Patent Publication Number: US-2023160882-A1

Title: Compositions and methods for low-volume biomolecule assays

Description:
CROSS-REFERENCE 
     The present application claims the benefit of U.S. Provisional Patent Application No. 63/236,654 filed Aug. 24, 2021, U.S. Provisional Patent Application No. 63/278,971 filed Nov. 12, 2021, and U.S. Provisional Patent Application No. 63/310,523 filed Feb. 15, 2022, each of which is incorporated herein by reference in its entirety. 
    
    
     BACKGROUND 
     Biological samples contain a wide variety of proteins and nucleic acids. Compositions and methods are needed for elucidating the presence and concentration of proteins and nucleic acids as well as any correlations between proteins and nucleic acids that may be indicative of a biological state. 
     SUMMARY 
     Provided herein are methods for assaying a plurality of biomolecules, the method comprising: labeling the plurality of biomolecules with distinguishable tags; contacting the plurality of biomolecules with one or more surfaces to thereby adsorb the plurality of biomolecules on the one or more surfaces; and assaying the plurality of biomolecules adsorbed on the one or more surfaces to identify at least a subset of the plurality of biomolecules based at least partially on the distinguishable tags. In some embodiments, the labeling is performed before the contacting. In some embodiments, the labeling is performed after the contacting. In some embodiments, the plurality of biomolecules is obtained from a plurality of biological samples, wherein the distinguishable tags are specific to each individual biological sample in the plurality of biological samples. In some embodiments, the method further comprises determining a relative quantity of a biomolecule in the plurality of biomolecules between a first sample in the plurality of biological samples and a second sample in the plurality of biological samples. In some embodiments, the plurality of biomolecules from the plurality of samples are combined into a single solution before assaying the plurality biomolecules. In some embodiments, the plurality of biomolecules comprises a dynamic range of at least about 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16. In some embodiments, the plurality of biomolecules comprises a dynamic range of at most about 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16. In some embodiments, the relative quantity of the biomolecule spans a dynamic range of at least about 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16. In some embodiments, the relative quantity of the biomolecule spans a dynamic range of at most about 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16. In some embodiments, a coefficient of variance of biomolecule is less than 50%, 40%, 30%, 20%, or 10%. In some embodiments, a coefficient of variance of biomolecule is greater than 50%, 40%, 30%, 20%, or 10%. 
     In some embodiments, the one or more surfaces are one or more particle surfaces. In some embodiments, the one or more particle surfaces are one or more nanoparticle surfaces. In some embodiments, the one or more particle surfaces are one or more microparticle surfaces. In some embodiments, the one or more particle surfaces are one or more porous particle surfaces. In some embodiments, the one or more particles of the one or more particle surfaces are paramagnetic. In some embodiments, the one or more particles of the one or more particle surfaces are superparamagnetic. In some embodiments, the one or more particles of the one or more particle surfaces comprise iron oxide. 
     In some embodiments, the plurality of biological samples comprises biological samples from different organisms. In some embodiments, the plurality of biological samples comprises biological samples from different individuals. In some embodiments, the plurality of biological samples comprises different cells of a single organism. In some embodiments, the individual biological samples of the plurality of biological samples each comprise from about 250 cells to about 2,000 cells. In some embodiments, the individual biological samples of the plurality of biological samples each comprise from about 500 cells to about 1,000 cells. In some embodiments, the individual biological samples of the plurality of biological samples each comprise at most about 100 cells of a single organism. In some embodiments, the individual biological samples of the plurality of biological samples each comprise a single cell. In some embodiments, the individual biological samples of the plurality of biological samples each comprise from about 10 nanograms (ng) to about 1000 ng of protein. In some embodiments, the individual biological samples of the plurality of biological samples each comprise from about 1 ng to about 100 ng of protein. In some embodiments, the individual biological samples of the plurality of biological samples each comprise from about 100 picograms (pg) to about 1 ng of protein. In some embodiments, the individual biological samples of the plurality of biological samples each comprise from about 100 microliters to about 1000 microliters of fluid. In some embodiments, the individual biological samples of the plurality of biological samples each comprise from about 50 microliters to about 500 microliters of fluid. In some embodiments, the individual biological samples of the plurality of biological samples each comprise from about 1 microliter to about 100 microliters of fluid. In some embodiments, a biological sample in the plurality of biological samples comprises plasma, serum, urine, cerebrospinal fluid, synovial fluid, tears, saliva, whole blood, milk, nipple aspirate, ductal lavage, vaginal fluid, nasal fluid, ear fluid, gastric fluid, pancreatic fluid, trabecular fluid, lung lavage, sweat, crevicular fluid, semen, prostatic fluid, sputum, fecal matter, bronchial lavage, fluid from swabbings, bronchial aspirants, fluidized solids, fine needle aspiration samples, tissue homogenates, lymphatic fluid, cell culture samples, or any combination thereof. In some embodiments, the biological sample comprise plasma or serum. 
     In some embodiments, the plurality of biomolecules comprises a biomolecule for a reporter channel. In some embodiments, the biomolecule comprises at least one protein or protein fragment in a known amount. 
     In some embodiments, the plurality of biomolecules is obtained from a plurality of locations within a single cell, wherein the distinguishable tags are specific to individual locations within the single cell. In some embodiments, the plurality of biomolecules are fractionated into a plurality of fractions. In some embodiments, the method further comprises determining for each fraction, one or both of (i) an amount of the distinguishable tags and an amount of individual biomolecules in the fraction, and (ii) an amount of biomolecules originating from a given location of the plurality of locations based at least partially on the amount of the distinguishable tags or the amount of the biomolecules. 
     In some embodiments, the distinguishable tags comprise tandem mass tags (TMT). In some embodiments, the tandem mass tags comprise TMT 0, TMT 2, TMT6/10, TMT 11, TMT Pro-zero, TMT Pro, TMTpro-126, TMTpro-127C, TMTpro-128C, TMTpro-129C, TMTpro-130C, TMTpro-131C, TMTpro-132C, TMTpro-133C, TMTpro-134C, TMTpro-127N, TMTpro-128N, TMTpro-129N, TMTpro-130N, TMTpro-131N, TMTpro-132N, TMTpro-133N, TMTpro-134N, TMTpro-135N, TMT6-126, TMT6-127, TMT6-128, TMT6-129, TMT6-130, TMT6-131, TMT10-126, TMT10-127N, TMT10-127C, TMT10-128N, TMT10-128C, TMT10-129N, TMT10-129C, TMT10-130N, TMT10-130C, TMT10-131, or any combination thereof. In some embodiments, (b) is carried out in a well comprising a surface that is both hydrophobic and oleophobic. In some embodiments, the method comprises assaying the plurality of biomolecules to identify at least a subset of the plurality of biomolecules based at least partially on the distinguishable tags. 
     Described herein are methods for quantification of proteins in samples, the method comprising: contacting (i) a first sample comprising a first plurality of proteins with a first set of one or more surfaces to generate a first plurality of adsorbed proteins, and (ii) a second sample comprising a second plurality of proteins with a second set of one or more surfaces to generate a second plurality of adsorbed proteins; proteolytically cleaving (i) the first plurality of adsorbed proteins to generate a first plurality of peptides, and (ii) the second plurality of adsorbed proteins to generate a second plurality of peptides; labeling (i) the first plurality of peptides with at least a first distinguishable tag, and (ii) the second plurality of peptides with at least a second distinguishable tag; performing tandem mass spectrometry using (i) the first plurality of peptides to generate a first plurality of mass spectra, and (ii) the second plurality of peptides to generate a second plurality of mass spectra; and determining (i) a first intensity of a first peptide in the first plurality of peptides based on a first quantity of the first distinguishable tag from the first plurality of mass spectra, and (ii) a second intensity of a second peptide in the second plurality of peptides based on a second quantity of the second distinguishable tag from the second plurality of mass spectra. In some embodiments, the method further comprises comparing the first intensity and the second intensity to determine a relative abundance of the first peptide and the second peptide between the first sample and the second sample. In some embodiments, the tandem mass spectrometry is performed on the first plurality of peptides and second plurality of peptides at the same time. In some embodiments, the first distinguishable tag and the second distinguishable tag comprise different isotopes of one or more elements. In some embodiments, the first sample has less than 1000 ng of proteins. In some embodiments, the second sample has less than 1000 ng of proteins. In some embodiments, the different isotopes of the one or more elements comprises C 12  and C 13 . In some embodiments, the different isotopes of the one or more elements comprises N 14  and N 15 . In some embodiments, the first distinguishable tag and the second distinguishable tag are configured to covalently bind to a primary amine. In some embodiments, the first distinguishable tag and the second distinguishable tag comprise different masses. In some embodiments, the first distinguishable tag and the second distinguishable tag are different in mass by about 4, 8, or 16 Daltons. In some embodiments, the first distinguishable tag and the second distinguishable tag comprise the same mass. In some embodiments, the first distinguishable tag and the second distinguishable tag are configured to generate different reporter ions. 
     Provided herein are methods for quantification of proteins in samples, the method comprising: incubating (i) a first cell in a first medium comprising a first isotope of an amino acid to generate a first daughter cell of the first cell, and (ii) a second cell in a second medium comprising a second isotope of an amino acid to generate a second daughter cell of the second cell; separating (i) a first plurality of proteins from the first cell to generate a first sample, wherein the first plurality of proteins comprises the first isotope, and (i) a second plurality of proteins from the second cell to generate a second sample, wherein the second plurality of proteins comprises the second isotope; contacting (i) the first sample with a first set of one or more surfaces to generate a first plurality of adsorbed proteins, and (ii) a second sample with a second set of one or more surfaces to generate a second plurality of adsorbed proteins; proteolytically cleaving (i) the first plurality of adsorbed proteins to generate a first plurality of peptides, and (ii) the second plurality of adsorbed proteins to generate a second plurality of peptides; performing tandem mass spectrometry using (i) the first plurality of peptides to generate a first plurality of mass spectra, and (ii) the second plurality of peptides to generate a second plurality of mass spectra; and determining (i) a first intensity of a first peptide in the first plurality of peptides, and (ii) a second intensity of a second peptide in the second plurality of peptides, wherein the first peptide and the second peptide are mass-shifted based on a difference in mass between the first isotope and the second isotope. In some embodiments, the method further comprises comparing the first intensity and the second intensity to determine a relative abundance of the first peptide and the second peptide between the first sample and the second sample. In some embodiments, the tandem mass spectrometry is performed on the first plurality of peptides and second plurality of peptides at the same time. In some embodiments, the first sample has less than 1000 ng of proteins. In some embodiments, the second sample has less than 1000 ng of proteins. In some embodiments, the method further comprises determining (i) a first plurality of peptide identifications by searching a database to match the first plurality of mass spectra to a first plurality of peptide identifications, and (ii) a second plurality of peptide identifications by searching the database to match the second plurality of mass spectra to a second plurality of peptide identifications. In some embodiments, the method further comprises grouping (i) the first plurality of peptide identifications to generate a first plurality of protein groups, and (ii) the second plurality of peptide identifications to generate a second plurality of protein groups. In some embodiments, the method further comprises determining (i) a first protein group intensity of a first protein group in the first plurality of protein groups, and (ii) a second protein group intensity of a second protein group in the second plurality of protein groups. In some embodiments, the method further comprises comparing the first protein group intensity and the second protein group intensity to determine a protein group relative abundance of the first protein group and the second protein group between the first sample and the second sample. In some embodiments, the first set of one or more surfaces and the second set of one or more surfaces comprise the same surface types. In some embodiments, the first set of one or more surfaces and the second set of one or more surfaces comprise different surface types. In some embodiments, the proteolytically cleaving is performed using protease. In some embodiments, the protease comprises trypsin, lysin, serine protease, or any combination thereof. In some embodiments, the tandem mass spectrometry comprises liquid chromatography-tandem mass spectrometry (LC-MS/MS). In some embodiments, the first peptide and the second peptide comprise the same chemical identity. In some embodiments, the first peptide and the second peptide comprise different chemical identities. In some embodiments, the first protein group and the second protein group comprise the same protein group. In some embodiments, the first protein group and the second protein group comprise different protein groups. In some embodiments, the relative abundance comprises a ratio of at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16. In some embodiments, the relative abundance comprises a ratio of at most about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16. In some embodiments, the protein group relative abundance comprises a ratio of at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16. In some embodiments, the protein group relative abundance comprises a ratio of at most about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16. 
     Provided herein are methods for quantification of a low abundance protein in a sample, the method comprising: contacting the sample comprising a plurality of proteins with a set of one or more surfaces to generate a plurality of adsorbed proteins; proteolytically cleaving the plurality of adsorbed proteins to generate a plurality of peptides; adding a predetermined amount of a peptide to the plurality of peptides to generate a modified sample; performing mass spectrometry using the modified sample to generate a plurality of mass spectra; and determining a quantity of the peptide in the sample based on (i) the predetermined amount of the peptide added to the plurality of peptides and (ii) an intensity of the peptide in the plurality of mass spectra. 
     Provided herein are methods for quantification of a low abundance protein in a sample, the method comprising: adding a predetermined amount of a protein to the sample comprising a plurality of proteins, thereby generating a modified sample; contacting the modified sample with a set of one or more surfaces to generate a plurality of adsorbed proteins; proteolytically cleaving the plurality of adsorbed proteins to generate a plurality of peptides; performing mass spectrometry using the modified sample to generate a plurality of mass spectra; performing a database search to match the plurality of mass spectra to a plurality of peptide identifications; grouping the plurality of peptide identifications to determine a plurality of protein groups; and determining a quantity of the protein in the sample based on (i) an intensity of a protein group in the plurality of protein groups associated with the protein, and (ii) the predetermined amount of the protein added to the sample. 
     Provided herein are kits for use in relative quantification of biomolecules, comprising: one or more substrates comprising one or more surfaces for adsorbing a biomolecule; a first distinguishable tag configured to covalently bind to the biomolecule; and a second distinguishable tag configured to covalently bind to the biomolecule; wherein the biomolecule generates a first ionic species comprising the biomolecule when the biomolecule is covalently bound with the first distinguishable tag, wherein the biomolecule generates a second ionic species comprising the second biomolecule when the biomolecule is covalently bound with the second distinguishable tag, and wherein the first ionic species and the second ionic species comprise distinguishable masses. In some embodiments, the kit further comprises a denaturing agent. In some embodiments, the denaturing agent comprises at least one of: sodium dodecyl sulfate, acetic acid, trichloroacetic acid, sulfosalicylic acid, sodium bicarbonate, ethanol, formaldehyde, glutaraldehyde, urea, guanidium chloride, lithium perchlorate, 2-mercaptoethanol, dithiothreitol, tris(2-carboxyethyl)phosphine (TCEP), or any combination thereof. In some embodiments, the kit further comprises a reducing agent. In some embodiments, the reducing agent comprises TCEP, dithiothreitol, beta-mercaptoethanol, glutathione, cysteine, or any combination thereof. In some embodiments, the kit further comprises an alkylating agent. In some embodiments, the alkylating agent comprises iodoacetamide, iodoacetic acid, acrylamide, chloroacetamide, or any combination thereof. In some embodiments, the kit further comprises a digesting agent. In some embodiments, the digesting agent comprises trypsin, lysin, serine protease, or any combination thereof. In some embodiments, the kit further comprises a buffer. In some embodiments, the buffer comprises triethylammonium bicarbonate, tris(hydroxymethyl)aminomethane, citrate, Tris, phosphate, ethylenediaminetetraacetic acid, or any combination thereof. In some embodiments, the kit further comprises an organic solvent. In some embodiments, the kit further comprises a cysteine blocking reagent. In some embodiments, the cysteine blocking reagent comprises methyl methanethiosulfonate, iodoacetamide, N-ethylmaleimide, methylsulfonyl benzothiazole, or any combination thereof. 
     Provided herein are systems for relative quantification of a biomolecule in a plurality of samples, comprising: a plurality of partitions comprising a first partition and a second partition; a plurality of reagent storages comprising a first reagent comprising a first distinguishable tag and a second reagent comprising a second distinguishable tag; a plurality of substrates comprising a first substrate comprising a first surface chemistry and a second substrate comprising a second surface chemistry; one or more transfer devices operably connected to the plurality of partitions, the plurality of reagent storages, and the plurality of substrates; a mass spectrometer; and a computer comprising at least one processor and instructions executable by the at least one processor to perform steps comprising: i) generating, using the one or more transfer devices, a first fluid composition in the first partition comprising the first substrate, the first reagent, and a first plurality of biomolecules, wherein the first plurality of biomolecules is adsorbed on the first substrate; ii) generating, using the one or more transfer devices, a second fluid composition in the second partition comprising the second substrate, the second reagent, and a second plurality of biomolecules, wherein the second plurality of biomolecules is adsorbed on the second substrate; and iii) inputting, using the one or more transfer devices, the first plurality of biomolecules and the second plurality of biomolecules into the mass spectrometer to generate a first plurality of mass spectra for the first plurality of biomolecules and a second plurality of mass spectra for the second plurality of biomolecules. 
     In some aspects, the present disclosure describes a method for assaying biomolecules, the method comprising: (a) obtaining a plurality of biomolecules, wherein individual biomolecules of at least a subset of the plurality of biomolecules are labeled with distinguishable tags; (b) contacting the plurality of biomolecules with a particle composition comprising at least one particle to thereby form a biomolecule corona with the particle composition, wherein the biomolecule corona comprises at least a subset of the individual biomolecules; and (c) assaying the biomolecule corona to identify the at least the subset of the individual biomolecules based at least partially on the distinguishable tags. 
     In some embodiments, the plurality of biomolecules are obtained from a plurality of biological samples, wherein the distinguishable tags are specific and corresponding to individual biological samples of the plurality of biological samples. 
     In some embodiments, the particle composition comprises a plurality of particles. 
     In some embodiments, the plurality of biological samples comprises biological samples from different organisms. 
     In some embodiments, the plurality of biological samples comprises different cells of a single organism. 
     In some embodiments, the individual biological samples of the plurality of biological samples each comprise from about 250 cells to about 2,000 cells. 
     In some embodiments, the individual biological samples of the plurality of biological samples each comprise from about 500 cells to about 1,000 cells. 
     In some embodiments, the individual biological samples of the plurality of biological samples each comprise at most about 100 cells of a single organism. 
     In some embodiments, the individual biological samples of the plurality of biological samples each comprise a single cell. 
     In some embodiments, the individual biological samples of the plurality of biological samples each comprise from about 10 nanograms (ng) to about 1000 ng of protein. 
     In some embodiments, the individual biological samples of the plurality of biological samples each comprise from about 1 ng to about 100 ng of protein. 
     In some embodiments, the individual biological samples of the plurality of biological samples each comprise from about 100 picograms (pg) to about 1 ng of protein. 
     In some embodiments, the plurality of biomolecules comprises a biomolecule for a reporter channel. 
     In some embodiments, the biomolecule comprises at least one protein or protein fragment in a known amount. 
     In some embodiments, the plurality of biomolecules is obtained from a plurality of locations within a single cell, wherein the distinguishable tags are specific to individual locations within the single cell. 
     In some embodiments, the plurality of biomolecules are fractionated into a plurality of fractions. 
     In some embodiments, the method further comprises, determining for each fraction, one or both of (i) an amount of the distinguishable tags and an amount of individual biomolecules in the fraction, and (ii) an amount of biomolecules originating from a given location of the plurality of locations based at least partially on the amount of the distinguishable tags or the amount of the biomolecules. 
     In some embodiments, the distinguishable tags comprise tandem mass tags. 
     In some embodiments, the tandem mass tags comprise TMT 0, TMT 2, TMT6/10, TMT 11, TMT Pro-zero, TMT Pro, TMTpro-126, TMTpro-127C, TMTpro-128C, TMTpro-129C, TMTpro-130C, TMTpro-131C, TMTpro-132C, TMTpro-133C, TMTpro-134C, TMTpro-127N, TMTpro-128N, TMTpro-129N, TMTpro-130N, TMTpro-131N, TMTpro-132N, TMTpro-133N, TMTpro-134N, TMTpro-135N, TMT6-126, TMT6-127, TMT6-128, TMT6-129, TMT6-130, TMT6-131, TMT10-126, TMT10-127N, TMT10-127C, TMT10-128N, TMT10-128C, TMT10-129N, TMT10-129C, TMT10-130N, TMT10-130C, TMT10-131, or any combination thereof. 
     In some embodiments, (b) is carried out in a well comprising a surface that is both hydrophobic and oleophobic. 
     In some embodiments, the surface comprises a fluorinated surface. 
     In some embodiments, the surface comprises a poly(tetrafluoro ethylene) surface. 
     INCORPORATION BY REFERENCE 
     All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The novel features of the invention are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the invention are utilized, and the accompanying drawings (also “Figure” and “FIG.” herein), of which: 
         FIG.  1    shows an illustrative workflow for assaying proteins and nucleic acids in a sample. 
         FIG.  2 A  summarizes the number of protein variations identified among proteins collected on a 10-particle panel from 29 separate samples. 
         FIG.  2 B  provides an example of a protein variant identification in  FIG.  2 A . Shown are multiple alleles identified in RNA from a sample enabled identification of glycine and arginine (circled amino acids) peptide variants from the sample. 
         FIG.  3    shows normalized mass spectrometric intensities for six separate BMP1 peptides (plots labeled  1 - 6 ) from multiple samples derived from cancer patients and healthy patients. 
         FIG.  4    shows the ratio of phosphorylated to unphosphorylated Heparin Co-factor 2 (Y-axis) across healthy, comorbid, early-stage lung cancer, and late-stage lung cancer patients (X-axis, left to right) in an experiment. 
         FIG.  5    illustrates a method for generating a subject-specific library of protein sequences and predicted mass spectrometric peptide signals from nucleic acid data. 
         FIG.  6    provides an example of a method for determining homo- or heterozygosity using nucleic acid and proteomic data. 
         FIG.  7    shows a workflow for using protein mass spectrometric data to determine expression patterns. 
         FIG.  8 A  illustrates the number of subjects in each sample group studied. 
         FIG.  8 B  provides the maximum number of commonly identified protein groups for different percentages of a non-small cell lung cancer (NSCLC) population. 
         FIG.  9    shows the number of peptide fragments identified from plasma proteins collected on particles and subjected to trypsin digestion. 
         FIG.  10    provides allele frequency distributions among 464 variants identified in 29 subjects (dark, lower bars) and among about 10 8  variants identified in 2504 subjects (light, higher bars). 
         FIG.  11    provides density plots for 464 alleles identified among a population of 29 subjects. 
         FIG.  12 A  outlines a method for identifying biological state-relevant protein isoforms. Briefly, fragments of a protein (‘Protein X’) are interrogated for differential expression between two biological states to identify proteins with biological state-dependent splicing variations 
         FIG.  12 B  ranks 16 identified non-small cell lung cancer protein biomarkers by their Open Target lung carcinoma association scores. 
         FIG.  12 C  plots the 16 identified NSCLC protein biomarkers from  FIG.  12 B  by known plasma protein abundance using concentrations from the Human Plasma Proteome Project. 
         FIG.  13    provides the number of protein variants identified in each of 29 subjects with late stage non-small cell lung cancer (NSCLC), early stage non-small cell lung cancer, co-morbidity, or healthy statuses. 
         FIG.  14    provides the number of variant forms of 7 lung cancer-associated candidate proteins observed in each of 29 subjects with late stage non-small cell lung cancer (NSCLC), early stage non-small cell lung cancer, co-morbidity, or healthy statuses. 
         FIG.  15    graphically illustrates advantages for some of the methods disclosed herein. 
         FIG.  16    schematically illustrates a parallel and configurable workflow for some of the methods disclosed herein. 
         FIG.  17    schematically illustrates a pipeline implementing some of the methods disclosed herein. Some of the methods disclosed herein may enable simplified and automated handling. Some of the methods disclosed herein may comprise fluidic handling and magnetic capture. Some of the methods disclosed herein may comprise a liquid handling instrument assay implementation. 
         FIG.  18    schematically illustrates a method for functionalizing SPIONs with some chemical structures. 
         FIG.  19 A  shows size and binding energy for some of the particles disclosed herein. Some of the nanoparticles disclosed herein are consistent in size, form, and composition. In some cases, characterization of the particles disclosed herein may be used for quality control purposes. 
         FIG.  19 B  shows composition data for some of the particles disclosed herein. 
         FIG.  20    illustrates a method for using a plurality of particles for analyzing the abundance of proteins and protein structural and functional groups. 
         FIG.  21    shows plots for a database of MS intensities, MS intensities detected in a depleted plasma without using nanoparticles of the present disclosure, a composite of MS intensities detected in a depleted plasma using a panel of 5 nanoparticles of the present disclosure, and 5 independent MS intensities detected in a depleted plasma each using one of the 5 nanoparticles of the present disclosure. Plasma samples from 141 subjects with NSCLC were used for this study. Proteins in a biological sample (e.g., plasma) may comprise a wide concentration range or a dynamic range. Even in samples where high abundance proteins are reduced in amount (e.g., depleted plasma), detecting proteins deeply (both high abundance proteins and low abundance proteins) and broadly (detecting the broad variety of proteins with minimal selective bias towards certain proteins) may be challenging. Proteins were ordered by the rank of MS intensities in the database. Proteins were plotted if the proteins were present in at least 25% of samples. In the composite plot, the color intensity indicates the highest detected value from the 5 distinct nanoparticles. The composite plot shows that the nanoparticles detected the entire spectrum of available plasma proteins more completely. Meanwhile, each individual nanoparticle also detected more proteins than direct MS analysis of the depleted plasma. Individual nanoparticles were able to assay nearly the full range of the plasma proteome. In some cases, the panel of nanoparticles may be optimized to cover the entire range of the proteome or a specific portion of the proteome. MS experiments on depleted plasma using nanoparticles may enable detecting less abundant proteins and/or detecting the proteome more broadly. 
         FIG.  22    shows experimental data for mass spectrometry (MS) feature intensity detected using some of the methods disclosed herein, for various peptides as a function of peptide concentration. Spike recovery experiments with MS data from nanoparticle coronas modeled against gold-standard ELISA demonstrates linearity in response to 4 polypeptides with 4 nanoparticles at 1×, 2×, 5×, 10×, and 100× endogenous levels of spiked protein. The data shows good accuracy and precision of the nanoparticle-based protein detections. Therefore, relative concentration or absolute concentration (with calibration) of proteins may be determined using some of the methods disclosed herein. 
         FIG.  23 A  shows a histogram of raw MS feature intensities from experiments with some particles disclosed herein. 
         FIG.  23 B  shows coefficient of variance (CV) of MS feature intensities of some particles disclosed herein. Three replicate experiments were conducted with for three nanoparticles (i.e., NP1, NP2, and NP3). The distribution of MS signals for various proteins were histogrammed. The replicate experiment results were overlaid in plots, showing the reproducibility of the experiments. The distribution of feature intensities by particles were conserved across replicate trials of experiments. Coefficient of variance was calculated for each nanoparticle. The results suggest that with 25 samples and measuring 2000 proteins, there is about 85% power to detect differences of 50% in protein concentrations. In this example, power refers to the probability that an experiment would find a significant difference for a particular result, given the expected effect size, sample size, and measurement accuracy. In this example, differences of 50% refers to the ratio of abundance of a protein (e.g., as measured by concentration) between two biological samples. 
         FIG.  24    shows experimental data for the number of peptides detected per protein for various proteins using some of the particles disclosed herein. Proteins were assayed from 141 healthy and early NSCLC subjects. Proteins present in at least 25% of the samples (1992 proteins) are plotted. The median value for the number of peptides detected per protein is about 7-8. 
         FIG.  25 A  shows receiver operating characteristic (ROC) curve for a trained machine learning classifier. 
         FIG.  25 B  shows feature importance ranks of input features to the trained machine learning classifier. The machine learning classifier was trained with multiple cross-validation to classify between healthy subjects and early NSCLC subjects. The trained machine learning classifier has an AUC of 0.91, sensitivity of 59%, and specificity of 98%. The feature importance rank shows which signal from which nanoparticle was important for classifying subjects. Majority of the importance features were newly discovered to be useful for studying NSCLC. One of the important features is tubulin, which is a target for paclitaxel. 
         FIG.  26    shows an example flowchart for analyzing proteins using nanoparticles in accordance with some of the methods disclosed herein. 
         FIG.  27    shows experimental measurements of modification ratios in cancerous samples and control samples for various exons in the human genome. Among the peptides detected in this study, six specific peptides came from various parts of Bone Morphogenic Protein 1 (BMP1). The short form of the BMP1 protein was expressed predominantly in cancer patients, whereas the long forms of the protein were seen more often or at a higher level among the healthy controls. As such, differential expression of protein isoforms by disease may be detected. 
         FIG.  28    shows an illustration of a phosphorylated peptide (phospho-peptide) compared to an unphosphorylated peptide. 
         FIG.  29    shows experimental measurements of protein sequence polymorphisms (e.g., single nucleotide variant mutations) from proteogenomic information. An amino acid substitution induced by 0.001% population frequency SNV was detected. 
         FIG.  30    shows a schematic of protein-protein interactions. 
         FIG.  31    shows an illustration of the human plasma interactome map. 
         FIG.  32    shows protein-protein interaction maps generated from the STRING PPI database using proteins detected in samples from 276 subjects. Dots represent individual proteins, with lighter shading representing higher abundance. The three circled clusters show differential expression of plasma interactome across healthy and diseased samples. 
         FIG.  33    shows a table listing various features of some of the compositions and methods described herein. 
         FIG.  34    schematically illustrates a pipeline implementing some of the methods disclosed herein. 
         FIG.  35    schematically illustrates a pipeline implementing some of the methods for assaying biomolecule coronas disclosed herein. 
         FIG.  36    shows illustrations, microscope images, and diameter and zeta potential measurements of some of the particles disclosed herein. 
         FIG.  37    shows an example of an automated system for assaying biomolecule coronas. 
         FIG.  38    shows a diagram of a multi-well assay plate. 
         FIG.  39    shows a diagram for a deck layout of an automated system for assaying biomolecule coronas. 
         FIG.  40    schematically illustrates an example of a method for assaying biomolecules coronas as disclosed herein. 
         FIG.  41    shows a diagram of a multi-well assay plate comprising wells for control experiments. 
         FIG.  42    shows experimental results performed with individual proteomics machines. 
         FIG.  43    shows results of biomolecule corona assays performed on 200 samples for an Alzheimer&#39;s disease study. 
         FIG.  44    shows panel protein group counts by sample using a biomolecule corona assay experiments compared to naked plasma counts experiments. 
         FIG.  45    shows an example of a data architecture for a biomolecule corona analysis workflow. 
         FIG.  46    shows an example of a data architecture for a biomolecule corona analysis workflow. 
         FIG.  47    shows an example of a graphical user interface (GUI) for a biomolecule corona analysis workflow. 
         FIG.  48    shows examples of some analytical tools and GUI elements as disclosed herein. 
         FIG.  49    shows examples of some analytical tools and GUI elements as disclosed herein. 
         FIG.  50    shows examples of some instruments as disclosed herein. 
         FIG.  51    shows results of manufacturing experiments for some particles disclosed herein. 
         FIG.  52    shows microscope images for some particles disclosed herein. 
         FIG.  53    shows some examples of dry compositions as disclosed herein. 
         FIG.  54 A  shows stability experiment results of size (diameter of nm) vs time (days) for some dry compositions as disclosed herein. 
         FIG.  54 B  shows stability experiment results of zeta potential (mV) vs time (days) for some dry compositions as disclosed herein. 
         FIG.  55    shows diameters for some particles and their dry compositions as disclosed herein as measured by DLS. 
         FIG.  56    shows zeta potentials for some particles and their dry compositions as disclosed herein. 
         FIG.  57    shows peptide counts and protein groups counts for a standard panel, a dry composition reconstituted with water before use, a dry composition use without reconstitution, and a control composition that comprises an excipient that is used without lyophilization. 
         FIG.  58    provides a schematic overview of a library variant detection method. 
         FIG.  59    diagrams a method for variant peptide detection and analysis. 
         FIG.  60    shows a computer system that is programmed or otherwise configured to implement methods provided herein. 
         FIG.  61    summarizes counts of detected genetic variants corresponding to heterozygous and homozygous alleles corresponding to reference or alternate allelic variants in 29 samples from separate subjects. 
         FIG.  62 A  summarizes alternate allele frequencies of variant proteins detected in 29 samples and provides a histogram with variants binned in 1% alternate allele frequency increments. 
         FIG.  62 B  provides a table with bins corresponding to 10% increments in alternate allele frequencies of variant proteins detected in 29 samples. 
         FIG.  63    summarizes counts of detected genetic variants corresponding to heterozygous and homozygous alleles corresponding greater than 10% and less than 10%/population level abundances. 
         FIG.  64 A  lists single amino acid polymorphism variants with alternate allele frequencies of less than 0.01 which were detected in at least 2 of 29 assayed samples. 
         FIG.  64 B  provides relative counts of reference and variant forms of coagulation factor V (F5) detected across 29 patient samples. 
         FIG.  64 C  provides relative counts of reference and variant forms of alpha-1 antitrypsin (SERPINA1) detected across 29 patient samples. 
         FIG.  64 D  provides relative counts of reference and variant forms of Apolipoprotein H (APOH) detected across 29 patient samples. 
         FIG.  64 E  provides relative counts of reference and variant forms of Apolipoprotein B (APOB) detected across 29 patient samples. 
         FIG.  64 F  provides relative counts of reference and variant forms of Inter-Alpha-Trypsin Inhibitor Heavy Chain 3 (ITIH3) detected across 29 patient samples. 
         FIG.  64 G  provides mass spectrometric intensities for alternate and reference forms of coagulation factor V (F5) detected across 29 patient samples. 
         FIG.  64 H  provides mass spectrometric intensities for alternate and reference forms of alpha-1 antitrypsin (SERPINA1) detected across 29 patient samples. 
         FIG.  64 I  provides mass spectrometric intensities for alternate and reference forms of Apolipoprotein H (APOH) detected across 29 patient samples. 
         FIG.  64 J  provides mass spectrometric intensities for alternate and reference forms of Apolipoprotein B (APOB) detected across 29 patient samples. 
         FIG.  64 K  provides mass spectrometric intensities for alternate and reference forms of Inter-Alpha-Trypsin Inhibitor Heavy Chain 3 (ITIH3) detected across 29 patient samples. 
         FIGS.  65 A- 65 B  indicate overlap between detected heterozygous alleles across 29 samples. 
         FIGS.  66 A- 66 B  indicate overlap between detected homozygous alleles across the 29 samples for variant peptides with alternate allele frequencies of less than 0.5. 
         FIGS.  67 A- 67 B  indicate overlap between detected homozygous alleles across the 29 samples for variant peptides with alternate allele frequencies greater than 0.5. 
         FIG.  68    schematically illustrates a method for partitioning samples in a 96 well-plate, in accordance with some embodiments. 
         FIG.  69    shows the mass of peptide quantified for each nanoparticle, in accordance with some embodiments. 
         FIG.  70    shows the number of protein groups identified using 5 nanoparticle enriched peptide and TMT Tandem Mass Tag workflow (Each individual NP TMT labeling workflow) in accordance with some embodiments. 
         FIG.  71    shows the intersection size of protein group identifications as a function of different particle combinations, using TMT Tandem Mass Tag workflow with 5 nanoparticles, in accordance with some embodiments. 
         FIG.  72    shows the percentage of protein groups identified using TMT Tandem Mass Tag workflow (with 1, 2, 3, 4, or 5 nanoparticles, in accordance with some embodiments. 
         FIG.  73    shows a five nanoparticles pooling procedure for pooled nanoparticle TMT Tandem Mass Tag workflow, in accordance with some embodiments. 
         FIG.  74    shows the number of protein group identifications for each pooled sample using pooled nanoparticle with T MT Tandem Mass Tag workflow, in accordance with some embodiments. 
         FIGS.  75 A- 75 B  show non-limiting examples of tandem mass tags (TMTpro 16plex reagents), in accordance with some embodiments. 
         FIG.  76    schematically illustrates LC (liquid chromatography) fractionated samples using Pooled Nanoparticle with Tandem Mass Tag (Pooled NP TMT) workflow, in accordance with some embodiments. 
         FIG.  77    shows the number of protein group identifications using various methods described herein, in accordance with some embodiments. Each column of data is subdivided based on the number of peptides comprising each protein group. 
         FIG.  78    shows TMT channel CV distribution across PSMs and proteins for the Pooled NP TMT workflow, in accordance with some embodiments. 
         FIG.  79 A  shows CV of PSM detected across different plates, in accordance with some embodiments. 
         FIG.  79 B  shows CV of PSM detected across different replicates, in accordance with some embodiments. 
         FIG.  80    shows the CV of protein abundances detected with 5 NPs in a Label-free LCMS analysis using two different automated systems of the present disclosure, in accordance with some embodiments. 
         FIG.  81    shows estimated protein concentrations using protein group identification data from a Pooled NP TMT experiment and the Human Protein Atlas (HPA), in accordance with some embodiments. 
         FIG.  82    shows protein group MS1 intensities, ranked from highest to lowest, in accordance with some embodiments. Some potential biomarkers identified using HPA are labeled in this plot. 
         FIG.  83    shows identified protein groups, binned into classes using HPA, in accordance with some embodiments. 
         FIG.  84    shows the diversity of functional annotations that are captured using Pooled NP TMT, in accordance with some embodiments. 
         FIG.  85    shows protein groups and protein group intensity CVs as measured from a Label-free DDA LC/MS experiments, in accordance with some embodiments. 
         FIG.  86    shows protein group intensity CVs and dynamic range as measured captured using Pooled NP TMT, in accordance with some embodiments. 
         FIG.  87 A  shows peptide intensity CVs as measured captured using Pooled NP TMT, in accordance with some embodiments. 
         FIG.  87 B  shows protein group intensity CVs as measured captured using Pooled NP TMT, in accordance with some embodiments. 
         FIG.  88    shows a method for relative quantification of biomolecules in different cells, in accordance with some embodiments. 
         FIG.  89    shows a method for relative quantification of biomolecules in different cells, in accordance with some embodiments. 
         FIG.  90    shows a method for relative quantification of biomolecules in different cells, in accordance with some embodiments. 
         FIG.  91 A  shows a surface, in accordance with some embodiments. A surface may be functionalized at one or more regions for capturing biomolecules. 
         FIG.  91 B  shows a surface, in accordance with some embodiments. A surface may comprise one or more wells or depressions for capturing biomolecules. For example, a functionalized surface may be disposed in a 96 well plate or a 384 well plate. 
         FIG.  91 C  shows a surface, in accordance with some embodiments. A surface may be disposed on one or more particles. In some embodiments, the one or more particles may be disposed in one or more wells or depressions. 
         FIG.  91 D  shows a surface, in accordance with some embodiments. A surface may be disposed on a plurality of particles packed in a channel or a porous material disposed in a channel. 
         FIG.  91 E  shows a surface, in accordance with some embodiments. A surface may be disposed on an inner surface of a channel. 
         FIGS.  91 F- 91 I  show surfaces, in accordance with some embodiments. A surface may comprise 1, 2, 3, 4 or any number of distinct surface regions. In some embodiments, a surface may be disposed on a particle. In some embodiments, a particle may be a porous particle. 
     
    
    
     DETAILED DESCRIPTION 
     While various embodiments of the invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions may occur to those skilled in the art without departing from the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed. 
     Though the human genome contains about 20,000 genes, some researchers estimate that the human proteome contains over 1 million proteins derived from those genes. A number of different proteoforms can be derived from a repertoire of various transcriptional, translational, and post-translational mechanisms (e.g., alternative splice forms, allelic variations, and protein modifications) that produce proteins that differ from those that comprise the canonical sequence expressed from the genes. Of the vast number of proteins estimated to exist in the human proteome, only a small fraction has thus been meaningfully identified and/or quantified in the human body. 
     Some of the challenges in identifying and quantifying the proteins is related to the rarity of certain proteins. For instance, a human cell can contain protein species over a dynamic range that exceeds 7 magnitudes, where thousands of low abundance proteins may each be less than 10 parts per million or less and where the least abundant proteins may be as few as 100 parts per billion or less. Liquid chromatography coupled with mass spectrometry (LC-MS) or tandem mass spectrometry (LC-MS/MS) can be used to identify protein species. However, due to the nature of the methods, only a fraction of ionic species that are generated at a time from a given sample may be detectable as a mass spectra. As a result, the presence of species that are highly abundant compared to the rare species can create an overwhelming amount of signals that make the detection of rare species elusive. 
     Some aspects of the PROTEOGRAPH™ technology aims to solve some of these challenges by “compressing” the dynamic range of protein species in a sample. Some aspects of the PROTEOGRAPH™ technology operates based on binding of proteins to nanoparticle surfaces to form protein coronas. Without requiring a presence of a specific entity that is configured for binding to a singular specific protein (e.g., as in immunoassays), the binding can result in a dynamic range compression of proteins bound to the nanoparticle surfaces while capturing a wide variety of proteins. In other words, the relative abundance of proteins in the sample can be modified on the nanoparticle surfaces, such that the rare proteins are relatively more abundant, and the highly abundant proteins are relatively less abundant compared to the original sample. The proteins can then be separated from the sample and analyzed, for example, with mass spectrometry. The compressed dynamic range can allow rare proteins to comprise a higher fraction of ionic species, thereby allowing higher probability for detecting those rare proteins in a MS experiment. Though the above example is described in terms of proteins, other biomolecule classes (e.g., lipids, sugars, etc.) can be similarly targeted. Other aspects of the PROTEOGRAPH™ technology include controlled automation of the PROTEOGRAPH™ workflow that increases speed/throughput and accuracy/reliability. 
     While the introduction of the PROTEOGRAPH™ technology increased the number of proteins that can be detected from samples, this improvement may be reduced for low volume or low mass samples (e.g., on the scale of a single or a few cells). These samples may also be less amenable to high throughput analysis using the PROTEOGRAPH™ technology. 
     In some aspects, the present application provides systems and methods for performing proteomics using distinguishable labels that bind to protein species in samples. The samples can comprise two or more cells. One distinguishable label can be used to label a first cell, and another distinguishable label can be used to label a second cell. The two samples may be assayed to determine relative differences in the compositions of the two samples. 
       FIG.  88    illustrates an example of a method for performing proteomics using single cells. Cells from different areas in a tissue sample ( 8801 ) can be extracted. For instance, a first cell ( 8802 ) may be a diseased cell (e.g., a tumor cell), while a second cell ( 8803 ) may be a healthy cell. The first cell can be lysed ( 8804 ) using a lyse buffer, and then a first set of biomolecules from the first cell can be labeled with a first label ( 8805 ). The second cell can also be lysed using a lyse buffer, and then biomolecules of the second cell can be labeled with a second label ( 8806 ). The first label and the second label can comprise different chemical entities, that can be distinguished with one another. The first set of labeled biomolecules ( 8807 ) of the first cell can be contacted with a first set of one or more surfaces ( 8808 ), adsorbing the first set of labeled biomolecules onto the first set of one or more surfaces. Similarly, the second set of labeled biomolecules ( 8809 ) of the second cell can also be contacted with a second set of one or more surfaces ( 8810 ), adsorbing the second set of labeled biomolecules onto the second set of one or more surfaces. 
     The first set of labeled biomolecules and the second set of labeled biomolecules may be released from the surface, and then multiplexed as a single sample ( 8811 ). The multiplexed sample can be analyzed using mass spectrometry ( 8812 ). The mass spectrometry results can allow identification of a biomolecule from both the first cell and the second cell. The first label and the second label may provide distinguishable signatures for biomolecules that originate from the first cell versus the second cell. For instance, the mass-to-charge ratio of the biomolecule with the first label, when it originates from the first cell, can be slightly different from the mass-to-charge ratio of the biomolecule with the second label originating from the second cell. The intensities of the biomolecule observed from mass spectrometry can be used to determine relative quantities of the biomolecule originating from the first cell versus the second cell. For example, relative quantities may be determined as a ratio or a difference in the measured intensity of the biomolecule in the first sample versus the second sample. 
     Multiplexing samples can provide several advantages. One advantage can be that factors which can influence the binding behavior of a biomolecule unequally among different samples may be equalized when the samples are multiplexed. Adsorption behavior of a biomolecule to a surface can be dependent on the chemical structure of the biomolecule, and it can also depend on (i) the solvent environment in which the adsorption takes place, and/or (ii) the competition of other biomolecules adsorbing to the surface. Thus, the same biomolecule in two samples of different compositions (e.g., the two samples may have different biomolecules or different concentrations of some biomolecules), may adsorb to a surface differently. Multiplexing the different samples into one sample can reduce biases from the solvent environment and/or the competitive adsorption of other biomolecules in the solvent. This can lead to more accurate determination of relative quantities of a biomolecule between samples. Another advantage of multiplexing samples can include in throughput; some studies may involve assaying hundreds or thousands of cells before gaining meaningful insight into understanding differences in the biomolecule compositions (e.g., proteome, transcriptome, or genome) of different cell lines. 
     In some cases, the biomolecules are labeled after desorption from the particles. In some cases, the biomolecules are labeled after proteolytic cleavage. In some cases, the biomolecules that are labeled after the proteolytic cleavage are peptides. In some cases, the label comprises stable isotope labeling using amino acids in cell culture (SILAC). SILAC may allow for protein level multiplexing based on metabolic labeling of biomolecules in the cell. In some cases, the capability to label biomolecules that are large (e.g., proteins and large peptide fragments) allows for middle down and top down proteomics. In some cases, the label after proteolytic cleavage comprises a tandem mass tag (TMT). In some cases, the biomolecules are labeled before proteolytic cleavage. In some cases, the biomolecules that are labeled before the proteolytic cleavage are proteins. In some cases, the biomolecules are labeled before the biomolecules are desorbed from the particles. 
       FIG.  89    illustrates another example of a method for performing proteomics using single cells. Cells from different areas in a tissue sample can be extracted. The first cell can be lysed using a lyse buffer, and then a first set of biomolecules from the first cell can be contacted with a first set of one or more surfaces, adsorbing the first set of biomolecules onto the first set of one or more surfaces. The first set of adsorbed biomolecules can be digested using a protease (e.g., trypsin, lysin, or a serine protease) to generate a first set of peptides. The first set of peptides can be labeled with a first label. The second cell can be lysed using a lyse buffer, and then a second set of biomolecules from the second cell can be contacted with a second set of one or more surfaces, adsorbing the second set of biomolecules onto the second set of one or more surfaces. The second set of adsorbed biomolecules can be catalyzed using a protease (e.g., trypsin, lysin, or a serine protease) to generate a second set of peptides. The second set of peptides can be labeled with a second label. The first set of labeled peptides and the second set of labeled peptides can be multiplexed. The multiplexed sample can be analyzed using mass spectrometry. 
       FIG.  90    illustrates another example of a method for performing proteomics using single cells. A first set of cells can be cultured with biomolecules having a first isotope such that the first set of cells (or daughters thereof) incorporate the first isotope into their own biomolecules. A second set of cells can be cultured with biomolecules having a second isotope. The first set of cells such that the second set of cells (or daughters thereof) incorporate the second isotope into their own biomolecules. The first set of cells and the second set of cells can be lysed, the proteins or proteolytically cleaved peptides thereof can be multiplexed into a sample, and then mass spectrometry can be performed on the sample. 
     Various classes of biomolecules can be labeled to allow relative quantifications. In some cases, the biomolecules can comprise proteins that are labeled with protein-specific labels. For instance, some of the labels disclosed herein may covalently bind to proteins, lipids, sugars, or nucleic acids. 
     In some cases, the systems and methods disclosed herein can provide spatially or temporally differential biomolecule compositions of small-volume samples (e.g., individual cells). Spatially differential biomolecule compositions can be obtained by sampling biomolecules from different portions in a cell (e.g., different compartments in a cell) or a tissue (e.g., healthy versus cancerous cells in a tumor, or cells from the epidermis, dermis, and hypodermis of skin). Temporally differential biomolecule compositions can be obtained by sampling biomolecules at different times (e.g., a cell before and after treatment with a potential therapeutic). In some cases, the systems and methods disclosed herein can provide differential biomolecule compositions across a population of subjects (e.g., tumor cells from those treated with a potential chemotherapeutic versus those who have not been treated). Various biomolecules can be targeted (e.g., proteins or nucleic acids) to provide differential transcriptomic or proteomic information between samples. 
     In some aspects, the present disclosure provides systems and methods for distinguishing between particular disease states (e.g., subtypes of cancer or stages of cancer) in biological samples. Biological samples are complex mixtures of various biomolecules, including proteins, nucleic acids, lipids, polysaccharides, and more. The presence or absence and concentration of various biomolecules, as well as correlations between various subsets of biomolecules (e.g., proteins and nucleic acids), may be indicative of the biological state of a sample (e.g., a healthy or a disease state). Disclosed herein are compositions and workflows for analysis of proteins, using a method comprising corona analysis of biomolecules on a particulate surface and nucleic acids (e.g., cell-free nucleic acids) using sequencing (e.g., next generation sequencing (NGS) techniques) in one or more samples. The one or more samples may comprise one or more biological samples. The one or more samples may be obtained from a subject. The one or more samples may be obtained from a plurality of subjects. The methods disclosed herein may identify a related pattern between proteins and nucleic acids, or between any of the various biomolecules disclosed herein, wherein the related pattern can be indicative of one or more biological states. In some cases, a biological state may be a healthy biological state or a disease state. 
     In an example workflow shown in  FIG.  1   , a proteogenomic method of the present disclosure is described, with optional steps shown with dashed lines and boxes. Initially, a biological sample is obtained  100 . The biological sample is optionally split in multiple portions  105  comprising a first portion of the sample and a second portion of the sample. The first portion of the sample may be contacted to a sensor element (e.g., a particle). Upon contacting, biomolecules (e.g., proteins or protein groups) from the sample may adsorb to the sensor element surface forming a biomolecule corona  110 . The particle(s) may be separated from unbound biomolecules in the sample  115 . Optionally, a sample or a portion of the sample may be subjected to nucleic acid analysis (e.g., optionally  130 , optionally  135 ,  140 , and  150 ) following contact with particles  110  or a subsequent separation of particles from unbound biomolecules  115 . The biomolecules in the corona may be released, e.g., by elution or trypsinization, from the particle surface  120 . The resulting biomolecules or fragments thereof (e.g., peptides and/or proteins) may be assayed using a number of qualitative or quantitative techniques, such as mass spectrometry  125 . The composition of the biomolecule corona and abundances of species (e.g., amount(s) of a protein or protein group(s)) within the biomolecule corona are, thus, identified thereby generating proteomic data. The sample or the second portion of the sample may undergo nucleic acid analysis. Nucleic acids may optionally be enriched, e.g., using amplification or pull-down probes (e.g., in solution or attached to a solid substrate)  130 . Optionally, a sample or a portion of a sample may be subjected to biomolecule corona analysis (e.g.,  110 , optionally  115 , optionally  120 ,  125 , and  145 ) following nucleic acid enrichment  130 . Nucleic acids may be contacted with reagents for nucleic acid analysis, such as sequencing  135 ,  140  to yield sequence information or genomic data  140 . The sequencing may comprise quantifying nucleic acid sequences from the biological sample. Sequencing may be carried out by sequencing by synthesis (NGS). Sequencing may be carried out by traditional Sanger sequencing. The generated proteomic data  125  may be used to identify peptides, proteins, or protein groups from the sample  145 , and the genomic data  140  may be used to identify nucleic acid sequences in the sample. Optionally, the nucleic acid sequences may inform peptide, protein, or protein group identification, or may affect biomolecule assaying (e.g., by informing data-dependent acquisition of mass spectrometric data). The peptides, proteins, or protein groups identified in a sample may also affect the identification of nucleic acid sequences. The identified peptides, proteins, protein groups and/or nucleic acid sequences may be combined to identify a biological state of the biological sample  155 . 
     For next generation sequencing methods, samples may be contacted to a reagent for cleaving nucleic acids into short sequence stretches, such as a nuclease. In instances where cell-free nucleic acid molecules are analyzed, cleavage may not be necessary, as cell-free nucleic acid molecules tend to already be present in short fragments. Next, nucleic acid molecules may be contacted to adaptors. Adaptors may be ligated to the nucleic acid molecules. Adaptor ligated nucleic acids may be amplified, for example by polymerase chain reaction (“PCR”), with the incorporation of nucleotides labeled with a detectable label. Samples may be imaged and the detectable labels may be detected by imaging in order to determine the sequence of the nucleic acids from the sample. 
     In a further example workflow, a biological sample may be obtained and then contacted with a particle that binds a nucleic acid from the sample. The particle may be functionalized with nucleic acid binding moieties (e.g., a protein with a DNA binding motif or an oligonucleotide with a single stranded region capable of hybridizing to a target nucleic acid). The captured nucleotides may be eluted from the particle and analyzed, for example by gel electrophoresis, in situ hybridization, or sequencing. In such a workflow, in a separate sample volume, the biological sample may also be contacted with particles lacking nucleic acid binding moieties, and allowing the formation of a biomolecule corona. The particle-corona may be isolated from the sample and assayed to identify or detect various biomolecules in the biomolecule corona, including proteins, thereby rendering a multi-omic snapshot of the biological sample. 
     The compositions and methods disclosed herein provide particles that may capture low abundance biomolecules from a sample and compress the dynamic range of biomolecules in a sample upon incubation of the sensor element with the sample. The methods disclosed herein may capture low abundance biomolecules even in low volume samples (e.g., a single cell), where biomolecule capture may be especially difficult. The methods of the present disclosure may further enable low abundance biomolecule capture from a sample that also comprises medium or high abundance biomolecules, thereby enriching the low abundance biomolecule. For example, after contacting a sample with a particle, a protein may be present at a higher relative abundance in a biomolecule corona than in the sample that it was collected from (e.g., when a protein constitutes 1 in 10 7  proteins in the sample and 1 in 10 5  proteins in the biomolecule corona). Low abundance biomolecule enrichment may be useful when analyzing blood, plasma, and serum samples, which contain proteins in the mg/ml range (e.g., albumin) and proteins in the pg/ml range (e.g., certain cytokines). The methods disclosed herein may allow for assaying of a greater number of proteins or protein groups from a biological sample compared to other mass spectrometry techniques (e.g., data-independent acquisition, DIA, 125 minute injection gradient). For instance, the particle-based assay methods disclosed herein can be capable of assaying 1.7 to 4.5 times more protein groups from a plasma sample than non-particle-based approaches for both depleted (reduced abundance of high abundance proteins) and un-depleted plasma samples (data-independent acquisition, DIA, 125 minute injection gradient). Low abundance biomolecule enrich may also be useful when analyzing single cell samples, which can contain a low amount of proteins in the entire sample, in some cases, less than 1 picogram (pg). 
     Provided herein are compositions of sensor elements (e.g., particles) that may be incubated with various biological samples. In some aspects, the compositions comprise various particle types, alone or in combination, which can be incubated with a wide range of biological samples to analyze the biomolecules (e.g., proteins) present in the biological sample based on binding to particle surfaces to form protein coronas. A single particle type may be used to assay the proteins in a particular biological sample or multiple particle types can be used together to assay the proteins in the biological sample. A protein corona analysis may be performed on a biological sample (e.g., a biofluid) by contacting the biological sample with a plurality of particles, incubating the biological sample with the plurality of particles to form biomolecule coronas (e.g., protein coronas), separating the particles from the biological sample, and analyzing the biomolecule coronas to determine the compositions of the biomolecule coronas. The protein corona analysis methods are compatible with parallel analysis of nucleic acids in the biological sample by sequencing. Some methods comprise mass spectrometric analysis of the protein coronas. Interrogation of a sample with a plurality of particles followed by analysis of the protein coronas formed on the plurality of particles may be referred to herein as “protein corona analysis.” A biological sample may be interrogated with one or more particle types. The protein corona of each particle type may be analyzed separately. The protein corona of one or more particle types may also be analyzed in combination. 
     The present disclosure provides several biological samples that can be assayed using the particles disclosed herein and the methods provided herein. Such biological samples may also be assayed by nucleic acid sequencing to analyze nucleic acid molecules (e.g., DNA, RNA, cDNA and the like) in cellular or cell-free portions of the sample(s). For example, a biological sample may be a biofluid sample such as cerebral spinal fluid (CSF), synovial fluid (SF), urine, plasma, serum, tears, crevicular fluid, semen, whole blood, milk, nipple aspirate, needle aspirate, ductal lavage, vaginal fluid, nasal fluid, ear fluid, gastric fluid, pancreatic fluid, trabecular fluid, lung lavage, prostatic fluid, sputum, fecal matter, bronchial lavage, fluid from swabbings, bronchial aspirants, sweat or saliva. A biofluid may be a fluidized solid, for example a tissue homogenate, or a fluid extracted from a biological sample. A biological sample may be, for example, a tissue sample or a fine needle aspiration (FNA) sample. A biological sample may be a cell culture sample. A biofluid may be a fluidized biological sample. A biofluid may be a cell extract. A biofluid may be a lysate. For example, a biofluid may be a fluidized cell culture extract. 
     Substrates 
     The compositions and methods of the present disclosure may be used or performed in a wide range of structures, devices, and apparatuses, hereinafter referred to as substrates. A substrate may comprise any substrate described in U.S. Patent Application Publication No. 2021/0285958, filed Mar. 29, 2021, the content of which is incorporated by reference in its entirety herein. A substrate may comprise a single partition (e.g., an Eppendorf tube) for holding a volume of sample or reagents, or may comprise a plurality of partitions (e.g., a 16 well plate, a 96 well plate, a 384 well plate, a plurality of wells in a microwell plate) for holding sample or reagent volumes. A partition may comprise a well, a channel (e.g., a microfluidic channel in a microfluidic device), or a compartment. A partition may comprise plasticware (e.g., a plastic multi-well plate), a metal structure (e.g., a metal multi-well plate), a carbon material structure (e.g., a carbon composite material multi-well plate), a gel, glassware, or any combination thereof. A substrate may comprise an imprinted structure. A substrate may comprise a fluidic channel or chamber. The fluidic channel or chamber may be a microfluidic or nanofluidic channel or chamber. A substrate may be sealed (e.g., with a removable plastic slip or a pierceable septum) or sealable (e.g., may comprise a reusable cap or lid). 
     A partition may be configured to hold a volume of at least 1 to 10 microliters (μl), at least 5 to 25 μl, at least 20 to 50 μl, at least 40 to 200 μl, at least 100 to 500 μl, at least 200 μl to 1 ml, at least 2 ml, at least 3 ml, or more. A partition may be configured to hold a volume of less than about 240 μl, 200 μl, 150 μl, 100 μl, 75 μl, 50 μl, 25 μl, 10 μl, 5 μl, 1 μl, or less. A partition may be temperature controlled. A partition may be configured to prevent or diminish evaporation. A partition may be designed to minimize the influx of ambient light. 
     A substrate may comprise a plurality of partitions, wherein the partitions may be grouped by particles, samples, control or any combination thereof, as shown in  FIG.  38   . In this example, the substrate comprises 8 rows and 12 columns that can be used with 5 types of particles (i.e., NP1, NP2, NP3, NP4, and NP5). Each nanoparticle occupies two columns, and up to 16 biological samples may be deposited. In this example, each biological sample is labeled as X1, X2, X3, and so forth, until X16. There may be two columns for control experiments, wherein each control well in the columns may receive a control particle composition, a control biological sample, or both. Each control well may be utilized at a certain step or between steps of an experiment so that an experimental procedure being followed can be troubleshooted. In some cases, particles may be populated in the partitions and then the biological samples may be added in after. In some cases, the biological samples may be populated in the partitions and then the particles may be added in after. 
     Any subset of the partitions may be grouped by particle or grouped by sample. In some cases, the plurality of partitions may comprise rows for samples and columns for particles. In some cases, the plurality of partitions may be grouped by a specific composition of particles. 
     In some cases, a substrate may comprise 2 rows or columns for controls. In some cases, a substrate may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 rows for controls. 
     In some cases, a partition may comprise a single particle for a single biological sample. In some cases, a partition may comprise a plurality of particles for a single biological sample. In some cases, a partition may comprise a single particle for a plurality of biological samples. In some cases, a partition may comprise a plurality of particles for a plurality of biological samples. 
     In some cases, a substrate may comprise at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100 rows or columns. In some cases, a substrate may comprise at most about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100 rows or columns. 
     A sample may be prepared or interrogated within a single substrate or substrate partition, may be divided between multiple substrates or substrate partitions, or may be sequentially transferred between multiple substrates or substrate partitions. For example, a 5 ml sample may be evenly divided between 500 partitions, resulting in separate 10 μl sample volumes. A sample may be mixed with reagents within a partition. A sample may undergo a dilution (e.g., with buffer) within a partition. 
     A substrate may comprise a surface that is configured to capture or interact with a biomolecule from a sample. For example, the surface may be functionalized with nucleic acid binding moieties, such as single stranded nucleic acids, which are capable of binding nucleic acids from a sample. A surface may comprise a sensor element capable of forming a biomolecule corona upon contacting a sample. The surface may comprise a portion of a partition, such as the side of a well in a well plate. 
     A substrate may be configured to allow the application of magnetic fields to the contents of a partition, as shown in  FIG.  40   . In some cases, an applied magnetic field may separate magnetic substances from non-magnetic substances within a partition. 
     A substrate may be coupled to an instrument to receive vibrational energy. In some cases, the substrate may be shaken, vibrated, or sonicated by an instrument, as shown in  FIG.  40   . 
     Sensor Elements 
     Methods of the present disclosure may utilize sensor elements to collect biomolecules from a sample or portion thereof. The sensor element may comprise any sensor elements described in U.S. Patent Application Publication No. 2021/0285957, filed Mar. 29, 2021, the content of which is incorporated by reference in its entirety herein. In some cases, a sensor element may refer to an element that is capable of binding to (e.g., non-specifically) or adsorbing (e.g., variably selective depending upon, physicochemical properties of particles) a plurality of biomolecules when in contact with a sample (e.g., a biological sample comprising biomolecules). A sensor element may collect biomolecules from a biological sample through variably selective adsorption. In some cases, variably selective adsorption comprises an interaction that is not a protein-ligand (an avidin-biotin interaction), protein-receptor, or protein-affinity reagent (e.g., epitope-antibody) interaction. For example, variably selective adsorption may comprise a plurality of analytes (e.g., biomolecules from a biological sample) making contact with a surface of a particle which does not comprise proteins, ligands, or affinity reagents immobilized (e.g., chemically tethered) thereto. Variably selective adsorption of biomolecules or biomolecule groups from a biological sample by a sensor element may generate a biomolecule corona comprising the biomolecules or biomolecule groups on a surface of the sensor element. In some cases, variably selective adsorption denotes binding a range of analytes with low affinities (as an illustrative but nonlimiting example, variably selective adsorption may comprise binding at least 50 analytes with a minimum dissociation constant of 50 μM). In some cases, variably selective adsorption may comprise binding at least 50 analytes with a minimum dissociation constant of at least about 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, or 500 μM. In some cases, variably selective adsorption may comprise binding at least 50 analytes with a minimum dissociation constant of at most about 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, or 500 μM. In some cases, variably selective adsorption denotes binding a range of analytes with slow binding kinetics (for example, with approximate pseudo-first order adsorption half-lives of 10 to 100 minutes). In some cases, a sensor element may be modified to have a higher variably selective adsorption affinity for a group of proteins and lower variably selective adsorption affinity for another group of proteins. In some cases, a sensor element may be modified to comprise charge to increase the affinity of the sensor element towards some oppositely charged biomolecules. In some cases, a sensor element may be modified to comprise specific binding moieties, such as peptides, proteins, or nucleic acids. 
     A sensor element may comprise a discrete structure (e.g., a particle) or a portion of a structure (e.g., a surface of a nanomaterial). In some cases, a particle may be or may comprise a sensor element. In some cases, a particle may be a nanoparticle which may be or may comprise a sensor element. In some cases, a composition comprising a particle or a nanoparticle may be or may comprise a sensor element. In some cases, the composition may be a dry composition. The dry composition may be or may comprise a sensor element. In some cases, sensor element can encompass a nanoscale sensor element. In some cases, a sensor element may comprise a porous structure (e.g., a polymer matrix). In some cases, a sensor element may comprise a projection from a single structure (e.g., a flexible oligomer extending from a rigid metal oxide surface). In many cases, a sensor element may comprise a dimension with a length from about 5 nanometers (nm) to about 50000 nm in at least one direction. Suitable sensor elements may include, for example, but are not limited to a sensor element from about 5 nm to about 50,000 nm in at least one direction, including, about 5 nm to about 40000 nm, alternatively about 5 nm to about 30000 nm, alternatively about 5 nm to about 20,000 nm, alternatively about 5 nm to about 10,000 nm, alternatively about 5 nm to about 5000 nm, alternatively about 5 nm to about 1000 nm, alternatively about 5 nm to about 500 nm, alternatively about 5 nm to 50 nm, alternatively about 10 nm to 100 nm, alternatively about 20 nm to 200 nm, alternatively about 30 nm to 300 nm, alternatively about 40 nm to 400 nm, alternatively about 50 nm to 500 nm, alternatively about 60 nm to 600 nm, alternatively about 70 nm to 700 nm, alternatively about 80 nm to 800 nm, alternatively about 90 nm to 900 nm, alternatively about 100 nm to 1000 nm, alternatively about 1000 nm to 10000 nm, alternatively about 10000 nm to 50000 nm and any combination or amount in between (e.g. 5 nm, 10 nm, 15 nm, 20 nm, 25 nm, 30 nm, 35 nm, 40 nm, 45 nm, 50 nm, 55 nm, 60 nm, 65 nm, 70 nm, 80 nm, 90 nm, 100 nm, 125 nm, 150 nm, 175 nm, 200 nm, 225 nm, 250 nm, 275 nm, 300 nm, 350 nm, 400 nm, 450 nm, 500 nm, 550 nm, 600 nm, 650 nm, 700 nm, 750 nm, 800 nm, 850 nm, 900 nm, 1000 nm, 1200 nm, 1300 nm, 1400 nm, 1500 nm, 1600 nm, 1700 nm, 1800 nm, 1900 nm, 2000 nm, 2500 nm, 3000 nm, 3500 nm, 4000 nm, 4500 nm, 5000 nm, 5500 nm, 6000 nm, 6500 nm, 7000 nm, 7500 nm, 8000 nm, 8500 nm, 9000 nm, 10000 nm, 11000 nm, 12000 nm, 13000 nm, 14000 nm, 15000 nm, 16000 nm, 17000 nm, 18000 nm, 19000 nm, 20000 nm, 25000 nm, 30000 nm, 35000 nm, 40000 nm, 45000 nm, 50000 nm and any number in between). In some cases, a nanoscale sensor element may refer to a sensor element that is less than 1 micron in at least one direction. Suitable examples of ranges of nanoscale sensor elements may include, but are not limited to, for example, elements from about 5 nm to about 1000 nm in one direction, including, from example, about 5 nm to about 500 nm, alternatively about 5 nm to about 400 nm, alternatively about 5 nm to about 300 nm, alternatively about 5 nm to about 200 nm, alternatively about 5 nm to about 100 nm, alternatively about 5 nm to about 50 nm, alternatively about 10 nm to about 1000 nm, alternatively about 10 nm to about 750 nm, alternatively about 10 nm to about 500 nm, alternatively about 10 nm to about 250 nm, alternatively about 10 nm to about 200 nm, alternatively about 10 nm to about 100 nm, alternatively about 50 nm to about 1000 nm, alternatively about 50 nm to about 500 nm, alternatively about 50 nm to about 250 nm, alternatively about 50 nm to about 200 nm, alternatively about 50 nm to about 100 nm, and any combinations, ranges or amount in-between (e.g. 5 nm, 10 nm, 15 nm, 20 nm, 25 nm, 30 nm, 35 nm, 40 nm, 45 nm, S0 nm, 55 nm, 60 nm, 65 nm, 70 nm, 80 nm, 90 nm, 100 nm, 125 nm, 150 nm, 175 nm, 200 nm, 225 nm, 250 nm, 275 nm, 300 nm, 350 nm, 400 nm, 450 nm, 500 nm, 550 nm, 600 nm, 650 nm, 700 nm, 750 nm, 800 nm, 850 nm, 900 nm, 1000 nm, etc.). In reference to the sensor elements described herein, the use of the term sensor element includes the use of a nanoscale sensor element for the sensor and associated methods. 
     A sensor element may form a biomolecule corona upon contact with a sample. In some cases, the term “biomolecule corona” can refer to the composition, signature, or pattern of different biomolecules that are bound to a sensor element. In some cases, the biomolecule corona not only refers to the different biomolecules but may also refer to the differences in the amount, level, or quantity of one or more biomolecules bound to the sensor element, differences in the charge or conformational state of the one or more biomolecules that are bound to the sensor element, or differences in the chemical (e.g., redox, post-transcriptional, or post translational) state of the one or more biomolecules that are bound to the sensor element. It is contemplated that the biomolecule corona of each sensor element may contain some of the same biomolecules, may contain distinct biomolecules with regard to the other sensor elements, and/or may differ in level or quantity, type or charge or conformation of the biomolecule. In some cases, a biomolecule corona may comprise a composition that is different from a provided biological sample. In some cases, a biomolecule corona may comprise a higher proportion of a subset of proteins and/or nucleic acids present in a provided biological sample than in the provided biological sample, for instance, proteins and/or nucleic acids of longer lengths or higher molecular weights. 
     The biomolecule corona may depend on not only the physicochemical properties of the sensor element, but may also depend on the nature of the sample and the duration of exposure to the sample. The type, amount, and categories of the biomolecules that make up these biomolecule coronas may be responsive to the physicochemical properties of the sensor elements as well as the complex interactions between the different biomolecules present in the sample. These interactions may lead to the production of a biomolecule coronas for each sensor element. 
     A biomolecule corona may comprise proteins, saccharides, lipids, metabolites, nucleic acids (e.g., DNA or RNA), or any combination thereof. In some cases, the biomolecule corona is a protein corona. In another case, the biomolecule corona is a polysaccharide corona. In yet another case, the biomolecule corona is a metabolite corona. In some cases, the biomolecule corona is a lipidomic corona. A biomolecule corona may comprise a plurality of layers of biomolecules. For instance, a biomolecule corona may comprise an average thickness of 2 nm to more than 50 nm, corresponding to from 1 to greater than 50 layers of biomolecules. A biomolecule corona may comprise nucleic acids of various lengths or various molecular weights. A biomolecule corona may comprise proteins of various lengths or various molecular weights. 
     Non-Specific Binding 
     A particle may form a biomolecule corona through variably selective adsorption (e.g., adsorption of biomolecules or biomolecule groups upon contacting the particle to a biological sample comprising the biomolecules or biomolecule groups, which adsorption is variably selective depending upon factors including e.g., physicochemical properties of the particle) or non-specific binding. Non-specific binding can refer to a class of binding interactions that exclude specific binding. Examples of specific binding may comprise protein-ligand binding interactions, antigen-antibody binding interactions, nucleic acid hybridizations, or a binding interaction between a template molecule and a target molecule wherein the template molecule provides a sequence or a 3D structure that favors the binding of a target molecule that comprise a complementary sequence or a complementary 3D structure, and disfavors the binding of a non-target molecule(s) that does not comprise the complementary sequence or the complementary 3D structure. 
     Non-specific binding may comprise one or a combination of a wide variety of chemical and physical interactions and effects. Non-specific binding may comprise electromagnetic forces, such as electrostatics interactions, London dispersion, Van der Waals interactions, or dipole-dipole interactions (e.g., between both permanent dipoles and induced dipoles). Non-specific binding may be mediated through covalent bonds, such as disulfide bridges. Non-specific binding may be mediated through hydrogen bonds. Non-specific binding may comprise solvophobic effects (e.g., hydrophobic effect), wherein one object is repelled by a solvent environment and is forced to the boundaries of the solvent, such as the surface of another object. Non-specific binding may comprise entropic effects, such as in depletion forces, or raising of the thermal energy above a critical solution temperature (e.g., a lower critical solution temperature). Non-specific binding may comprise kinetic effects, wherein one binding molecule may have faster binding kinetics than another binding molecule. 
     Non-specific binding may comprise a plurality of non-specific binding affinities for a plurality of targets (e.g., at least 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10,000, 20,000, 30,000, 40,000, 50,000 different targets adsorbed to a single particle). The plurality of targets may have similar non-specific binding affinities that are within about one, two, or three magnitudes (e.g., as measured by non-specific binding free energy, equilibrium constants, competitive adsorption, etc.). This may be contrasted with specific binding, which may comprise a higher binding affinity for a given target molecule than non-target molecules. 
     Biomolecules may adsorb onto a surface through non-specific binding on a surface at various densities. In some cases, biomolecules may adsorb at a density at least about 10 9  milligrams (mg) of biomolecules per square millimeter (mm 2 ). In some cases, proteins may adsorb at a density at least about 10 −9  milligrams (mg) of biomolecules per square millimeter (mm 2 ). In some cases, biomolecules or proteins may adsorb at a density of at least about 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 fg/mm 2 . In some cases, biomolecules or proteins may adsorb at a density of at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 pg/mm 2 . In some cases, biomolecules or proteins may adsorb at a density of at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 ng/mm 2 . In some cases, biomolecules or proteins may adsorb at a density of at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 μg/mm 2 . In some cases, biomolecules or proteins may adsorb at a density of at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 mg/mm 2 . In some cases, biomolecules or proteins may adsorb at a density of at most about 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 fg/mm. In some cases, biomolecules or proteins may adsorb at a density of at most about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 pg/mm 2 . In some cases, biomolecules or proteins may adsorb at a density of at most about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 ng/mm 2 . In some cases, biomolecules or proteins may adsorb at a density of at most about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 μg/mm 2 . In some cases, biomolecules or proteins may adsorb at a density of at most about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 mg/mm 2 . 
     Adsorbed biomolecules may comprise various types of proteins. In some cases, adsorbed proteins may comprise at least 5 types of proteins. In some cases, adsorbed proteins may comprise at least 200 types of proteins. In some cases, adsorbed proteins may comprise at least 500 types of proteins. In some cases, adsorbed proteins may comprise from 5 to 1000 types of proteins. In some cases, adsorbed proteins may comprise from 20 to 200 types of proteins. In some cases, adsorbed proteins may comprise at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, or 10000 types of proteins. In some cases, adsorbed proteins may comprise at most about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, or 10000 types of proteins. 
     In some cases, proteins in a biological sample may comprise at least 1 orders of magnitudes in concentration. In some cases, proteins in a biological sample may comprise at least 2 orders of magnitudes in concentration. In some cases, proteins in a biological sample may comprise at least 3 orders of magnitudes in concentration. In some cases, proteins in a biological sample may comprise at least 4 orders of magnitudes in concentration. In some cases, proteins in a biological sample may comprise at least 5 orders of magnitudes in concentration. In some cases, proteins in a biological sample may comprise at least 6 orders of magnitudes in concentration. 
     Particle Types 
     A sensor element may be or may comprise a particle. Particles of various types disclosed herein can be made from various materials. For example, particle materials may be made from materials comprising metals, polymers, magnetic materials, oxides, and/or lipids. Magnetic particles may be iron oxide particles. Examples of metal materials include any one of or any combination of gold, silver, copper, nickel, cobalt, palladium, platinum, iridium, osmium, rhodium, ruthenium, rhenium, vanadium, chromium, manganese, niobium, molybdenum, tungsten, tantalum, iron and cadmium, or any other material described in U.S. Pat. No. 7,749,299. Examples of oxide materials include any one of or any combination of magnesium oxide, silica, titanium oxide, vanadium oxide, or nickel oxide. In some cases, a particle material may be made from silicon. A particle may be a magnetic particle, such as a superparamagnetic iron oxide nanoparticle (SPION). 
     Examples of polymers include any one of or any combination of polyethylenes, polycarbonates, polyanhydrides, polyhydroxyacids, polypropylfumerates, polycaprolactones, polyamides, polyacetals, polyethers, polyesters, poly(orthoesters), polycyanoacrylates, polyvinyl alcohols, polyurethanes, polyphosphazenes, polyacrylates, polymethacrylates, polycyanoacrylates, polyureas, polystyrenes, or polyamines, a polyalkylene glycol (e.g., polyethylene glycol (PEG)), a polyester (e.g., poly(lactide-co-glycolide) (PLGA), polylactic acid, or polycaprolactone), or a copolymer of two or more polymers, such as a copolymer of a polyalkylene glycol (e.g., PEG) and a polyester (e.g., PLGA). The polymer may be a lipid-terminated polyalkylene glycol and a polyester, or any other material disclosed in U.S. Pat. No. 9,549,901. 
     In some cases, a polymer may comprise polymers with linear topology, branched topology, star topology, dendritic topology, hyperbranched topology, bottlebrush topology, ring topology, catenated topology, or any combination thereof. In some cases, a polymer may comprise 3-armed topology, 4-armed topology, 5-armed topology, 6-armed topology, 7-armed topology, 8-armed topology, 9-armed topology, or 10-armed topology. In some cases, a polymer may comprise a crosslinker. 
     In some cases, a polymer may comprise at least about 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, or 100000 monomers. In some cases, a polymer may comprise at most about 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, or 100000 monomers. 
     Examples of lipids that can be used to form the particles of the present disclosure include cationic, anionic, and neutrally charged lipids. For example, particles can be made of any one of or any combination of dioleoylphosphatidylglycerol (DOPG), diacylphosphatidylcholine, diacylphosphatidylethanolamine, ceramide, sphingomyelin, cephalin, cholesterol, cerebrosides and diacylglycerols, dioleoylphosphatidylcholine (DOPC), dimyristoylphosphatidylcholine (DMPC), and dioleoylphosphatidylseine (DOPS), phosphatidylglycerol, cardiolipin, diacylphosphatidylserine, diacylphosphatidic acid, N-dodecanoyl phosphatidylethanolamines, N-succinyl phosphatidylethanolamines, N-glutarylphosphatidylethanolamines, lysylphosphatidylglycerols, palmitoyloleyolphosphatidylglycerol (POPG), lecithin, lysolecithin, phosphatidylethanolamine, lysophosphatidylethanolamine, dioleoylphosphatidylethanolamine (DOPE), dipalmitoyl phosphatidyl ethanolamine (DPPE), dimyristoylphosphoethanolamine (DMPE), distearoyl-phosphatidyl-ethanolamine (DSPE), palmitoyloleoyl-phosphatidylethanolamine (POPE) palmitoyloleoylphosphatidylcholine (POPC), egg phosphatidylcholine (EPC), distearoylphosphatidylcholine (DSPC), dioleoylphosphatidylcholine (DOPC), dipalmitoylphosphatidylcholine (DPPC), dioleoylphosphatidylglycerol (DOPG), dipalmitoylphosphatidylglycerol (DPPG), palmitoyloleyolphosphatidylglycerol (POPG), 16-O-monomethyl PE, 16-O-dimethyl PE, 18-1-trans PE, palmitoyloleoyl-phosphatidylethanolamine (POPE), 1-stearoyl-2-oleoyl-phosphatidyethanolamine (SOPE), phosphatidylserine, phosphatidylinositol, sphingomyelin, cephalin, cardiolipin, phosphatidic acid, cerebrosides, dicetyliphosphate, and cholesterol, or any other material listed in U.S. Pat. No. 9,445,994, which is incorporated herein by reference in its entirety. 
     Examples of particles of the present disclosure are provided in TABLE 1. 
     
       
         
           
               
             
               
                 TABLE 1 
               
             
            
               
                   
               
               
                 Example particles of the present disclosure 
               
            
           
           
               
               
               
               
            
               
                 Batch 
                   
                 Particle 
                   
               
               
                 No. 
                 Type 
                 ID 
                 Description 
               
               
                   
               
               
                 S-001-001 
                 HX-13 
                 SP-001 
                 Carboxylate (Citrate) superparamagnetic 
               
               
                   
                   
                   
                 iron oxide NPs (SPION) 
               
               
                 S-002-001 
                 HX-19 
                 SP-002 
                 Phenol-formaldehyde coated SPION 
               
               
                 S-003-001 
                 HX-20 
                 SP-003 
                 Silica-coated superparamagnetic iron 
               
               
                   
                   
                   
                 oxide NPs (SPION) 
               
               
                 S-004-001 
                 HX-31 
                 SP-004 
                 Polystyrene coated SPION 
               
               
                 S-005-001 
                 HX-38 
                 SP-005 
                 Carboxylated Poly(styrene-co-methacrylic 
               
               
                   
                   
                   
                 acid), P(St-co-MAA) coated SPION 
               
               
                 S-006-001 
                 HX-42 
                 SP-006 
                 N-(3-Trimethoxysilylpropyl)diethylenetri- 
               
               
                   
                   
                   
                 amine coated SPION 
               
               
                 S-007-001 
                 HX-56 
                 SP-007 
                 poly(N-(3-(dimethylamino)propyl) 
               
               
                   
                   
                   
                 methacrylamide) (PDMAPMA)-coated 
               
               
                   
                   
                   
                 SPION 
               
               
                 S-008-001 
                 HX-57 
                 SP-008 
                 1,2,4,5-Benzenetetracarboxylic acid 
               
               
                   
                   
                   
                 coated SPION 
               
               
                 S-009-001 
                 HX-58 
                 SP-009 
                 poly(vinylbenzyltrimethylammonium 
               
               
                   
                   
                   
                 chloride) (PVBTMAC) coated SPION 
               
               
                 S-010-001 
                 HX-59 
                 SP-010 
                 Carboxylate, PAA coated SPION 
               
               
                 S-011-001 
                 HX-86 
                 SP-011 
                 poly(oligo(ethylene glycol) methyl ether 
               
               
                   
                   
                   
                 methacrylate) (POEGMA)-coated SPION 
               
               
                 P-033-001 
                 P33 
                 SP-333 
                 Carboxylate microparticle, surfactant free 
               
               
                 P-039-003 
                 P39 
                 SP-329 
                 Polystyrene carboxyl functionalized 
               
               
                 P-041-001 
                 P41 
                 SP-341 
                 Carboxylic acid 
               
               
                 P-047-001 
                 P47 
                 SP-365 
                 Silica 
               
               
                 P-048-001 
                 P48 
                 SP-348 
                 Carboxylic acid, 150 nm 
               
               
                 P-053-001 
                 P53 
                 SP-353 
                 Amino surface microparticle, 0.4-0.6 μm 
               
               
                 P-056-001 
                 P56 
                 SP-356 
                 Silica amino functionalized microparticle, 
               
               
                   
                   
                   
                 0.1-0.39 μm 
               
               
                 P-063-001 
                 P63 
                 SP-363 
                 Jeffamine surface, 0.1-0.39 μm 
               
               
                 P-064-001 
                 P64 
                 SP-364 
                 Polystyrene microparticle, 2.0-2.9 μm 
               
               
                 P-065-001 
                 P65 
                 SP-365 
                 Silica 
               
               
                 P-069-001 
                 P69 
                 SP-369 
                 Carboxylated Original coating, 50 nm 
               
               
                 P-073-001 
                 P73 
                 SP-373 
                 Dextran based coating, 0.13 μm 
               
               
                 P-074-001 
                 P74 
                 SP-374 
                 Silica Silanol coated with lower acidity 
               
               
                 — 
                 S-118 
                 — 
                 Glucose 6-phosphate functionalized 
               
               
                   
                   
                   
                 SPION 
               
               
                 — 
                 S-128 
                 — 
                 Mixed amide, carboxylate functionalized, 
               
               
                   
                   
                   
                 silica-coated SPION 
               
               
                 — 
                 S-229 
                 — 
                 N 1 -(3-(trimethoxysilyl)propyl)hexane-1,6- 
               
               
                   
                   
                   
                 diamine functionalized, silica-coated 
               
               
                   
                   
                   
                 SPION 
               
               
                   
               
            
           
         
       
     
     A particle of the present disclosure may be a synthesized particle. A particle may be surface functionalized. An example of a particle type of the present disclosure may be a carboxylate (Citrate) superparamagnetic iron oxide nanoparticle (SPION), a phenol-formaldehyde coated SPION, a silica-coated SPION, a polystyrene coated SPION, a carboxylated poly(styrene-co-methacrylic acid) coated SPION, a N-(3-Trimethoxysilylpropyl)diethylenetriamine coated SPION, a poly(N-(3-(dimethylamino)propyl) methacrylamide) (PDMAPMA)-coated SPION, a 1,2,4,5-Benzenetetracarboxylic acid coated SPION, a poly(Vinylbenzyltrimethylammonium chloride) (PVBTMAC) coated SPION, a carboxylate, PAA coated SPION, a poly(oligo(ethylene glycol) methyl ether methacrylate) (POEGMA)-coated SPION, a carboxylate microparticle, a polystyrene carboxyl functionalized particle, a carboxylic acid coated particle, a silica particle, a carboxylic acid particle of about 150 nm in diameter, an amino surface microparticle of about 0.4-0.6 μm in diameter, a silica amino functionalized microparticle of about 0.1-0.39 μm in diameter, a Jeffamine surface particle of about 0.1-0.39 μm in diameter, a polystyrene microparticle of about 2.0-2.9 μm in diameter, a silica particle, a carboxylated particle with an original coating of about 50 nm in diameter, a particle coated with a dextran based coating of about 0.13 μm in diameter, or a silica silanol coated particle with low acidity. In some cases, a particle may lack functionalized proteins for specific binding on its surface. In some cases, a surface functionalized particle does not comprise an antibody or a T cell receptor, a chimeric antigen receptor, a receptor protein, or a variant or fragment thereof. 
     Particles of the present disclosure can be made and used in methods of forming protein coronas after incubation in a biofluid at a wide range of sizes. A particle of the present disclosure may be a nanoparticle. A nanoparticle of the present disclosure may be from about 10 nm to about 1000 nm in diameter. For example, the nanoparticles disclosed herein can be at least 10 nm, at least 100 nm, at least 200 nm, at least 300 nm, at least 400 nm, at least 500 nm, at least 600 nm, at least 700 nm, at least 800 nm, at least 900 nm, from 10 nm to 50 nm, from 50 nm to 100 nm, from 100 nm to 150 nm, from 150 nm to 200 nm, from 200 nm to 250 nm, from 250 nm to 300 nm, from 300 nm to 350 nm, from 350 nm to 400 nm, from 400 nm to 450 nm, from 450 nm to 500 nm, from 500 nm to 550 nm, from 550 nm to 600 nm, from 600 nm to 650 nm, from 650 nm to 700 nm, from 700 nm to 750 nm, from 750 nm to 800 nm, from 800 nm to 850 nm, from 850 nm to 900 nm, from 100 nm to 300 nm, from 150 nm to 350 nm, from 200 nm to 400 nm, from 250 nm to 450 nm, from 300 nm to 500 nm, from 350 nm to 550 nm, from 400 nm to 600 nm, from 450 nm to 650 nm, from 500 nm to 700 nm, from 550 nm to 750 nm, from 600 nm to 800 nm, from 650 nm to 850 nm, from 700 nm to 900 nm, or from 10 nm to 900 nm in diameter. A nanoparticle may be less than 1000 nm in diameter. 
     A particle of the present disclosure may be a microparticle. A microparticle may be a particle that is from about 1 μm to about 1000 μm in diameter. For example, the microparticles disclosed here can be at least 1 μm, at least 10 μm, at least 100 μm, at least 200 μm, at least 300 μm, at least 400 μm, at least 500 μm, at least 600 μm, at least 700 μm, at least 800 μm, at least 900 μm, from 10 μm to 50 μm, from 50 μm to 100 μm, from 100 μm to 150 μm, from 150 μm to 200 μm, from 200 μm to 250 μm, from 250 μm to 300 μm, from 300 μm to 350 μm, from 350 μm to 400 μm, from 400 μm to 450 μm, from 450 μm to 500 μm, from 500 μm to 550 μm, from 550 μm to 600 μm, from 600 μm to 650 μm, from 650 μm to 700 μm, from 700 μm to 750 μm, from 750 μm to 800 μm, from 800 μm to 850 μm, from 850 μm to 900 μm, from 100 μm to 300 μm, from 150 μm to 350 μm, from 200 μm to 400 μm, from 250 μm to 450 μm, from 300 μm to 500 μm, from 350 μm to 550 μm, from 400 μm to 600 μm, from 450 μm to 650 μm, from 500 μm to 700 μm, from 550 μm to 750 μm, from 600 μm to 800 μm, from 650 μm to 850 μm, from 700 μm to 900 μm, or from 10 μm to 900 μm in diameter. A microparticle may be less than 1000 μm in diameter. 
     The ratio between surface area and mass can be a determinant of a particle&#39;s properties. For example, the number and types of biomolecules that a particle adsorbs from a solution may vary with the particle&#39;s surface area to mass ratio. The particles disclosed herein can have surface area to mass ratios of 3 to 30 cm 2 /mg, 5 to 50 cm 2 /mg, 10 to 60 cm 2 /mg, 15 to 70 cm 2 /mg, 20 to 80 cm 2 /mg, 30 to 100 cm 2 /mg, 35 to 120 cm 2 /mg, 40 to 130 cm 2 /mg, 45 to 150 cm 2 /mg, 50 to 160 cm 2 /mg, 60 to 180 cm 2 /mg, 70 to 200 cm 2 /mg, 80 to 220 cm 2 /mg, 90 to 240 cm 2 /mg, 100 to 270 cm 2 /mg, 120 to 300 cm 2 /mg, 200 to 500 cm 2 /mg, 10 to 300 cm 2 /mg, 1 to 3000 cm 2 /mg, 20 to 150 cm 2 /mg, 25 to 120 cm 2 /mg, or from 40 to 85 cm 2 /mg. Small particles (e.g., with diameters of 50 nm or less) can have significantly higher surface area to mass ratios, stemming in part from the higher order dependence on diameter by mass than by surface area. In some cases (e.g., for small particles), the particles can have surface area to mass ratios of 200 to 1000 cm 2 /mg, 500 to 2000 cm 2 /mg, 1000 to 4000 cm 2 /mg, 2000 to 8000 cm 2 /mg, or 4000 to 10000 cm 2 /mg. In some cases (e.g., for large particles), the particles can have surface area to mass ratios of 1 to 3 cm 2 /mg, 0.5 to 2 cm 2 /mg, 0.25 to 1.5 cm 2 /mg, or 0.1 to 1 cm 2 /mg. 
     In some cases, a plurality of particles (e.g., of a particle panel) used with the methods described herein may have a range of surface area to mass ratios. In some cases, the range of surface area to mass ratios for a plurality of particles is less than 100 cm 2 /mg, 80 cm 2 /mg, 60 cm 2 /mg, 40 cm 2 /mg, 20 cm 2 /mg, 10 cm 2 /mg, 5 cm 2 /mg, or 2 cm 2 /mg. In some cases, the surface area to mass ratios for a plurality of particles varies by no more than 40%, 30%, 20%, 10%, 5%, 3%, 2%, or 1% between the particles in the plurality. In some cases, the plurality of particles may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 15, 20, or more different types of particles. 
     In some cases, a plurality of particles (e.g., in a particle panel) may have a wider range of surface area to mass ratios. In some cases, the range of surface area to mass ratios for a plurality of particles is greater than 100 cm 2 /mg, 150 cm 2 /mg, 200 cm 2 /mg, 250 cm 2 /mg, 300 cm 2 /mg, 400 cm 2 /mg, 500 cm 2 /mg, 800 cm 2 /mg, 1000 cm 2 /mg, 1200 cm 2 /mg, 1500 cm 2 /mg, 2000 cm 2 /mg, 3000 cm 2 /mg, 5000 cm 2 /mg, 7500 cm 2 /mg, 10000 cm 2 /mg, or more. In some cases, the surface area to mass ratios for a plurality of particles (e.g., within a panel) can vary by more than 100%, 200%, 300%, 400%, 5000%, 1000%, 10000% or more. In some cases, the plurality of particles with a wide range of surface area to mass ratios comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 15, 20, or more different types of particles. 
     A particle may comprise a wide array of physical properties. A physical property of a particle may include composition, size, surface charge, hydrophobicity, hydrophilicity, amphipathicity, surface functionality, surface topography, surface curvature, porosity, core material, shell material, shape, zeta potential, and any combination thereof. A particle may have a core-shell structure. In some cases, a core material may comprise metals, polymers, magnetic materials, oxides, and/or lipids. In some cases, a shell material may comprise metals, polymers, magnetic materials, oxides, and/or lipids. 
     In some cases, surface topography may comprise roughness of various scales, for instance, a roughness may have a dimension lateral to a surface of at least about 0.1 nm, 0.2 nm, 0.3 nm, 0.4 nm, 0.5 nm, 0.6 nm, 0.7 nm, 0.8 nm, 0.9 nm, 1 nm, 2 nm, 3 nm, 4 nm, 5 nm, 6 nm, 7 nm, 8 nm, 9 nm, 10 nm, 20 nm, 30 nm, 40 nm, 50 nm, 60 nm, 70 nm, 80 nm, 90 nm, 100 nm, 200 nm, 300 nm, 400 nm, 500 nm, 600 nm, 700 nm, 800 nm, 900 nm, 1 μm, 2 μm, 3 μm, 4 μm, 5 μm, 6 μm, 7 μm, 8 μm, 9 μm, 10 μm, 20 μm, 30 μm, 40 μm, 50 μm, 60 μm, 70 μm, 80 μm, 90 μm, 100 μm, 200 μm, 300 μm, 400 μm, 500 μm, 600 μm, 700 μm, 800 μm, 900 μm, or 1000 μm. In some cases, a roughness may have a dimension lateral to a surface of at most about 0.1 nm, 0.2 nm, 0.3 nm, 0.4 nm, 0.5 nm, 0.6 nm, 0.7 nm, 0.8 nm, 0.9 nm, 1 nm, 2 nm, 3 nm, 4 nm, 5 nm, 6 nm, 7 nm, 8 nm, 9 nm, 10 nm, 20 nm, 30 nm, 40 nm, 50 nm, 60 nm, 70 nm, 80 nm, 90 nm, 100 nm, 200 nm, 300 nm, 400 nm, 500 nm, 600 nm, 700 nm, 800 nm, 900 nm, 1 μm, 2 μm, 3 μm, 4 μm, 5 μm, 6 μm, 7 μm, 8 μm, 9 μm, 10 μm, 20 μm, 30 μm, 40 μm, 50 μm, 60 μm, 70 μm, 80 μm, 90 μm, 100 μm, 200 μm, 300 μm, 400 μm, 500 μm, 600 μm, 700 μm, 800 μm, 900 μm, or 1000 μm. 
     In some cases a roughness may have a depth at least about 0.1 nm, 0.2 nm, 0.3 nm, 0.4 nm, 0.5 nm, 0.6 nm, 0.7 nm, 0.8 nm, 0.9 nm, 1 nm, 2 nm, 3 nm, 4 nm, 5 nm, 6 nm, 7 nm, 8 nm, 9 nm, 10 nm, 20 nm, 30 nm, 40 nm, 50 nm, 60 nm, 70 nm, 80 nm, 90 nm, 100 nm, 200 nm, 300 nm, 400 nm, 500 nm, 600 nm, 700 nm, 800 nm, 900 nm, 1 μm, 2 μm, 3 μm, 4 μm, 5 μm, 6 μm, 7 μm, 8 μm, 9 μm, 10 μm, 20 μm, 30 μm, 40 μm, 50 μm, 60 μm, 70 μm, 80 μm, 90 μm, 100 μm, 200 μm, 300 μm, 400 μm, 500 μm, 600 μm, 700 μm, 800 μm, 900 μm, or 1000 μm. In some cases a roughness may have a depth at most about 0.1 nm, 0.2 nm, 0.3 nm, 0.4 nm, 0.5 nm, 0.6 nm, 0.7 nm, 0.8 nm, 0.9 nm, 1 nm, 2 nm, 3 nm, 4 nm, 5 nm, 6 nm, 7 nm, 8 nm, 9 nm, 10 nm, 20 nm, 30 nm, 40 nm, 50 nm, 60 nm, 70 nm, 80 nm, 90 nm, 100 nm, 200 nm, 300 nm, 400 nm, 500 nm, 600 nm, 700 nm, 800 nm, 900 nm, 1 μm, 2 μm, 3 μm, 4 μm, 5 μm, 6 μm, 7 μm, 8 μm, 9 μm, 10 μm, 20 μm, 30 μm, 40 μm, 50 μm, 60 μm, 70 μm, 80 μm, 90 μm, 100 μm, 200 μm, 300 μm, 400 μm, 500 μm, 600 μm, 700 μm, 800 μm, 900 μm, or 1000 μm. 
     A surface functionality may comprise a polymerizable functional group, a positively or negatively charged functional group, a zwitterionic functional group, an acidic or basic functional group, a polar functional group, a nonpolar functional group, or any combination thereof. A surface functionality may comprise carboxyl groups, hydroxyl groups, thiol groups, cyano groups, nitro groups, ammonium groups, alkyl groups, imidazolium groups, sulfonium groups, pyridinium groups, pyrrolidinium groups, phosphonium groups, aminopropyl groups, amine groups, boronic acid groups, N-succinimidyl ester groups, PEG groups, streptavidin, methyl ether groups, triethoxylpropylaminosilane groups, PCP groups, citrate groups, lipoic acid groups, BPEI groups, or any combination thereof. A particle from among the plurality of particles may be selected from the group consisting of: micelles, liposomes, iron oxide particles, silver particles, gold particles, palladium particles, quantum dots, platinum particles, titanium particles, silica particles, metal or inorganic oxide particles, synthetic polymer particles, copolymer particles, terpolymer particles, polymeric particles with metal cores, polymeric particles with metal oxide cores, polystyrene sulfonate particles, polyethylene oxide particles, polyoxyethylene glycol particles, polyethylene imine particles, polylactic acid particles, polycaprolactone particles, polyglycolic acid particles, poly(lactide-co-glycolide polymer particles, cellulose ether polymer particles, polyvinylpyrrolidone particles, polyvinyl acetate particles, polyvinylpyrrolidone-vinyl acetate copolymer particles, polyvinyl alcohol particles, acrylate particles, polyacrylic acid particles, crotonic acid copolymer particles, polyethlene phosphonate particles, polyalkylene particles, carboxy vinyl polymer particles, sodium alginate particles, carrageenan particles, xanthan gum particles, gum acacia particles, Arabic gum particles, guar gum particles, pullulan particles, agar particles, chitin particles, chitosan particles, pectin particles, karaya tum particles, locust bean gum particles, maltodextrin particles, amylose particles, corn starch particles, potato starch particles, rice starch particles, tapioca starch particles, pea starch particles, sweet potato starch particles, barley starch particles, wheat starch particles, hydroxypropylated high amylose starch particles, dextrin particles, levan particles, elsinan particles, gluten particles, collagen particles, whey protein isolate particles, casein particles, milk protein particles, soy protein particles, keratin particles, polyethylene particles, polycarbonate particles, polyanhydride particles, polyhydroxyacid particles, polypropylfumerate particles, polycaprolactone particles, polyamine particles, polyacetal particles, polyether particles, polyester particles, poly(orthoester) particles, polycyanoacrylate particles, polyurethane particles, polyphosphazene particles, polyacrylate particles, polymethacrylate particles, polycyanoacrylate particles, polyurea particles, polyamine particles, polystyrene particles, poly(lysine) particles, chitosan particles, dextran particles, poly(acrylamide) particles, derivatized poly(acrylamide) particles, gelatin particles, starch particles, chitosan particles, dextran particles, gelatin particles, starch particles, poly-β-amino-ester particles, poly(amido amine) particles, poly lactic-co-glycolic acid particles, polyanhydride particles, bioreducible polymer particles, and 2-(3-aminopropylamino)ethanol particles, and any combination thereof. 
     In some cases, a surface functionality may comprise a primary amine, a secondary amine, a tertiary amine, an amide, an alcohol, an acetic acid, a carboxylic acid, a pyridine, a pyrimidine, a pyrrolidine, or any combination thereof. 
       FIG.  18    shows some surface functionalities for particles. In some cases, a surface functionality may comprise butan-1-amine, propan-2-amine, ethane-1,2-diamine, 1,3-phenylenedimethanamine, 2-aminoethan-1-ol, 2-phenylpyrrolidine, hexan-1-amine, diethylamine, (3s,5s,7s)-adamantan-1-amine, pyridine-2-ylmethanamine, (S)-1,2,3,4-tetrahydronaphthalen-1-amine, phenylmethanamine, tert-butyl (2-aminoethyl)carbamate, 3-aminophenol, benzene-1,4-diamine, 1-(2-aminoethyl)-1H-pyrrole-2,5-dione, 2,2′-azanediyldiacetic acid, (S)-2,3-dihydro-1H-inden-1-amine, 6-aminohexan-1-ol, 4,4′-methylenebis(cyclohexan-1-amine), N 1 ,N 1 -dimethylethane-1,2-diamine, hexane-1,6-diamine, O-(2-aminoethyl)polyethylene glycol, silica, poly(N-(3-(dimethylamino)propyl)methacrylamide) (PDMAPMA), glucose-6-phosphate, N 1 -(2-aminoethyl)-N 2 -butylethane-1,2-diamine, a stereoisomer thereof, a salt thereof, or any combination thereof. 
     Surface functionalities can influence the composition of a particle&#39;s biomolecule corona. In some cases, a particle with a first surface functionality and a particle with a second surface functionality may form a biomolecule corona comprising at most 80% of types of proteins common to both biomolecule coronas. In some cases, two particles with different surface functionalities may commonly comprise at most about 0.1%, 0.2%, 0.3%, 0.4%, 0.5%, 0.6%, 0.7%, 0.8%, 0.9%4, 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, 99.1%, 99.2%, 99.3%, 99.4%, 99.5%, 99.6%, 99.7%, 99.8%, or 99.9% of the types of proteins in a biological sample. 
     The present disclosure includes compositions and methods that comprise two or more particles from among differing in at least one physicochemical property. Such compositions and methods may comprise at least 2 to at least 20 particles from among the plurality of particles differ in at least one physicochemical property. Such compositions and methods may comprise at least 3 to at least 6 particles from among the plurality of particles differ in at least one physicochemical property. Such compositions and methods may comprise at least 4 to at least 8 particles from among the plurality of particles differ in at least one physicochemical property. Such compositions and methods may comprise at least 4 to at least 10 particles from among the plurality of particles differ in at least one physicochemical property. Such compositions and methods may comprise at least 5 to at least 12 particles from among the plurality of particles differ in at least one physicochemical property. Such compositions and methods may comprise at least 6 to at least 14 particles from among the plurality of particles differ in at least one physicochemical property. Such compositions and methods may comprise at least 8 to at least 15 particles from among the plurality of particles differ in at least one physicochemical property. Such compositions and methods may comprise at least 10 to at least 20 particles from among the plurality of particles differ in at least one physicochemical property. Such compositions and methods may comprise at least 2 distinct particle types, at least 3 distinct particle types, at least 4 distinct particle types, at least 5 distinct particle types, at least 6 distinct particle types, at least 7 distinct particle types, at least 8 distinct particle types, at least 9 distinct particle types, at least 10 distinct particle types, at least 11 distinct particle types, at least 12 distinct particle types, at least 13 distinct particle types, at least 14 distinct particle types, at least 15 distinct particle types, at least 20 distinct particle types, at least 25 particle types, or at least 30 distinct particle types. 
     Compositions described herein include particle panels comprising one or more than one distinct particle types. Particle panels described herein can vary in the number of particle types and the diversity of particle types in a single panel. For example, particles in a panel may vary based on size, polydispersity, shape and morphology, surface charge, surface chemistry and functionalization, and base material. Panels may be incubated with a sample to be analyzed for proteins and protein concentrations. Proteins in the sample adsorb to the surface of the different particle types in the particle panel to form a protein corona. The exact protein and the concentration of protein that adsorbs to a certain particle type in the particle panel may depend on the composition, size, and surface charge of the particle type. Thus, each particle type in a panel may have different protein coronas due to adsorbing a different set of proteins, different concentrations of a particular protein, or a combination thereof. Each particle type in a panel may have mutually exclusive protein coronas or may have overlapping protein coronas. Overlapping protein coronas can overlap in protein identity, in protein concentration, or both. 
     The present disclosure also provides methods for selecting a particle types for inclusion in a panel depending on the sample type. Particle types included in a panel may be a combination of particles that are optimized for removal of highly abundant proteins. Particle types included in a panel may be a combination of particles that are optimized for adsorbing low abundance proteins. Particle types also consistent for inclusion in a panel are those selected for adsorbing particular proteins of interest. The particles can comprise nanoparticles. The particles can comprise microparticles. The particles can comprise a combination of nanoparticles and microparticles. 
     The particle panels disclosed herein can be used to identify the number of distinct proteins disclosed herein, and/or any of the specific proteins disclosed herein, over a wide dynamic range. For example, the particle panels disclosed herein comprising distinct particle types, can enrich for proteins in a sample, which can be identified using the biomolecule assay workflow, over the entire dynamic range at which proteins are present in a sample (e.g., a plasma sample). In some cases, a particle panel including any number of distinct particle types disclosed herein, enriches and identifies proteins over a dynamic range of at least 2. In some cases, a particle panel including any number of distinct particle types disclosed herein, enriches and identifies proteins over a dynamic range of at least 3. In some cases, a particle panel including any number of distinct particle types disclosed herein, enriches and identifies proteins over a dynamic range of at least 4. In some cases, a particle panel including any number of distinct particle types disclosed herein, enriches and identifies proteins over a dynamic range of at least 5. In some cases, a particle panel including any number of distinct particle types disclosed herein, enriches and identifies proteins over a dynamic range of at least 6. In some cases, a particle panel including any number of distinct particle types disclosed herein, enriches and identifies proteins over a dynamic range of at least 7. In some cases, a particle panel including any number of distinct particle types disclosed herein, enriches and identifies proteins over a dynamic range of at least 8. In some cases, a particle panel including any number of distinct particle types disclosed herein, enriches and identifies proteins over a dynamic range of at least 9. In some cases, a particle panel including any number of distinct particle types disclosed herein, enriches and identifies proteins over a dynamic range of at least 10. In some cases, a particle panel including any number of distinct particle types disclosed herein, enriches and identifies proteins over a dynamic range of at least 11. In some cases, a particle panel including any number of distinct particle types disclosed herein, enriches and identifies proteins over a dynamic range of at least 12. In some cases, a particle panel including any number of distinct particle types disclosed herein, enriches and identifies proteins over a dynamic range of at least 13. In some cases, a particle panel including any number of distinct particle types disclosed herein, enriches and identifies proteins over a dynamic range of at least 14. In some cases, a particle panel including any number of distinct particle types disclosed herein, enriches and identifies proteins over a dynamic range of at least 15. In some cases, a particle panel including any number of distinct particle types disclosed herein, enriches and identifies proteins over a dynamic range of at least 20. In some cases, a particle panel including any number of distinct particle types disclosed herein, enriches and identifies proteins over a dynamic range of from 2 to 100. In some cases, a particle panel including any number of distinct particle types disclosed herein, enriches and identifies proteins over a dynamic range of from 2 to 20. In some cases, a particle panel including any number of distinct particle types disclosed herein, enriches and identifies proteins over a dynamic range of from 2 to 10. In some cases, a particle panel including any number of distinct particle types disclosed herein, enriches and identifies proteins over a dynamic range of from 2 to 5. In some cases, a particle panel including any number of distinct particle types disclosed herein, enriches and identifies proteins over a dynamic range of from 5 to 10. 
     A particle panel including any number of distinct particle types disclosed herein, can enrich and identify a single protein or protein group. In some cases, the single protein or protein group may comprise proteins having different post-translational modifications. For example, a first particle type in the particle panel may enrich a protein or protein group having a first post-translational modification, a second particle type in the particle panel may enrich the same protein or same protein group having a second post-translational modification, and a third particle type in the particle panel may enrich the same protein or same protein group lacking a post-translational modification. In some cases, the particle panel including any number of distinct particle types disclosed herein, enriches and identifies a single protein or protein group by binding different domains, sequences, or epitopes of the single protein or protein group. For example, a first particle type in the particle panel may enrich a protein or protein group by binding to a first domain of the protein or protein group, and a second particle type in the particle panel may enrich the same protein or same protein group by binding to a second domain of the protein or protein group. 
     A particle panel may comprise a combination of particles with silica and polymer surfaces. For example, a particle panel may comprise a SPION coated with a thin layer of silica, a SPION coated with poly(dimethyl aminopropyl methacrylamide) (PDMAPMA), and a SPION coated with poly(ethylene glycol) (PEG). A particle panel consistent with the present disclosure could also comprise two or more particles selected from the group consisting of silica coated SPION, an N-(3-Trimethoxysilylpropyl) diethylenetriamine coated SPION, a PDMAPMA coated SPION, a carboxyl-functionalized polyacrylic acid coated SPION, an amino surface functionalized SPION, a polystyrene carboxyl functionalized SPION, a silica particle, and a dextran coated SPION. A particle panel consistent with the present disclosure may also comprise two or more particles selected from the group consisting of a surfactant free carboxylate microparticle, a carboxyl functionalized polystyrene particle, a silica coated particle, a silica particle, a dextran coated particle, an oleic acid coated particle, a boronated nanopowder coated particle, a PDMAPMA coated particle, a Poly(glycidyl methacrylate-benzylamine) coated particle, and a Poly(N-[3-(Dimethylamino)propyl]methacrylamide-co-[2-(methacryloyloxy)ethyl]dimethyl-(3-sulfopropyl)ammonium hydroxide, P(DMAPMA-co-SBMA) coated particle. A particle panel consistent with the present disclosure may comprise silica-coated particles, N-(3-Trimethoxysilylpropyl)diethylenetriamine coated particles, poly(N-(3-(dimethylamino)propyl) methacrylamide) (PDMAPMA)-coated particles, phosphate-sugar functionalized polystyrene particles, amine functionalized polystyrene particles, polystyrene carboxyl functionalized particles, ubiquitin functionalized polystyrene particles, dextran coated particles, or any combination thereof. 
     A particle of the present disclosure may be contacted with a biological sample (e.g., a biofluid) to form a biomolecule corona. Upon contacting the complex biological sample, one or more types of particles of a plurality of particles may adsorb 100 or more types of proteins (e.g., in a 100 μl aliquot of a biological sample comprising 100 pM of a type of particle, the about 10 10  particles of the given type collectively may adsorb 100 or more types of proteins). The particle and biomolecule corona may be separated from the biological sample, for example by centrifugation, magnetic separation, filtration, or gravitational separation. The particle types and biomolecule corona may be separated from the biological sample using a number of separation techniques. Non-limiting examples of separation techniques include comprises magnetic separation, column-based separation, filtration, spin column-based separation, centrifugation, ultracentrifugation, density or gradient-based centrifugation, gravitational separation, or any combination thereof. A protein corona analysis may be performed on the separated particle and biomolecule corona. A protein corona analysis may comprise identifying one or more proteins in the biomolecule corona, for example by mass spectrometry. A method may comprise contacting a single particle type (e.g., a particle of a type listed in TABLE 1) to a biological sample. A method may also comprise contacting a plurality of particle types (e.g., a plurality of the particle types provided in TABLE 1) to a biological sample. The plurality of particle types may be combined and contacted to the biological sample in a single sample volume. The plurality of particle types may be sequentially contacted to a biological sample and separated from the biological sample prior to contacting a subsequent particle type to the biological sample. Protein corona analysis of the biomolecule corona may compress the dynamic range of the analysis compared to a total protein analysis method. 
     Contacting a biological sample with a particle or plurality of particles may comprise adding a defined concentration of particles to the biological sample. Contacting a biological sample with a particle or plurality of particles may comprise adding from 1 pM to 100 nM of particles to the biological sample. Contacting a biological sample with a particle or plurality of particles may comprise adding from 1 pM to 500 pM of particles to the biological sample. Contacting a biological sample with a particle or plurality of particles may comprise adding from 10 pM to 1 nM of particles to the biological sample. Contacting a biological sample with a particle or plurality of particles may comprise adding from 100 pM to 10 nM of particles to the biological sample. Contacting a biological sample with a particle or plurality of particles may comprise adding from 500 pM to 100 nM of particles to the biological sample. Contacting a biological sample with a particle or plurality of particles may comprise adding from 50 μg/ml to 300 μg/ml (particle mass to biological sample volume) of particles to the biological sample. Contacting a biological sample with a particle or plurality of particles may comprise adding from 100 μg/ml to 500 μg/ml of particles to a biological sample. Contacting a biological sample with a particle or plurality of particles may comprise adding from 250 μg/ml to 750 μg/ml of particles to the biological sample. Contacting a biological sample with a particle or plurality of particles may comprise adding from 400 μg/ml to 1 mg/ml of particles to the biological sample. Contacting a biological sample with a particle or plurality of particles may comprise adding from 600 μg/ml to 1.5 mg/ml of particles to the biological sample. Contacting a biological sample with a particle or plurality of particles may comprise adding from 800 μg/ml to 2 mg/ml of particles to the biological sample. Contacting a biological sample with a particle or plurality of particles may comprise adding from 1 mg/ml to 3 mg/ml of particles to the biological sample. Contacting a biological sample with a particle or plurality of particles may comprise adding from 2 mg/ml to 5 mg/ml of particles to the biological sample. Contacting a biological sample with a particle or plurality of particles may comprise adding less than 5 mg/ml of particles to the biological sample. Contacting a biological sample with a particle or plurality of particles may comprise adding greater than 5 mg/ml of particles to the biological sample. Contacting a biological sample with a particle or plurality of particles may comprise adding greater than 10 mg/ml of particles to the biological sample. Contacting a biological sample with a particle or plurality of particles may comprise adding greater than 15 mg/ml of particles to the biological sample. 
     In some cases, a biological sample may comprise greater than about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000, 200000, 300000, 400000, or 500000 types of proteins. In some cases, a biological sample may comprise less than about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000, 200000, 300000, 400000, or 500000 types of proteins. 
     Particles in a plurality of particles may have varying degrees of size and shape uniformity. The standard deviation in diameter for a collection of particles of a particular type may be less than 20%, 10%, 5%, or 2% of the average diameter for the particle type (e.g., less than 2 nm for a particle with an average diameter of 100 nm). This may correspond to a low polydispersity index for a sample comprising a plurality of particles, less than 2, less than 1, less than 0.8, less than 0.6, less than 0.5, less than 0.4, less than 0.3, less than 0.2, less than 0.1, or less than 0.05. Conversely, a plurality of particles may have a high degree of variance in average size and shape. The polydispersity index for a sample comprising a plurality of particles may be greater than 3, greater than 4, greater than 5, greater than 8, greater than 10, greater than 12, greater than 15, or greater than 20. Size and shape uniformity among a plurality of particles can affect the number and types of biomolecules that adsorb to the particles. For some methods, size uniformity (e.g., a low polydispersity index) among particles can enable greater enrichment of particular biomolecules, and a stronger correspondence between enriched biomolecule abundance and particle type. For some methods, low size uniformity can enable collection of a greater number of types of biomolecules. 
     Particles may comprise various diameters. In some cases, a diameter may be measured by dynamic light scattering. In some cases, a particle may comprise a diameter of at least about 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, 330, 340, 350, 360, 370, 380, 390, 400, 410, 420, 430, 440, 450, 460, 470, 480, 490, 500, 600, 700, 800, 900, or 1000 nm. In some cases, a particle may comprise a diameter of at most about 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, 330, 340, 350, 360, 370, 380, 390, 400, 410, 420, 430, 440, 450, 460, 470, 480, 490, 500, 600, 700, 800, 900, or 1000 nm. 
     Particles may comprise various zeta potentials in a solvent. In some cases, a particle may comprise a zeta potential between at least about −100 mV and at most about 100 mV. In some cases, a particle may comprise a zeta potential between at least about −50 mV and at most about 50 mV. In some cases, a particle may comprise a zeta potential between at least about −40 mV and at most about −20 mV. In some cases, a particle may comprise a zeta potential between at least about −20 mV and at most about 0 mV. In some cases, a particle may comprise a zeta potential between at least about 0 mV and at most about 20 mV. In some cases, a particle may comprise a zeta potential between at least about 20 mV and at most about 40 mV. In some cases, a particle may comprise a zeta potential greater than about −1000, −900, −800, −700, −600, −500, −400, −300, −200, −100, −90, −80, −70, −60, −50, −40, −30, −20, −10, 0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, or 10000 mV. In some cases, a particle may comprise a zeta potential less than about −1000, −900, −800, −700, −600, −500, −400, −300, −200, −100, −90, −80, −70, −60, −50, −40, −30, −20, −10, 0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, or 10000 mV. 
     In some cases, a solvent may comprise water, methanol, ethanol, isopropyl alcohol, acetone, or any combination thereof. In some cases, a solvent may a buffer solution. In some cases, a solvent may comprise a crowding agent. In some cases, a solvent may comprise a surfactant. 
     In some cases, a solvent may comprise a salt. In some cases, a salt may comprise LiF, LiCl, LiBr, LiI, Li 2 SO 4 , BeF 2 , BeCl 2 , BeBr 2 , BeI 2 , BeSO 4 , NaF, NaCl, NaBr, NaI, Na 2 SO 4 , MgF 2 , MgCl 2 , MgBr 2 , MgI 2 , MgSO 4 , KF, KCl, KBr, KI, K 2 SO 4 , CaF 2 , CaCl 2 , CaBr 2 , CaI 2 , KSO 4 , NH 4 F, NH 4 Cl, NH 4 Br, NHI, (NH 4 ) 2 SO 4 , or any combination thereof. 
     In some cases, a solvent may comprise various acids or bases. In some cases, an acid may comprise hydrochloric, acetic acid, sulfuric acid, nitric acid, citric acid, or any combination thereof. In some cases, a base may comprise NaOH, KOH, Ca(OH) 2 , NH 4 OH, or any combination thereof. 
     In some cases, a solvent may comprise various pH values. In some cases, a solvent may comprise a pH of about physiological pH. In some cases, a solvent may comprise a pH of at least about 6.9 to at most about 7.0, at least about 7.0 to at most about 7.1, at least about 7.1 to at most about 7.2, at least about 7.2 to at most about 7.3, at least about 7.3 to at most about 7.4, at least about 7.4 to at most about 7.5, at least about 7.5 to at most about 7.6, at least about 7.6 to at most about 7.7, at least about 7.7 to at most about 7.8, or at least about 7.9 to at most about 8.0. In some cases, a solvent may comprise a pH of at least about 1 to at most about 2, at least about 2 to at most about 3, at least about 3 to at most about 4, at least about 4 to at most about 5, at least about 5 to at most about 6, at least about 6 to at most about 7, at least about 7 to at most about 8, at least about 8 to at most about 9, at least about 9 to at most about 10, at least about 10 to at most about 11, at least about 11 to at most about 12, at least about 12 to at most about 13, or at least about 13 to at most about 14. 
     In some cases, a solvent may comprise a sterile solvent. In some cases, sterile or being sterile can refer to a substance that comprises biological substances less than an amount acceptable for a certain experiment, a certain composition, a certain method, and the like. The amount acceptable to be considered sterile may vary from experiment to experiment, from composition to composition, and from method to method. In some cases, a sterile solvent used for mass spectroscopy may comprise less than about 100 μg/mL, 10 μg/mL, 1 μg/mL, 100 ng/mL, 10 ng/mL, 1 ng/mL, 100 pg/mL, 10 pg/mL, 1 pg/mL, 100 fg/mL, 10 fg/mL, or 1 fg/mL of added biological substances. In some cases, a sterile solvent may comprise added biological substances in an amount less than the detectable limit. 
     In some cases, a particle may be a binding bait particle. In some cases, a particle may be a mechanistic bait particle. In some cases, a particle may be capable of intrinsic signaling. 
     In some cases, a particle may be designed to broaden selectivity. In some cases, a particle may be designed to narrow selectivity. In some cases, selectivity may broadened or narrowed by altering the surface chemistry of a particle. In some cases, altering the surface chemistry of a particle may comprise adhering new or different functional groups, oxidizing a surface, hydrogenating a surface, irradiating a surface. In some cases, selectivity may be broadened or narrowed by placing a specific molecule on the surface of the particle. In some cases, selectivity may be broadened or narrowed by placing a protein on the particle surface. In some cases, selectivity may be broadened or narrowed by placing an antibody or an antigen for capturing a very specific protein. 
     Sample Collection and Extraction Methods 
     A variety of samples may be assayed in accordance with the methods and compositions of this disclosure. The samples disclosed herein may be analyzed by biomolecule corona analysis after serially interrogating the sample with various types of sensor elements. A sample may be fractioned or depleted prior to protein corona analysis. A method of this disclosure may comprise contacting a sample with one or more particle types and performing a biomolecule corona analysis on the sample. 
     A sample may be a biological sample. For example, a biological sample may be a biofluid sample such as cerebrospinal fluid (CSF), synovial fluid (SF), urine, plasma, serum, tears, crevicular fluid, semen, whole blood, milk, nipple aspirate, needle aspirate, ductal lavage, vaginal fluid, nasal fluid, ear fluid, gastric fluid, pancreatic fluid, trabecular fluid, lung lavage, prostatic fluid, sputum, fecal matter, bronchial lavage, fluid from swabbings, bronchial aspirants, sweat or saliva. A biofluid may be a fluidized solid, for example a tissue homogenate, or a fluid extracted from a biological sample. A biological sample may be, for example, a tissue sample or a fine needle aspiration (FNA) sample. A biological sample may be a cell culture sample. For example, a sample that may be used in the methods disclosed herein can either include cells grow in cell culture or can include acellular material taken from cell cultures. A biofluid may be a fluidized biological sample. For example, a biofluid may be a fluidized cell culture extract. A sample may be extracted from a fluid sample, or a sample may be extracted from a solid sample. For example, a sample may comprise gaseous molecules extracted from a fluidized solid (e.g., a volatile organic compound). 
     The biomolecule corona analysis methods described herein may comprise assaying proteins in a sample of the present disclosure across a wide dynamic range. The dynamic range of biomolecules assayed in a sample may be a range of measured signals of biomolecule abundances as measured by an assay method (e.g., mass spectrometry, peptide sequencing, peptide affinity capture, chromatography, gel electrophoresis, spectroscopy, or immunoassays) for the biomolecules contained within a sample. For example, an assay capable of detecting proteins across a wide dynamic range may be capable of detecting proteins of very low abundance to proteins of very high abundance. The dynamic range of an assay may be directly related to the slope of assay signal intensity as a function of biomolecule abundance. For example, an assay with a low dynamic range may have a low (but positive) slope of the assay signal intensity as a function of biomolecule abundance, e.g., the ratio of the signal detected for a high abundance biomolecule to the ratio of the signal detected for a low abundance biomolecule may be lower for an assay with a low dynamic range than an assay with a high dynamic range. The biomolecule corona analysis methods described herein may compress the dynamic range of an assay. The dynamic range of an assay may be compressed relative to another assay if the slope of the assay signal intensity as a function of biomolecule abundance is lower than that of the other assay. For example, a plasma sample assayed using biomolecule corona analysis with mass spectrometry may have a compressed dynamic range compared to a plasma sample assayed using mass spectrometry alone, directly on the sample or compared to provided abundance values for plasma biomolecules in databases (e.g., the database provided in Keshishian et al.,  Mol. Cell Proteomics  14, 2375-2393 (2015), also referred to herein as the “Carr database”). The compressed dynamic range may enable the detection of more low abundance biomolecules in the plasma sample using biomolecule corona analysis with mass spectrometry than using mass spectrometry alone. 
     Compression of a dynamic range of an assay may enable the detection of low abundance biomolecules using the methods disclosed herein (e.g., serial interrogation with a particle followed by an assay for quantitating protein abundance such as mass spectrometry). For example, an assay (e.g., mass spectrometry) may be capable of detecting a dynamic range of 3 orders of magnitude. In a sample comprising five proteins, A, B, C, D, and E, in abundances of 1 ng/mL, 10 ng/mL, 100 ng/mL, 1,000 ng/mL, and 10,000 ng/mL, respectively, the assay (e.g., mass spectrometry) may detect proteins B, C, D, and E. However, using the methods disclosed herein of incubating the sample with a particle, proteins A, B, C, D, and E may have different affinities for the particle surface and may adsorb to the surface of the particle to form the biomolecule corona at different abundancies than present in the sample. For example, proteins A, B, C, D, and E may be present in the biomolecule corona at abundancies of 1 ng/mL, 231 ng/mL, 463 ng/mL, 694 ng/mL, and 926 ng/mL, respectively. Thus, using the particles disclosed herein in methods of interrogating a sample can result in compressing the dynamic range to 2 orders of magnitude, and the resulting assay (e.g., mass spectrometry) may detect all five proteins. 
     In some aspects, the dynamic range of the plurality of biomolecules in the first biomolecule corona is a first ratio of: a) a signal produced by a higher abundance biomolecules of the plurality of biomolecules in the first biomolecule corona; and b) a signal produced by a lower abundance biomolecule of the plurality of biomolecules in the first biomolecule corona. In some aspects, the dynamic range of the plurality of biomolecules in the first biomolecule corona is a first ratio of a concentration of the highest abundance biomolecule to a concentration of the lowest abundance biomolecule in the plurality of proteins in the first biomolecule corona. In some aspects, the dynamic range of the plurality of biomolecules in the first biomolecule corona is a first ratio of a top decile of biomolecules to a bottom decile of biomolecules in the plurality of proteins in the first biomolecule corona. In some aspects, the dynamic range of the plurality of biomolecules in the first biomolecule corona is a first ratio comprising a span of the interquartile range of biomolecules in the plurality of biomolecules in the first biomolecule corona. In some aspects, the dynamic range of the plurality of biomolecules in the first biomolecule corona is a first ratio comprising a slope of fitted data in a plot of all concentrations of biomolecules in the plurality of biomolecules in the first biomolecule corona versus known concentrations of the same biomolecules in the sample. 
     In some aspects, the dynamic range of the plurality of biomolecules in the sample, as measured by a total biomolecule analysis method (e.g., a total protein analysis method), is a second ratio comprising a span of the interquartile range of biomolecules in the plurality of biomolecules in the sample. In some aspects, the dynamic range of the plurality of biomolecules in the sample, as measured by a total biomolecule analysis method, is a second ratio comprising a slope of fitted data in a plot of all concentrations of biomolecules in the plurality of biomolecules in the sample versus known concentrations of the same biomolecules in the sample. In some aspects, the known concentrations of the same biomolecules in the sample are obtained from a database. In some aspects, the compressing the dynamic range comprises a decreased first ratio relative to the second ratio. In further aspects, the decreased first ratio is at least 1.1-fold, at least 1.2-fold, at least 1.3-fold, at least 1.4-fold, at least 1.5-fold, at least 2-fold, at least 2.5-fold, at least 3-fold, at least 3.5-fold, at least 4-fold, at least 5-fold, at least 10-fold, at least 100-fold, at least 1000-fold, or at least 10,000-fold less than the second ratio. 
     A biomolecule of interest (e.g., a low abundance protein) may be enriched in a biomolecule corona relative to the untreated sample (e.g., a sample that is not assayed using particles). A level of enrichment may be the percent increase or fold increase in relative abundance of the biomolecule of interest relative to the total quantity of biomolecules in the biomolecule corona as compared to the untreated sample. A biomolecule of interest may be enriched in a biomolecule corona by increasing the relative abundance of the biomolecule of interest in the biomolecule corona as compared to the sample that has not been contacted to a particle. A biomolecule of interest may be enriched by decreasing the relative abundance of a high abundance biomolecule in the biomolecule corona as compared to the sample that has not been contacted to a particle. A biomolecule corona analysis assay may be used to rapidly identify low abundance biomolecules in a biological sample (e.g., a biofluid). A biomolecule corona analysis assay may identify at least about 500 low abundance biomolecules in a biological sample in no more than about 8 hours from first contacting the biological sample with a particle. A biomolecule corona analysis assay may identify at least about 1000 low abundance biomolecules in a biological sample in no more than about 8 hours from first contacting the biological sample with a particle. A biomolecule corona analysis assay may identify at least about 500 low abundance biomolecules in a biological sample in no more than about 4 hours from first contacting the biological sample with a particle. A biomolecule corona analysis assay may identify at least about 1000 low abundance biomolecules in a biological sample in no more than about 4 hours from first contacting the biological sample with a particle. 
     Multi-Sample Analysis 
     A method may comprise analysis of multiple samples from a subject (e.g., a cancer patient). For instance, multiple samples may include a nucleic acid sample and a protein sample. The nucleic acid sample and the protein sample may be derived from one or more biological samples, such as a blood sample. 
     Biomolecule distribution can be uneven between sample types and/and tissue types (e.g., histology). For many biological states, different samples from a subject can comprise distinct and, in some cases, can comprise divergent sets of biomarkers (e.g., proteins or genes). For example, human plasma can comprise relatively low nucleic acid content and a subset of the human proteome that varies strongly with biological state, while a tissue homogenate may comprise biological state-sensitive genomic content but a protein distribution that is stable across a wide range of biological states. A useful sample for nucleic acid analysis may be a poor sample for protein analysis, while a useful sample for protein analysis may contain low nucleic acid content. In some cases (e.g., for many forms of cancer), cell homogenates can provide extensive genomic and transcriptomic information reflective of a biological state, while simultaneously displaying diminutive variations in protein expression that are insufficient for biological state analysis. Plasma protein abundances can be sensitive to a subject&#39;s biological state, while plasma nucleic acid concentrations can be prohibitively low for analysis. 
     A method may overcome these limitations by utilizing different types of samples for proteomic and nucleic acid assays. For example, for a subject suspected of having cancer, a biopsy on potentially cancerous tissue may be used for nucleic acid analysis, while plasma may be used for proteomic analysis. A method may also utilize different portions of a sample for protein and nucleic acid analysis. For example, an assay may utilize the buffy coat from a blood sample for nucleic acid analysis, and the plasma portion of a sample for proteomic analysis. 
     A method of the present disclosure may comprise performing nucleic acid analysis on a first sample from a subject and performing protein analysis on a second sample from a subject. The subject may have or be suspected of having a disease or cancer. A method consistent with the present disclosure may comprise performing nucleic acid analysis or protein analysis on multiple sample types from a subject (e.g., a buccal swabbing and urine). A method of the present disclosure may comprise performing nucleic acid and protein analysis on the same sample. A method of the present disclosure may comprise first collecting proteins from a sample, and then collecting nucleic acids from the sample. A method of the present disclosure may comprise first collecting nucleic acids from a sample, and then collecting proteins from the sample. A method of the present disclosure may comprise simultaneously purifying nucleic acids and proteins from a sample (e.g., a phenol:chloroform isoamyl alcohol extraction to separate nucleic acids and proteins into separate phases). A method of the present disclosure may comprise separating DNA from RNA in the sample, and optionally converting RNA to cDNA by reverse transcription for sequencing analysis. A method of the present disclosure may comprise separating species based on size, charge, isoelectric point, or any combination thereof. A method of the present disclosure may comprise performing lysis on a sample. A method of the present disclosure may comprise performing chromatographic separation on a sample. 
     Assaying Biomolecule Coronas 
     A method for assaying a biological sample may comprise preparing analytes from a biomolecule corona for further analysis (e.g., mass spectrometric analysis). The biomolecule corona may be separated from the supernatant (the portion of the biological sample not bound to a sensor element) by removing the supernatant and then desorbing a plurality of biomolecules from the biomolecule corona into a separate solution. In some methods, a first portion of biomolecules from a biomolecule corona are desorbed from the biomolecule corona and discarded, and a second portion of biomolecules from a biomolecule corona are desorbed from the biomolecule corona and collected (e.g., for analysis). Multiple portions of biomolecules from a biomolecule corona may be separately desorbed, collected, and analyzed. The separate portions may comprise different compositions of biomolecules, and the differences between the portions may be used to fingerprint a sample. 
     In some cases, a method for assaying a biological sample may produce a signal. In some cases, a signal may comprise or be used for determining proteomic information, genomic information, or both. In some cases, a signal can refer to the proteomic or genotypic information that is emitted from a source comprising proteomic or genotypic information in the form of chemical signals, ion signals, fluorescence signals, another form of signal, or any combination thereof. In some cases, a signal may be assignable to a protein. In some cases, a signal may be assignable to a nucleic acid. In some cases, a method for assaying a biological sample may produce a plurality of signals which may be assignable to biomolecules such as proteins, nucleic acid molecules, or a combination thereof. In some cases, the plurality of signals can comprise at least 20000, 50000, 100,000, 1,000,000 distinguishable signals, or more. 
     Biomolecules from a biomolecule corona may denatured, fragmented, chemically modified, or any combination thereof. These treatments may be performed on desorbed biomolecules or on biomolecules within biomolecule coronas. The plurality of biomolecules desorbed from a biomolecule corona may comprise 1%, 2%, 3%, 4%, 5%, 6%, 8%, 10%, 12%, 15%, 20%, 25%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 98%, 99%, or greater than 99% of the biomolecules from the biomolecule corona. The desorption may be performed for different lengths of time, including 5 seconds, 15 seconds, 30 seconds, 1 minute, 2 minutes, 3 minutes, 4 minutes, 5 minutes, 6 minutes, 8 minutes, 10 minutes, 12 minutes, 15 minutes, 20 minutes, 30 minutes, 40 minutes, 50 minutes, 1 hour, 1.5 hours, 2 hours, 3 hours, 4 hours, 5 hours, 6 hours, 8 hours, 12 hours, or longer. In some cases, the desorption comprises physical agitation, such as shaking or sonication. The percent of biomolecules desorbed from a biomolecule corona may depend on the desorption time, the chemical composition the solution into which biomolecules are desorbed (e.g., pH or buffer-type), the desorption temperature, the form and intensity of physical agitation applied, or any combination thereof. The types of biomolecules desorbed from a biomolecule corona may differ by 1%, 2%, 3%, 4%, 5%, 6%, 8%, 10%, 12%, 15%, 20%, 25%, 30%, 40%, 50%, 60%, or more between two desorption conditions or methods. 
     Biomolecules collected from a biomolecule corona may be subjected to further chemical treatment prior to analysis. This can include digesting the biomolecule corona, a subset of biomolecules within the biomolecule corona, or biomolecules desorbed from the biomolecule corona to form a digested sample in the automated apparatus. Biomolecule treatment may also comprise chemically modifying a biomolecule from the biomolecule corona, such as methylating or reducing the biomolecule. In some cases, separation of biomolecules from a biomolecule comprise intact biomolecule separation. The intact biomolecule separation may product intact biomolecules (e.g., proteins) which may be subject to subsequent processing and analyses (e.g., mass spectrometric analysis). 
     A method may comprise multiple rounds of preparing biomolecules from a biomolecule corona for analysis. A method may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more rounds of preparation, wherein a plurality of the rounds produce separate samples for analysis (e.g., desorbed biomolecules may be collected after each round and subjected to mass spectrometric analysis). Two rounds may also comprise different desorption methods or conditions, such as different desorbate solution volumes, different desorbate solution types (e.g., desorbate solutions comprising different buffers or osmolarities), different temperatures, or different types and degrees of physical agitation. Two or more successive rounds of preparation from a single biomolecule corona (e.g., desorption and collection of a first subset of biomolecules from a biomolecule corona followed by desorption and collection of a second subset of biomolecules from a biomolecule corona) may generate two sets of biomolecules, even in cases where the desorption methods are identical between rounds. This may inform detection or analysis of biomolecule interactions within a protein corona. 
     A method may comprise immobilizing a sensor element (e.g., a particle) within a partition. The immobilization may prevent the sensor element from being removed from a sample volume (e.g., a well in a well plate) when a portion of the sample volume is removed. Immobilization may be performed chemically, and may comprise affixing a sensor element directly or indirectly (e.g., via a linker) to a surface, such as a wall within a container. Immobilization may be achieved by applying a magnetic field to hold a magnetic sensor element (e.g., a magnetic particle) within a sample container. Immobilization may be achieved by forming or embedding a sensor element on a surface, such as on the inside surface of a microplate well. 
     Sensor element immobilization may allow a biomolecule corona to be separated from a sensor element. This may comprise desorbing a plurality of biomolecules from a biomolecule corona associated with a sensor element, immobilizing the sensor element, and then collecting the solution with the plurality of biomolecules from the biomolecule corona, thereby separating at least a portion of the biomolecule corona from the sensor element. Alternatively, a sensor element may be immobilized prior to a portion of its biomolecule corona being desorbed. 
     The methods disclosed herein may comprise a filtering step. The filtering may separate a sensor element or a type of biomolecule (e.g., a protease) from a sample. For example, the method may comprise desorbing a plurality of biomolecules from a biomolecule corona associated with a sensor element and filtering the solution such that the sensor element is collected on the filter and the plurality of biomolecules remain in solution. The filtering may be performed after denaturation (e.g., digestion). The filtering may also remove a plurality of biomolecules or biological species such as intact proteins (e.g., undigested proteins from the biological sample or proteases added to the sample to fragment proteins). 
     A method may comprise a purification step. A purification step may comprise transferring a biological sample (e.g., biomolecules eluted and collected from a biomolecule corona) to a purification unit (e.g., a chromatography column) or partition within a purification unit. The purification unit may comprise a solid-phase extraction or chromatography column. The purification step may remove reagents (e.g., chemicals and enzymes) from the sample following post-collection preparation steps. Following purification, the biological sample may be recollected for further enrichment or chemical treatment, or may be subjected to a form of analysis (e.g., mass spectrometric analysis). 
     Collectively, the methods of the present disclosure may enable a high degree of profiling depth for biological samples. A plurality of biomolecules collected in the methods of the present disclosure may enable, without further manipulation or modification of the plurality of biomolecules, mass spectrometric detection of at least 2%, at least 3%, at least 4%, at least 5%, at least 6%, at least 7%, at least 8%, at least 9%, at least 10%, at least 12%, at least 15%, at least 20%, at least 25%, at least 30%, at least 40%, at least 50%, at least 60%, or more than 60% of the types of biomolecules in the biological sample from which the subset of biomolecules were collected. The plurality of biomolecules may enable, without further manipulation or modification of the plurality of biomolecules, mass spectrometric detection of at least 2%, at least 3%, at least 4%, at least 5%, at least 6%, at least 7%, at least 8%, at least 9%, at least 10%, at least 12%, at least 15%, at least 20%, at least 25%, at least 30%, at least 40%, at least 50%, or more than 50% of the types of proteins in a sample. The plurality of biomolecules collected on a sensor element or prepared for analysis may enable, without further manipulation or modification of the plurality of biomolecules, simultaneous mass spectrometric detection of two biomolecules (e.g., proteins) spanning 6, 7, 8, 9, 10, 11, 12 or more orders of magnitude in a sample. For example, the two biomolecules may be desorbed and collected at concentrations within 6 orders of magnitude, fragmented, and then submitted for mass spectrometric analysis. 
     Protein Corona Analysis in Biological Samples 
     The particles and methods of use thereof disclosed herein can bind a large number of different proteins or protein groups in a biological sample (e.g., a biofluid). Non-limiting examples of biological samples that may be analyzed using the protein corona analysis methods described herein include biofluid samples (e.g., cerebral spinal fluid (CSF), synovial fluid (SF), urine, plasma, serum, tears, semen, whole blood, milk, nipple aspirate, needle aspirate, ductal lavage, vaginal fluid, nasal fluid, ear fluid, gastric fluid, pancreatic fluid, trabecular fluid, lung lavage, prostatic fluid, sputum, fecal matter, bronchial lavage, fluid from swabbings, bronchial aspirants, sweat or saliva), fluidized solids (e.g., a tissue homogenate), or samples derived from cell culture. For example, a particle disclosed herein can be incubated with any biological sample disclosed herein to form a protein corona comprising at least 40 proteins or protein groups, at least 60 proteins or protein groups, at least 80 proteins or protein groups, at least 100 proteins or protein groups, at least 120 proteins or protein groups, at least 140 proteins or protein groups, at least 160 proteins or protein groups, at least 180 proteins or protein groups, at least 200 proteins or protein groups, at least 220 proteins or protein groups, at least 240 proteins or protein groups, at least 260 proteins or protein groups, at least 280 proteins or protein groups, at least 300 proteins or protein groups, at least 320 proteins or protein groups, at least 340 proteins or protein groups, at least 360 proteins or protein groups, at least 380 proteins or protein groups, at least 400 proteins or protein groups, at least 420 proteins or protein groups, at least 440 proteins or protein groups, at least 460 proteins or protein groups, at least 480 proteins or protein groups, at least 500 proteins or protein groups, at least 520 proteins or protein groups, at least 540 proteins or protein groups, at least 560 proteins or protein groups, at least 580 proteins or protein groups, at least 600 proteins or protein groups, at least 620 proteins or protein groups, at least 640 proteins or protein groups, at least 660 proteins or protein groups, at least 680 proteins or protein groups, at least 700 proteins or protein groups, at least 720 proteins or protein groups, at least 740 proteins or protein groups, at least 760 proteins or protein groups, at least 780 proteins or protein groups, at least 800 proteins or protein groups, at least 820 proteins or protein groups, at least 840 proteins or protein groups, at least 860 proteins or protein groups, at least 880 proteins or protein groups, at least 900 proteins or protein groups, at least 920 proteins or protein groups, at least 940 proteins or protein groups, at least 960 proteins or protein groups, at least 980 proteins or protein groups, at least 1000 proteins or protein groups, from 100 to 1000 proteins or protein groups, from 150 to 950 proteins or protein groups, from 200 to 900 proteins or protein groups, from 250 to 850 proteins or protein groups, from 300 to 800 proteins or protein groups, from 350 to 750 proteins or protein groups, from 400 to 700 proteins or protein groups, from 450 to 650 proteins or protein groups, from 500 to 600 proteins or protein groups, from 200 to 250 proteins or protein groups, from 250 to 300 proteins or protein groups, from 300 to 350 proteins or protein groups, from 350 to 400 proteins or protein groups, from 400 to 450 proteins or protein groups, from 450 to 500 proteins or protein groups, from 500 to 550 proteins or protein groups, from 550 to 600 proteins or protein groups, from 600 to 650 proteins or protein groups, from 650 to 700 proteins or protein groups, from 700 to 750 proteins or protein groups, from 750 to 800 proteins or protein groups, from 800 to 850 proteins or protein groups, from 850 to 900 proteins or protein groups, from 900 to 950 proteins or protein groups, from 950 to 1000 proteins or protein groups. In some cases, a particle disclosed herein can be incubated with any biological sample disclosed herein to form a protein corona comprising at least about 50 to 500 proteins or protein groups. 
     In some cases, a particle disclosed herein can be incubated with a biological sample to form a protein corona comprising at least about 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10,000, 20,000, 30,000, 40,000, 50,000, 60,000, 70,000, 80,000, 90,000, 100,000, 200,000, 300,000, 400,000, or 500,000 proteins or protein groups. In some cases, a particle disclosed herein can be incubated with a biological sample to form a protein corona comprising at most about 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10,000, 20,000, 30,000, 40,000, 50,000, 60,000, 70,000, 80,000, 90,000, 100,000, 200,000, 300,000, 400,000, or 500,000 proteins or protein groups. 
     In some cases, a particle disclosed herein can identify within a protein corona at least about 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10,000, 20,000, 30,000, 40,000, 50,000, 60,000, 70,000, 80,000, 90,000, 100,000, 200,000, 300,000, 400,000, or 500,000 proteins or protein groups. In some cases, a particle disclosed herein can identify within a protein corona at most about 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10,000, 20,000, 30,000, 40,000, 50,000, 60,000, 70,000, 80,000, 90,000, 100,000, 200,000, 300,000, 400,000, or 500,000 proteins or protein groups. 
     In some cases, an assay may comprise several different types of particles, separately or in combination, to identify large numbers of proteins or protein groups in a particular biological sample. In some cases, particles can be multiplexed in order to bind and identify large numbers of proteins or protein groups in a biological sample. In some cases, at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100 particles may be used in combination. In some cases, particles used in combination can bind and identify at least about 250 to about 25,000 proteins or protein groups. In some cases, particles used in combination can bind and identify at least about 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 15000, 20000, 25000, 30000, 35000, 40000, 45000, or 50000 proteins or protein groups. In some cases, particles used in combination can bind and identify at most about 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 15000, 20000, 25000, 30000, 35000, 40000, 45000, or 50000 proteins or protein groups. 
     In some cases, a particle disclosed herein can be incubated with a biological sample from a single subject or a plurality of subjects. In some cases, a biological sample may be from at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 subjects. In some cases, a biological sample may be from at most about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 subjects. 
     In some cases, a particle disclosed herein can identify or quantify about 50 to 500 types of proteins or protein groups. In some cases, a particle disclosed herein can identify or quantify about 5 to 5000 types of proteins or protein groups. In some cases, a particle disclosed herein can identify or quantify at least about 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, or 5000 types of proteins or protein groups. In some cases, a particle disclosed herein can identify or quantify at most about 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, or 5000 types of proteins or protein groups. 
     In some cases, a plurality of particles disclosed herein can identify or quantify about 250 to 25000 types of proteins or protein groups. In some cases, a plurality of particles disclosed herein can identify or quantify at least about 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 15000, 20000, 25000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, or 10000 types of proteins or protein groups. In some cases, a particle disclosed herein can identify or quantify at most about 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 15000, 20000, 25000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, or 10000 types of proteins or protein groups. 
     Furthermore, the methods of the present disclosure can enable simultaneous quantification of proteins or protein groups enriched from a sample. A shortcoming of some diagnostic methods is that the concentration or relative state distribution (e.g., phosphorylated vs. unphosphorylated TrkA) of an individual biomarker (e.g., the concentration of IL-10 in the blood) can have greater variance between subjects or greater dependencies on extraneous factors (e.g., how recently a subject ate before donating a biological sample) than for biological states. However, as is further presented herein, the abundance ratios of a large number of proteins can be strongly diagnostic for particular biological states, and can even differentiate similar biological states (e.g., healthy vs. prediabetes, or stage 1 vs stage 2 of chronic lymphocytic leukemia). The methods described in the present disclosure can provide the ability to distinguish relative or absolute protein abundances from individual particles or particle types. Two particle types may be analyzed or assayed separately, thus allowing the relative abundances of a large number of proteins (e.g., 70 types of proteins) to be compared across a plurality of particle types. 
     Protein corona analysis of the biomolecule corona may compress the dynamic range of the analysis compared to a total protein analysis method. Many analytical techniques (e.g., mass spectrometry) have concentration range limits for single measurements. For example, some mass spectrometric detection methods may lack the capability of simultaneously detecting two peptides present at concentrations differing by more than 6 orders of magnitude. Thus, crude analysis on bulk samples may accentuate signals from abundant analytes (e.g., albumin in plasma) while not resolving signals from low abundant targets (e.g., interleukins in plasma). The methods of the present disclosure may increase the number of types of proteins present within 2, 3, 4, 5, or 6 orders of magnitude of concentration, which can enable detection of a greater number of proteins from the sample in parallel. 
     For example, a method comprising biomolecule corona formation may increase the number of types of biomolecules whose concentrations are within 6 orders of magnitude of the most concentrated biomolecule in the sample by at least 25%, 50%, 100/6, 200%, 300%, 500%, or 1000%. Analogously, the compressed dynamic range may comprise an increase in the number of types of proteins whose concentrations are within 6 orders of magnitude of the most abundant biomolecule in the sample. The method may increase the number of types of proteins whose concentrations are within 6 orders of magnitude of the most concentrated protein in the sample by at least 25%, 50%, 100%, 200%, 300/6, 500%, or 1000%. The method may enrich a subset of biomolecules from a biological sample, and the subset of biomolecules may comprise at least 10% of the types of biomolecules from the biological sample within a 6 order of magnitude concentration range. The method may enrich a subset of biomolecules from a biological sample, and the subset of biomolecules may comprise at least 20% of the types of biomolecules from the biological sample within a 6 order of magnitude concentration range. The method may enrich a subset of biomolecules from a biological sample, and the subset of biomolecules may comprise at least 30% of the types of biomolecules from the biological sample within a 6 order of magnitude concentration range. The method may enrich a subset of biomolecules from a biological sample, and the subset of biomolecules may comprise at least 40% of the types of biomolecules from the biological sample within a 6 order of magnitude concentration range. The method may enrich a subset of biomolecules from a biological sample, and the subset of biomolecules may comprise at least 50% of the types of biomolecules from the biological sample within a 6 order of magnitude concentration range. The method may enrich a subset of biomolecules from a biological sample, and the subset of biomolecules may comprise at least 60% of the types of biomolecules from the biological sample within a 6 order of magnitude concentration range. The method may enrich a subset of biomolecules from a biological sample, and the subset of biomolecules may comprise at least 70% of the types of biomolecules from the biological sample within a 6 order of magnitude concentration range. The method may enrich a subset of biomolecules from a biological sample, and the subset of biomolecules may comprise at least 10% of the types of proteins from the biological sample within a 6 order of magnitude concentration range. The method may enrich a subset of biomolecules from a biological sample, and the subset of biomolecules may comprise at least 20% of the types of proteins from the biological sample within a 6 order of magnitude concentration range. The method may enrich a subset of biomolecules from a biological sample, and the subset of biomolecules may comprise at least 30% of the types of proteins from the biological sample within a 6 order of magnitude concentration range. The method may enrich a subset of biomolecules from a biological sample, and the subset of biomolecules may comprise at least 40% of the types of proteins from the biological sample within a 6 order of magnitude concentration range. The method may enrich a subset of biomolecules from a biological sample, and the subset of biomolecules may comprise at least 50% of the types of proteins from the biological sample within a 6 order of magnitude concentration range. The method may enrich a subset of biomolecules from a biological sample, and the subset of biomolecules may comprise at least 60% of the types of proteins from the biological sample within a 6 order of magnitude concentration range. The method may enrich a subset of biomolecules from a biological sample, and the subset of biomolecules may comprise at least 70% of the types of proteins from the biological sample within a 6 order of magnitude concentration range. 
     The methods and sensor elements of the present disclosure may be tailored so that biomolecule corona composition is invariant with respect to sample lipid concentration. Changes of at most 10% in the lipid concentration in a biological sample may result in changes of less than 5%, 2%, 1%, or 0.1% in the composition of the proteins in a biomolecule corona. Changes of at most 10% in the lipid concentration in a biological sample may result in changes of less than 5%, 2%, 1%, or 0.1% in the number of types of proteins in a biomolecule corona. Changes of at most 10% in the lipid concentration in a biological sample may result in changes of less than 5%, 2%, 1%, or 0.1% in the total number of proteins in a biomolecule corona. 
     In some cases, the biological sample may comprise blood, plasma, or serum, and a biomolecule corona may comprise a lower proportion of albumin to non-albumin proteins than the biological sample. The ratio of albumin to non-albumin proteins may be 20%, 30%, 40%, 50%, 60%, or 70% lower in a biomolecule corona than in the sample from which proteins were adsorbed. 
     In some cases, proteomic information or data can refer to information about substances comprising a peptide and/or a protein component. In some cases, proteomic information may comprise primary structure information, secondary structure information, tertiary structure information, or quaternary information about the peptide or a protein. In some cases, proteomic information may comprise information about protein-ligand interactions, wherein a ligand may comprise any one of various biological molecules and substances that may be found in living organisms, such as, nucleotides, nucleic acids, amino acids, peptides, proteins, monosaccharides, polysaccharides, lipids, phospholipids, hormones, or any combination thereof. 
     In some cases, proteomic information may comprise information about a single cell, a tissue, an organ, a system of tissues and/or organs (such as cardiovascular, respiratory, digestive, or nervous systems), or an entire multicellular organism. In some cases, proteomic information may comprise information about an individual (e.g., an individual human being or an individual bacterium), or a population of individuals (e.g., human beings with diagnosed with cancer or a colony of bacteria). Proteomic information may comprise information from various forms of life, including forms of life from the Archaea, the Bacteria, the Eukarya, the Protozoa, the Chromista, the Plantae, the Fungi, or from the Animalia. In some cases, proteomic information may comprise information from viruses. 
     In some cases, proteomic information may comprise information relating exons and introns in the code of life. In some cases, proteomic information may comprise information regarding variations in the primary structure, variations in the secondary structure, variations in the tertiary structure, or variations in the quaternary structure of peptides and/or proteins. In some cases, proteomic information may comprise information regarding variations in the expression of exons, including alternative splicing variations, structural variations, or both. In some cases, proteomic information may comprise conformation information, post-translational modification information, chemical modification information (e.g., phosphorylation), cofactor (e.g., salts or other regulatory chemicals) association information, or substrate association information of peptides and/or proteins. In some cases, post-translation modification may comprise acylation, alkylation, prenylation, flavination, amidation, amination, deamination, carboxylation, decarboxylation, nitrosylation, formylation, citrullination, glycosylation, glycation, halogenation, hydroxylation, phosphorylation, sulfurylation, glutathionylation, succinylation, carbonylation, carbamylation, oxidation, oxygenation, reduction, ubiquitination, SUMOylation, neddylation, or any combination thereof. In some cases, proteomic information may comprise a rate or prevalence of apoptosis of a healthy cell or a diseased cell. In some cases, proteomic information may comprise a state of a cell, such as a healthy state or a diseased state. 
     The methods and compositions of the present disclosure can provide identification and measurement of particular proteins in the biological samples. This may comprise processing of the proteomic data via digestion of coronas formed on the surface of particles. Examples of proteins that can be identified and measured include highly abundant proteins, proteins of medium abundance, and low-abundance proteins. In some cases, a low abundance protein may be present in a sample at concentrations at or below about 10 ng/mL. In some cases, a high abundance protein may be present in a sample at concentrations at or above about 10 μg/mL. A protein of moderate abundance may be present in a sample at concentrations between about 10 ng/mL and about 10 μg/mL. Examples of proteins that may be highly abundant proteins in some biological samples include albumin, IgG, and the top 14 proteins in abundance that contribute about 95% of the protein mass in plasma. In some cases, proteins that are purified using a conventional depletion column may be directly detected in a sample using a particle, a particle panel, or a particle composition disclosed herein. Examples of proteins may be any protein listed in published databases such as Keshishian et al. (Mol Cell Proteomics. 2015 September; 14(9):2375-93. doi: 10.1074/mcp.M114.046813. Epub 2015 Feb. 27.), Farr et al. (J Proteome Res. 2014 Jan. 3; 13(1):60-75. doi: 10.1021/pr4010037. Epub 2013 Dec. 6.), or Pernemalm et al. (Expert Rev Proteomics. 2014 August; 11(4):431-48. doi: 10.1586/14789450.2014.901157. Epub 2014 Mar. 24.). 
     Examples of proteins that can be measured and identified using the methods and compositions disclosed herein may include albumin, IgG, lysozyme, CEA, HER-2/neu, bladder tumor antigen, thyroglobulin, alpha-fetoprotein, PSA, CA125, CA19.9, CA 15.3, leptin, prolactin, osteopontin, IGF-II, CD98, fascin, sPigR, 14-3-3 eta, troponin I, B-type natriuretic peptide, BRCA1, c-Myc, IL-6, fibrinogen. EGFR, gastrin, PH, G-CSF, desmin. NSE, FSH, VEGF, P21, PCNA, calcitonin, PR, CA125, LH, somatostatin. S100, insulin. alpha-prolactin, ACTH, Bcl-2, ER alpha, Ki-67, p53, cathepsin D, beta catenin. VW F, CD15, k-ras, caspase 3, EPN, CD10, FAS, BRCA2. CD30L, CD30, CGA, CRP, prothrombin, CD44, APEX, transferrin, GM-CSF, E-cadherin, IL-2, Bax, IFN-gamma, beta-2-MG, TNF alpha, c-erbB-2, trypsin, cyclin DI, MG B, XBP-1, HG-1, YKL-40, S-gamma, NESP-55, netrin-1, geminin, GADD45A, CDK-6, CCL21, BrMS1, 17betaHDI, PDGFRA, Pcaf, CCL5, MMP3, claudin-4, and claudin-3. Other examples of proteins that can be measured and identified using the particle panels disclosed herein are any proteins or protein groups listed in the open targets database for a particular disease indication of interest (e.g., prostate cancer, lung cancer, or Alzheimer&#39;s disease). 
     The proteomic data of the biological sample can be identified, measured, and quantified using a number of different analytical techniques. For example, proteomic data can be analyzed using SDS-PAGE or any gel-based separation technique. Peptides and proteins can also be identified, measured, and quantified using an immunoassay, such as ELISA. Alternatively, proteomic data can be identified, measured, and quantified using mass spectrometry, high performance liquid chromatography, LC-MS/MS, Edman Degradation, immunoaffinity techniques, methods disclosed in EP3548652, WO2019083856, WO2019133892, each of which is incorporated herein by reference in its entirety, and other protein separation techniques. 
     In some cases, a measurement technique identifies protein groups. A measurement technique designed to detect proteins may also detect protein groups. In some cases, protein groups can refer to two or more proteins that are identified by a shared peptide sequence. In some cases, protein groups can refer to two or more proteins that are identified by a shared function. In some cases, protein groups comprise proteoforms of a given protein. In some cases, protein groups can refer to two or more proteins that are identified by their participation in a same biochemical pathway. In some cases, protein groups can refer to two or more proteins that are identified by their shared localization in a cell, tissue, or an organ. In some cases, protein groups can refer to two or more proteins that are identified by a shared affinity for a particle disclosed herein. Alternatively or in addition, a protein group can refer to one protein that is identified using a identifying sequence. For example, if in a sample, a peptide sequence is assayed that is shared between two proteins (Protein 1: XYZZX and Protein 2: XYZYZ), a protein group could be the “XYZ protein group” having two members (protein 1 and protein 2). Alternatively, if the peptide sequence is to a single protein (Protein 1), a protein group could be the “ZZX” protein group having one member (Protein 1). Each protein group can be supported by more than one peptide sequence. Protein detected or identified according to the instant disclosure can refer to a distinct protein detected in the sample (e.g., distinct relative other proteins detected using mass spectrometry). Thus, analysis of proteins present in distinct coronas corresponding to the distinct particle types in a particle panel, yields a high number of feature intensities. 
     A protein group may be a group of proteins with similar or indistinguishable mass spectrometric fingerprints. The number of protein groups identified in an assay may correlate with the number of proteins detected. In some cases, a protein group may comprise a set of protein isoforms. In some cases, a protein group may comprise proteins from multiple protein families. In some cases, a protein group may consist of proteins from a single protein family. In some cases, a protein group may comprise a single type of protein. 
       FIG.  15    graphically illustrates advantages for some of the methods disclosed herein. Some methods of the present disclosure may be used to study polymorphisms, pos-translation modifications, peptides, proteins, protein interactions, and/or pathways. Some methods of the present disclosure may be used to study proteomics with deep resolution (e.g., polymorphisms) and with high context (e.g., pathways). Some methods of the present disclosure may be scalable. Some methods of the present disclosure may not be biased. 
       FIG.  16    schematically illustrates a parallel and configurable workflow for some of the methods disclosed herein. As opposed to highly-complex and conventional laboratory set ups, some of the methods of the present disclosure may be implemented in a simpler and more efficient format. Some of the methods of the present disclosure may be implemented with parallel and configurable workflows. 
       FIG.  17    schematically illustrates a pipeline implementing some methods of the present disclosure. Some methods of the present disclosure may enable simplified and automated handling, may comprise fluidic handling and magnetic capture, and/or may comprise a liquid handling instrument assay implementation. 
     Peptide Variants 
     In some cases, a peptide variant may be detected using an assay. In some cases, peptide variant can refer to a peptide that is expressed from a set of coding regions in DNA, wherein the same set or a subset of the coding regions in DNA can express a plurality of peptides each comprising a primary structure. In some cases, a given set of coding regions in DNA may express a variety of peptides through, for example, constitutive splicing, exon skipping, intron retention, mutually excluding exons, alternative splicing, alternative 5′ splicing, alternative 3′ splicing, variable promoter usage, post-transcriptional modifications, or any combination thereof. 
     In some cases, a peptide variant may be detected using a proteomic assay, wherein the proteomic assay detects a peptide sequence that can be identified to be a variant sequence. 
     In some cases, a peptide variant may be detected using a genotypic assay, wherein the genotypic assay detects an mRNA that comprises a sequence that can be identified to be a variant sequence encoding a peptide variant. 
     Dynamic Range 
     The biomolecule corona analysis methods described herein may comprise assaying biomolecules in a sample of the present disclosure across a wide dynamic range. The dynamic range of biomolecules assayed in a sample may be a range of concentrations of biomolecules resolved (e.g., for which there are signals above a defined signal-to-noise threshold) or identified in an assay (e.g., mass spectrometry, chromatography, gel electrophoresis, spectroscopy, or immunoassays). For example, an assay capable of detecting proteins across a wide dynamic range may be capable of detecting proteins of very low abundance to proteins of very high abundance. The dynamic range of an assay may be directly related to the slope of assay signal intensity as a function of biomolecule abundance. For example, an assay with a low dynamic range may have a low (but positive) slope of the assay signal intensity as a function of biomolecule abundance, e.g., the ratio of the signal detected for a high abundance biomolecule to the ratio of the signal detected for a low abundance biomolecule may be lower for an assay with a low dynamic range than an assay with a high dynamic range. In specific cases, dynamic range may refer to the dynamic range of proteins within a sample or assaying method. 
     The biomolecule corona analysis methods described herein may compress the dynamic range of an assay. The dynamic range of an assay may be compressed relative to another assay if the slope of the assay signal intensity as a function of biomolecule abundance is lower than that of the other assay. For example, a plasma sample assayed using protein corona analysis with mass spectrometry may have a compressed dynamic range compared to a plasma sample assayed using mass spectrometry alone, directly on the sample or compared to provided abundance values for plasma proteins in databases (e.g., the database provided in Keshishian et al., Mol. Cell Proteomics 14, 2375-2393 (2015), also referred to herein as the “Carr database”). The compressed dynamic range may enable the detection of more low abundance biomolecules in a biological sample using biomolecule corona analysis with mass spectrometry than using mass spectrometry alone. 
     The dynamic range of a proteomic analysis assay may be the ratio of the signal produced by highest abundance proteins (e.g., the highest 10% of proteins by abundance) to the signal produced by the lowest abundance proteins (e.g., the lowest 10% of proteins by abundance). Compressing the dynamic range of a proteomic analysis may comprise decreasing the ratio of the signal produced by the highest abundance proteins to the signal produced by the lowest abundance proteins for a first proteomic analysis assay relative to that of a second proteomic analysis assay. The protein corona analysis assays disclosed herein may compress the dynamic range relative to the dynamic range of a total protein analysis method (e.g., mass spectrometry, gel electrophoresis, or liquid chromatography). 
     Provided herein are several methods for compressing the dynamic range of a biomolecular analysis assay to facilitate the detection of low abundance biomolecules relative to high abundance biomolecules. For example, a particle type of the present disclosure can be used to serially interrogate a sample. Upon incubation of the particle type in the sample, a biomolecule corona comprising forms on the surface of the particle type. If biomolecules are directly detected in the sample without the use of the particle types, for example by direct mass spectrometric analysis of the sample, the dynamic range may span a wider range of concentrations, or more orders of magnitude, than if the biomolecules are directed on the surface of the particle type. Thus, using the particle types disclosed herein may be used to compress the dynamic range of biomolecules in a sample. Without being limited by theory, this effect may be observed due to more capture of higher affinity, lower abundance biomolecules in the biomolecule corona of the particle type and less capture of lower affinity, higher abundance biomolecules in the biomolecule corona of the particle type. 
     A dynamic range of a proteomic analysis assay may be the slope of a plot of a protein signal measured by the proteomic analysis assay as a function of total abundance of the protein in the sample. Compressing the dynamic range may comprise decreasing the slope of the plot of a protein signal measured by a proteomic analysis assay as a function of total abundance of the protein in the sample relative to the slope of the plot of a protein signal measured by a second proteomic analysis assay as a function of total abundance of the protein in the sample. The protein corona analysis assays disclosed herein may compress the dynamic range relative to the dynamic range of a total protein analysis method (e.g., mass spectrometry, gel electrophoresis, or liquid chromatography). 
     Proteomic analysis may be enhanced by coupling the proteomic analysis to nucleic acid analysis. This may facilitate the accurate identification of proteins and peptides which may be otherwise unidentifiable or assigned an inaccurate identification in the absence of such a coupled approach. Nucleic acid analysis may increase the number of identifiable peptides from proteomic data (e.g., mass spectrometric data of peptide fragments). For example, genomic or transcriptomic data may enable identification of an otherwise unassignable mass spectrometric feature. Profiling nucleic acids from a subject may also identify sub-populations or individual proteins from among a protein group, and furthermore may determine the abundance or relative abundances of proteins from among the protein group (e.g., by determining that a protein group consists of two isoforms present in a 99:1 abundance ratio). In some cases, the determination of the relative abundance of a protein from a protein group may identify a protein at an abundance or concentration below the detection limit of a protein analysis method. Thus, coupling protein and nucleic acid analysis with protein analysis may increase the sensitivity of the protein analysis by 1 order of magnitude, 2 orders of magnitude, 3 orders of magnitude, 4 orders of magnitude, or more. For example, a method comprising nucleic acid and protein analysis may identify proteins or protein groups over a broader concentration range than a method comprising the protein analysis alone. 
     An example of such a determination is provided in  FIG.  2 B , which summarizes the identification of a minor allele of prekallikrein with a frequency of 0.01% relative to the major prekallikrein form. Such an identification of variant (e.g., allele or splicing) frequencies can be used to refine protein abundance data (e.g., obtained by a protein analysis method of the present disclosure), and split a single protein group abundance into a multiple protein or protein subgroup abundances. In the case of  FIG.  2 B , the mass spectrometrically determined prekallikrein abundance could be divided into a major form present at 99.99% of the total abundance and a minor form at 0.01% of the total abundance. 
     Nucleic Acid Analysis 
     The present disclosure provides various compositions and methods for analyzing (e.g., detecting or sequencing) nucleic acids. In some cases, genotypic (or genomic) information may be obtained using some of the compositions and methods of the present disclosure. In some cases, genotypic information can refer to information about substances comprising a nucleotide and/or a nucleic acid component. In some cases, genotypic information may comprise epigenetic information. In some cases, epigenetic information may comprise histone modification, DNA methylation, accessibility of different regions in a genome, dynamics changes thereof, or any combination thereof. In some cases, genotypic information may comprise primary structure information, secondary structure information, tertiary structure information, or quaternary information about a nucleic acid. In some cases, genotypic information may comprise information about nucleic acid-ligand interactions, wherein a ligand may comprise any one of various biological molecules and substances that may be found in living organisms, such as, nucleotides, nucleic acids, amino acids, peptides, proteins, monosaccharides, polysaccharides, lipids, phospholipids, hormones, or any combination thereof. In some cases, genotypic information may comprise a rate or prevalence of apoptosis of a healthy cell or a diseased cell. In some cases, genotypic information may comprise a state of a cell, such as a healthy state or a diseased state. In some cases, genotypic information may comprise chemical modification information of a nucleic acid molecule. In some cases, a chemical modification may comprise methylation, demethylation, amination, deamination, acetylation, oxidation, oxygenation, reduction, or any combination thereof. In some cases, genotypic information may comprise information regarding from which type of cell a biological sample originates. In some cases, genotypic information may comprise information about an untranslated region of nucleic acids. 
     In some cases, genotypic information may comprise information about a single cell, a tissue, an organ, a system of tissues and/or organs (such as cardiovascular, respiratory, digestive, or nervous systems), or an entire multicellular organism. In some cases, genotypic information may comprise information about an individual (e.g., an individual human being or an individual bacterium), or a population of individuals (e.g., human beings with diagnosed with cancer or a colony of bacteria). Genotypic information may comprise information from various forms of life, including forms of life from the Archaea, the Bacteria, the Eukarya, the Protozoa, the Chromista, the Plantae, the Fungi, or from the Animalia. In some cases, genotypic information may comprise information from viruses. 
     In some cases, genotypic information may comprise information relating exons and introns in the code of life. In some cases, genotypic information may comprise information regarding variations or mutations in the primary structure of nucleic acids, including base substitutions, deletions, insertions, or any combination thereof. In some cases, genotypic information may comprise information regarding the inclusion of non-canonical nucleobases in nucleic acids. In some cases, genotypic information may comprise information regarding variations or mutations in epigenetics. 
     In some cases, genotypic information may comprise information regarding variations in the primary structure, variations in the secondary structure, variations in the tertiary structure, or variations in the quaternary structure of peptides and/or proteins that one or more nucleic acids encode. 
     Such compositions and methods may be applied in assays that target multiple types of biomolecules. For example, an assay may analyze proteins and nucleic acids from a single sample. 
     A wide range of disease and pre-disease states are evidenced by detectable changes in nuclear, cytoplasmic, and cell free nucleic acids. However, many nucleic acid disease markers may be insufficient indicators for particular biological states, and thus by themselves cannot be used for accurate diagnostics. This, in part, may be due to the fact that many genetic markers correlate with multiple diseases, as is the case with high levels of insulin encoding cell-free DNA (cfDNA), which can result from a number of diseases including diabetes and polycystic ovary syndrome (PCOS). Additionally, the presence of a genetic marker associated with a disease state may not always correlate with the disease itself. For instance, in the realm of cancer detection, non-tumorigenic cells may be found to bear more oncogenes than a corresponding cancer cell from the same subject. Thus, while nucleic acid biomarkers can provide a panoply of information about a subject, that information can be difficult to leverage for accurate diagnostics. 
     The present disclosure provides methods that enable accurate analytic techniques and diagnostics from nucleic acid data and with other types of biomolecule data, such as proteomic data. By combining multiple forms of biomolecular analysis with nucleic acid analysis, individual biomarkers (e.g., genetic markers) that weakly correlate with or are not known to correlate with a particular disease state can be used for highly accurate diagnostics. Furthermore, by measuring and analyzing large numbers of biomarkers, the noise stemming from inter-subject variation and extraneous factors (e.g., short-term changes in gene expression due to stress) can be differentiated from true-positive results for a disease or condition. 
     In some assays, different types of biomolecules can be enriched or analyzed in separate sample partitions. For example, an assay may comprise analyzing nucleic acids in a first sample partition, analyzing proteins in a second sample partition, and optionally analyzing lipids and metabolites in a third sample partition. In some assays, multiple types of biomolecules can be enriched or analyzed within a single sample partition (e.g., nucleic acids and peptides can be enriched from a single volume of sample). 
     Various reagents for sequencing and methods of sequencing nucleic acids are consistent with the compositions and methods disclosed herein of parallel assaying for proteins (e.g., using corona analysis) and nucleic acids (e.g., using a sequencing method). The methods disclosed herein may comprise enriching one or more nucleic acid molecules from a sample. This may comprise enrichment in solution, enrichment on a sensor element (e.g., a particle), enrichment on a substrate (e.g., a surface of an Eppendorf tube), or selective removal of a nucleic acid (e.g., by sequence-specific affinity precipitation). Enrichment may comprise amplification, including differential amplification of two or more different target nucleic acids. Differential amplification may be based on sequence, CG-content, or post-transcriptional modifications, such as methylation state. Enrichment may also comprise hybridization methods, such as pull-down methods. For example, a substrate partition may comprise immobilized nucleic acids capable of hybridizing to nucleic acids of a particular sequence, and thereby capable of isolating particular nucleic acids from a complex biological solution. Hybridization may target genes, exons, introns, regulatory regions, splice sites, reassembly genes, among other nucleic acid targets. Hybridization can utilize a pool of nucleic acid probes that are designed to target multiple distinct sequences, or to tile a single sequence. 
     Enrichment may comprise a hybridization reaction and may generate a subset of nucleic acid molecules from a biological sample. Hybridization may be performed in solution, on a substrate surface (e.g., a wall of a well in a microwell plate), on a sensor element, or any combination thereof. A hybridization method may be sensitive for single nucleotide polymorphisms. For example, a hybridization method may comprise molecular inversion probes. 
     Enrichment may also comprise amplification. Suitable amplification methods include polymerase chain reaction (PCR), solid-phase PCR, RT-PCR, qPCR, multiplex PCR, touchdown PCR, nanoPCR, nested PCR, hot start PCR, helicase-dependent amplification, loop mediated isothermal amplification (LAMP), self-sustained sequence replication, nucleic acid sequence based amplification, strand displacement amplification, rolling circle amplification, ligase chain reaction, and any other suitable amplification technique. 
     The sequencing may target a specific sequence or region of a genome. The sequencing may target a type of sequence, such as exons. In some cases, the sequencing comprises exome sequencing. In some cases, the sequencing comprises whole exome sequencing. The sequencing may target chromatinated or non-chromatinated nucleic acids. The sequencing may be sequence-non specific (e.g., provide a reading regardless of the target sequence). The sequencing may target a polymerase accessible region of the genome. The sequencing may target nucleic acids localized in a part of a cell, such as the mitochondria or the cytoplasm. The sequencing may target nucleic acids localized in a cell, tissue, or an organ. The sequencing may target RNA, DNA, any other nucleic acid, or any combination thereof. 
     ‘Nucleic acid’ may refer to a polymeric form of nucleotides of any length, in single-, double- or multi-stranded form. A nucleic acid may comprise any combination of ribonucleotides, deoxyribonucleotides, and natural and non-natural analogues thereof, including 5-bromouracil, peptide nucleic acids, locked nucleotides, glycol nucleotides, threose nucleotides, dideoxynucleotides, 3′-deoxyribonucleotides, dideoxyribonucleotides, 7-deaza-GTP, fluorophores-bound nucleotides, thiol containing nucleotides, biotin linked nucleotides, methyl-7-guanosine, methylated nucleotides, inosine, thiouridine, pseudourdine, dihydrouridine, queuosine, and wyosine. A nucleic acid may comprise a gene, a portion of a gene, an exon, an intron, messenger RNA (mRNA), transfer RNA (tRNA), ribosomal RNA (rRNA), short interfering RNA (siRNA), short-hairpin RNA (shRNA), micro-RNA (miRNA), a ribozyme, cDNA, a recombinant nucleic acid, a branched nucleic acid, a plasmid, cell-free DNA (cfDNA), cell-free RNA (cfRNA), genomic DNA, mitochondrial DNA (mtDNA), circulating tumor DNA (ctDNA), long non-coding RNA, telomerase RNA, Piwi-interacting RNA, small nuclear RNA (snRNA), small interfering RNA, YRNA, circular RNA, small nucleolar RNA, or pseudogene RNA. A nucleic acid may comprise a DNA or RNA molecule. A nucleic acid may also have a defined 3-dimensional structure. In some cases, a nucleic acid may comprise a non-canonical nucleobase or a nucleotide, such as hypoxanthine, xanthine, 7-methylguanine, 5,6-dihydrouracil, 5-methylcytosine, or any combination thereof. Nucleic acids may also comprise non-nucleic acid molecules. 
     A nucleic acid may be derived from various sources. In some cases, a nucleic acid may be derived from an exosome, an apoptotic body, a tumor cell, a healthy cell, a virtosome, an extracellular membrane vesicle, a neutrophil extracellular trap (NET), or any combination thereof. 
     A nucleic acid may comprise various lengths. In some cases, a nucleic acid may comprise at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, or 10000 nucleotides. In some cases, a nucleic acid may comprise at most about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, or 10000 nucleotides. 
     Various reagents may be used for sequencing. In some cases, a reagent may comprise primers, oligonucleotides, switch oligonucleotides, adapters, amplification adapters, polymerases, dNTPs, co-factors, buffers, enzymes, ionic co-factors, ligase, reverse transcriptase, restriction enzymes, endonucleases, transposase, protease, proteinase K, DNase, RNase, lysis agents, lysozymes, achromopeptidase, lysostaphin, labiase, kitalase, lyticase, inhibitors, inactivating agents, chelating agents, EDTA, crowding agents, reducing agents, DTT, surfactants, TritonX-IOO, Tween 20, sodium dodecyl sulfate, sarcosyl, or any combination thereof. 
     Various methods for sequencing nucleic acids may be used. In some cases, a nucleic acid sequencing method may comprise high-throughput sequencing, next-generation sequencing, flow sequencing, massively-parallel sequencing, shotgun sequencing, single-molecule real-time sequencing, ion semiconductor sequencing, electrophoretic sequencing, pyrosequencing, sequencing by synthesis, combinatorial probe anchor synthesis sequencing, sequencing by ligation, nanopore sequencing, GenapSys sequencing, chain termination sequencing, polony sequencing, 454 pyrosequencing, reversible terminated chemistry sequencing, heliscope single molecule sequencing, tunneling currents DNA sequencing, sequencing by hybridization, clonal single molecule array sequencing, sequencing with MS, DNA-seq, RNA-seq, ATAC-seq, methyl-seq, ChIP-seq, or any combination thereof. 
     Reagents for sequencing and methods for sequencing nucleic acids include those described in WO2012050920, WO2020023744, WO2019108851, WO2019084158, WO2020023744, US20190177803, and US20190316185, all of which are incorporated herein by reference in their entirety. 
     As disclosed herein, nucleic acids may be processed by standard molecular biology techniques for downstream applications. Nucleic acids may be prepared from nucleic acids isolated from a sample of the present disclosure. The nucleic acids may subsequently be attached to an adaptor polynucleotide sequence, which may comprise a double stranded nucleic acid. The nucleic acids may be end repaired prior to attaching to the adaptor polynucleotide sequences. Adaptor polynucleotides may be attached to one or both ends of the nucleotide sequences. The same or different adaptor may be bound to each end of the fragment, thereby producing an “adaptor-nucleic acid-adaptor” construct. A plurality of the same or different adaptor may be bound to each end of the fragment. In some cases, different adaptors may be attached to each end of the nucleic acid when adaptors are attached to both ends of the nucleic acid. Various methods of attaching nucleic acid adaptors to a nucleic acid of interest are consistent with the compositions and methods disclosed herein including those using standard molecular cloning techniques (Sambrook and Russell, Molecular Cloning, A Laboratory Manual, 3 edition Cold Spring Harbor Laboratory Press (2001), herein incorporated by reference). 
     An oligonucleotide tag complementary to a sequencing primer may be incorporated with adaptors attached to a target nucleic acid. For analysis of multiple samples, different oligonucleotide tags complementary to separate sequencing primers may be incorporated with adaptors attached to a target nucleic acid. 
     An oligonucleotide index tag may also be incorporated with adaptors attached to a target nucleic acid. In cases in which deletion products are generated from a plurality of polynucleotides prior to hybridizing the deletion products to a nucleic acids immobilized on a structure (e.g., a sensor element such as a particle), polynucleotides corresponding to different nucleic acids of interest may first be attached to different oligonucleotide tags such that subsequently generated deletion products corresponding to different nucleic acids of interest may be grouped or differentiated. Consequently, deletion products derived from the same nucleic acid of interest may have the same oligonucleotide index tag such that the index tag identifies sequencing reads derived from the same nucleic acid of interest. Likewise, deletion products derived from different nucleic acids of interest will have different oligonucleotide index tags to allow them to be grouped or differentiated such as on a sensor element. Oligonucleotide index tags may range in length from about 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 75, to 100 nucleotides or base pairs, or any length in between. 
     The oligonucleotide index tags may be added separately or in conjunction with a primer, primer binding site or other component. Conversely, a pair-end read may be performed, wherein the read from the first end may comprise a portion of the sequence of interest and the read from the other (second) end may be utilized as a tag to identify the fragment from which the first read originated. 
     A sequencing read may be initiated from the point of incorporation of the modified nucleotide into the extended capture probe. A sequencing primer may be hybridized to extended capture probes or their complements, which may be optionally amplified prior to initiating a sequence read, and extended in the presence of natural nucleotides. Extension of the sequencing primer may stall at the point of incorporation of the first modified nucleotide incorporated in the template, and a complementary modified nucleotide may be incorporated at the point of stall using a polymerase capable of incorporating a modified nucleotide (e.g. TiTaq polymerase). A sequencing read may be initiated at the first base after the stall or point of modified nucleotide incorporation. In a sequencing-by-synthesis method, a sequencing read may be initiated at the first base after the stall or point of modified nucleotide incorporation. 
     Aspects of the present disclosure comprise methods and compositions related to nucleic acid (polynucleotide) sequencing. The methods of the present disclosure provide for identification and quantification of nucleic acids in a subject or a sample. In some methods and compositions described herein, the nucleotide sequence of a portion of a target nucleic acid or fragment thereof may be determined using a variety of methods and devices. Examples of sequencing methods include electrophoretic, sequencing by synthesis, sequencing by ligation, sequencing by hybridization, single-molecule sequencing, and real time sequencing methods. The process to determine the nucleotide sequence of a target nucleic acid or fragment thereof may be an automated process. In certain amplification reactions, capture probes may function as primers permitting the priming of a nucleotide synthesis reaction using a polynucleotide from the nucleic acid sample as a template. In this way, information regarding the sequence of the polynucleotides supplied to the array may be obtained. Polynucleotides hybridized to capture probes on the array may serve as sequencing templates if primers that hybridize to the polynucleotides bound to the capture probes and sequencing reagents are further supplied to the array. Methods of sequencing using arrays have been described previously in the art. 
     Nucleic acid analysis methods may generate paired end reads on nucleic acid clusters. Methods for obtaining paired end reads are described in WO/07010252 and WO/07091077, each of which is incorporated herein by reference in its entirety. In such methods, a nucleic acid cluster may be immobilized on a sensor element, such as a surface. Paired end sequencing facilitates reading both the forward and reverse template strands of each cluster during one paired-end read. Generally, template clusters may be amplified on the surface of a substrate (e.g. a flow-cell) by bridge amplification and sequenced by paired primers sequentially. Upon amplification of the template strands, a bridged double stranded structure may be produced. This may be treated to release a portion of one of the strands of each duplex from the surface. The single stranded nucleic acid may be available for sequencing, primer hybridization and cycles of primer extension. After the first sequencing run, the ends of the first single stranded template may be hybridized to the immobilized primers remaining from the initial cluster amplification procedure. The immobilized primers may be extended using the hybridized first single strand as a template to resynthesize the original double stranded structure. The double stranded structure may be treated to remove at least a portion of the first template strand to leave the resynthesized strand immobilized in single stranded form. The resynthesized strand may be sequenced to determine a second read, whose location originates from the opposite end of the original template fragment obtained from the fragmentation process. 
     Nucleic acid sequencing may be single-molecule sequencing or sequencing by synthesis. Sequencing may be massively parallel array sequencing (e.g., Illumina™ sequencing), which may be performed using template nucleic acid molecules immobilized on a support, such as a flow cell. A high-throughput sequencing method may sequence simultaneously (or substantially simultaneously) at least about 10,000, 100,000, 1 million, 10 million, 100 million, 1 billion, or more polynucleotide molecules. Sequencing methods may include, but are not limited to: pyrosequencing, sequencing-by synthesis, single-molecule sequencing, nanopore sequencing, semiconductor sequencing, sequencing-by-ligation, sequencing-by-hybridization, Digital Gene Expression (Helicos), massively parallel sequencing, e.g., Helicos, Clonal Single Molecule Array (Solexa/Illumina), sequencing using PacBio, SOLiD, Ion Torrent, or Nanopore platforms. Sequencing may comprise a first-generation sequencing method, such as Maxam-Gilbert or Sanger sequencing, or a high-throughput sequencing (e.g., next-generation sequencing or NGS) method. 
     Sequencing methods disclosed herein may involve sequencing a whole genome or portions thereof. Sequencing may comprise sequencing a whole genome, a whole exome, portions thereof (e.g., a panel of genes, including potentially coding and non-coding regions thereof). Sequencing may comprise sequencing a transcriptome or portion thereof. Sequencing may comprise sequencing an exome or portion thereof. Sequencing coverage may be optimized based on analytical or experimental setup, or desired sequencing footprint. 
     The sequencing methods of the present disclosure may be able to detect germline susceptibility loci, somatic single nucleotide polymorphisms (SNPs), small insertion and deletion (indel) mutations, copy number variations (CNVs) and structural variants (SVs). 
     The sequencing methods of the present disclosure may involve sequence analysis of RNA. RNA sequences or expression levels may be analyzed by using a reverse transcription reaction to generate complementary DNA (cDNA) molecules from RNA for sequencing or by using reverse transcription polymerase chain reaction for quantification of expression levels. The sequencing methods of the present disclosure may detect RNA structural variants and isoforms, such as splicing variants and structural variants. The sequencing methods of the present disclosure may quantify RNA sequences or structural variants. 
     Furthermore, the sequencing methods of the present disclosure may quantify a nucleic acid, thus allowing sequence variations within an individual sample may be identified and quantified (e.g., a first percent of a gene is unmutated and a second percent of a gene present in a sample contains an indel). 
     Nucleic acid analysis methods may comprise physical analysis of nucleic acids collected from a biological sample. A method may distinguish nucleic acids based on their mass, post-transcriptional modification state (e.g., capping), histonylation, circularization (e.g., to detect extrachromosomal circular DNA elements), or melting temperature. For example, an assay may comprise restriction fragment length polymorphism (RFLP) or electrophoretic analysis on DNA collected from a biological sample. In some cases, post-transcriptional modification may comprise 5′ capping, 3′ cleavage, 3′ polyadenylation, splicing, or any combination thereof. 
     Nucleic acid analysis may also include sequence-specific interrogation. An assay for sequence-specific interrogation may target a particular sequence to determine its presence, absence or relative abundance in a biological sample. For example, an assay may comprise a southern blot, qPCR, fluorescence in situ hybridization (FISH), array-Comparative Genomic Hybridization (array-CGH), quantitative fluorescence PCR (QF-PCR), nanopore sequencing, sequencing by hybridization, sequencing by synthesis, sequencing by ligation, or capture by nucleic acid binding moieties (e.g., single stranded nucleotides or nucleic acid binding proteins) to determine the presence of a gene of interest (e.g., an oncogene) in a sample collected from a subject. An assay may also couple sequence specific collection with sequencing analysis. For example, an assay may comprise generating a particular sticky-end motif in nucleic acids comprising a specific target sequence, ligating an adaptor to nucleic acids with the particular sticky-end motif, and sequencing the adaptor-ligated nucleic acids to determine the presence or prevalence of mutations in a gene of interest. 
     Genomic Variant 
     In some cases, a genomic variant may be detected using an assay. In some cases, a genomic variant can refer to a nucleic acid sequence originating from a DNA address(es) in a sample that comprises a sequence that is different a nucleic acid sequence originating from the same DNA address(es) in a reference sample. In some cases, a genomic variant may comprise a mutation such as an insertion mutation, deletion mutations, substitution mutation, copy number variations, transversions, translocations, inversion, aneuploidy, partial aneuploidy, polyploidy, chromosomal instability, chromosomal structure alterations, gene fusions, chromosome fusions, gene truncations, gene amplification, gene duplications, chromosomal lesions, DNA lesions, abnormal changes in nucleic acid chemical modifications, abnormal changes in epigenetic patterns, abnormal changes in nucleic acid methylation infection, chromosal lesions, DNA lesions, or any combination thereof. In some cases, a set of genomic variants may comprise a single nucleotide polymorphism (SNP). 
     In some cases, a genomic variant may be detected from DNA or copies thereof, such as RNA, or such as nucleic acid libraries amplified from DNA or RNA. 
     In some cases, a genomic variant may be detected using a proteomic assay, wherein the proteomic assay detects a peptide sequence that can be identified to have a mutation in its primary sequence. 
     Dual Protein-Nucleic Acid Assays 
     The present disclosure provides methods for parallel identification of proteins and nucleic acids from a sample. The methods may include those described in International Publication No. WO2022/046804, filed Aug. 24, 2021, the content of which is incorporated by reference in its entirety herein. Coupling these two forms of analysis can overcome limitations inherent to each type. In particular, performing protein or nucleic acid analysis individually can generate indeterminate identifications, such as uncertain genomic copy numbers or inconclusive protein isoform assignments. In many cases, properly coupling nucleic acid and protein analysis can overcome these indeterminacies and can increase the level of diagnostic insight beyond the sum of what protein and nucleic acid analysis would provide individually. 
     Some methods may comprise parallel collection of proteins and nucleic acids on a sensor element (e.g., a particle). For example, a method may comprise simultaneous adsorption of proteins and nucleic acids on a sensor element, followed by nucleic acid sequencing and protein analysis by mass spectrometry. A method may also comprise simultaneous adsorption of proteins and nucleic acids on a sensor element and collection of the proteins and nucleic acids from the sensor element for parallel protein analysis (e.g., mass spectrometry) and nucleic acid sequencing. Such a method may comprise separation of the proteins from the nucleic acids, such as by chromatography, separate elution of the proteins and nucleic acids from a sensor element, differential precipitation, phase separation, or affinity capture. Alternatively, a method may comprise adsorption of proteins on a sensor element, followed by collection of nucleic acids from the sample. Further, a method may comprise dividing a sample into separate portions for protein (e.g., biomolecule corona) and nucleic acid analysis. 
     Nucleic acid analysis may guide or inform protein (e.g., biomolecule corona) analysis. The results of nucleic acid analysis may contribute to a protein identification. In some cases, protein analysis may determine whether a protein is present, and nucleic acid analysis may determine the exact sequence of the protein. This can occur when mass spectrometric data identifies only a portion of a protein or peptide sequence. In such cases, nucleic acid data, such as the identification of a particular RNA isoform in a sample, may be used to discern the identity or full sequence of the protein or peptide. As an example, cases in which protein domain transpositions (e.g., an HRAS protein kinase domain transpositions leading to constitutive activity and possible increased cancer risk) do not alter peptide fragment digestion patterns can be difficult to ascertain through protein analysis alone, but may be elucidated by a combination of biomolecule corona analysis and genomic analysis, wherein the biomolecule corona analysis may identify the presence of the protein, and genomic analysis can determine its transposition state. 
     Nucleic acid (e.g., transcriptomic) analysis may be used to determine which protein splicing variants are present in a sample. RNA analysis may further be used to determine the relative abundances of the protein splicing variants. Protein analysis may be used to determine the RNA variants (e.g., mRNA splicing variants) present in a sample. 
     Nucleic acid analysis may also distinguish an individual protein from among an experimentally identified protein group. Biomolecule corona analysis may identify protein groups comprising pluralities of proteins. In such cases, nucleic acid information such as a genomic sequence, an RNA sequence (e.g., a particular RNA isoform or splicing variant), or expression modulating nucleic acid modification (e.g., methylation) may be used to discern the protein or set of proteins that are present from among the protein group. For example, biomolecule corona analysis may identify a protein group consisting of seven related proteins (e.g., the seven confirmed 14-3-3 protein isoforms found in mammalian cells), while subsequent nucleic acid analysis may determine that RNA encoding two of the seven related proteins are present in the sample, thereby determining the proteins from among the protein group present in the sample. 
     In this way, nucleic acid analysis may increase the number of proteins or protein groups identified by a protein assay. Nucleic acid analysis may determine the particular proteins present within an identified protein group, or may identify protein subgroups from among a protein group. Coupling nucleic acid analysis with protein analysis may thus increase the number of identified proteins or protein groups by at least 5%, at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 40%, at least 50%, at least 60%, at least 80%, or at least 100% relative to an assay comprising protein analysis only. 
     Nucleic acid analysis may also guide protein (e.g., protein corona) and biomolecule corona analysis. In some cases, mass spectrometric analysis (and thereby a biomolecule corona method) comprises data-dependent acquisition, in which a number of ions (e.g., particular m/z ratios) are pre-selected for tandem mass spectrometric analysis. An ion or plurality of ions of the data-dependent acquisition may be selected based on nucleic acid analysis results. For example, nucleic acid analysis may identify two protein variants with predicted peptide fragments that share a mass but vary in sequence and provide instructions to a mass spectrometric instrument to include the mass of the peptide fragment in a data-dependent acquisition. Mass spectrometric analysis may also comprise data-independent acquisition, in which a mass/charge range is preselected for tandem mass spectrometric analysis. In such cases, nucleic acid analysis may dictate or partially dictate the mass/charge ranges analyzed. Nucleic acid analysis may also guide ionization methodology. For example, results from nucleic acid analysis may determine laser power for a matrix assisted laser desorption/ionization (MALDI) mass spectrometric experiment, and thereby affect the biomolecule fragments generated for analysis. 
     Subject-Specific Libraries 
     Nucleic acid and protein analysis may be used individually or in combination to develop subject-specific (e.g., patient-specific) libraries that can expedite and expand the depth and accuracy of mass spectrometric analyses. Some mass spectrometric analyses are limited by degrees of ambiguity in protein assignments. In some cases, only a portion of a protein&#39;s sequence may be covered by mass spectrometric signals, thereby rendering the assay blind to variations in the remaining unsequenced portion. Furthermore, mass spectrometric analysis can be incapable of identifying particular transpositions (e.g., domain transpositions) and splicing variations. Rectifying such shortcomings can be expensive and time consuming. For example, expanding mass spectrometric assays to include multiple forms of digestions can increase sequence coverage at the expense of increased user input. 
     Generating a subject-specific library can allow faster and deeper analysis of mass spectrometric data from the subject. A subject-specific library may comprise proteins present in a subject. A subject-specific library may comprise nucleic acids (e.g., genes) present in a subject. A subject-specific library may be used to generate a specific spectrum library comprising predicted experimental signals (e.g., mass spectrometric signals corresponding to peptide fragments or DNA electrophoresis bands) from the subject. A subject-specific library may be generated with proteomic data, nucleic acid data, metabolomic data (e.g., measuring lactose hydrolysis to determine the presence of lactase), lipidomic data, or any combination thereof. 
     A subject-specific library may increase the precision of protein or nucleic acid identifications. In some cases, possible protein identifications may be limited to potential protein sequences identified in a subject&#39;s genome. For example, a protein group encompassing 8 allelic variants may be narrowed to a specific form based on nucleic acid data from a subject. 
       FIG.  5    illustrates a method for generating and utilizing a subject specific library. This method shows how coupled nucleic acid and protein analysis can increase diagnostic depth and precision. A subject-specific library can be constructed from nucleic acid data  501 . The data may be processed to identify sequence variants  502  (e.g., based at least on alignment with a reference sequence), leading to a library of subject-specific nucleic acid variants  503 . The nucleic acid data may be derived from comprise whole genome sequencing or targeted sequencing using a specific or enriched portion of a genome or transcriptome. Furthermore, the screening may comprise exome sequencing to thereby identify splicing variants from a sample. 
     Nucleic acid sequences (e.g., gene variants) may be translated in-silico  504  to generate a subject-specific protein sequence database  505 . A database may comprise protein sequences which may aid in protein or protein group identifications from mass spectrometric data on a sample. In many cases, the database may be used to determine which proteins from among a protein group are present in a sample. The database may also comprise abundances or relative abundances of protein sequences. For example, the database may comprise the relative abundances of different isoforms of a protein in a sample or the mutation rate for a gene or among multiple genes. 
     The subject-specific protein sequence database  505  may be used to computationally generate  506  subject-specific spectrum libraries  507 , which may comprise expected or putative mass spectrometric signals from samples from the subject, based in part on the data generated in  504 . The computational prediction of mass spectrometric features may account for experimental variables, such as sample purification and digestion methods. The subject-specific spectrum library may comprise expected tandem mass spectrometric features, as well as predicted relative intensities of mass spectrometric features. The subject-specific spectrum library may also comprise empirically derived mass spectrometric features. For example, peptide variants may be identified  508  from data-dependent acquisition mass spectrometric experiments  509 . 
     The subject-specific spectrum library  507  may be used to deconvolute mass spectrometric data (e.g., data-independent acquisition mass spectrometric data  510 ) collected from samples from the subject, and to thus identify particular genomic variants in a sample  512 . A shortcoming of some mass spectrometric experiments is that signals may only be obtained for portions of a target protein, such that the mass spectrometric analysis is blind to sequence variations in the unresolved portion of the protein sequence. The subject-specific spectrum library  507 , as described herein, can overcome this limitation (when present) by correlating mass spectrometric features with known proteins or protein variants, in some cases allowing the mass spectrometric data to be used to identify partial or complete protein sequences  511 . Furthermore, the subject-specific spectrum library  507  can aid in quantifying (e.g., determining the abundance in the subject sample) proteins from mass spectrometric data. This in part may comprise apportioning a common mass spectrometric signal (e.g., an m/z common to multiple proteins) between multiple proteins identified in a sample. 
     A utility of subject-specific libraries is that they may differentiate and enable the identification of proteins from groups (e.g., protein groups) that are difficult to distinguish solely through protein analysis. In some cases, the subject-specific library can also enable relative or absolute quantification (e.g., concentration in a biological sample) of a protein or set of proteins. A subject-specific library can also determine the presence of mutations, such as point mutations or transpositions, which may not be detectable through protein analysis (e.g., mass spectrometry) alone. 
     Heterozygous pairs can be particularly difficult to detect through mass spectrometric analysis alone. In some cases, the distinct points or regions of a heterozygous pair may not be detected during protein analysis. For example, mass spectrometric analysis might not produce signals covering the region or regions that differ between proteins arising from multiple alleles. Pairing nucleic acid analysis can determine whether a subject is homozygous or heterozygous for a particular gene, and can further determine the allele or alleles that are present. 
     An example of such a method is provided in  FIG.  6   . Sequencing the subject&#39;s genome  601  may reveal homozygosity or heterozygosity  602  for a particular gene. The sequencing may target the particular gene, may cover a portion or portions of the subject&#39;s genome, or may cover the entirety of the subject&#39;s genome. Nucleic acid sequences obtained for the subject may be translated in silico to construct a subject-specific protein sequence database  603  containing predicted protein sequences present in the subject. Multiple protein sequences may be predicted for a single gene, such as in the case of heterozygosity or alternative splicing. The protein sequences may be used to generate predicted mass spectrometric signals from a subject sample  604 . This can simplify the analysis of a protein mass spectrometry data from a subject and enhance its specificity and accuracy as well. For example, where a set of mass spectrometric signals identifies a protein group from a sample, tandem nucleic acid sequences and mass spectrometric signals may identify a particular protein or set of proteins present in the sample, such as a pair of proteins arising from two alleles for a gene. 
     Furthermore, protein data may be used to determine expression levels in a subject. While nucleic acid analysis may identify a number of genes present in a subject, protein analysis on samples from the subject can determine which genes are being expressed and translated. This concept is illustrated in  FIG.  7   , which shows mass spectrometric data  701  determining that one allele from a heterozygous gene pair is being expressed in a particular subject. 
     Disease Detection 
     The compositions and methods disclosed herein can be used to identify various biological states in a particular biological sample. For example, a biological state can refer to an elevated or low level of a particular protein or a set of proteins. In other examples, a biological state can refer to a disease, such as cancer. In some cases, a biological state can be healthy state. In some cases, identification of a biological state may comprise determining a probability to a certain state for the biological sample. One or more particle types can be incubated with a sample (e.g., CSF), allowing for formation of a protein corona. The protein corona can then be analyzed by gel electrophoresis or mass spectrometry in order to identify a pattern of proteins or protein groups. Analysis of protein corona (e.g., by mass spectrometry or gel electrophoresis) may be referred to as corona analysis. The pattern of proteins or protein groups can be compared to the same methods carried out on a control sample. Upon comparison of the patterns of proteins or protein groups, it may be identified that the first sample comprises an elevated level of markers corresponding to some biological states (e.g., brain cancer). The particles and methods of use thereof, can thus be used to diagnose a particular disease state. 
     The methods described herein can be used generate biomolecule fingerprints (e.g., the relative abundances of 50 proteins and 10 nucleic acid sequences in a sample) which are consistent with a particular biological (e.g., disease) state. The biological state may be a disease, disorder, or tissue abnormality. The disease state may be an early, intermediate, or late phase disease state. 
     In some cases, a biomolecule fingerprint can be used to determine the disease state of a subject, diagnose or prognose a disease in a subject or identify patterns of biomarkers that are associated with a disease state or a disease or disorder. For example, the changes in the biomolecule fingerprint in a subject over time (days, months, years) allows for the ability to track a disease or disorder in a subject (e.g. disease state) which may be broadly applicable to determination of a biomolecule fingerprint that can be associated with the early stage of a disease or any other disease state. As disclosed herein, the ability to detect a disease early on, for example cancer, even before it fully develops or metastasizes allows for a significant increase in positive outcomes for those patients and the ability to increase life expectancy and lower mortality associated with that disease. 
     The methods disclosed herein can provide biomolecule fingerprints associated with the pre-stages or precursor states of the disease in a high-throughput fashion. The methods of the present disclosure enable large scale, fast processing of samples to generate biomolecule fingerprints in a highly parallelized manner, thereby allowing for rapid and large scale determination of disease state of a subject, diagnosis or prognosis a disease in a subject or identification of patterns of biomarkers that are associated with a disease state or a disease or disorder, across many subjects. 
     The disease or disorder may be cancer. The term “cancer” is meant to encompass any cancer, neoplastic and preneoplastic disease that is characterized by abnormal growth of cells, including tumors and benign growths. Cancer may, for example, be lung cancer, pancreatic cancer, or skin cancer. The present disclosure provides compositions and methods which may diagnose cancer and also distinguish the particular type and stage of cancer (e.g. determine if a subject (a) does not have cancer, (b) is in a pre-cancer development stage, (c) is in early stage of cancer, (d) is in a late stage of cancer) from a sample. 
     The methods of the present disclosure can additionally be used to detect other cancers, such as acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), cancer in adolescents, adrenocortical carcinoma, childhood adrenocortical carcinoma, unusual cancers of childhood, AIDS-related cancers, kaposi sarcoma (soft tissue sarcoma), AIDS-related lymphoma (lymphoma), primary central nervous system (CNS) lymphoma (lymphoma), anal cancer, appendix cancer, gastrointestinal carcinoid tumors, astrocytomas, childhood brain cancer, atypical teratoid/rhabdoid tumor, central nervous system brain cancer, central nervous system brain cancer, basal cell carcinoma of the skin, skin cancer, bile duct cancer, bladder cancer, childhood bladder cancer, bone cancer, Ewing sarcoma, osteosarcoma, malignant fibrous histiocytoma, brain tumors, breast cancer, childhood breast cancer, bronchial tumors, childhood Burkitt lymphoma, Burkitt lymphoma, non-Hodgkin lymphoma, gastrointestinal carcinoid tumor, carcinoid tumor, childhood carcinoid tumors, unknown primary carcinoma, childhood unknown primary carcinoma, childhood cardiac (heart) tumors, cardiac tumors, tumors in the central nervous system, atypical teratoid/rhabdoid tumor, childhood brain cancer, embryonal tumors, germ cell tumor, cervical cancer, childhood cervical cancer, childhood cancers, unusual childhood cancers, cholangiocarcinoma, bile duct cancer, childhood chordoma, chordoma, chronic lymphocytic leukemia (CLL), chronic myelogenous leukemia (CML), chronic myeloproliferative neoplasms, colorectal cancer, childhood colorectal cancer, craniopharyngioma, cutaneous t-cell lymphoma, mycosis fungoides, Sézary syndrome, ductal carcinoma in situ (DCIS), embryonal tumors, endometrial cancer, uterine cancer, ependymoma, esophageal cancer, childhood esophageal cancer, esthesioneuroblastoma, head and neck cancer, Ewing sarcoma, bone cancer, childhood extracranial germ cell tumor, extracranial germ cell tumor, extragonadal germ cell tumor, eye cancer, childhood intraocular melanoma, intraocular melanoma, retinoblastoma, fallopian tube cancer, fibrous histiocytoma of bone, malignant, and osteosarcoma, gallbladder cancer, gastric (stomach) cancer, childhood gastric (stomach) cancer, gastrointestinal carcinoid tumor, gastrointestinal stromal tumors (GIST), soft tissue sarcoma, childhood gastrointestinal stromal tumors, germ cell tumors, childhood central nervous system germ cell tumors, childhood extracranial germ cell tumors, extragonadal germ cell tumors, ovarian germ cell tumors, testicular cancer, gestational trophoblastic disease, hairy cell leukemia, childhood heart tumors, heart tumors, hepatocellular (liver) cancer, histiocytosis, Langerhans cell, Hodgkin lymphoma, hypopharyngeal cancer, intraocular melanoma, childhood intraocular melanoma, Islet cell tumors, pancreatic neuroendocrine tumors, Kaposi sarcoma, soft tissue sarcoma, kidney (renal cell) cancer, Langerhans cell histiocytosis, laryngeal cancer, leukemia, lip and oral cavity cancer, liver cancer, lung cancer (non-small cell and small cell), childhood lung cancer, lymphoma, male breast cancer, malignant fibrous histiocytoma of bone, osteosarcoma, melanoma, childhood melanoma, intraocular melanoma, childhood intraocular melanoma, Merkel cell carcinoma, skin cancer, malignant mesothelioma, childhood mesothelioma, metastatic cancer, metastatic squamous neck cancer with occult primary, midline tract carcinoma with nut gene changes, mouth cancer, multiple endocrine neoplasia syndromes, multiple myeloma/plasma cell neoplasms, mycosis fungoides, myelodysplastic syndromes, myelodysplastic/myeloproliferative neoplasms, chronic myelogenous leukemia, acute myeloid leukemia, chronic myeloproliferative neoplasms, nasal cavity and paranasal sinus cancer, nasopharyngeal cancer, neuroblastoma, Non-hodgkin lymphoma, non-small cell lung cancer, oral cancer, lip and oral cavity cancer, oropharyngeal cancer, osteosarcoma, malignant fibrous histiocytoma of bone, ovarian cancer, childhood ovarian cancer, pancreatic cancer, childhood pancreatic cancer, pancreatic neuroendocrine tumors (Islet cell tumors), childhood laryngeal papillomatosis, papillomatosis, paraganglioma, childhood paraganglioma, paranasal sinus and nasal cavity cancer, parathyroid cancer, penile cancer, pharyngeal cancer, pheochromocytoma, childhood pheochromocytoma, pituitary tumor, plasma cell neoplasm/multiple myeloma, pleuropulmonary blastoma, pregnancy and breast cancer, primary central nervous system (CNS) lymphoma, primary peritoneal cancer, prostate cancer, rectal cancer, recurrent cancer, renal cell (kidney) cancer, retinoblastoma, rhabdomyosarcoma, childhood soft tissue sarcoma, salivary gland cancer, sarcoma, childhood rhabdomyosarcoma, soft tissue sarcoma, childhood vascular tumors, Ewing sarcoma, Kaposi sarcoma, osteosarcoma, soft tissue sarcoma, uterine sarcoma, Sezary syndrome (lymphoma), skin cancer, childhood skin cancer, small cell lung cancer, small intestine cancer, soft tissue sarcoma, squamous cell carcinoma of the skin, squamous neck cancer with occult primary, metastatic head and neck cancer, stomach (gastric) cancer, childhood stomach cancer, cutaneous T-cell lymphoma, T-cell lymphoma, mycosis fungoides and Sezary syndrome, testicular cancer, childhood testicular cancer, throat cancer, nasopharyngeal cancer, oropharyngeal cancer, hypopharyngeal cancer, thymoma and thymic carcinoma, thyroid cancer, transitional cell cancer of the renal pelvis and ureter, unknown primary carcinoma, unknown primary childhood cancer, unusual cancers of childhood, transitional cell cancer of the ureter and renal pelvis, urethral cancer, uterine cancer, endometrial, uterine sarcoma, vaginal cancer, childhood vaginal cancer, vascular tumors, vulvar cancer, Wilms tumor and other childhood kidney tumors, or cancer in young adults. 
     In some cases, the disease or disorder may comprise a cardiovascular disease. As used herein, the terms “cardiovascular disease” (CVD) or “cardiovascular disorder” can refer to a classification of numerous conditions affecting the heart, heart valves, and vasculature (e.g., veins and arteries) of the body and encompasses diseases and conditions including, but not limited to atherosclerosis, myocardial infarction, acute coronary syndrome, angina, congestive heart failure, aortic aneurysm, aortic dissection, iliac or femoral aneurysm, pulmonary embolism, atrial fibrillation, stroke, transient ischemic attack, systolic dysfunction, diastolic dysfunction, myocarditis, atrial tachycardia, ventricular fibrillation, endocarditis, peripheral vascular disease, and coronary artery disease (CAD). Further, the term cardiovascular disease can refer to subjects that ultimately have a cardiovascular event or cardiovascular complication, referring to the manifestation of an adverse condition in a subject brought on by cardiovascular disease, such as sudden cardiac death or acute coronary syndrome, including, but not limited to, myocardial infarction, unstable angina, aneurysm, stroke, heart failure, non-fatal myocardial infarction, stroke, angina pectoris, transient ischemic attacks, aortic aneurysm, aortic dissection, cardiomyopathy, abnormal cardiac catheterization, abnormal cardiac imaging, stent or graft revascularization, risk of experiencing an abnormal stress test, risk of experiencing abnormal myocardial perfusion, and death. 
     As used herein, the ability to detect, diagnose or prognose cardiovascular disease, for example, atherosclerosis, can include determining if the subject is in a pre-stage of cardiovascular disease, has developed early, moderate or severe forms of cardiovascular disease, or has suffered one or more cardiovascular event or complication associated with cardiovascular disease. 
     Atherosclerosis (also known as arteriosclerotic vascular disease or ASVD) can refer to the cardiovascular disease in which an artery-wall thickens as a result of invasion and accumulation and deposition of arterial plaques containing white blood cells on the innermost layer of the walls of arteries resulting in the narrowing and hardening of the arteries. The arterial plaque can refer to an accumulation of macrophage cells or debris, and can contains lipids (cholesterol and fatty acids), calcium and a variable amount of fibrous connective tissue. Diseases associated with atherosclerosis include, but are not limited to, atherothrombosis, coronary heart disease, deep venous thrombosis, carotid artery disease, angina pectoris, peripheral arterial disease, chronic kidney disease, acute coronary syndrome, vascular stenosis, myocardial infarction, aneurysm or stroke. The methods of the present disclosure may distinguish the different stages of atherosclerosis, including, but not limited to, the different degrees of stenosis in a subject. 
     In some cases, the disease or disorder is an endocrine disease. The term “endocrine disease” can refer to a disorder associated with dysregulation of endocrine system of a subject. Endocrine diseases may result from a gland producing too much or too little of an endocrine hormone causing a hormonal imbalance, or due to the development of lesions (such as nodules or tumors) in the endocrine system, which may or may not affect hormone levels. Suitable endocrine diseases able to be treated include, but are not limited to, e.g., Acromegaly, Addison&#39;s Disease, Adrenal Cancer, Adrenal Disorders, Anaplastic Thyroid Cancer, Cushing&#39;s Syndrome, De Quervain&#39;s Thyroiditis, Diabetes, Follicular Thyroid Cancer, Gestational Diabetes, Goiters, Graves&#39; Disease, Growth Disorders, Growth Hormone Deficiency, Hashimoto&#39;s Thyroiditis, Hurthle Cell Thyroid Cancer, Hyperglycemia, Hyperparathyroidism, Hyperthyroidism, Hypoglycemia, Hypoparathyroidism, Hypothyroidism, Low Testosterone, Medullary Thyroid Cancer, MEN 1, MEN 2A, MEN 2B, Menopause, Metabolic Syndrome, Obesity, Osteoporosis, Papillary Thyroid Cancer, Parathyroid Diseases, Pheochromocytoma, Pituitary Disorders, Pituitary Tumors, Polycystic Ovary Syndrome, Prediabetes, Silent, Thyroiditis, Thyroid Cancer, Thyroid Diseases, Thyroid Nodules, Thyroiditis, Turner Syndrome, Type 1 Diabetes, Type 2 Diabetes, and the like. 
     In some cases, the disease or disorder is an inflammatory disease. As referred to herein, inflammatory disease can refer to a disease caused by uncontrolled inflammation in the body of a subject. Inflammation may be a biological response of the subject to a harmful stimulus which may be external or internal such as pathogens, necrosed cells and tissues, irritants etc. However, when the inflammatory response becomes abnormal, it can result in self-tissue injury and may lead to various diseases and disorders. Inflammatory diseases can include, but are not limited to, asthma, glomerulonephritis, inflammatory bowel disease, rheumatoid arthritis, hypersensitivities, pelvic inflammatory disease, autoimmune diseases, arthritis: necrotizing enterocolitis (NEC), gastroenteritis, pelvic inflammatory disease (PID), emphysema, pleurisy, pyelitis, pharyngitis, angina, acne vulgaris, urinary tract infection, appendicitis, bursitis, colitis, cystitis, dermatitis, phlebitis, rhinitis, tendonitis, tonsillitis, vasculitis, autoimmune diseases; celiac disease; chronic prostatitis, hypersensitivities, reperfusion injury; sarcoidosis, transplant rejection, vasculitis, interstitial cystitis, hay fever, periodontitis, atherosclerosis, psoriasis, ankylosing spondylitis, juvenile idiopathic arthritis, Behcet&#39;s disease, spondyloarthritis, uveitis, systemic lupus erythematosus, and cancer. For example, arthritis may include rheumatoid arthritis, psoriatic arthritis, osteoarthritis or juvenile idiopathic arthritis, and the like. 
     The disease or disorder may be a neurological disease. Neurological disorders or neurological diseases can be used interchangeably and can refer to diseases of the brain, spine and the nerves that connect them. Neurological diseases include, but are not limited to, brain tumors, epilepsy, Parkinson&#39;s disease, Alzheimer&#39;s disease, ALS, arteriovenous malformation, cerebrovascular disease, brain aneurysms, epilepsy, multiple sclerosis, Peripheral Neuropathy, Post-Herpetic Neuralgia, stroke, frontotemporal dementia, demyelinating disease (including but are not limited to, multiple sclerosis, Devic&#39;s disease (i.e. neuromyelitis optica), central pontine myelinolysis, progressive multifocal leukoencephalopathy, leukodystrophies, Guillain-Barre syndrome, progressing inflammatory neuropathy, Charcot-Marie-Tooth disease, chronic inflammatory demyelinating polyneuropathy, and anti-MAG peripheral neuropathy) and the like. Neurological disorders also include immune-mediated neurological disorders (IMNDs), which include diseases with at least one component of the immune system reacts against host proteins present in the central or peripheral nervous system and contributes to disease pathology. IMNDs may include, but are not limited to, demyelinating disease, paraneoplastic neurological syndromes, immune-mediated encephalomyelitis, immune-mediated autonomic neuropathy, myasthenia gravis, autoantibody-associated encephalopathy, and acute disseminated encephalomyelitis. 
     Methods of the present disclosure may be able to accurately distinguish between subjects with or without Alzheimer&#39;s disease. These may also be able to detect subjects who are pre-symptomatic and may develop Alzheimer&#39;s disease several years after the screening. This can provide advantages of being able to treat a disease at a very early stage, even before development of the disease. 
     The methods of the present disclosure can detect a pre-disease stage of a disease or disorder. A pre-disease stage is a stage at which the subject has not developed any signs or symptoms of the disease. A pre-cancerous stage would be a stage in which cancer or tumor or cancerous cells have not be identified within the subject. A pre-neurological disease stage can refer to a stage in which a person has not developed one or more symptom of the neurological disease. The ability to diagnose a disease before one or more sign or symptom of the disease can allow for close monitoring of the subject and the ability to treat the disease at a very early stage, increasing the prospect of being able to halt progression, to cure, or to reduce the severity of the disease. 
     Methods of the present disclosure may be able to detect the early stages of a disease or disorder. Early stages of the disease can refer to when the first signs or symptoms of a disease may manifest within a subject. The early stage of a disease may be a stage at which there are no outward signs or symptoms. For example, in Alzheimer&#39;s disease an early stage may be a pre-Alzheimer&#39;s stage in which no symptoms are detected yet the subject will develop Alzheimer&#39;s months or years later. 
     Identifying a disease in either pre-disease development or in the early states may often lead to a higher likelihood for a positive outcome for the subject. For example, diagnosing cancer at an early stage (stage 0 or stage 1) can increase the likelihood of survival by over 80%. Stage 0 cancer can describe a cancer before it has begun to spread to nearby tissues. This stage of cancer is often highly curable, usually by removing the entire tumor with surgery. Stage 1 cancer may usually be a small cancer or tumor that has not grown deeply into nearby tissue and has not spread to lymph nodes or other parts of the body. 
     The methods of the present disclosure may be able to detect intermediate stages of a disease. Intermediate states of the disease can describe stages of the disease that have passed the first signs and symptoms and the subject may be experiencing one or more symptom of the disease. For example, for cancer, stage II or III cancers are considered intermediate stages, indicating larger cancers or tumors that have grown more deeply into nearby tissue. In some instances, stage II or III cancers may have also spread to lymph nodes but not to other parts of the body. 
     Further, the methods may be able to detect late or advanced stages of the disease. Late or advanced stages of the disease may also be called “severe” or “advanced” and usually indicates that the subject is suffering from multiple symptoms and effects of the disease. For example, severe stage cancer includes stage IV, where the cancer has spread to other organs or parts of the body and is sometimes referred to as advanced or metastatic cancer. 
     In some cases, the methods of the present disclosure may be able to distinguish not only between different types of diseases, but also between the different stages of the disease (e.g. early stages of a cancer). This can comprise distinguishing healthy subjects from pre-disease state subjects. The pre-disease state may be stage 0 or stage 1 cancer or an early phase of a neurodegenerative disease, dementia, a coronary disease, a kidney disease, a cardiovascular disease (e.g., coronary artery disease), diabetes, or a liver disease. Distinguishing between different stages of the disease can comprise distinguishing between two stages of a cancer (e.g., stage 0 vs stage 1 or stage 1 vs stage 3). 
     Disease detection may comprise analyzing or processing nucleic acid and protein data from a subject. In some cases, nucleic acid data can guide protein analysis. A common shortcoming of nucleic acid analysis is that the presence of a gene or transcript does not necessarily imply expression or translation, respectively. For example, in some cases an oncogenic mutation may or may not result in disease or even altered expression. A number of methods of the present disclosure can address this by at least directly identifying proteins relevant to genetic and/or transcriptome data obtained from a subject. 
     In some cases, protein analysis can guide nucleic acid analysis. While sequencing an entire genome, transcriptome, or exome can be time consuming and expensive, sequencing or querying an individual nucleic acid is often cheap, fast, and accurate. Thus, some methods of the present disclosure comprise protein analysis followed by targeted nucleic acid analysis. Some methods of the present disclosure comprise targeted nucleic acid analysis followed by protein analysis. Some methods of the present disclosure comprise performing targeted nucleic acid analysis and protein analysis in parallel. For example, plasma proteome analysis indicating that a subject may have early stage non-small cell lung cancer (NSCLC) can be followed by nucleic acid analysis targeting potential NSCLC oncogenes. 
     Dry Compositions and Kits 
     Compositions disclosed herein may be lyophilized. Lyophilization can refer to the method of freezing a substance comprising a solvent and then sublimating the solvent by reducing pressure, raising temperature, or both, to cause solid phase to gas phase transition of the solvent. The freezing may comprise contacting the substance (e.g., immersing the substance within) a cryogen, such as liquid nitrogen. The freezing may comprise contacting the substance to a cold surface, such as a cryogen cooled plate. In certain instances disclosed herein, the freezing comprises dropping a defined volume of the substance into a cryogen, thereby forming a frozen bead with the defined volume. A lyophilized composition, or a dry composition, can refer to a substance that has been lyophilized. 
     Various particles or various compositions thereof as disclosed herein may be lyophilized. Various solvents as disclosed herein may be used as the solvent for lyophilization. In some cases, particle compositions as disclosed herein are lyophilized using water as the solvent. In some cases, the liquid comprises an organic solvent. The liquid may also be an organic, aqueous mixture, such as a water, methanol mixture, an organic solvent, such as chloroform, or an organic solvent mixture, such as a dimethylsulfoxide, acetonitrile mixture. In some cases, the organic solvent comprises acetone, acetonitrile, benzene, butanol, butanone, tert-butyl alcohol, carbon tetrachloride, chlorobenzene, chloroform, cyclohexane, 1,2-dichloroethane, diethylene glycol, diethyl ether, 1,2-dimethoxy-,ethane (glyme, DME), dimethyl-formamide (DMF), dimethyl sulfoxide (DMSO), 1,4-dioxane, ethanol, ethyl acetate, ethylene glycol, glycerin, heptane, hexamethylphosphoramide, (HMPA), hexamethylphosphorous, triamide (HMPT), hexane, methanol, methyl t-butyl, ether (MTBE), methylene chloride, N-methyl-2-pyrrolidinone (NMP), nitromethane, pentane, propanol, propanol, pyridine, tetrahydrofuran (THF), toluene, triethyl amine, o-xylene, m-xylene, p-xylene, or any combination thereof. 
     In some cases, the substance is lyophilized within a support. For example, the substance may be flash frozen and subjected to solvent sublimating conditions within a plurality of wells (e.g., wells of a well-plate) or tubes. The support containing the lyophilized substance may later be used for a biological sample analysis, as disclosed further herein. 
     Various support agents may be used for lyophilizing a composition. In some cases, a support agent may comprise an excipient. In some cases, an excipient may comprise dextran, PEG, sucrose, glucose, trehalose, lactose, polysorbates, amino acids, mannitol, glycine, glycerol, or any combination or variation thereof. In some cases, a support agent may comprise a salt. 
     Support agents may be present in various amounts for lyophilization. In some cases, support agents may have a concentration that is less than about 5 mg/mL. In some cases, support agents may have a concentration that is less than about 50 mg/mL. In some cases, support agents may have a concentration that is less than about 250 mg/mL. In some cases, support agents may have a concentration that is greater than about 250 mg/mL. In some cases, support agents may have a concentration that is between about 100 mg/mL and 200 mg/mL. In some cases, support agents may have a concentration that is greater than about 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 mg/mL. In some cases, support agents may have a concentration that is less than about 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 mg/mL. In some cases, support agents may be present at an amount from at least about 60 wt % to 70 wt %. In some cases, support agents may be present at an amount from at least about 75 wt % to 85 wt %. In some cases, support agents may be present at an amount from at least about 97.5 wt %. In some cases, support agents may be present at an amount at least about 50, 55, 60, 65, 70, 75, 80, 85, 90, 91, 92, 93, 94, 95, 96, 97, 98, or 99 wt/o. In some cases, support agents may be present at an amount at most about 50, 55, 60, 65, 70, 75, 80, 85, 90, 91, 92, 93, 94, 95, 96, 97, 98, or 99 wt/o. 
     Particles may be present in various amounts for lyophilization. In some cases, a solution or suspension may have a particle concentration of greater than about 5 mg/mL. In some cases, a solution or suspension may have a particle concentration of less than about 100 mg/mL. In some cases, a solution or suspension may have a particle concentration of between about 10 mg/mL and about 100 mg/mL. In some cases, a solution or suspension may have a particle concentration of between about 15 mg/mL and about 80 mg/mL. In some cases, a solution or suspension may have a particle concentration greater than about 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 mg/mL. In some cases, a solution or suspension may have a particle concentration less than about 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 mg/mL. 
     Particles may comprise various surface modifications, including ones provided by the present disclosure. In some cases, a surface modification may comprise silica coating, a tri-amine functionalization, a PDMAPMA-polymer functionalization, a glucose-6-phosphate functionalization, or a mono-amine surface functionalization. In some cases, a surface modification may comprise a metal oxide coating. In some cases, a surface modification may comprise at least one exposed primary amine group, secondary amine group, tertiary amine group. In some cases, a surface modification may comprise at least one monosaccharide. In some cases, the surface modification may comprise a silica coating, a PDMAPMA-polymer functionalization, a glucose-6-phosphate functionalization, a polystyrene carboxyl functionalization, a dextran functionalization, an amide functionalization, a carboxyl functionalization, a tri-amine functionalization, a diamine functionalization, a mono-amine surface functionalization, or any combination thereof. In some cases, the surface modification may comprise a N-(3-Trimethoxysilylpropyl)diethylenetriamine functionalization, 1,6-hexanediamine functionalization, N1-(3-(trimethoxysilyl)propyl)hexane-1,6-diamine, or any combination thereof. 
     Various volumes of a solution or a suspension may be lyophilized. For example, a volume of a solution or a suspension may be dropped into a cryosolvent to form a frozen bead of the solution or suspension, which bead may then be freeze dried to form a lyophilized bead comprising at least a portion of the original volume of the solution. In some cases, a solution or a suspension may have a volume that is greater than about 1 μL. In some cases, a solution or a suspension may have a volume less than about 100 μL. In some cases, a solution or suspension may have a volume between 2 μL and 60 μL. In some cases, a solution or suspension may have a volume between 25 μL and 45 μL. In some cases, a solution or suspension may have a volume of at least about 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, or 10000 μL. In some cases, a solution or suspension may have a volume of at most about 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, or 10000 μL. 
     Lyophilized compositions may comprise dry compositions. In some cases, dry or being dry can refer to a state of a composition comprising less than a certain amount of liquid phase such as water or another solvent. In some case, a dry composition can comprise a composition comprising less than about 10, 1, 0.1, 0.01, 0.001, 0.0001, or 0.00001 wt % of solvent. In some cases, a dry composition can comprise a composition comprising less than about 10, 1, 0.1, 0.01, 0.001, 0.0001, or 0.00001 vol % of solvent. In some cases, dry compositions may comprise a bead comprising a spherical shape, a cylindrical shape, a rectangular shape, or any other shape. 
     In some cases, a dry composition may comprise at least about 0.5 mg of surface modified particle per bead. In some cases, a dry composition may comprise between about 0.5 mg to about 5 mg of surface modified particle per bead. In some cases, a dry composition may comprise at least about 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100 mg of particle per bead. In some cases, a dry composition may comprise at most about 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100 mg of particle per bead. 
     Lyophilization can impart stability to substances. In some cases, formulating nanoparticles in a lyophilized form can allow for stable physicochemical properties over an extended period of time. In some cases, the lyophilized particles may be inert or stable at refrigerated temperatures or room temperature. 
     A particle may comprise stability in some of the compositions described herein. In some cases, stability or being stable, can be attributed to a property of a substance that changes less than a threshold amount while retaining the utility or the efficacy of the substance over a period of time. Various properties of various substances may be attributed with stability for various periods of time based on various measure of utility or efficacy. 
     Lyophilized compositions may comprise various physicochemical properties as stable. The physicochemical properties may comprise a zeta potential. The physicochemical properties may comprise a distribution of zeta potentials in a nanoparticle composition. The physicochemical properties may comprise a mean zeta potential in a nanoparticle composition. The physicochemical properties may comprise a standard deviation of zeta potentials in a nanoparticle composition. In some cases, zeta potential is measured by electrophoresis, electroosmosis, streaming potential measurements, or sedimentation potential measurements. In some cases, the physicochemical properties may comprise particle size. The physicochemical properties may comprise a distribution of particle sizes in a nanoparticle composition. The physicochemical properties may comprise a mean particle size in a nanoparticle composition. The physicochemical properties may comprise a standard deviation of particle sizes in a nanoparticle composition. 
     In some cases, lyophilized particles may comprise a diameter that is between 90% and 110% of the diameter in the solution or suspension. In some cases, lyophilized particles may comprise a diameter that is between 80% and 120% of the diameter in the solution or suspension. In some cases, lyophilized particles may comprise a diameter that is between 95% and 105% of the diameter in the solution or suspension. In some cases, lyophilized particles may comprise a diameter that is between 98% and 102% of the diameter in the solution or suspension. In some cases, lyophilized particles may comprise a diameter that is between 99% and 101% of the diameter in the solution or suspension. 
     In some cases, lyophilized particles may comprise a mean diameter that is between 90% and 110% of the mean diameter in the solution or suspension. In some cases, lyophilized particles may comprise a mean diameter that is between 80% and 120% of the mean diameter in the solution or suspension. In some cases, lyophilized particles may comprise a mean diameter that is between 95% and 105% of the mean diameter in the solution or suspension. In some cases, lyophilized particles may comprise a mean diameter that is between 98% and 102% of the mean diameter in the solution or suspension. In some cases, lyophilized particles may comprise a mean diameter that is between 99% and 101% of the mean diameter in the solution or suspension. 
     In some cases, lyophilized particles may comprise a zeta potential that is between 90% and 110% of the zeta potential in the solution or suspension. In some cases, lyophilized particles may comprise a zeta potential that is between 80% and 120% of the zeta potential in the solution or suspension. In some cases, lyophilized particles may comprise a zeta potential that is between 95% and 105% of the zeta potential in the solution or suspension. In some cases, lyophilized particles may comprise a zeta potential that is between 98% and 102% of the zeta potential in the solution or suspension. In some cases, lyophilized particles may comprise a zeta potential that is between 99% and 101% of the zeta potential in the solution or suspension. 
     In some cases, lyophilized particles may comprise a mean zeta potential that is between 90% and 110% of the mean zeta potential in the solution or suspension. In some cases, lyophilized particles may comprise a mean zeta potential that is between 80% and 120% of the mean zeta potential in the solution or suspension. In some cases, lyophilized particles may comprise a mean zeta potential that is between 95% and 105% of the mean zeta potential in the solution or suspension. In some cases, lyophilized particles may comprise a mean zeta potential that is between 98% and 102% of the mean zeta potential in the solution or suspension. In some cases, lyophilized particles may comprise a mean zeta potential that is between 99% and 101% of the mean zeta potential in the solution or suspension. 
     In some cases, upon reconstitution of the dry composition in a solution, a particle may have a mean zeta potential that is between 85% to 115% of the zeta potential of a same particle dissolved in a same solution in the absence of lyophilization, as determined by zeta potential measurements (e.g., electrophoresis, electroosmosis, streaming potential measurements, or sedimentation potential measurements). In some cases, upon reconstitution of the dry composition in a solution, a particle may have a mean zeta potential that is between 90% to 110% of the zeta potential of a same particle dissolved in a same solution in the absence of lyophilization, as determined by zeta potential measurements. In some cases, upon reconstitution of the dry composition in a solution, a particle may have a mean zeta potential that is between 95% to 105% of the zeta potential of a same particle dissolved in a same solution in the absence of lyophilization, as determined by zeta potential measurements. In some cases, upon reconstitution of the dry composition in a solution, a particle may have a mean zeta potential that is between 98% to 102% of the zeta potential of a same particle dissolved in a same solution in the absence of lyophilization, as determined by zeta potential measurements. In some cases, upon reconstitution of the dry composition in a solution, a particle may have a mean zeta potential standard deviation that is between 85% to 115% of the zeta potential standard deviation of a same particle dissolved in a same solution in the absence of lyophilization, as determined by zeta potential measurements. In some cases, upon reconstitution of the dry composition in a solution, a particle may have a mean zeta potential standard deviation that is between 90% to 110% of the zeta potential standard deviation of a same particle dissolved in a same solution in the absence of lyophilization, as determined by zeta potential measurements. In some cases, upon reconstitution of the dry composition in a solution, a particle may have a zeta potential standard deviation that is between 95% to 105% of the zeta potential standard deviation of a same particle dissolved in a same solution in the absence of lyophilization, as determined by zeta potential measurements. In some cases, upon reconstitution of the dry composition in a solution, a particle may have a zeta potential standard deviation that is between 98% to 102% of the zeta potential standard deviation of a same particle dissolved in a same solution in the absence of lyophilization, as determined by zeta potential measurements. 
     In some cases, upon reconstitution of the dry composition in a solution, a particle may have a mean diameter that is between 85% to 115% of the mean diameter of a same particle dissolved in a same solution in the absence of lyophilization, as determined by DLS. In some cases, upon reconstitution of the dry composition in a solution, a particle may have a mean diameter that is between 90% to 110% of the mean diameter of a same particle dissolved in a same solution in the absence of lyophilization, as determined by DLS. In some cases, upon reconstitution of the dry composition in a solution, a particle may have a mean diameter that is between 95% to 105% of the mean diameter of a same particle dissolved in a same solution in the absence of lyophilization, as determined by DLS. In some cases, upon reconstitution of the dry composition in a solution, a particle may have a mean diameter that is between 98% to 102% of the mean diameter of a same particle dissolved in a same solution in the absence of lyophilization, as determined by DLS. In some cases, upon reconstitution of the dry composition in a solution, a particle may have a diameter standard deviation that is between 85% to 115% of the diameter standard deviation of a same particle dissolved in a same solution in the absence of lyophilization, as determined by DLS. In some cases, upon reconstitution of the dry composition in a solution, a particle may have a diameter standard deviation that is between 90% to 110% of the diameter standard deviation of a same particle dissolved in a same solution in the absence of lyophilization, as determined by DLS. In some cases, upon reconstitution of the dry composition in a solution, a particle may have a diameter standard deviation that is between 95% to 105% of the diameter standard deviation of a same particle dissolved in a same solution in the absence of lyophilization, as determined by DLS. In some cases, upon reconstitution of the dry composition in a solution, a particle may have a diameter standard deviation that is between 98% to 102% of the diameter standard deviation of a same particle dissolved in a same solution in the absence of lyophilization, as determined by DLS. 
     In some cases, upon reconstitution of a dry composition in a solution, a particle may adsorb at least 85% of biomolecules in a biological sample that the particle dissolved in a same solution in the absence of lyophilization would adsorb from the same biological sample. In some cases, upon reconstitution of a dry composition in a solution, a particle may adsorb at least 90% of biomolecules in a biological sample that the particle dissolved in a same solution in the absence of lyophilization would adsorb from the same biological sample. In some cases, upon reconstitution of a dry composition in a solution, a particle may adsorb at least 95% of biomolecules in a biological sample that the particle dissolved in a same solution in the absence of lyophilization would adsorb from the same biological sample. In some cases, upon reconstitution of a dry composition in a solution, a particle may adsorb at least 96% of biomolecules in a biological sample that the particle dissolved in a same solution in the absence of lyophilization would adsorb from the same biological sample. In some cases, upon reconstitution of a dry composition in a solution, a particle may adsorb at least 97% of biomolecules in a biological sample that the particle dissolved in a same solution in the absence of lyophilization would adsorb from the same biological sample. In some cases, upon reconstitution of a dry composition in a solution, a particle may adsorb at least 98% of biomolecules in a biological sample that the particle dissolved in a same solution in the absence of lyophilization would adsorb from the same biological sample. In some cases, upon reconstitution of a dry composition in a solution, a particle may adsorb at least 99% of biomolecules in a biological sample that the particle dissolved in a same solution in the absence of lyophilization would adsorb from the same biological sample. 
     Lyophilized compositions may have stable physicochemical properties over various periods of time. In some cases, the period of time may comprise a period of at least about 12 days, at least about 14 days, at least about 30 days, at least 40 days, at least about 2 months, at least about 3 months, at least about 4 months, at least about 5 months, at least about 6 months, at least about 7 months, at least about 8 months, at least about 9 months, at least about 10 months, at least about 11 months, or at least about 1 year. 
     Lyophilized compositions may have stable physicochemical properties at various temperatures. In some cases, the temperature may be about room temperature. In some cases, the temperature may be about 37° C. In some cases, the temperature may be about 60° C. In some cases, the temperature may be about −26° C. to about 0° C. In some cases, the temperature may be about −10° C. to about −5° C. In some cases, the temperature may be about 0° C. to 20° C. In some cases, the temperature may be about 0° C. to about 10° C. In some cases, the temperature may be about 25° C. to about 60° C. In some cases, the temperature may be about 35° C. to about 40° C. In some cases, the dry composition or lyophilized composition is stable at about 37° C. for at least 40 days. In some cases, the dry composition or lyophilized composition is stable at ambient temperature for at least 11 months. 
     Dry compositions may be packaged into a kit with various other contents. In some cases, a kit may comprise a dry composition comprising a particle (e.g., a surface modified particle) and a lyophilized support agent, comprising a substrate configured to receive and retain the dry composition. In some cases, the substrate may be a tube, a well, a multi-well, or a microfluidic channel or chamber in a microfluidic device. In some cases, a multi-well may be a a 12 well plate, a 24 well plate, a 48 well plate, a 72 well plate, 96 well plate, a 192 well plate, or a 384 well plate. In some cases, a substrate may comprise a plurality of spatially isolated locations (e.g., individual wells of a multi-well plate, or individual microfluidic channels of a microfluidic device) each of which may comprise a dry composition. In some cases, dry compositions comprised in the individual locations may differ from each other in at least one physicochemical property of particles in the compositions. The particles may be configured to adsorb different biomolecules or biomolecule groups from a sample. In some cases, individual locations of a plurality of spatially isolated locations may be individually and/or independently addressable. 
       FIG.  19 A  illustrates characterization of three superparamagnetic iron oxide nanoparticles (SPIONs) shown in the left-most first column, which from top to bottom, are: silica-coated SPION, poly(N-(3-(dimethylamino)propyl) methacrylamide) (PDMAPMA)-coated SPION, and poly(oligo(ethylene glycol) methyl ether methacrylate) (POEGMA)-coated SPION, by the following methods: scanning electron microscopy (SEM, second column of images), dynamic light scattering (DLS, third column of graphs), transmission electron microscopy (TEM, fourth column of images), high-resolution transmission electron microscopy (HRTEM, fifth column), and X-ray photoelectron spectroscopy (XPS, sixth column, respectively. DLS shows three replicates of each particle type. The HRTEM pictures were recorded at the surface of individual particles. A particle, when synthesized, may comprise a distribution of sizes or compositions. In some cases, particles of one type may be manufactured reproducibly to a certain size, form, composition, or composition profile. In some cases, manufactured particles may be characterized for quality control. 
     Method of Using Dry Compositions and Kits 
     Dry compositions, as described herein, can be contacted with a biological sample to produce a biomolecule corona on the surfaces of surface-modified particles. In some cases, the dry composition may first be reconstituted before contacting the composition with a biological sample. 
     Various aspects of the present disclosure provide a method for assaying a biological sample comprising: providing a dry composition comprising a particle and a support agent; reconstituting the dry composition in a liquid to form a reconstituted composition; and contacting the biological sample with the reconstituted composition to bind at least a portion of biomolecules or biomolecule groups from the sample to the particle. In some cases, the dry composition comprises a lyophilized bead consistent with the present disclosure. In some cases, the dry composition is a lyophilized bead or a plurality of lyophilized beads. 
     In some cases, reconstitution can refer to dissolving or suspending a solid in a sterile solvent to form a liquid mixture. In some cases, a dry composition may be reconstituted, before contacting the mixture with a biological sample. 
     In some cases, the dry composition is provided in a volume of a multi-well plate, a fluidic channel, a fluidic chamber, a microfluidic device, or a tube. In such cases, the dry composition may be reconstituted within the volume of the multi-well plate, the fluidic channel, the fluidic chamber, the microfluidic device, or the tube. The reconstituted composition may also be contacted to the biological sample within the volume of the multi-well plate, the fluidic channel, the fluidic chamber, the microfluidic device, or the tube. For example, the method may comprise reconstituting the dry composition within a well of a multi-well plate, and then adding a volume of the biological sample to the well. 
     In some cases, the particle is a surface modified particle, such as a surface modified particle of TABLE 1. The particle may comprise a physicochemical property for variably selective binding of biomolecules from the biological sample. For example, the particle may comprise an aliphatic, non-polar surface functionalization which disfavors charged analyte adsorption relative to adsorption of neutral, nonpolar analytes. The particle may comprise a plurality of particles. In some cases, individual particles of the plurality of particles comprise different surfaces. In some cases, individual particles of the plurality of particles comprise different physicochemical properties. For example, the particle may comprise an amine functionalized particle, a carboxylate functionalized particle, and a styrene functionalized particle. The different physicochemical properties of the particles may affect their variably selective adsorption of biomolecules or biomolecule groups from the biological sample. 
     A method of using a dry composition may comprise various rates for reconstitution. In some cases, reconstitution may comprise a rate of at least 0.1 min −1  at 25° C. In some cases, reconstitution may comprise a rate of at least 0.5 min −1  at 25° C. In some cases, reconstitution may comprise a rate of at least about 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 2, 3, 4, 5, 6, 7, 8, 9 min −1  at about 25° C. In some cases, reconstitution may comprise a rate of at least about 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 2, 3, 4, 5, 6, 7, 8, 9 min −1  at about 37° C. In some cases, reconstitution may comprise a rate of at most about 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 2, 3, 4, 5, 6, 7, 8, 9 min −1  at about 25° C. In some cases, reconstitution may comprise a rate of at most about 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 2, 3, 4, 5, 6, 7, 8, 9 min −1  at about 37° C. 
     In some cases, reconstitution may comprise a rate of at least 0.2 mg of particle per minute at about 25° C. In some cases, reconstitution may comprise a rate of at least about 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 2, 3, 4, 5, 6, 7, 8, 9 mg of particle per min at about 25° C. In some cases, reconstitution may comprise a rate of at most about 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 2, 3, 4, 5, 6, 7, 8, 9 mg of particle per min at about 25° C. In some cases, reconstitution may comprise a rate of at least about 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 2, 3, 4, 5, 6, 7, 8, 9 mg of particle per min at about 37° C. In some cases, reconstitution may comprise a rate of at most about 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 2, 3, 4, 5, 6, 7, 8, 9 mg of particle per min at about 37° C. 
     In some cases, reconstitution may be performed for at most 20 minutes. In some cases, reconstitution may be performed for at most 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60 minutes. After such times, the reconstitution may be at least 85% complete, at least 90% complete, at least 95% complete, at least 98% complete, at least 99% complete, or at least 99.5% complete. 
     In some cases, reconstitution may comprise physical perturbation to speed up reconstitution. In some cases, reconstitution may comprise sonication, mixing, or shaking. In some cases, reconstitution may not comprise physical perturbation. 
     Reconstituting a dry composition may revert to the original properties of the particle composition before lyophilization. In some cases, subsequent to reconstitution, the surface modified particle is substantially free of particle aggregates. In some cases, subsequent to reconstitution, less than about 10% of the surface modified particles may be present as particle aggregates. In some cases, subsequent to reconstitution, less than about 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, 1%, 0.9%, 0.8%, 0.7%, 0.6%, 0.5%, 0.4%, 0.3%, 0.2%, 0.1%, 0.09%, 0.08%, 0.07%, 0.06%, 0.05%, 0.04%, 0.03%, 0.02%, 0.01%, 0.009%, 0.008%, 0.007%, 0.006%, 0.005%, 0.004%, 0.003%, 0.002%, 00.01%, 0.0009%, 0.0008%, 0.0007%, 0.0006%, 0.0005%, 0.0004%, 0.0003%, 0.0002%, or 0.0001% of the surface modified particles may be present as particle aggregates. 
     In some cases, subsequent to reconstitution, the liquid comprises a pH between about 5 and about 9. In some cases, subsequent to reconstitution, the liquid comprises a pH between about 6 and about 8. In some cases, subsequent to reconstitution, the liquid comprises a pH between about 7 and about 8. In some cases, subsequent to reconstitution, the liquid comprises a pH between about 7.2 and about 7.7. In some cases, subsequent to reconstitution, the liquid comprises a pH of about 7.5. In some cases, subsequent to reconstitution, the liquid comprises a pH of at least 5. In some cases, subsequent to reconstitution, the liquid comprises a pH of at least 6. In some cases, subsequent to reconstitution, the liquid comprises a pH of at most 9. In some cases, subsequent to reconstitution, the liquid comprises a pH of at most 8. 
     In some cases, prior to reconstitution, the liquid has an ion concentration of at most about 500 mM, at most about 350 mM, at most about 250 mM, at most about 200 mM, at most about 150 mM, at most about 100 mM, at most about 50 mM, at most about 30 mM, at most about 10 mM, at most about 5 mM, at most about 1 mM, at most about 0.5 mM, at most about 0.1 mM, or at most about 0.05 mM. In some cases, prior to reconstitution, the liquid has an ion concentration of at least about 500 mM, at least about 350 mM, at least about 250 mM, at least about 200 mM, at least about 150 mM, at least about 100 mM, at least about 50 mM, at least about 30 mM, at least about 10 mM, at least about 5 mM, at least about 1 mM, at least about 0.5 mM, at least about 0.1 mM, or at least about 0.05 mM. In some cases, subsequent to reconstitution, the liquid has an ion concentration of at most about 500 mM, at most about 350 mM, at most about 250 mM, at most about 200 mM, at most about 150 mM, at most about 100 mM, at most about 50 mM, at most about 30 mM, at most about 10 mM, at most about 5 mM, at most about 1 mM, at most about 0.5 mM, at most about 0.1 mM, or at most about 0.05 mM. In some cases, subsequent to reconstitution, the liquid has an ion concentration of at least about 500 mM, at least about 350 mM, at least about 250 mM, at least about 200 mM, at least about 150 mM, at least about 100 mM, at least about 50 mM, at least about 30 mM, at least about 10 mM, at least about 5 mM, at least about 1 mM, at least about 0.5 mM, at least about 0.1 mM, or at least about 0.05 mM. 
     In some cases, the dry compositions may be contacted with a biological sample without first reconstituting them in a solvent. The dry composition may dissolve or suspend within the biofluidic sample. For example, a method consistent with the present disclosure may comprise providing a dry composition comprising a particle and a lyophilized support agent, and contacting a biofluidic sample (e.g., plasma) with the dry composition in the absence of reconstitution of the dry composition to adsorb biomolecules or biomolecule groups from the biofluidic sample to the particle. For example, the dry composition may be contacted with blood, plasma, serum, CSF, urine, tear, cell lysates, tissue lysates, cell homogenates, tissue homogenates, nipple aspirates, needle aspirates, fecal samples, synovial fluid, whole blood, saliva, or a combination thereof. 
     The biological sample may be diluted with various amounts of a solvent. In some cases, the biological sample may be diluted in a buffer solution. In some case, the biological sample may be diluted at a volume ratio of about 1 part biological sample to at least about 1 part buffer solution. In some case, the biological sample may be diluted at a volume ratio of about 1 part biological sample to at least about 2 parts buffer solution. In some case, the biological sample may be diluted at a volume ratio of about 1 part biological sample to at least about 5 parts buffer solution. In some case, the biological sample may be diluted at a volume ratio of about 1 part biological sample to at least about 10 parts buffer solution. In some case, the biological sample may be diluted at a volume ratio of about 1 part biological sample to at least about 20 parts buffer solution. 
     In some cases, subsequent to contacting with a biological sample, the particles in the dry composition may be individually solvated in the biological sample. In some cases, at least about 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, 99.1%, 99.2%, 99.3%, 99.4%, 99.5%, 99.6%, 99.7%, 99.8%, 99.9%, 99.91%, 99.92%, 99.93%, 99.94%, 99.95%, 99.96%, 99.97%, 99.98%, or 99.99% of the particles may be individually solvated in the biological sample. 
     Classification Using Machine Learning 
     The method of determining a set of biomolecules associated with the disease or disorder and/or disease state can include the analysis of the biomolecule corona of at least two samples. This determination, analysis or statistical classification can be performed by methods, including, but not limited to, for example, a wide variety of supervised and unsupervised data analysis, machine learning, deep learning, and clustering approaches including hierarchical cluster analysis (HCA), principal component analysis (PCA), Partial least squares Discriminant Analysis (PLS-DA), random forest, logistic regression, decision trees, support vector machine (SVM), k-nearest neighbors, naive Bayes, linear regression, polynomial regression, SVM for regression, K-means clustering, and hidden Markov models, among others. In other words, the biomolecules in the corona of each sample are compared/analyzed with each other to determine with statistical significance what patterns are common between the individual corona to determine a set of biomolecules that is associated with the disease or disorder or disease state. 
     In some cases, machine learning algorithms can be used to construct models that accurately assign class labels to examples based on the input features that describe the example. In some case it may be advantageous to employ machine learning and/or deep learning approaches for the methods described herein. For example, machine learning can be used to associate the biomolecule corona with various disease states (e.g. no disease, precursor to a disease, having early or late stage of the disease, etc.). For example, in some cases, one or more machine learning algorithms can be employed in connection with the methods disclosed hereinto analyze data detected and obtained by the biomolecule corona and sets of biomolecules derived therefrom. For example, machine learning can be coupled with genomic and proteomic information obtained using the methods described herein to determine not only if a subject has a pre-stage of cancer, cancer or does not have or develop cancer, and also to distinguish the type of cancer. 
     Machine learning algorithms may also be used to associate the results from protein corona analysis and results from nucleic acid sequencing analysis and further associate any trends or correlations between proteins and nucleic acids to a biological state (e.g., disease state, health state, subtypes of disease such as stages of disease are cancer subtypes). 
     Machine learning may be used to cluster proteins detected using a plurality of particles.  FIG.  20    illustrates a method for using a plurality of particles for analyzing the abundance of proteins and protein structural and functional groups. In some cases, a library of particles may be used to assay proteins from one or more biological samples. In some cases, particles in the library of particles may comprise diverse physicochemical properties. In some cases, proteins detected by the library of particles may be clustered using a clustering algorithm. In some cases, proteins detected by the library of particles may be clustered based at least partially on the intensities of detected protein signals, particle chemical properties, protein structural and/or functional groups, or any combination thereof. 
     A library of particles may comprise any number of particles. In some cases, a library of particles may comprise at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 particles. In some cases, a library of particles may comprise at most about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 particles. 
     A physicochemical property of a particle may comprise various properties disclosed herein. In some cases, a physicochemical property may comprise charge, hydrophobicity, hydrophilicity, amphipathicity, coordinating, reaction class, surface free energy, various functional groups/modifications (e.g., sugar, polymer, amine, amide, epoxy, crosslinker, hydroxyl, aromatic, or phosphate groups). In some cases, reaction class can refer to the type of reaction that provides the functionalization on a particle (e.g., Stober process). In some cases, specific reaction classes can have class specific reaction efficiencies, and can yield one or more byproducts, which influence particle properties. 
     In some cases, a clustering algorithm can refer to a method of grouping samples in a dataset by some measure of similarity. In some cases, samples can be grouped in a set space, for example, element ‘a’ is in set ‘A’. In some cases, samples can be grouped in a continuous space, for example, element ‘a’ is a point in Euclidean space with distance ‘I’ away from the centroid of elements comprising cluster ‘A’. In some cases, samples can be grouped in a graph space, for example, element ‘a’ is highly connected to elements comprising cluster ‘A’. In some cases, clustering can refer to the principle of organizing a plurality of elements into groups in some mathematical space based on some measure of similarity. 
     In some cases, clustering can comprise grouping any number of proteins in a dataset by any quantitative measure of similarity. In some cases, clustering can comprise K-means clustering. In some cases, clustering can comprise hierarchical clustering. In some cases, clustering can comprise using random forest models. In some cases, clustering can comprise boosted tree models. In some cases, clustering can comprise using support vector machines. In some cases, clustering can comprise calculating one or more N−1 dimensional surfaces in N-dimensional space that partitions a dataset into clusters. In some cases, clustering can comprise distribution-based clustering. In some cases, clustering can comprise fitting a plurality of prior distributions over the data distributed in N-dimensional space. In some cases, clustering can comprise using density-based clustering. In some cases, clustering can comprise using fuzzy clustering. In some cases, clustering can comprise computing probability values of a data point belonging to a cluster. In some cases, clustering can comprise using constraints. In some cases, clustering can comprise using supervised learning. In some embodiments, clustering can comprise using unsupervised learning. 
     In some cases, clustering can comprise grouping proteins based on similarity. In some cases, clustering can comprise grouping proteins based on quantitative similarity. In some cases, clustering can comprise grouping proteins based on one or more features of each protein. In some cases, clustering can comprise grouping proteins based on one or more labels of each protein. In some cases, clustering can comprise grouping proteins based on Euclidean coordinates in a numerical representation of proteins. In some cases, clustering can comprise grouping proteins based on protein structural groups or functional groups (e.g., protein structures, substructures, or functional groups from protein databases such as Protein Data Bank or CATH Protein Structure Classification database). In some cases, a protein structural group or functional group may comprise protein primary structure, secondary structure, tertiary structure, or quaternary structure. In some cases, a protein structural group or functional group may be based at least partially on alpha helices, beta sheets, relative distribution of amino acids with different properties (e.g., aliphatic, aromatic, hydrophilic, acidic, basic, etc.), a structural families (e.g., TIM barrel and beta barrel fold), protein domains (e.g., Death effector domain). In some cases, a protein structural group or functional group may be based at least partially on functional or spatial properties (e.g., functional groups—group of immune globulins, cytokines, cytoskeletal proteins, etc.). 
     Automated Systems 
     Some of the methods and compositions in the present disclosure may be integrated with an automated system. The automated system may comprise any automated system described in U.S. Patent Application Publication No. 2021/0285958, filed Mar. 29, 2021, the content of which is incorporated by reference in its entirety herein. An advantage of integrating compositions and methods into an automated system is that experiments can be streamlined, saving users time and improving efficiency in a research, clinical, or an applied setting. An automated system can offer repeatability of experiments, faster turnaround, and better communication between researchers and clinicians sharing useful protocols that may be followed using the automated system. An automated system can be engineered to run numerous experiments in parallel, can enable high-throughput approaches, and can be used to generate data for some of the machine learning methods described herein. 
     An automated system for assaying a biological sample may comprise: a substrate comprising a dry composition which comprises a particle and a support agent; a sample storage unit comprising a biological sample; a loading unit that is operably coupled to the substrate and the sample storage unit; and a computer readable medium comprising machine-executable code that, upon execution by a processor, implements a method comprising: (a) transferring the biological sample or a portion thereof from the sample storage unit to the substrate using the loading unit; (b) directing the biological sample into contact with the dry composition to produce a biomolecule corona comprising a plurality of biomolecules or biomolecule groups. 
     The substrate may comprise any one of the various substrates described in the present disclosure. In some cases, the substrate is a single well, a multi-well plate, a tube, a multi-tube apparatus, or a microfluidic device. In some cases, the automated system may comprise a plurality of multi-well plates. 
     The substrate may comprise one or more of any of the various compositions described in the present disclosure. In some cases, the substrate comprises a plurality of dry compositions, wherein at least one subset of particles comprised in individual dry compositions of the plurality of dry compositions may be different from another subset. In some cases, at least one subset of particles may differ from another subset in at least one physicochemical property. In some cases, the plurality of dry compositions comprises at least two dry compositions each comprising: silica coated SPION, tri-amine functionalized nanoparticles, PDMAPMA-polymer functionalized nanoparticles, glucose-6-phosphate functionalized nanoparticles, mono-amine functionalized nanoparticles, or a combination thereof. In some cases, each well in a multi-well plate comprises an individual dry composition. 
     An automated system can run experiments with different biological samples at once. In some cases, the sample storage unit can comprise a plurality of different biological samples. In some cases, transferring of a biological sample can comprise transferring each of the plurality of different biological samples to a different well of a multi-well plate. 
     An automated system can run experiments with different portions of biological samples. In some cases, a biological sample comprises a plurality of portions. For instance, a portion may be a fraction of a fractionated biological sample. In some cases, a portion may be a subsection of a tissue sample or a fraction of a whole blood sample (e.g., a portion of a buffy coat). In some cases, a portion may be a supernatant of a biological sample lysate. A portion of a biological sample can be transferred into a well. A portion of a biological sample may be diluted (e.g., with an aqueous buffer such as pH 6 phosphate buffer). The biological sample may be diluted by at least 2-fold, at least 3-fold, at least 4-fold, at least 5-fold, at least 6-fold, at least 8-fold, at least 10-fold, at least 15-fold, or at least 20-fold. In some cases, the transfer may be performed simultaneously by the automated system. 
     An automated system can be configured to contact a biological sample with a particle composition for various amounts of time. In some cases, a biological sample can remain in contact with a particle composition for a time period of at least about 10 seconds. In some cases, a biological sample can remain in contact with a dry composition for a time period of at least about 10 seconds. In some cases, the time period is at least about 1 minute. In some cases, the time period is at least about 5 minutes. 
     An automated system can be configured to add steps or remove various experimental steps. An automated system can be configured to rearrange various experimental steps. In some cases, the automated system can be configured to run a wash step. For example, the automated system may be configured to wash a biomolecule corona with resuspension. In some cases, the automated system can be configured to run a step for washing biomolecule corona without resuspension. In some cases, the automated system can be configured to run a step for producing a lysate. For example, the automated system may sonicate or apply an electric field to lyse exosomes present in a biological sample. In some cases, the automated system can be configured to run a step for reducing a lysate. In some cases, the automated system can be configured to run a step for filtering a lysate. In some cases, the automated system can be configured to run a step for alkylating a lysate. In some cases, the automated system can be configured to run a step for denaturing a biomolecule corona. In some cases, the automated system can be configured to run a step for denaturing a biomolecule corona with a step-wise denaturing process. In some cases, the automated system can be configured to run a step to digest a biomolecule corona. The digestion step may comprise a protease such as trypsin, chymotrypsin, endoproteinase Asp-N, endoproteinase Arg-C, endoproteinase Lys-C, pepsin, thermolysin, elastase, papain, proteinase K, subtilisin, clostripain, carboxypeptidase, cathepsin C, or any combination thereof. The digestion step may comprise a chemical peptide cleavage agent, such as cyanogen bromide. The automated system may be configured to run a series of digestion steps, which may comprise different conditions, proteases, or chemical cleavage agents. A digestion step may use at most 50 ng/mL, at most 100 ng/mL, at most 200 ng/mL, at most 500 ng/mL, at most 1 μg/mL, at most 2 μg/mL, at most 5 μg/mL, at most 10 μg/mL, at most 25 μg/mL, at most 50 μg/mL, at most 100 μg/mL, at most 200 μg/mL, or at most 500 μg/mL of a protease. A digestion step may utilize at least 500 μg/mL, at least 200 μg/mL, at least 100 μg/mL, at least 50 μg/mL, at least 25 μg/mL, at least 10 μg/mL, at least 5 μg/mL, at least 2 μg/mL, at least 1 μg/mL, at least 500 ng/mL, at least 200 ng/mL, at least 100 ng/mL or at least 50 ng/mL of a protease. In some cases, the automated system can be configured to run a step to digest a biomolecule corona with trypsin at a concentration of at least about 200 nanograms per milliliter (ng/mL) to about 200 micrograms per milliliter (μg/mL). In some cases, the automated system can be configured to run a step to digest a biomolecule corona with trypsin at a concentration of at least about 100 micrograms per milliliter (μg/mL) to about 0.1 g/L. In some cases, the automated system can be configured to run a step to digest a biomolecule corona with lysC at a concentration of at least about 200 nanograms per milliliter (ng/mL) to about 200 micrograms per milliliter (μg/mL). In some cases, the automated system can be configured to run a step to digest a biomolecule corona with lysC at a concentration of at least about 20 micrograms per milliliter (μg/mL) to about 0.02 g/L. In some cases, the digestion step is performed for at most 3 hours. In some cases, the digestion step is performed for at most 1 hour. In some cases, the digestion step is performed for at most 30 minutes. In some cases, the digestion step generates peptides with an average mass of at least 1000 Da, at least 2000 Da, at least 3000 Da, at least 4000 Da, at least 5000 Da, at least 6000 Da, at least 8000 Da, or at least 10000 Da. In some cases, the digestion step generates peptides with an average mass of at most 10000 Da, at most 8000 Da, at most 6000 Da, at most 5000 Da, at most 4000 Da, at most 3000 Da, at most 2000 Da, or at most 1000 Da. In some cases, the digestion step generates peptides with an average mass of about 1000 Da to about 4000 Da. In some cases, the digestion step is preceded by elution of at least a subset of biomolecules or biomolecule groups from a biomolecule corona, for example such that the biomolecules or biomolecule groups are digested in solution. The elution may comprise dilution, heating, physical perturbation, addition of a chemical agent (e.g., a mild chaotropic agent), or any combination thereof. 
     In some cases, the automated system can be configured to elute a biomolecule corona or a portion of a biomolecule corona (e.g., selectively elute the soft portion of a biomolecule corona from a particle while leaving the hard portion of the biomolecule corona adsorbed to the particle). In some cases, the automated system can be configured to perform liquid chromatography on a biomolecule corona. In some cases, the automated system can be configured to separate a portion of a dry composition from a portion of the biological sample. In some cases, the automated system can be configured to separate a portion of a dry composition from a portion of the biological sample using a magnetic field. In some cases, the automated system can be configured run a proteomic experiment. In some cases, the automated system can be configured run a genomic experiment. In some cases, the automated system can be configured run a proteogenomic experiment. In some cases, the automated system can be configured run a mass spectroscopy experiment. In some cases, the automated system can be configured run a sequencing experiment. 
     An automated system can be configured run various experimental steps at various temperatures. In some cases, an automated system can be configured to run an experimental step at about −20, −19, −18, −17, −16, −15, −14, −13, −12, −11, −10, −9, −8, −7, −6, −5, −4, −3, −2, −1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or 100° C. 
     An automated system can be configured run various experimental steps for various durations of time. In some cases, an automated system can be configured to run an experimental step at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 30, 40, 50, or 60 minutes. In some cases, an automated system can be configured to run an experimental step at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 hours. In some cases, an automated system can be configured to run an experimental step at most about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 30, 40, 50, or 60 minutes. In some cases, an automated system can be configured to run an experimental step at most about 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 hours. 
     In some cases, the eluting step may comprise eluting with at most about 2× in volume of solution. In some cases, the eluting step may comprise eluting with at most about 4× in volume of solution. In some cases, the eluting step may comprise eluting with at most about 8× in volume of solution. In some cases, the eluting step may comprise eluting with at most about 16× in volume of solution. In some cases, the eluting comprises dilution. The dilution may be no more than 20-fold, no more than 10-fold, no more than 8-fold, no more than 5-fold, no more than 2-fold, or no more than 1.5-fold dilution. The elution may comprise a physical perturbation such as heating, sonication, shaking, or stirring. In some cases, the eluting comprises releasing an intact biomolecule (e.g., an intact protein) from the particle. 
     In some cases, the automated apparatus may perform solid phase extraction. The solid phase extraction may separate analytes (e.g., peptides digested from biomolecule corona proteins) from reagents (e.g., proteases), biomacromolecules and supramolecular biological structures (e.g., ribosomes and portions of cell walls), and species not amenable to downstream analysis (e.g., analytes incompatible with a liquid chromatography column). In some cases, the solid phase extraction utilizes a solid phase extraction plate comprising TF, iST, or C18. The solid phase extraction may be performed above atmospheric pressure. The pressure may be at least 25 pounds per square inch (psi), at least about 50 psi, at least about 100 psi, at least about 200 psi, at least about 300 psi, at least about 400 psi, or at least about 500 psi. In some cases, the solid phase extraction step may comprise eluting from a solid phase extraction plate with at most about 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100 psi. In some cases, the solid phase extraction step may comprise eluting from a solid phase extraction plate with at least about 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100 psi. 
     An automated system can comprise using a set of barcodes to identify biological samples, dry compositions, experimental steps, a substrate, a partition or volume within a substrate (e.g., a plasticware substrate), or reagents. An automated system may be configured to transfer a substrate based at least partially on a substrate (e.g., plateware) barcode. For example, the automated system may transfer a multi-well plate from a heater to a magnet array to immobilize magnetic particles contained in volumes of the multi-well plate. An automated system may be configured to transfer dry compositions based at least partially on a dry composition barcode. An automated system may be configured to transfer biological samples based at least partially on a biological sample barcode. An automated system may be configured to transfer samples and/or reagents between partitions or volumes of a substrate. An automated system may be configured to transfer reagents based at least partially on a reagent barcode. An automated system may be configured to set up experimental steps based at least partially on an experimental step barcode. 
     In some cases, a barcode may comprise information for plasticware, particle, reagent, kit, inventor management system, automated system, plate layout, or any combination thereof. 
     In some cases, an automated system may be in communication with a customer laboratory information management system (LIMS), an inventory management system, a MS machine, a personal computer, the cloud, the internet, or any combination thereof. 
     In some cases, an automated system may communicate barcodes, barcode information, plate layouts, experiment logs, MS files, biological sample information, analytical results of proteomic or genomic assays, or any combination thereof. 
     Single-Cell and Spatial Proteomics 
     A single cell or a biological sample with a small sample volume or amount can be assayed using a method described the current disclosure. 
     In some cases, a method may comprise (a) obtaining a plurality of biomolecules, wherein individual biomolecules of at least a subset of the plurality of biomolecules are labeled with distinguishable tags; (b) contacting the plurality of biomolecules with a particle composition comprising at least one particle to thereby form a biomolecule corona with the particle composition, wherein the biomolecule corona comprises at least a subset of the individual biomolecules; and (c) assaying the biomolecule corona to identify the at least the subset of the individual biomolecules based at least partially on the distinguishable tags. 
     In some cases, the plurality of biomolecules is obtained from a plurality of biological samples, wherein the distinguishable tags are specific and corresponding to individual biological samples of the plurality of biological samples. 
     In some cases, the individual biological samples of the plurality of biological samples may each originate from different organisms. In some cases, distinguishable tags may be specific and corresponding to the different organisms. 
     In some cases, the individual biological samples of the plurality of biological samples may each originate from different conditions. In some cases, distinguishable tags may be specific and corresponding to the different conditions of biological samples. 
     In some cases, the individual biological samples of the plurality of biological samples may each originate from different cells of a single organism. In some cases, distinguishable tags may be specific and corresponding to the different cells of the single organisms. 
     In some cases, the individual biological samples of the plurality of biological samples may each originate from different components of a single cell. In some cases, distinguishable tags may be specific and corresponding to the different components of the single cell. 
     In some cases, the individual biological samples of the plurality of biological samples may each originate from at least about 250 cells to at most about 3,000 cells of a single organism. In some cases, the individual biological samples of the plurality of biological samples may each originate from at least about 500 cells to at most about 1,000 cells of a single organism. In some cases, the individual biological samples of the plurality of biological samples may each originate from at most about 100 cells of a single organism. In some cases, the individual biological samples of the plurality of biological samples may each originate from a single cell of a single organism. In some cases, the individual biological samples of the plurality of biological samples may each originate from at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, or 10000 cells. In some cases, the individual biological samples of the plurality of biological samples may each originate from at most about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, or 10000 cells. 
     In some cases, the individual biological samples of the plurality of biological samples may each comprise from at least about 10 ng to at most about 1000 ng of protein. In some cases, the individual biological samples of the plurality of biological samples may each comprise from at least about 1 ng to at most about 100 ng of protein. In some cases, the individual biological samples of the plurality of biological samples may each comprise from at least about 100 pg to at most about 1 ng of protein. In some cases, the individual biological samples of the plurality of biological samples may each comprise from at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, or 900 pg of protein. In some cases, the individual biological samples of the plurality of biological samples may each comprise from at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, or 900 ng of protein. In some cases, the individual biological samples of the plurality of biological samples may each comprise from at most about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, or 900 μg of protein. In some cases, the individual biological samples of the plurality of biological samples may each comprise from at most about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, or 900 ng of protein. 
     In some cases, the particle composition may comprise a plurality of particles. In some cases, a particle may comprise any one of the various particles disclosed herein. 
     In some cases, the plurality of biomolecules may comprise a biomolecule for a reporter channel. In some cases, the biomolecule for the reporter channel may comprise at least one protein or protein fragment in a known amount. In some cases, a reporter channel may comprise a protein present in a biological sample. In some cases, a reporter channel may comprise a low-abundance protein present in a biological sample. 
     In some cases, the plurality of biomolecules may be obtained from a plurality of locations within a single cell, wherein the distinguishable tags are specific to individual locations within the single cell. 
     In some cases, the plurality of biomolecules may be fractionated into a plurality of fractions. The plurality of biomolecules may be fractionated into any number of fractions. In some cases, the plurality of biomolecules may be fractionated into at least about 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100 fractions. 
     In some cases, the method may further comprise, determining for each fraction, one or both of (i) an amount of the distinguishable tags and an amount of individual biomolecules in the fraction, and (ii) an amount of biomolecules originating from a given location of the plurality of locations based at least partially on the amount of the distinguishable tags or the amount of the biomolecules. 
     In some cases, step (b) may be carried out in a well comprising a surface that is both hydrophobic and oleophobic. In some cases, a surface that is both hydrophobic and oleophobic may comprise a fluorinated surface. In some cases, a fluorinated surface may comprise a polytetrafluoroethylene surface. 
     Distinguishable Tags 
     A distinguishable tag may comprise various molecules that can be associated with a biomolecule to help identify the sample origin of the biomolecule in an assay. In some cases, a first sample may be labeled with a first distinguishable tag, and a second sample may be labeled with a second distinguishable tag. In some cases, various samples may be labeled with different distinguishable tags and assayed. In some cases, at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16 samples may be tagged with different distinguishable tags. In some case, at most 64, 32, 20, 16, 12, 10, or 8 samples may be tagged with different distinguishable tags. 
     In some cases, the first distinguishable tag and the second distinguishable tag comprise different isotopes of one or more elements. In some cases, the different isotopes of the one or more elements comprises C12 and C13. In some cases, the different isotopes of the one or more elements comprises N14 and N15. 
     The distinguishable tag can be configured to bind to various functional groups of biomolecules. In some cases, the distinguishable tag can be configured to covalently bind to an amine (e.g., a primary, a secondary, a tertiary, or a quaternary amine). In some cases, the distinguishable tag can be configured to covalently bind to an amide (e.g., a primary, a secondary, a tertiary, or a quaternary amide). In some cases, the distinguishable tag can be configured to covalently bind a carboxylic acid group. In some cases, the distinguishable tag can be configured to covalently bind a thiol group. In some cases, the distinguishable tag binds to a reactive moiety of the biomolecule. In some cases, the distinguishable tag comprises an isobaric tag. In some cases, the isobaric tag may be distinguished at the MS2 level. In some cases, the distinguishable tag comprises a nonisobaric tag. In some cases, the nonisobaric tag may be distinguished at the MS1 level m/z shift. Labeling of biomolecules with the distinguishable tag are described in Mari Enoksson, Jingwei Li, Melanie M. Ivancic, John C. Timmer, Eric Wildfang, Alexey Eroshkin, Guy S. Salvesen, and W. Andy Tao. “Identification of Proteolytic Cleavage Sites by Quantitative Proteomics.” Journal of Proteome Research 2007 6 (7), 2850-2858. doi: 10.1021/pr701052, which in incorporated by reference in its entirety herein. 
     In some cases, the first distinguishable tag and the second distinguishable tag comprise different masses (or reporter masses). For instance, the first distinguishable tag and the second distinguishable tag are different in mass by about 1, 4, 8, or 16 Daltons. In some cases, the first distinguishable tag and the second distinguishable tag can be different in mass by at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100 Daltons, or any other amount. In some cases, the first distinguishable tag and the second distinguishable tag can be different in mass by at most about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100 Daltons, or any other amount. 
     In some cases, the first distinguishable tag and the second distinguishable tag comprise the same mass. The first distinguishable tag and the second distinguishable tag can comprise the same molecular structure. The first distinguishable tag and the second distinguishable tag can comprise different isotopes of an element or reporter mass (e.g. in the case of TMT pro 16 plex) at one or more positions in the molecular structure. For instance, a first atom of the first distinguishable tag may comprise a lighter isotope, while a second atom of the second distinguishable tag may comprise a heavier isotope, wherein the first atom and the second atom are at the same position in the molecular structure. The first distinguishable tag and the second distinguishable tag may generate reporter ions comprising different masses, for example, in tandem mass spectrometry. For instance, the first distinguishable tag and the second distinguishable tag can be configured to fragment into ions of the same molecular structure; however, when the distribution of heavy isotopes in the first distinguishable tag and the second distinguishable tag are different, the generated ions may be different in mass. 
     In some cases, a distinguishable tag may comprise a tandem mass tag (TMT). In some cases, a tandem mass tag may comprise TMT 0, TMT 2, TMT6/10, TMT 11, TMT Pro-zero, TMT Pro, TMTpro-126, TMTpro-127C, TMTpro-128C, TMTpro-129C, TMTpro-130C, TMTpro-131C, TMTpro-132C, TMTpro-133C, TMTpro-134C, TMTpro-127N, TMTpro-128N, TMTpro-129N, TMTpro-130N, TMTpro-131N, TMTpro-132N, TMTpro-133N, TMTpro-134N, TMTpro-135N, TMT6-126, TMT6-127, TMT6-128, TMT6-129, TMT6-130, TMT6-131, TMT10-126, TMT10-127N, TMT10-127C, TMT10-128N, TMT10-128C, TMT10-129N, TMT10-129C, TMT10-130N, TMT10-130C, TMT10-131, variants thereof, any other tandem mass tag, or combinations thereof. 
     A distinguishable tag can comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or any number of heavy isotopes. A heavy isotope can be a stable isotope. A heavy isotope can be C 13 , N 12 , H 2 , S 33 , S 34 , or S 36 . A light isotope can be C 12 , N 14 , H 1 , or S 32 . 
     In some cases, a distinguishable tag can comprise an amino acid. The amino acid can be alanine, arginine, asparagine, aspartic acid, cysteine, glutamic acid, glutamine, glycine, histidine, isoleucine, leucine, lysine, methionine, phenylalanine, proline, serine, threonine, tryptophan, tyrosine, valine, selenocysteine, or pyrrolysine. The amino acid can comprise any number of heavy isotopes. 
     In some cases, a distinguishable tag can comprise a lipid. The lipid can be a fatty acid, a saturated fatty acid, an unsaturated fatty acid, a glyceride, a neutral glyceride, a phosphoglyceride, a triglyceride, a sphingolipid, a steroid, a cholesterol, a spingomyeline, a glycolipid, a lipoprotein. The lipid can comprise any number of heavy isotopes. 
     Computer Systems 
     The present disclosure provides computer systems that are programmed to implement methods of the disclosure.  FIG.  60    shows a computer system  6001  that is programmed or otherwise configured to, for example, contact one or more biological samples with one or more particles to form one or more biomolecule coronas and analyze the one or more biomolecule coronas with a proteomic method, genomic method, or both. 
     The computer system  6001  may regulate various aspects of analysis, calculation, and generation of the present disclosure, such as, for example, contacting one or more biological samples with one or more particles to form one or more biomolecule coronas and analyzing the one or more biomolecule coronas with a proteomic method, genomic method, or both. The computer system  6001  may be an electronic device of a user or a computer system that is remotely located with respect to the electronic device. The electronic device may be a mobile electronic device. The electronic device may comprise a wireless keyboard and a mouse. The electronic device may comprise a display mount (e.g., Hamilton arm). 
     The computer system  6001  includes a central processing unit (CPU, also “processor” and “computer processor” herein)  6005 , which may be a single core or multi core processor, or a plurality of processors for parallel processing. The computer system  6001  also includes memory or memory location  6010  (e.g., random-access memory, read-only memory, flash memory), electronic storage unit  6015  (e.g., hard disk), communication interface  6020  (e.g., network adapter) for communicating with one or more other systems, and peripheral devices  6025 , such as cache, other memory, data storage and/or electronic display adapters. The memory  6010 , storage unit  6015 , interface  6020  and peripheral devices  6025  are in communication with the CPU  6005  through a communication bus (solid lines), such as a motherboard. The storage unit  6015  may be a data storage unit (or data repository) for storing data. The computer system  6001  may be operatively coupled to a computer network (“network”)  6030  with the aid of the communication interface  6020 . The network  6030  may be the Internet, an internet and/or extranet, or an intranet and/or extranet that is in communication with the Internet. 
     The network  6030  in some cases is a telecommunication and/or data network. The network  6030  may include one or more computer servers, which may enable distributed computing, such as cloud computing. For example, one or more computer servers may enable cloud computing over the network  6030  (“the cloud”) to perform various aspects of analysis, calculation, and generation of the present disclosure, such as, for example, contacting one or more biological samples with one or more particles to form one or more biomolecule coronas and analyzing the one or more biomolecule coronas with a proteomic method, genomic method, or both. Such cloud computing may be provided by cloud computing platforms such as, for example, Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform, and IBM cloud. The network  6030 , in some cases with the aid of the computer system  6001 , may implement a peer-to-peer network, which may enable devices coupled to the computer system  6001  to behave as a client or a server. 
     The CPU  6005  may comprise one or more computer processors and/or one or more graphics processing units (GPUs). The CPU  6005  may execute a sequence of machine-readable instructions, which may be embodied in a program or software. The instructions may be stored in a memory location, such as the memory  6010 . The instructions may be directed to the CPU  6005 , which may subsequently program or otherwise configure the CPU  6005  to implement methods of the present disclosure. Examples of operations performed by the CPU  6005  may include fetch, decode, execute, and writeback. 
     The CPU  6005  may be part of a circuit, such as an integrated circuit. One or more other components of the system  6001  may be included in the circuit. In some cases, the circuit is an application specific integrated circuit (ASIC). 
     The storage unit  6015  may store files, such as drivers, libraries and saved programs. The storage unit  6015  may store user data, e.g., user preferences and user programs. The computer system  6001  in some cases may include one or more additional data storage units that are external to the computer system  6001 , such as located on a remote server that is in communication with the computer system  6001  through an intranet or the Internet. 
     The computer system  6001  may communicate with one or more remote computer systems through the network  6030 . For instance, the computer system  6001  may communicate with a remote computer system of a user. Examples of remote computer systems include personal computers (e.g., portable PC), slate or tablet PC&#39;s (e.g., Apple® iPad, Samsung® Galaxy Tab), telephones, Smart phones (e.g., Apple® iPhone, Android-enabled device, Blackberry®), or personal digital assistants. The user may access the computer system  6001  via the network  6030 . 
     Methods as described herein may be implemented by way of machine (e.g., computer processor) executable code stored on an electronic storage location of the computer system  6001 , such as, for example, on the memory  6010  or electronic storage unit  6015 . The machine executable or machine readable code may be provided in the form of software. During use, the code may be executed by the processor  6005 . In some cases, the code may be retrieved from the storage unit  6015  and stored on the memory  6010  for ready access by the processor  6005 . In some situations, the electronic storage unit  6015  may be precluded, and machine-executable instructions are stored on memory  6010 . 
     The code may be pre-compiled and configured for use with a machine having a processer adapted to execute the code, or may be compiled during runtime. The code may be supplied in a programming language that may be selected to enable the code to execute in a pre-compiled or as-compiled fashion. 
     Aspects of the systems and methods provided herein, such as the computer system  6001 , may be embodied in programming. Various aspects of the technology may be thought of as “products” or “articles of manufacture” typically in the form of machine (or processor) executable code and/or associated data that is carried on or embodied in a type of machine readable medium. Machine-executable code may be stored on an electronic storage unit, such as memory (e.g., read-only memory, random-access memory, flash memory) or a hard disk. “Storage” type media may include any or all of the tangible memory of the computers, processors or the like, or associated modules thereof, such as various semiconductor memories, tape drives, disk drives and the like, which may provide non-transitory storage at any time for the software programming. All or portions of the software may at times be communicated through the Internet or various other telecommunication networks. Such communications, for example, may enable loading of the software from one computer or processor into another, for example, from a management server or host computer into the computer platform of an application server. Thus, another type of media that may bear the software elements includes optical, electrical and electromagnetic waves, such as used across physical interfaces between local devices, through wired and optical landline networks and over various air-links. The physical elements that carry such waves, such as wired or wireless links, optical links or the like, also may be considered as media bearing the software. As used herein, unless restricted to non-transitory, tangible “storage” media, terms such as computer or machine “readable medium” refer to any medium that participates in providing instructions to a processor for execution. 
     Hence, a machine readable medium, such as computer-executable code, may take many forms, including but not limited to, a tangible storage medium, a carrier wave medium or physical transmission medium. Non-volatile storage media include, for example, optical or magnetic disks, such as any of the storage devices in any computer(s) or the like, such as may be used to implement the databases, etc. shown in the drawings. Volatile storage media include dynamic memory, such as main memory of such a computer platform. Tangible transmission media include coaxial cables; copper wire and fiber optics, including the wires that comprise a bus within a computer system. Carrier-wave transmission media may take the form of electric or electromagnetic signals, or acoustic or light waves such as those generated during radio frequency (RF) and infrared (IR) data communications. Common forms of computer-readable media therefore include for example: a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD or DVD-ROM, any other optical medium, punch cards paper tape, any other physical storage medium with patterns of holes, a RAM, a ROM, a PROM and EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave transporting data or instructions, cables or links transporting such a carrier wave, or any other medium from which a computer may read programming code and/or data. Many of these forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to a processor for execution. 
     The computer system  6001  may include or be in communication with an electronic display  6035  that comprises a user interface (UI)  6040  for providing, for example, contacting one or more biological samples with one or more particles to form one or more biomolecule coronas and analyzing the one or more biomolecule coronas with a proteomic method, genomic method, or both. Examples of UIs include, without limitation, a graphical user interface (GUI) and web-based user interface. 
     Methods and systems of the present disclosure may be implemented by way of one or more algorithms. An algorithm may be implemented by way of software upon execution by the central processing unit  6005 . The algorithm can, for example, contacting one or more biological samples with one or more particles to form one or more biomolecule coronas and analyzing the one or more biomolecule coronas with a proteomic method, genomic method, or both. 
     Unless otherwise defined, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise. Any reference to “or” herein is intended to encompass “and/or” unless otherwise stated. 
     Whenever the term “at least,” “greater than,” or “greater than or equal to” precedes the first numerical value in a series of two or more numerical values, the term “at least,” “greater than” or “greater than or equal to” applies to each of the numerical values in that series of numerical values. For example, greater than or equal to 1, 2, or 3 is equivalent to greater than or equal to 1, greater than or equal to 2, or greater than or equal to 3. 
     Whenever the term “no more than,” “less than,” “less than or equal to,” or “at most” precedes the first numerical value in a series of two or more numerical values, the term “no more than,” “less than” or “less than or equal to,” or “at most” applies to each of the numerical values in that series of numerical values. For example, less than or equal to 3, 2, or 1 is equivalent to less than or equal to 3, less than or equal to 2, or less than or equal to 1. 
     Where values are described as ranges, it will be understood that such disclosure includes the disclosure of all possible sub-ranges within such ranges, as well as specific numerical values that fall within such ranges irrespective of whether a specific numerical value or specific sub-range is expressly stated. 
     EXAMPLES 
     The following examples are illustrative and non-limiting to the scope of the devices, methods, systems, and kits described herein. 
     Example 1 
     Parallel Analysis of Proteins and Nucleic Acids 
     This example describes parallel analysis of proteins and nucleic acids. A biological sample is obtained. Optionally, the biological sample is split in two parts. Part of the sample is contacted to a particle. The particle adsorbs proteins (and other biomolecules) from the sample onto its surface forming a biomolecule corona. The particle is separated from the sample. The biomolecule is trypsinized. The trypsinized peptides are analyzed by mass spectrometry and peptides identities and concentrations are identified. 
     In parallel, another part of the sample is contacted with reagents for sequencing, including adaptors, labeled nucleotides (labeled with optically detectable labels), primers, polymerases. The contacting may take place on a substrate for sequencing. Nucleic acids in the sample are amplified, for example by PCR amplification. Samples or substrates for sequencing are imaged and the sequence of nucleic acids in the sample is determined. 
     The composition and concentrations of proteins in the sample as determined by protein corona analysis are compared to the composition and concentration of nucleic acids in the ample as determined by sequencing. These comparisons are correlated to samples from a control source (e.g., healthy biological state) and samples from a known experimental source (e.g., a disease biological state). Trained classifiers and machine learning algorithms are used to classify the biological state of samples based on the proteins and nucleic acids present in the sample. The biological state of the assayed sample is determined based on the proteins present in the sample (as determined by corona analysis) and the nucleic acids present in the sample (as determined by sequencing). 
     Example 2 
     Parallel Analysis of Proteins and Nucleic Acids 
     This example describes proteogenomic analysis on samples from subjects with early- and late-stage non-small-cell lung carcinoma (NSCLC). Identifying protein variants (such as isoforms) can be a major challenge in proteomic analysis. Often, methods capable of identifying proteins are blind to minor sequence variations, such as single amino acid substitutions. 
     In the present example, exon-based sequencing and proteomic analysis were performed in parallel on 29 plasma samples from subjects with early- and late-stage NSCLC. For the proteomic analysis, the plasma samples were apportioned and separately contacted with ten different types of SPIONs shown in TABLE 2 and varying in size (as determined by dynamic light scattering, ‘DLS’), polydispersity index (‘PDI’, as determined by DLS), and mean zeta potential). The particle-containing samples were subjected to multiple wash cycles, and then exposed to conditions suitable to elute proteins bound to the particles. The eluted proteins were digested and submitted for mass spectrometric analysis, thereby generating proteomic data. 
     
       
         
           
               
             
               
                 TABLE 2 
               
             
            
               
                   
               
               
                 Particles Used for NSCLC Sample Analysis 
               
            
           
           
               
               
               
               
               
            
               
                   
                   
                   
                   
                 Mean 
               
               
                   
                   
                 DLS 
                   
                 zeta 
               
               
                 Batch 
                   
                 size 
                 DLS 
                 potential 
               
               
                 No. 
                 Description 
                 (nm) 
                 PDI 
                 (mV) 
               
               
                   
               
            
           
           
               
               
               
               
               
            
               
                 SP-007 
                 Poly(dimethyl aminopropyl 
                 283 
                 0.09 
                 25.8 
               
               
                   
                 methacrylamide) (Dimethylamine) 
                   
                   
                   
               
               
                   
                 coated 
                   
                   
                   
               
               
                 SP-047 
                 Mixed chemistry based on amine- 
                 1255 
                 0.54 
                 18.1 
               
               
                   
                 epoxy chemistry 
                   
                   
                   
               
               
                 SP-064 
                 Polyzwitterion coated (Poly(N-[3- 
                 302 
                 0.25 
                 27.7 
               
               
                   
                 (Dimethylamino)propyl]meth- 
                   
                   
                   
               
               
                   
                 acrylamide-co-[2- 
                   
                   
                   
               
               
                   
                 (methacryloyloxy)ethyl]dimethyl- 
                   
                   
                   
               
               
                   
                 (3-sulfopropyl)ammonium hydroxide, 
                   
                   
                   
               
               
                   
                 P(DMAPMA-co-SBMA)) 
                   
                   
                   
               
               
                 SP-333 
                 Carboxylate microparticle 
                 1348 
                 0.66 
                 −28.5 
               
               
                 SP-339 
                 Carboxylated polystyrene 
                 410 
                 0.03 
                 −31.4 
               
               
                 SP-347 
                 Silica coated 
                 281 
                 0.18 
                 −21.8 
               
               
                 SP-365 
                 Strongly acidic silica surface 
                 231 
                 0.02 
                 −39 
               
               
                 SP-373 
                 Dextran-based coating 
                 169 
                 0.07 
                 −0.6 
               
               
                 SP-390 
                 Oleic acid- Hydrophilic/hydrophobic 
                 98 
                 0.1 
                 −38 
               
               
                   
                 surface 
                   
                   
                   
               
               
                 SP-406 
                 Boronated surface 
                 491 
                 0.45 
                 −40.7 
               
               
                   
               
            
           
         
       
     
     A total of 1189 proteins were identified across the 10 samples. In addition, peptide variations (including single amino acid substitutions) were identified in each sample.  FIG.  2 A  summarizes the number of protein variations identified from each sample. An average of approximately 70 peptide variations were identified across the 29 plasma samples, with the numbers from individual samples ranging from just above 50 to just under 125. 
       FIG.  2    panel B provides an example of protein variant identification based on the genomic and proteomic data. In one sample, genomic analysis identified heterozygosity for the KLKB1 gene, which codes for the protein prekallikrein. The sample contained the reference allele for KLKB1 and a minor allele encoding a glycine to arginine substitution, indicated by the red amino acids circled in  FIG.  2    panel B. Even though the exon sequencing identified a minor allele frequency of 0.01%, proteomic analysis identified forms of prekallikrein corresponding to the reference and minor alleles, demonstrating that genomic profiling can allow protein variants to be discerned from a complex sample. 
     Example 3 
     Parallel Analysis of Proteins and Nucleic Acids 
     In this example, genomic and proteomic analysis were used to determine the presence of Bone Morphogenic Protein 1 (BMP1) variants in samples from healthy and cancer patients. Alternate splicing is responsible for seven BMP1 variants at the RNA level and four variants at the protein level. Of these protein variants, two are the long form and two are the short form of the BMP1 protein. Simultaneous genomic and proteomic analysis allowed the four BMP1 protein variants to be quantified across 80 healthy and 61 early-stage non-small cell lung cancer patients. 
     Proteomic analysis was performed by contacting each sample with a particle panel, digesting proteins collected on the particles, and analyzing the resulting peptide fragments by mass spectrometry. Plasma samples from the 141 subjects were interrogated with a panel of 5 SPIONS with different physicochemical properties (summarized in TABLE 3). Plasma samples from each subject were diluted in TE buffer, mixed 1:1 with 2.5-15 mg/ml of each particle from the 5 SPION panel, and incubated for 1 hour at 37° C., resulting in the formation of plasma protein coronas. Following particle collection and wash steps, the protein coronas were digested on the particles for LC-MS/MS analysis. 
     
       
         
           
               
             
               
                 TABLE 3 
               
             
            
               
                   
               
               
                 Particles Used for Early Stage NSCLC 
               
               
                 and Healthy Sample Analysis 
               
            
           
           
               
               
            
               
                 Batch No. 
                 Description 
               
               
                   
               
               
                 SP-003 
                 Silica-coated superparamagnetic iron oxide NPs (SPION) 
               
               
                 SP-006 
                 N-(3-Trimethoxysilylpropyl)diethylenetriamine coated SPION 
               
               
                 SP-007 
                 poly(N-(3-(dimethylamino)propyl) methacrylamide) 
               
               
                   
                 (PDMAPMA)-coated SPION 
               
               
                 SP-333 
                 Carboxylate microparticle 
               
               
                 SP-339 
                 Carboxylated polystyrene 
               
               
                   
               
            
           
         
       
     
     A total of 7 peptide fragments were identified for BMP1. The peptides were mapped to 4 isoforms identified as coding transcripts from the subjects, and provided partial coverage of each of the 4 isoforms.  FIG.  3 A  provides exon-intron structures for the 4 identified BMP1 isoforms. The longest of the isoforms, BMP-202 ( FIG.  3 A , top), contained all 7 detected BMP1 peptide fragments. The next longest isoform, BMP-201 ( FIG.  3 A , 2 nd  from top), contained 6 of the 7 fragments. The two shorter isoforms, BMP-204 and BMP-203 ( FIG.  3 A , bottom two rows), only contained peptide fragments 1 and 2.  FIG.  3 B  provides normalized mass spectrometric intensities for the seven BMP1 peptide fragments in healthy and early-stage NSCLC plasma samples, of which two are more abundant in NSCLC and five are more abundant in healthy controls. 
     This example demonstrates that combined nucleic acid (e.g., transcript identification) and protein (e.g., peptide abundance) analysis can be combined to identify and quantify peptide isoforms present in a sample. 
     Example 4 
     Parallel Analysis of Proteins and Nucleic Acids 
     This example covers the identification of post-translational modifications in samples from healthy and cancer patients. Post-translational modifications can impart major changes in protein activity, signaling, and homeostasis. Techniques that characterize protein sequences without identifying post-translational activity can thus miss crucial information needed to identify biological states. For example, while Heparin Co-factor 2 overexpression can play a role in cancer development, its phosphorylation state can also indicate the presence and stage for a number of diseases. 
     In this example, the ratio of phosphorylated to unphosphorylated Heparin Co-factor 2 was measured across 14 samples collected from early- and late-stage lung cancer patients, healthy patients, and comorbid controls.  FIG.  4    shows the ratio of phosphorylated to unphosphorylated (‘modification ratio’ in  FIG.  4   ) across the four sample types. As can be seen from the figure, Heparin Co-factor 2 phosphorylation is higher in healthy patients than in lung cancer patients. The most pronounced difference, however, is between early-stage and late-stage lung cancer patients, with late stage lung cancer samples comprising multiple-fold lower Heparin Co-factor 2 phosphorylation. The results demonstrate that combined protein and post-translational modification analysis can enable disease state and disease stage diagnosis. 
     Example 5 
     Peptide Signal Multiplicity in Particle-Based Proteomic Analysis 
     This example covers signal multiplicity and reproducibility in particle-based proteomic analysis. The diagnostic power of proteomic methods often correlates with the number of signals obtained per target protein. This is in part due to conserved sequence motifs across disparate protein families, which can cause dissimilar proteins to produce similar signals during analysis. Thus, only a fraction of the signals obtained for a particular protein may be useful in identifying the protein. Furthermore, increasing the number of signals obtained for a single protein can increase the degree of sequence coverage for that protein. As such, a method that generates more signals for a target protein is more likely to identify sequence variations within that protein, and may provide a higher degree of repeatability across disparate patient samples. 
     The present example provides a proteogenomic assay capable of repeatably identifying thousands of proteins across a diverse set of patients (e.g., a population of patients with different health profiles), enabling accurate diagnostics for a wide range of diseases and conditions. The protein content of 141 plasma samples from a collection of healthy and early-stage non-small cell lung cancer (NSCLC) patients were separately analyzed using a 5 particle panel and mass spectrometry.  FIG.  8 A  summarizes the subject distribution of 61 early-stage NSCLC patients and 80 healthy patients. The plasma sample was first diluted 1:5 in a buffer composed of 10 mM Tris, 1 mM disodium ethylenediaminetetraacetic acid (EDTA), 150 mM potassium chloride, and 0.05% 3-((3-cholamidopropyl) dimethylammonio)-1-propanesulfonate (CHAPS). A nanoparticle mixture containing 5 particles provided in dried, powdered form was reconstituted by sonication and vortexing in deionized water, and mixed 1:1 (volume to volume) with the diluted plasma sample. The mixtures were then sealed and incubated for one hour at 37° C. under 300 rpm shaking. After incubation, the particles were magnetically separated from the supernatant. The proteins bound to the nanoparticles were subjected to trypsin digestion, and the resulting peptide fragments analyzed by LC-MS/MS. 
     A total of 2499 protein groups were detected across all subjects, with 1992 of the protein groups detected in at least 25% of the subjects.  FIG.  8 B  summarizes the number of protein groups detected across various percentages of the subjects in the study. About 50% of the detected proteins could be commonly identified across 70% of the subject population, while about 80% of the detected proteins were commonly detected across about 25% of the population. Thus, any two NSCLC patients selected from among the population studied were likely to share greater than 1000 identified protein groups. About 500 proteins (20% of the protein groups identified in the study) were commonly identified among all patients. 
       FIG.  9    displays the number of peptide fragments identified and correlated to each identified protein. A Gaussian-like distribution ranging from 1 to greater than 30 peptides were identified for the proteins identified in the assay, with a mean of around 12 peptide fragments were identified for each protein identified from the sample. The majority of proteins were identified on the basis of 10 or fewer peptides, with many of the proteins corresponding to 5 or fewer identified peptides. 
     These results indicate that the number of peptides identified for each protein identified for proteins from a sample often follow a statistical distribution. Some proteins were thoroughly covered by the assay, while others were identified on the basis of a small number (e.g., 3 or fewer) of peptides. The number of peptides identified for a particular protein or protein group can depend on assay methods, such as the protease, proteases, or chemical agents used to fragment the proteins. Thus, a method can be tailored to obtain a high peptide count for particular proteins of interest. 
     Example 6 
     Peptide Signal Multiplicity in Particle-Based Proteomic Analysis 
     This example covers allele detection and frequency across patient populations. Exon sequencing was used to generate personalized mass spectrometry search libraries for 29 subjects. A total of 464 amino acid variants were detected across the subject population. Analysis of the proteins containing these variants suggested putative allele specific presence in at least 178 separate genes. 
       FIG.  10    provides alternate allele frequency counts (y-axis) across the 464 protein variants identified in the 29 subjects studied (dark lines,  1010 ), and for allele frequencies for over 10 8  variants identified by whole genome sequencing of 2504 individuals (light lines,  1020 ). The degree of correspondence between the frequency distributions of the two datasets shows validates the unbiased nature of the present methods. 
       FIG.  11    provides density plots for the 464 alleles identified in the study  1110  and of the variants identified as showing allele specific expression  1120 , which denotes cases for which one or more peptides map only to the reference or only to the alternative allele, but not both, in all subjects with that genotype. As can be seen from plot, allele specific expression exhibits an increased prevalence for low frequency alleles, suggesting potential functions for these genotype-specific alleles. 
     Example 7 
     Peptide Signal Multiplicity in Particle-Based Proteomic Analysis 
     This example covers protein isoform identification. Plasma samples from 80 healthy and 61 early stage non-small cell lung cancer subjects were interrogated with a 5 particle panel summarized in TABLE 3. Briefly, plasma samples from the patients were diluted 1:5 in 10 mM Tris buffer containing 1 mM Na 2  (EDTA), 150 mM KCl, and 0.05% CHAPS. 100 Nanoparticles (about 2.5-15 mg/ml per particle type) were mixed 1:1 with the diluted biological samples, sealed, and incubated at 37° C. for 1 h with 300 rpm shaking. The particles were magnetically separated from the supernatant, washed, and then subjected to trypsinization conditions for on-particle protein digestion. Eluted peptides were analyzed by LC-MS/MS with a 20 minute LC-gradient. 
     1992 identified proteins identified across the 141 samples were filtered to select proteins present in at least 50% of subjects from either heathy or early cases and searched for peptides that had differential abundance between controls and cancer (p&lt;0.05; Benjamini-Hochberg corrected). To identify NSCLC-relevant protein isoforms, the 1992 identified proteins were screened to distinguish proteins with at least one peptide with significantly lower healthy plasma abundance and at least one peptide with significantly higher healthy plasma abundance (relative to early stage NSCLC abundance). This method is outlined in  FIG.  12 A , which depicts a hypothetical protein with 7 detected peptide fragments from LC-MS/MS analysis. While the plasma abundances of four of the peptide fragments are invariant across the healthy and early stage NSCLC groups, 3 of the peptides (inside dashed boxes and indicated with ***) are more prevalent in either the healthy or early stage NSCLC samples, suggesting that they belong to an isoform with enhanced or suppressed expression in early stage NSCLC. 
     A total of 16 proteins (summarized in TABLE 4) with differential early stage NSCLC isoform expression were identified.  FIG.  12 B  ranks the protein hits by Open Target lung carcinoma association score. While seven of the proteins (Table 4 ‘High’) have Open Target scores above 0.3, and thus known associations with lung carcinoma, nine of the proteins have low Open Target scores below 0.1, indicating little to no known associations with lung carcinoma. These nine proteins (TABLE 4 ‘Low’) constitute new lung carcinoma biomarkers discovered through differential isoform analysis. 
     
       
         
           
               
             
               
                 TABLE 4 
               
             
            
               
                   
               
               
                 Proteins with Differential NSCLC Isoform Abundances 
               
            
           
           
               
               
               
            
               
                   
                   
                 Associated Open 
               
               
                   
                   
                 Targets Lung 
               
               
                 Protein 
                 Abbreviation 
                 Carcinoma Score 
               
               
                   
               
               
                 Apolipoprotein B 
                 APOB 
                 0.80 
               
               
                 Ras-related protein Rap-1b 
                 RAP1B 
                 0.77 
               
               
                 Vinculin 
                 VCL 
                 0.76 
               
               
                 Talin-1 
                 TLN1 
                 0.76 
               
               
                 Filamin-A 
                 FLNA 
                 0.75 
               
               
                 Bone morphogenetic protein 1 
                 BMP1 
                 0.36 
               
               
                 Collagen alpha-3(VI) chain 
                 COL6A3 
                 0.36 
               
               
                 Proteoglycan 4 
                 PRG4 
                 0.05 
               
               
                 Lactate Dehydrogenase B 
                 LDHB 
                 0.03 
               
               
                 Reticulon 4 
                 RTN4 
                 0.03 
               
               
                 Fermitin Family Member 3 
                 FERMT3 
                 0.02 
               
               
                 Hydroxyacyl-CoA Dehydrogenase 
                 HADHA 
                 0.01 
               
               
                 Trifunctional Multienzyme Complex 
                   
                   
               
               
                 Subunit Alpha 
                   
                   
               
               
                 Thrombospondin-3 
                 THBS3 
                 0.01 
               
               
                 Inter-Alpha-Trypsin Inhibitor Heavy 
                 ITIH1 
                 — 
               
               
                 Chain 1 
                   
                   
               
               
                 Complement C4-A 
                 C4A 
                 — 
               
               
                 Complement C1r 
                 C1R 
                 — 
               
               
                   
               
            
           
         
       
     
       FIG.  12 C  plots the 16 identified NSCLC proteins by known plasma protein abundance using concentrations from the Human Plasma Proteome Project. Fifteen of the sixteen identified proteins have known plasma abundances, and span roughly 5 orders of magnitude in human plasma concentration, with Complement C4-A (P0C0L4) and Apolipoprotein B (APOB) having the highest concentrations at nearly 100 μg/ml, and Bone morphogenetic protein 1 having the lowest concentration of around 1 ng/ml (more than 7 orders of magnitude lower in plasma concentration than albumin). The methods of the present disclosure are thus able to distinguish protein isoforms, even for rare proteins from a biological sample. The present example also demonstrates that these methods may be used to identify biomarkers based on differential isoform expression, irrespective of total protein expression levels. 
     Example 8 
     Peptide Signal Multiplicity in Particle-Based Proteomic Analysis 
     This example illustrates protein variant detection at the single-sample level. The complexity of proteomic data can limit its utility for differentiating similar species, such as variant forms of a single protein. Accordingly, a number of high-throughput techniques, such as data-dependent acquisition mass spectrometry (DDA-MS), are often considered infeasible for complex sample analysis. Combining particle-based sample fractionation with nucleic acid analysis addresses this problem from multiple angles, simplifying both the data and the data analysis from such endeavors. 
     Combined protein and nucleic acid analysis of plasma samples from healthy, co-morbid, early stage non-small cell lung cancer (NSCLC), and late stage non-small cell lung cancer patients elucidated 464 peptide variants. Samples were obtained from 4 healthy, 11 co-morbid, 5 early stage NSCLC, and 9 late stage NSCLC patients. Plasma samples from each subject were fractionated with a particle panel (e.g., the 5-particle panel of TABLE 3 as outlined in EXAMPLE 7), and interrogated with DDA-MS. Patient-specific proteomic libraries, generated from translated patient genomes, guided peptide variant identification from the DDA-MS data. 
       FIG.  13    outlines the total number of protein variants identified in each of the 29 subjects. In this figure, each bar depicts the number of protein variants detected in a single subject. A total of 464 peptide variants were identified across the subject population, with the numbers of variants ranging from about 50 to about 150 per subject. The 464 variants mapped to 7 out of the 16 lung cancer-associated candidate proteins outlined in TABLE 4, namely APOB, COL6A3, FERMT3, FLNA, ITIH1, PRG4, and TLN1. 
       FIG.  14    provides the number of variant proteins identified in each subject for the 7 lung cancer-associated candidate proteins. Each bar represents the number of variants identified in a single subject for a given lung cancer-associated candidate protein. The results demonstrate that combined nucleic acid and biomolecule corona analysis can generate unbiased and deep plasma proteome profiles that enable identification of protein variants and peptides present in plasma at a scale sufficient for population-scale proteomic studies. 
     Example 9 
     Forming Lyophilized Beads 
     This example illustrates lyophilization of formulations comprising particles into lyophilized beads. Fixed volume droplets of formulations comprising particles and excipients were flash frozen in liquid nitrogen and then lyophilized. The concentration of particles in the formulations ranged from 18.75 mg/mL to 75 mg/mL. The volume of the droplets ranged from 30 μL to 40 μL. The volume of the droplets may be reduced to as low as 2 μL or as high as 60 μL with no adverse effects on the formulation. Various excipients were used, including sucrose, d-mannitol, trehalose, and combinations thereof. The concentration of the excipients ranged from about 100 mg/mL to 160 mg/mL. Particle concentration of 75 mg/mL and droplet volume of 40 μL corresponded to about 3 mg of particles per droplet. The concentration of particles may be reduced below 18.75 mg/ml or higher than 75 mg/mL with no adverse effects on the formulation. The lyophilized beads were packaged individually in PCR stripes with desiccant for future use. A suitable number of beads may be preloaded into tubes. 
     Experiments were conducted on the lyophilized beads to assess their stability.  FIGS.  54 A- 54 B  shows experimental measurements of the stability in the particle size and the particle mean zeta potential for lot S-003-121. A subset of the lyophilized beads was held at 37° C. for up to 12 days after lyophilization and another subset of the lyophilized beads were held at 60° C. for up to 12 days after lyophilization. After 1 day, 2 days, 5 days, 6 days, and 12 days, particles were reconstituted in water and the diameter was measured with dynamic light scattering (DLS) and the mean zeta potential was measured with Malvern ZetaSizer NanoZS.  FIG.  55    and  FIG.  56    show size measurements and mean zeta potential measurements, respectively, for various formulations: S-118-103, S-18-104, S-118-109, S-128-W6, S-128-066, S-229-055, S-229-056, and S-229-057. Lot numbers and corresponding formulations are listed in Table 5, shown below. 
     
       
         
           
               
             
               
                 TABLE 5 
               
             
            
               
                   
               
               
                 Lyophilized formulations 
               
            
           
           
               
               
               
               
               
               
               
            
               
                   
                 Doped Feed 
                   
                 NP conc. 
                 NP mg/mL, 
                 uL per 
                 mg NP/ 
               
               
                 Lot 
                 Buffer/Surfactant 
                 Excipient 
                 mg/mL 
                 formulated 
                 bead 
                 bead 
               
               
                   
               
            
           
           
               
               
               
               
               
               
               
            
               
                 S-003-111 
                   
                 sucrose 
                 40 
                 30 
                 30 
                 0.900 
               
               
                 S-003-111 
                   
                 d-mannitol 
                 40 
                 30 
                 30 
                 0.900 
               
               
                 S-003-111 
                   
                 trehalose 
                 40 
                 30 
                 30 
                 0.900 
               
               
                 S-007-020 
                   
                 sucrose 
                 33.8 
                 25.35 
                 30 
                 0.761 
               
               
                 S-007-020 
                   
                 d-mannitol 
                 33.8 
                 25.35 
                 30 
                 0.761 
               
               
                 S-007-020 
                   
                 trehalose 
                 33.8 
                 25.35 
                 30 
                 0.761 
               
               
                 S-106-039 
                   
                 sucrose 
                 40 
                 30 
                 30 
                 0.900 
               
               
                 S-106-039 
                   
                 d-mannitol 
                 40 
                 30 
                 30 
                 0.900 
               
               
                 S-106-039 
                   
                 trehalose 
                 40 
                 30 
                 30 
                 0.900 
               
               
                 S-006-020 
                   
                 sucrose 
                 40 
                 30 
                 30 
                 0.900 
               
               
                 S-006-020 
                   
                 d-mannitol 
                 40 
                 30 
                 30 
                 0.900 
               
               
                 S-006-020 
                   
                 trehalose 
                 40 
                 30 
                 30 
                 0.900 
               
               
                 S-006-019 
                   
                 d-mannitol 
                 40 
                 30 
                 30 
                 0.900 
               
               
                 S-006-016 
                   
                 d-mannitol 
                 40 
                 30 
                 30 
                 0.900 
               
               
                 P-073-010 
                   
                 d-mannitol 
                 25 
                 18.75 
                 30 
                 0.563 
               
               
                 S-118-023 
                   
                 d-mannitol 
                 40 
                 30 
                 30 
                 0.900 
               
               
                 S-118-024 
                   
                 d-mannitol 
                 40 
                 30 
                 30 
                 0.900 
               
               
                 S-145-018 
                   
                 d-mannitol 
                 40 
                 30 
                 30 
                 0.900 
               
               
                 S-145-019 
                   
                 d-mannitol 
                 40 
                 30 
                 30 
                 0.900 
               
               
                 S-106-092 
                   
                 d-mannitol 
                 40 
                 30 
                 30 
                 0.900 
               
               
                 S-106-102 
                   
                 d-mannitol 
                 40 
                 30 
                 30 
                 0.900 
               
               
                 S-010-022 
                   
                 d-mannitol 
                 40 
                 30 
                 30 
                 0.900 
               
               
                 S-010-023 
                   
                 d-mannitol 
                 40 
                 30 
                 30 
                 0.900 
               
               
                 S-006-028 
                 Control (no 
                 d-mannitol 
                 40 
                 30 
                 30 
                 0.900 
               
               
                   
                 acetate) 
               
               
                 S-006-028 
                 80 mM Acetate 
                 d-mannitol 
                 40 
                 30 
                 30 
                 0.900 
               
               
                   
                 pH 3.6 
               
               
                 S-006-028 
                 40 mM Acetate 
                 d-mannitol 
                 40 
                 30 
                 30 
                 0.900 
               
               
                   
                 pH 3.6 
               
               
                 S-006-028 
                 20 mM Acetate 
                 d-mannitol 
                 40 
                 30 
                 30 
                 0.900 
               
               
                   
                 pH 3.6 
               
               
                 S-006-023 
                 40 mM Acetate 
                 d-mannitol 
                 40 
                 30 
                 30 
                 0.900 
               
               
                   
                 pH 3.6 
               
               
                 S-006-028 
                 50 mM HCl pH 
                 d-mannitol 
                 40 
                 30 
                 30 
                 0.900 
               
               
                   
                 NA 
               
               
                 S-006-028 
                 40 mM Acetate 
                 d-mannitol 
                 40 
                 30 
                 30 
                 0.900 
               
               
                   
                 pH 3.6, then 
               
               
                   
                 3 × DIwash 
               
               
                 S-006-028 
                 50 mM HCl 
                 d-mannitol 
                 40 
                 30 
                 30 
                 0.900 
               
               
                   
                 3 × DIwash 
               
               
                 S-006-024 
                 40 mM Acetate 
                 d-mannitol 
                 40 
                 30 
                 30 
                 0.900 
               
               
                   
                 pH 3.6 
               
               
                 S-006-025 
                 40 mM Acetate 
                 d-mannitol 
                 40 
                 30 
                 30 
                 0.900 
               
               
                   
                 pH 3.6 
               
               
                 S-006-028 
                 0.01% CTAB 
                 d-mannitol 
                 40 
                 30 
                 30 
                 0.900 
               
               
                 S-006-025 
                 20 mM Acetate 
                 d-mannitol 
                 40 
                 30 
                 30 
                 0.900 
               
               
                   
                 pH 3.6 
               
               
                 S-128-008 
                   
                 d-mannitol 
                 42 
                 31.5 
                 30 
                 0.945 
               
               
                 S-128-009 
                   
                 d-mannitol 
                 44 
                 33 
                 30 
                 0.990 
               
               
                 S-240-001 
                   
                 d-mannitol 
                 42 
                 31.5 
                 30 
                 0.945 
               
               
                 S-229-002 
                   
                 d-mannitol 
                 38 
                 28.5 
                 30 
                 0.855 
               
               
                 S-118-061 
                   
                 d-mannitol 
                 32 
                 24 
                 30 
                 0.720 
               
               
                 P-073-011 
                   
                 d-mannitol 
                 25 
                 18.75 
                 30 
                 0.563 
               
               
                 P-039-010 
                   
                 d-mannitol 
                 25 
                 18.75 
                 30 
                 0.563 
               
               
                 S-003-121 
                   
                 d-mannitol 
                 99 
                 74.25 
                 40 
                 2.970 
               
               
                 S-006-032 
                 40 mM acetate 
                 d-mannitol 
                 99 
                 74.25 
                 40 
                 2.970 
               
               
                   
                 pH 3.6 
               
               
                 S-007-032 
                   
                 d-mannitol 
                 104 
                 78 
                 40 
                 3.120 
               
               
                 S-118-069 
                   
                 d-mannitol 
                 50 
                 37.5 
                 40 
                 1.500 
               
               
                 S-118-069 
                   
                 sucrose 
                 50 
                 37.5 
                 40 
                 1.500 
               
               
                 S-118-069 
                   
                 d-mannitol 
                 100 
                 75 
                 40 
                 3.000 
               
               
                 S-118-069 
                   
                 sucrose 
                 100 
                 75 
                 40 
                 3.000 
               
               
                 S-128-055 
                   
                 d-mannitol 
                 50 
                 37.5 
                 40 
                 1.500 
               
               
                 S-128-055 
                   
                 trehalose 
                 50 
                 37.5 
                 40 
                 1.500 
               
               
                 S-128-055 
                   
                 d-mannitol 
                 100 
                 75 
                 40 
                 3.000 
               
               
                 S-128-055 
                   
                 trehalose 
                 100 
                 75 
                 40 
                 3.000 
               
               
                 S-229-052 
                   
                 d-mannitol 
                 50 
                 37.5 
                 40 
                 1.500 
               
               
                 S-229-052 
                   
                 trehalose 
                 50 
                 37.5 
                 40 
                 1.500 
               
               
                 S-229-052 
                   
                 d-mannitol 
                 100 
                 75 
                 40 
                 3.000 
               
               
                 S-229-052 
                   
                 trehalose 
                 100 
                 75 
                 40 
                 3.000 
               
               
                 S-118-103 
                   
                 sucrose 
                 100 
                 75 
                 40 
                 3.000 
               
               
                 S-118-104 
                   
                 sucrose 
                 100 
                 75 
                 40 
                 3.000 
               
               
                 S-118-109 
                   
                 sucrose 
                 100 
                 75 
                 40 
                 3.000 
               
               
                 S-128-064 
                   
                 trehalose 
                 100 
                 75 
                 40 
                 3.000 
               
               
                 S-128-065 
                   
                 trehalose 
                 100 
                 75 
                 40 
                 3.000 
               
               
                 S-128-066 
                   
                 trehalose 
                 100 
                 75 
                 40 
                 3.000 
               
               
                 S-229-055 
                   
                 d-mannitol 
                 100 
                 75 
                 40 
                 3.000 
               
               
                 S-229-056 
                   
                 d-mannitol 
                 100 
                 75 
                 40 
                 3.000 
               
               
                 S-229-057 
                   
                 d-mannitol 
                 100 
                 75 
                 40 
                 3.000 
               
               
                   
               
            
           
         
       
     
     A subset of the lyophilized beads was reconstituted and assays were conducted with them to measure protein group counts and peptide counts.  FIG.  57    shows results for four different conditions. For Standard Panel, a liquid panel of particles was used at a standard concentration. For Condition 12, lyophilized beads were combined with 40 μL water to produce a nanoparticle concentration for each particle and then contacted with 40 μL of plasma. For Liquid Lyo Control, the same composition of liquid material used to produce the lyophilized beads (i.e., without having been lyophilized) was contacted with 40 μL of plasma. With Condition 4, 40 μL of plasma was added directly to the dry lyophilized bead with no added water. Each MS analysis was conducted while matching a standard MS injection concentration (about 500 ng peptide in 4 μL buffer). The experimental results show consistency across various conditions. The Liquid Lyo Control and Condition 12 (lyophilized beads used with reconstitution) performs statistically equivalent to the standard panel. Condition 4 (lyophilized beads without reconstitution, direct contact with plasma) detects statistically equivalent number of peptide groups as the standard panel, however, it also detects more peptides (with statistical significance, n=10) compared to the standard panel. 
     Example 10 
     Automated System 
     This example illustrates use of an automated system (an instrument) for a proteogenomics method.  FIG.  34    shows a pipeline comprising providing various consumable materials (e.g., nanoparticle formulations, solvents, reagents, etc.), using an automated system to conduct assays, using a mass spectrometer to produce assay results, and then data analysis software to analyze the results and display results to a user. 
     The following describes an example method implemented on an automated system comprising a computer readable medium comprising machine-executable code. (1) A user (i.e., an operator) prepares samples (e.g., by thawing frozen samples), reagents (e.g., diluting reagents), and particles (e.g., reconstituting lyophilized beads). The prepared samples, reagents, and particles are loaded into the automated system. The automated system then automatically carries out experimental steps from this point forward, including: (2) device initialization (Chassis, MPE 2 , Hamilton Heater Shaker (HHS), Inheco CPAC) that executed within 5 minutes, (3) Pipetting samples to assay plate executed within 5 minutes, (4) pipetting particles to assay plate executed within 15 minutes, (5) incubation at 37° C. executed within 60 minutes, (6) assay plate washing executed within 30 minutes, (7) addition of lysis, reduction, and alkylation buffer to assay plate executed within 10 minutes, (8) incubation at 95° C. executed on HHS within 10 minutes, (9) assay plate cool down at room temperature executed within 20 minutes, (10) addition of trypsin/LysC enzyme executed within 8 minutes, (11) incubation at 37° C. with HHS executed within 180 minutes, (12) addition of stop solution executed within 3 minutes, (13) pull down of particles executed within 5 minutes, (14) processing samples using SPE plate on MPE 2  executed within 8 minutes, (15) processing samples with Wash A using SPE plate on MPE 2  executed within 8 minutes, (16) processing samples with Wash B-1 using SPE plate on MPE 2  executed within 8 minutes, (17) processing samples with Wash B-2 using SPE plate on MPE 2  executed within 8 minutes, and (18) eluting samples using SPE plate on MPE 2  executed within 5 minutes. (19) The user can then clean-up the automated system after the end of the experiment. The total duration of the experiment is about 7 hours. 
     The previously described series of experimental steps may include extra steps, may exclude some steps, or may have variations in each step.  FIG.  40    shows an example of a method that may be implemented on the automated system with variations. These variations may be implemented such that a user can select which variation is to be used. For example, there may be variations in step (1), wherein the user can dilute a sample (e.g., a plasma sample up to 20 times its original volume), select a different volume for the assay (e.g., anywhere from 40 μL to 100 μL), thaw a sample to a specific temperature (e.g., room temperature or 4° C.), single-plex or multiplex nanoparticles (e.g., 2, 3, 4, 5, or any number of nanoparticles per partition), or carry out interference steps on the sample (e.g., hemolysis/lipid concentration). In some cases, a background of biomolecules other than proteins may change protein coronas depending on the physicochemical properties of a particle. In some cases, the background of biomolecules may also form a part of a biomolecule corona. In some cases, an interference step may comprise titrating different concentrations of certain biomolecules (e.g., of lipids) at different concentration. 
     There may be variations in any of the incubations steps, wherein the duration of time for incubation can be varied (e.g., 5 min or overnight), the pH of the solution being incubated can be varied (e.g., pH of 3.8, 5.0, or 7.4), the ionic strength of the solution being incubated can be varied (e.g., 0, 50, or 150 mM), and the rate at which the solution being incubated is shaken can be varied (e.g., 0, 150, or 300 RPM). 
     There may be variations in any of the wash steps, wherein some or all of the constituents in a solution can be resuspended, or not resuspended. Some or all of the constituents in a solution can be separated, for example, by applying a magnetic field to capture magnetic particles. 
     There may be variations in the lysis, reduction, or alkylation steps, wherein a step-wise denaturation can take place. The temperature of the solution can be varied (e.g., 50° C. or 95° C.). There may be steps where proteins or peptides are digested, for example, by using trypsin at various concentrations (1×, 2× concentration of a standard amount of trypsin) for various durations of time (e.g., 3 hours or overnight). In some cases, standard amount for trypsin may range from about 1/10 to about 1/100 mass of trypsin compared to the mass of proteins. Proteins or peptides may be digested in a stepwise fashion, for example, by using Trypsin/LysC. 
     There may be variations in the elution step. The elution volume can be varied (e.g., 75, 150, or 300 μL), clean dry air (CDA) or nitrogen can be supplied at various pressures (anywhere from 0 to 50 psi), different types of solid phase extraction (SPE) plates may be used (e.g., Thermal Fisher SPE plates, iST, C18 or other substrates). 
       FIG.  38    shows a plate layout that can be used with the automated system. The assay plate comprises of two columns, each column corresponding to 5 nanoparticles per sample, plus an additional column for controls. The assay plate comprises 8 rows, wherein each row can be populated with samples.  FIG.  39    shows a deck layout for the automated system. The deck comprises numerous modules, each of which is equipped with serve or perform a particular function. The list of different modules and their descriptions are listed below in Table 6. 
     In some cases, the automated system can be configured to run control experiments.  FIG.  41    shows layout of a plate wherein some partitions are designated to be for running control experiments. Because some of the methods described herein comprise multiple distinct steps, control experiments can be designed to indicate success/failure of a step or a group of steps. The control experiments can comprise process control experiments (PC3+S-003, labeled as AC), digestion control experiments (PC3 (1:5 dilution), labeled as DC), MPE 2  Control experiments (Peptide mix, labeled as CC), and mass spectrometry control experiments (Peptide mix, labeled as MC). These control experiments may be configured run at or between certain steps of an experiment, as shown in  FIG.  40   . MPE 2  may be a component of an automated system that can be used to drive a positive pressure on a filter plate. In some cases, MPE 2  can refer to a Monitored Multi-Flow Positive Pressure Evaporative Extraction module (Hamilton). 
     
       
         
           
               
             
               
                 TABLE 6 
               
             
            
               
                   
               
               
                 Modules for the Automated system 
               
            
           
           
               
               
            
               
                 Number 
                 Description 
               
               
                   
               
            
           
           
               
               
            
               
                 1 
                 CO-RE 96 Probe Head 
               
               
                 2 
                 MPE 2  Filter 
               
               
                 3 
                 Magnet Position 
               
               
                 4 
                 HHS 
               
               
                 5 
                 Magnet Position 
               
               
                 6 
                 Nanoparticle &amp; Plasma Samples Tubes 
               
               
                 7 
                 Plate-Stack Module 
               
               
                 8 
                 TE Buffer Reservoir 
               
               
                 9 
                 Wetting Reagent (100% Methanol) Reservoir 
               
               
                 10 
                 Condition Reagent (H 2 O) 
               
               
                 11 
                 Plate Stack Module 
               
               
                 12 
                 NTR Module 
               
               
                 13 
                 Lid Park Position 
               
               
                 14 
                 Lid Park Position 
               
               
                 15 
                 Plate Carrier Position 
               
               
                 16 
                 Plate Carrier Position 
               
               
                 17 
                 Nested Tip Rack (NTR) Stack Module for Multi-Probe Head 
               
               
                   
                 (MPH) 
               
               
                 18 
                 NTR Stack Module for MPH 
               
               
                 19 
                 NTR Stack Module for MPH 
               
               
                 20 
                 NTR Stack Module for MPH 
               
               
                 21 
                 NTR Stack Module for MPH 
               
               
                 22 
                 NTR Stack Module for Channels 
               
               
                 23 
                 NTR Stack Module for Channels 
               
               
                 24 
                 Inheco Cold Plate Air Cooled (CPAC) 
               
               
                 25 
                 Tip Waste 
               
               
                 26 
                 Compressed O-Ring Expanion (CO-RE) Paddles 
               
               
                 27 
                 Autoload 
               
               
                 28 
                 STARlet Chassis 
               
               
                   
               
            
           
         
       
     
     In some cases, the automated system can be configured run 8 to 16 samples at one time. Biomolecules in a biological sample (i.e., biofluid) can be measured with 5 different approaches per sample. Measurements can be conducted on multiple biofluids including plasma, cell extracts, and lysates. Measurements can be done automatically and be completed 7-8 hours, with peptides ready to be injected into liquid chromatography (LC) or MS for detection. Unbiased measurements allow for reduced LC/MS time, and these measurements can be agnostic of the LC/MS detector or approach, for instance: no more than 30 min gradient length (sample to sample) per fraction using DIA SWATH (data independent acquisition) approach on Sciex 6600+, and/or no more than 1 hour gradient length DDA (data dependent acquisition) approach on Thermo Orbitrap Lumos. DIA SWATH (data independent acquisition) and DDA (data independent acquisition) are modes for MS and differ in the ways that peptides are analyzed and the ways that proteins are computationally reconstructed based on the MS raw data. Because measurements can be done on intact proteins, the measurements may reveal protein-protein interactions in the experimental data. 
     In some cases, the automated system can comprise a 96 well plate that can accommodate up to 16 samples with 5 nanoparticles interrogation. In some cases, the amount of required sample volume can be less than or equal to 240 μL or 40 μL. In some cases, reagents can be stored while retaining stability for greater than 9 months at 4° C. or great than 6 months at room temperature. In some cases, the assay can run within 7 hours. In some cases, MS experiment run time can be within 120 minutes. In some cases, MS experiment may be run with ScanningSWATH. In some cases, ScanningSWATH can refer to a rapid MS acquisition mode for short gradients, down to a few minutes. In some cases, ScanningSWATH can refer to a rapid MS acquisition mode using a scanning quadrupole. In some cases, ScanningSWATH can use Sciex timTOF rapid IMS-IMS, which can involve ion mobility separation and can involve upfront separation of ions based on their charge/dipole and shape properties. In some cases, the automated system can comprise analysis tools including visualization (e.g., group-analysis, PCA) tools or quality control tools, which may be integrated into a cloud-based computing system. In some cases, the protein detection method implemented on the automated system can show 5× superiority (i.e., superiority in the number of protein groups detected) over shallow plasma methods and 3× superiority over depleted plasma methods. In some cases, the protein detection method implemented on the automated system can have 5% improvement in precision (lower CV) over published datasets (e.g., Geyer et al.  Mol. Syst. Biol.  13, 942 (2017). 
     A study was conducted to measure the assay pass rate with the automated systems. Experiments were conducted for a set of 400 biological samples using the substrate shown in  FIG.  41   . Each biological sample was contacted with 5 particle compositions in separate wells (for a total of 2000 wells). 
       FIG.  42    shows experimental results of three automated systems. Identical sets of experiments were conducted on each of the three automated systems, and the results were equivalent. The peptide group counts and the peptide counts were statistically equivalent (n=10). Depth of plasma as a function of plasma proteins ranked by database intensity yielded nearly identical results for each automated system. 
       FIG.  43    shows results of a set of control experiments (i.e., process control, digestion control, MPE 2  control, and mass spec control) conducted with two different automated systems (System-1 and System-2) on multiple plates. The well pass rate/yield was calculated based on the total number of wells for which acceptable number of peptides were detected. The assay pass rate/yield was calculated based on the total number of biological samples for which acceptable number of peptides were detected for all 5 wells with different particle compositions. About 99.9% of the experiments (well pass rate/yield, i.e., percentage calculated by-well) and about 99.5% of the experiments (assay pass rate/yield, i.e., percentage calculated by-sample) were successfully carried out, furthermore, the results between System-1 and System-2 were almost identical. The root cause of failure for the small percentage of unsuccessful (i.e., outlier) experiments were identified to be due to reagent carrier position in those cases. 
       FIG.  44    shows results of experiments conducted with samples from an NSCLC study with the automated system. There were 14 samples which spanned different disease classes, sites, and qualities. The experiments on the samples were run on ThermoFisher (TF) Lumos MS (DDA) using plates processed with the automated system. 1810 protein groups were seen (identified) in 25% of the 14 samples with 2334 total protein groups across any of the 14 samples. The 2334 protein groups were 6.1× greater in amount than the amount found in the digested neat plasma baseline. Experiments conducted with plasma alone consistently detected a smaller number of protein groups than the experiments conducted with nanoparticles panel. Depending on the sample, the experiments with the nanoparticles panel detected from 2.74 times to 6.65 times greater than the experiments conducted with plasma only. Table 7 below lists each sample and its description. 
     
       
         
           
               
             
               
                 TABLE 7 
               
             
            
               
                   
               
               
                 Sample descriptions for NSCLC study. 
               
            
           
           
               
               
               
               
            
               
                 Sample 
                   
                 Sample 
                   
               
               
                 Name 
                 Description 
                 Name 
                 Description 
               
               
                   
               
               
                 021-0004 
                 NSCLC_EARLY 
                 001-0044 
                 HEALTHY 
               
               
                 PC3- 
                 Pool of plasma from 
                 007-0025 
                 NSCLC_EARLY 
               
               
                 minipool 
                 30 healthy 
                   
                   
               
               
                   
                 individuals 
                   
                   
               
               
                 008-0014 
                 CO-MORBID 
                 009-0006 
                 HEALTHY 
               
               
                 020-0091 
                 HEALTHY 
                 005-0032 
                 NSCLC_EARLY 
               
               
                 022-0016 
                 NSCLC_LATE 
                 14-LC-pool 
                 Pool from 14 samples 
               
               
                   
                   
                   
                 used in the NSCLC 
               
               
                   
                   
                   
                 study 
               
               
                 002-0081 
                 CO-MORBID 
                 023-0003 
                 NSCLC_LATE 
               
               
                 014-0066 
                 HEALTHY 
                 029-0005 
                 NSCLC_EARLY 
               
               
                 008-0009 
                 CO-MORBID 
                 018-0004 
                 NSCLC_LATE 
               
               
                   
               
            
           
         
       
     
     Example 11 
     Data Architecture 
       FIG.  45    and  FIG.  46    schematically illustrate a data architecture for managing a platform. The data architecture enables users to integrate data from multiple platforms with the data generated by various instruments (including MS instrument) and automated systems using plates of the platforms disclosed herein. The integrated data is automatically loaded into the data architecture, as shown in  FIG.  45   , which stores and manipulates data to convey appropriate information between computing devices, platforms, and instruments (e.g., MS). 
     The data architecture makes use of barcodes to facilitate the experimental process and the data management process. The data architecture receives barcodes ( 4502 ) from a kit ( 4501 ) containing a biological sample, which conveys information regarding the specific methodology that is to be followed when experimenting with the samples within the kit. The barcodes ( 4502 ) convey the specific analysis that is to be carried out when analyzing the experimental results. The barcodes ( 4502 ) convey the plate layout ( 4506 ) information to the customer laboratory information systems (LIMS,  4508 ). The barcodes ( 4502 ) convey information to the inventory management system ( 4503 ) which materials are to be used. 
     The data architecture coordinates various instruments and systems to carry out some of the methods disclosed herein. Metadata (e.g., date kit was received, from whom it was received from, and experimental log files) and output data (experimental results) are communicated through appropriate channels so that systems and devices (e.g., protein analysis platforms ( 4504 ), MS ( 4509 ), personal computers ( 4512 ), customer LIMS ( 4508 )). The data architecture can coordinates experiments and analysis through digital communication channels. Mass spec ( 4509 ) results ( 4510 ) can be passed to the cloud ( 4513 ). The data architecture allows users to integrate data from multiple instruments ( 4504 ) with the data generated by running a plasticware of the present disclosure. Log files ( 4505 ) comprising experimental results, histories, and other metadata are sent to the cloud ( 4513 ). Results of experiments are analyzed on the cloud ( 4513 ) to produce genomic or proteomic information ( 4511 ) which is communicated to the customer LIMS ( 4508 ). 
     In another example, as shown in  FIG.  46   , barcodes of a kit ( 4601 ) are associated with various articles, such as plasticware ( 4602 ), nanoparticles ( 4603 ), reagents ( 4604 ), kits ( 4605 ). The barcodes can be used to track the inventory of these articles through an inventory management system ( 4606 ). The barcodes may also be used in quality control and/or troubleshooting of any of the various methods disclosed herein. The barcodes may be communicated to an automated system ( 4607 ) for coordinating an assay. 
     The automated system ( 4607 ) receives also the sample barcode ( 4614 ) from the customer LIMS ( 4612 ) and conveys the plate layout ( 4611 ) to the customer LIMS. The automated system also conveys log files (which can capture experimental history, outcomes, etc.) to the internet ( 4609 ) where a logging system stores ( 4610 ) the log files. The customer LIMS ( 4613 ) can convey experiment information to a LC-MS machine ( 4613 ) to generate data, which is received back to the customer LIMS ( 4613 ). The customer LIMS conveys MS files ( 4616 ), MS file name and plate layout ( 4615 ) to the cloud ( 4618 ). The customer LIMS also conveys sample information ( 4617 ) to the cloud ( 4618 ). 
     Example 12 
     Analytics and GUI 
     This example describes various analytical methods and graphical elements for carrying out or displaying results of the analytical methods. 
       FIG.  47    illustrates a graphical user interface (GUI) comprising a set of buttons with which a user can interact with. A GUI such as shown in  FIG.  47    can be accessed through a laptop, smart phone, or a computer installed into an automated system. 
       FIG.  48    and  FIG.  49    illustrates various analytical tools that may be incorporated into a pipeline, as described herein. Analytical tools comprises a data screen ( 4801 ) for listing of experiments (e.g., a columns for sample used, sample volume, particle used, the instrument, the MS protocol), a plot showing protein group counts and peptide intensity distribution for 2 particles against specific sample and conditions ( 4802 ), an upset plot showing overlap of protein groups found under different conditions and subsets of conditions ( 4803 ), a plot of proteins found mapped against their reference abundance ( 4804 ), a plot showing peptide quantity results for two particles under different conditions ( 4805 ), a controls monitor ( 4901 ), and a clustering algorithm and visualization ( 4902 . In some cases, the analytical tools may display one or more graphical elements on a GUI. In some cases, the analytical tools may comprise a tool for analyzing post-translation modifications, sequence variants, differential exons, protein-protein interactions, or any combination thereof. 
     Example 13 
     Biomolecule Abundance Determination from Raw Mass Spectrometry Data 
     Mass spectrometric signal intensities often depend on a number of factors including analyte structure, sample conditions, and methodology (e.g., ionization method, length of chromatography gradients). Accordingly, two analytes (e.g., fragments of a single protein) derived from a single sample may generate different mass spectrometric signal intensities, a phenomenon often referred to as “flyability.” This inherent signal variation often renders signal intensity comparison and analyte abundance determination infeasible without time and resource intensive spike-in, calibration series, or tagging experiments. 
     This example provides a method for determining flyability values by comparing signal intensities across multiple samples, and then for using these flyability values to determine absolute abundances for biomolecules in a sample. While the foregoing example pertains to two biomolecules (e.g., two protein variants), this method can be extended to any number of biomolecules, so long as the biomolecules (1) share a common signal and (2) each comprise a unique signal (i.e., not overlapping with signals from other species). For example, this method could be used to determine abundances of 6 sialic acids sharing a common signal and each having a unique signal. Furthermore, the method may be extended to groups of biomolecules, such as alleles comprising multiple isoforms or classes of proteins sharing common sequences. 
     In this example, three individuals sharing a common heterozygous allele ‘A’ with allele ‘A ref ’ and allele ‘A alt ’ submit plasma samples for mass spectrometric analysis. Both alleles share a common signal and each have a unique signal. Assuming that the flyability of each signal is linear, the abundance of each allele (A alt  and A ref ) and the total abundance of heterozygous allele A can be expressed as the products of their flyabilities and associated signal intensities. For example, if A alt  is associated with signals S 1  and S2 corresponding to peptides P 1  and P 2  and A ref  is associated with signals S 1  and S 3  corresponding to peptides P 1  and P 3 , then the abundance of A alt  may be expressed as the product of S 2  intensity and P 2  flyability, the abundance of A ref  may be expressed as the product S 3  intensity and P 3  flyability, and the combined abundances of A alt  and A ref  (the total abundance of heterozygous allele A) may be expressed as the product of S 1  intensity and P 1  flyability. 
     The flyabilities can be assumed to be constant across the three samples. Accordingly, if the intensities of the signals associated with A alt , A ref , and A vary between the three samples, the flyability value for each signal may be uniquely determined. As the abundances A, A alt , and A ref  are the product of flyability and signal intensity, the abundances of A, A alt  and A ref  may be determined from the mass spectrometric data alone, and without further sample manipulation or calibration data. 
     Example 14 
     Deep and Broad Proteome Coverage Using Particles 
     Proteins in a biological sample (e.g., plasma) may comprise a wide concentration range or a dynamic range. Even in samples where high abundance proteins are reduced in amount (e.g., depleted plasma), detecting proteins deeply (both high abundance proteins and low abundance proteins) and broadly (detecting the broad variety of proteins with minimal selective bias towards certain proteins) can be challenging. This example shows the ability of a particle-based proteomic assay to provide deep and broad coverage of the proteome. 
       FIG.  21    shows plots for a database of MS intensities, MS intensities detected in a depleted plasma without using nanoparticles of the present disclosure, a composite (e.g., combined) MS intensities detected in a depleted plasma using a panel of 5 nanoparticles of the present disclosure, and 5 independent MS intensities detected in a depleted plasma each using one of the 5 nanoparticles of the present disclosure. Plasma samples from 141 subjects with NSCLC were used for this study. Proteins were ordered by the rank of MS intensities in the database. Proteins were plotted if the proteins were present in at least 25% of samples. In the composite plot, the color intensity indicates the highest detected value from the 5 distinct nanoparticles. The composite plot shows that the nanoparticles detected the entire spectrum of available plasma proteins more completely. Meanwhile, each individual nanoparticle also detected more proteins than direct MS analysis of the depleted plasma. Individual nanoparticles were able to assay nearly the full range of the plasma proteome. In some cases, the panel of nanoparticles may be optimized to cover the entire range of the proteome or a specific portion of the proteome. MS experiments on depleted plasma using nanoparticles may enable detecting less abundant proteins and/or detecting the proteome more completely. 
     Example 15 
     Allelic Distributions Across Subject Samples 
     This example covers variant protein detection with mass spectrometry. Mass spectrometric biomolecule corona analyses were performed on 29 samples from separate subjects with a 10-particle panel outlined in TABLE 8 below. A total of 464 peptide variants were detected using personalized mass spectrometry search libraries from the 29 subjects. Genetic variants captured within the 464 peptide variants were then binned based on if the variant is heterozygous or homozygous (for either the reference or alternative allele). 
     
       
         
           
               
             
               
                 TABLE 8 
               
             
            
               
                   
               
               
                 Particle panel for exome search library guided analysis 
               
            
           
           
               
               
            
               
                 Batch No. 
                 Description 
               
               
                   
               
               
                 S-003 
                 Silica-coated superparamagnetic iron oxide NPs (SPION) 
               
               
                 S-006 
                 N-(3-Trimethoxysilylpropyl)diethylenetriamine coated SPION 
               
               
                 S-007 
                 poly(N-(3-(dimethylamino)propyl) methacrylamide) 
               
               
                   
                 (PDMAPMA)-coated SPION 
               
               
                 S-010 
                 Carboxylate, PAA coated SPION 
               
               
                 P-033 
                 Carboxylate microparticle, surfactant free 
               
               
                 P-039 
                 Polystyrene carboxyl functionalized 
               
               
                 P-047 
                 Silica 
               
               
                 P-053 
                 Amino surface microparticle, 0.4-0.6 μm 
               
               
                 P-065 
                 Silica 
               
               
                 P-073 
                 Dextran based coating, 0.13 μm 
               
               
                   
               
            
           
         
       
     
       FIG.  61    summarizes counts of detected genetic variants corresponding to heterozygous (central bar in each plot, ‘het’) and homozygous alleles corresponding to reference (right bar in each plot, ‘ref’) or alternate (left bar in each plot, ‘alt’) allelic variants. Each plot corresponds to a unique sample, with the plots collectively covering each of the 29 samples. As can be seen from the plots, the combined genomic and proteomic detection method was able to observe and distinguish homozygous and heterozygous allelic expression. The majority of samples exhibited greater abundances of heterozygous than homozygous alleles. 
       FIG.  62 A  provides a histogram of alternate allele frequencies, as based on the gnomAD human reference genome consortium, for the 464 peptides observed across the 29 samples.  FIG.  62 B  summarizes the alternate allele frequencies grouped into bins spanning 10% increments. While the majority of alternate alleles are properly annotated based on their frequencies, 89 of the observed peptides corresponded to alleles with higher ‘alternate’ than ‘reference’ allele frequencies. With the goal to stratify variants by commonness,  FIG.  63    corrects for this discrepancy by re-annotating the homozygous alleles as major forms having a relative frequencies of greater than 0.1 (central column in each plot, ‘&gt;’) and minor forms having relative frequencies of less than or equal to 0.1 (leftmost column in each plot, (‘&lt;=’). As can be seen from these plots, the exome sequence-guided proteomic analyses resolved low and high frequency homozygous alleles. 
       FIG.  64    summarizes detected single amino acid polymorphism variants with alternate allele frequencies of less than 0.01.  FIG.  64 A  provides a table listing the five detected variants, with column 2 providing the detected mutation, column 3 indicating the number of subjects in which the variant was detected, and column 4 providing the gene name for each variant.  FIGS.  64    B-F provide relative counts of ‘reference’ (upper) and ‘alternate’ (lower) forms in the 29 samples.  FIGS.  64    G-K provide relative mass spectrometric intensities for the ‘reference’ (right) and ‘alternate’ (left) variant forms in the 29 samples. As can be seen from these plots, allele abundances can differ across variant forms. For example, for SERPINA1 (data shown in  FIGS.  64    C and H), the ‘alternate’ form of the allele is nearly one order of magnitude more abundant than the ‘reference’ form in samples in which allelic expression was detected. Conversely, for APOB (data shown in  FIGS.  64    E and J), the ‘reference’ form has about 1 order of magnitude greater abundance than the ‘alternate’ forms in samples in which allelic expression was detected. 
       FIGS.  65    A-B indicate overlap between detected heterozygous alleles across the 29 samples.  FIG.  65 A  provides sets of peptides ordered by count. As can be seen from the plot, the majority of the high-count groups correspond to single samples, indicating that unique heterozygous alleles were detected for each sample.  FIG.  65 B  provides sets of peptides ranked by degree of overlap. As can be seen from this plot, no set of two peptides was detected in more than 7 of the 29 samples. 
       FIGS.  66    A-B indicate overlap between detected homozygous alleles across the 29 samples for variant peptides with alternate allele frequencies of less than 0.5.  FIGS.  67    A-B indicate overlap between detected homozygous alleles across the 29 samples for variant peptides with alternate allele frequencies greater than 0.5. As can be seen from the plots, many variant peptides are unique to each subject, while a small number of variant peptides are shared across many subjects. 
     Example 16 
     Low Volume Proteomics 
     A challenge in low volume proteomics is the limited amount of starting material that can be assayed. Some examples of low volume proteomics may be proteomics for single cells, a few cells, a small sample/biopsy of tissue, or select cellular components of a cell (e.g., the nucleus). Various preprocessing steps, assay steps, or analysis steps may each contribute to a loss of protein material that can take away from the overall yield of proteins that are detected by a proteomics method. This example demonstrates several methods and compositions that can improve the yield of proteins detected by some of the methods described herein. 
     Particle Multiplexing 
     A biological sample can comprise a plurality of proteins, each with a specific set of thermodynamic affinities and binding kinetics against a variety of nanoparticles. Therefore, by contacting a biological sample of a low volume biological sample (e.g., a single-cell, which may have a small amount of detectable protein material) to a nanoparticle composition comprising a plurality of different nanoparticle types, a greater amount of biomolecules in the biological sample may be detected. That is, a greater overall yield of proteins detected can be achieved because the proteins are able to interact with a wider variety of surfaces, which provide each protein more varieties of surfaces to interact with that the protein may favorably bind or adsorb onto. As a result, each nanoparticle in the plurality of different nanoparticle types may bind a set of proteins specific to each particle type. The following paragraph describes an example experimental procedure that may be carried out to implement this idea. 
     A single cell is lysed and centrifugated. The supernatant is collected and the pellet is discarded. The supernatant is transferred into a well comprising nanoparticle composition comprising 5 different nanoparticle types. The well is incubated at a pH of 3.8 and at an ionic strength of 50 mM for 2 hours while vibrating the well. After incubation, the particles are washed. The protein corona on the particles are first denatured at a temperature of 50° C. for 30 minutes and then digested with trypsin for 2 hours to convert the proteins into peptides. The peptides are then eluted with 100 μL of buffer solution and injected into a MS machine. 
     Sample Multiplexing 
     Non-specific adsorption can involve a thermodynamic phenomenon, wherein thermodynamic forces balance to distribute chemicals around in a chemical system. A concept that is employed in this example is to increase the chemical potential of proteins in a solution that is contacted with nanoparticles, so that more proteins are favorably adsorbed onto nanoparticle surfaces. By multiplexing a plurality of low volume biological sample (e.g., a plurality of single-cells, which may individually or collectively have a small amount of detectable protein material), each labeled with a specific tag, the chemical potential of proteins in a solution for an assay can be raised, leading to more protein adsorbed onto nanoparticles. Meanwhile, the cellular origin of each protein may be traced based on the specific tag that it is found with. The following paragraph describes an example experimental procedure that may be carried out to implement this idea. 
     16 single-cell samples are collected, and each are labeled with a specific tandem mass tag protein. The samples are combined (pooled) into a single well, then they are lysed and centrifugated. By combining the samples, there exists a higher concentration of certain types of peptides or proteins in the well. The supernatant is collected and the pellet is discarded. The supernatant is transferred into a well comprising a nanoparticle composition. The well is incubated at a pH of 3.8 and at an ionic strength of 50 mM for 2 hours while vibrating the well. After incubation, the particles are washed. The protein corona on the particles are first denatured at a temperature of 50° C. for 30 minutes and then digested with trypsin for 2 hours to convert the proteins into peptides. The peptides are then eluted with 100 μL of buffer solution and injected into a MS machine. 
     Reducing Surface Contact 
     A biological sample may come into contact with numerous surfaces throughout an experimental procedure. Proteins of a biological sample may adsorb onto surfaces even when such adsorption events can incur losses in detection yield. By reducing the amount of surface area that the biological sample comes into contact with (other than the surfaces of particles) and by using engineered surfaces that minimize unwanted adsorption of protein, the overall protein yield may be increased. This example describes an example experimental procedure for reducing the amount of proteins that may be lost to surfaces during an experiment. 
     A single cell is placed in a well comprising a fluorinated surface, e.g., poly(tetrafluoroethylene). Fluorinated surfaces can comprise both hydrophobic (for repelling water and polar moieties) and oleophobic (for repelling oily and nonpolar moieties) properties. Thus, less protein is expected to adsorb on the well surface. The single cell is lysed and centrifugated. The pellet is removed while the supernatant remains within the well. The well is incubated at a pH of 3.8 and at an ionic strength of 50 mM for 2 hours while vibrating the well. After incubation, the particles are washed within the well. The protein corona on the particles are first denatured at a temperature of 50° C. for 30 minutes and then digested with trypsin for 2 hours to convert the proteins into peptides within the well. The peptides are then eluted with 100 μL of buffer solution within the well and injected into a MS machine. 
     While the above strategies for low volume proteomics have been given examples in the context of single-cell proteomics, various other biological samples with low-volume may also be used. 
     Example 17 
     Reporter Channels 
     In some MS experiments, statistical analysis of detected moieties yields confidence intervals for the relative amounts of proteins available in a sample. In some cases, it may be challenging to determine a tight confidence interval for the relative amount of a low abundance protein in a biological sample. In some cases, it may be challenging to determine a tight confidence interval for the relative amount of proteins in cases the amount of protein available in a sample is very low, for instance, with a single cell. In these cases, reporter channels can be used to spike the biological samples with known amounts of known proteins. The reporter channels improve the signal strength of low-abundance proteins, for instance, cytokines. The following example describes an example experimental procedure for using reporter channels. 
     A single cell is lysed and centrifugated. The supernatant is collected and the pellet is discarded. The supernatant is transferred into a well comprising nanoparticle composition comprising 5 different nanoparticle types. The well is incubated at a pH of 3.8 and at an ionic strength of 50 mM for 2 hours while vibrating the well. After incubation, the particles are washed. The protein corona on the particles are first denatured at a temperature of 50° C. for 30 minutes and then digested with trypsin for 2 hours to convert the proteins into peptides. The peptides are then eluted with 100 μL of buffer solution and combined with a solution comprising proteins for reporter channels (e.g., various cytokines). The mixture is injected into a MS machine. The resultant signal of the MS will have dominant peaks from the reporter channel proteins with contributions from the single-cell. 
     Example 18 
     Single-Cell Spatial Proteomics 
     A biological cell can comprise a heterogeneous environment, wherein the amounts and the kinds of proteins that may be found can vary from one cellular location to another. This spatial heterogeneity in protein distribution within a cell can be revealed with single-cell spatial proteomics. In some cases, single-cell spatial proteomics may also face similar challenges with low volume proteomics, and thus, similar strategies may be employed for single-cell spatial proteomics. The following describes an example experimental procedure for conducting single-cell spatial proteomics. 
     Tandem mass tags are used to label proteins that are known to be localized at different compartments/portions of a single cell. For instance, the mitochondria is tagged with one tandem mass tag, the Golgi apparatus is tagged with another tandem mass tag, and the centrosome is tagged with yet another tandem mass tag. The cell is lysed and fractionated into multiple subsamples. Each subsample then comprises a different amount of each tandem mass tag, which will then directly correlate with the amount of protein originating from the tagged locations that the subsample comprises. 
     Each subsample is transferred into individual wells, each comprising nanoparticle composition comprising 5 different nanoparticle types. The wells are incubated at a pH of 3.8 and at an ionic strength of 50 mM for 2 hours while vibrating the wells. After incubation, the particles in each well are washed. The protein corona on the particles in each well are first denatured at a temperature of 50° C. for 30 minutes and then digested with trypsin for 2 hours to convert the proteins into peptides. The peptides in each well are then eluted with 100 μL of buffer solution and is injected into a MS machine. The MS results will reveal a specific protein profile for each subsample. The amount of tandem mass tag detected in each profile will correlate with the amount of protein derived from the location that each tandem mass tag originates from. Therefore, analysis of the MS results can show the heterogeneous distribution of proteins in a single cell. 
     Example 19 
     Performance Evaluation of Label Free Quantitation, Sample Pooling, and TMT Multiplexing Approaches 
     Human plasma can comprise a large dynamic range of circulating proteins and a broad diversity of proteoforms. Comprehensive characterization of the plasma proteome is a challenge that the present disclosure addresses. This example illustrates that by combining immunodepletion of high abundance proteins, peptide fractionation, and sample multiplexing approaches (e.g., TMT), throughput of analysis and sensitivity can be significantly improved. In this example, the performance enhancement of using TMT multiplexing is systematically evaluated. Advancements in sample preparation, improved mass spectrometry instrument sensitivity and speed, enables the quantification of thousands of proteins from plasma with insubstantial compromise on throughput or reproducibility, creating opportunities to detect robust protein biomarkers for complex diseases. This example describes the performance of label-free and TMT multiplexing methods with a set of control plasma samples for deep plasma proteomic analysis. 
     Sample Preparation 
     A total of 4 different pooled human plasma samples were assayed using a panel of 5 nanoparticles using a 96 well-plate and an automated system described herein.  FIG.  68    schematically illustrates the 96-well plate configuration. The 4 human plasma samples (plasma controls: PC2, PC3, PC4, and PC5) were each distributed on a 96-well plate as shown. Each well received 250 microlites (μL) of plasma. 
     The human plasma samples were contacted with nanoparticles that were provided in a kit, and then the nanoparticles were separated from the supernatant. For each well, proteins adsorbed on the nanoparticles were digested and desalted to yield peptides. 
     Label Free Quantitation (LFQ) 
     Following sample preparation, the peptides were quantified by nanodrop and analyzed with LFQ. The amount of peptide quantified for each nanoparticle is shown in  FIG.  69   . A total of 250 nanograms (ng) of peptides was separated in a 60 min gradient using a C18 Aurora column (IonOpticks) mounted on a Proxeon EASY nanoLC, coupled to an Orbitrap Fusion Lumos equipped with FAIMS Pro Interface (CV −60, −80). Data was analyzed with SpectroMine software (Biognosys). 
       FIG.  70    shows the number of protein group identifications using LFQ. Each nanoparticle contributed to the identification of between 1,100 and 1,700 protein groups. In combination, a total of 2,402 protein groups were identified. 
       FIG.  71    shows the intersection size of protein group identifications as a function of different particle combinations. The intersection size is the number of protein groups that are identified using information from a given set of particles. For instance, the first column shows that 580 protein groups were identified using spectroscopic data obtained from all 5 nanoparticles. Meanwhile, the last column shows that 243 protein groups were identified using spectroscopic data solely from NP5. 
       FIG.  72    shows the percentage of protein groups that were identified using 1, 2, 3, 4, or 5 nanoparticles. About one-quarter of the protein groups were identified using information only from one particle. About three-quarters of the protein groups were identified using information from a plurality of particles. 
     Pooled Nanoparticle LFQ 
     Following sample preparation, peptide samples were pooled. The pooling procedure is schematically illustrated in  FIG.  73   . A total of 250 ng of peptide of each pooled NP sample was separated in an 80 min gradient using a C18 Aurora column mounted on a Proxeon EASY nanoLC coupled to an Orbitrap Fusion Lumos equipped with FAIMS Pro (CV −50, −70, and −80). 
     Data was analyzed with Spectromine. 
       FIG.  74    shows the number of protein group identifications for each pooled sample. Between 650 and 1250 protein groups were identified for each pooled sample (PC2, PC3, PC4, and PC5). 
     In a separate experiment, the pooled sample was processed with two different automated systems on two separate days each, resulting in a total of 4 batches (plates). Each batch contained 16 replicates of the pooled sample enriched with 5 nanoparticles to produce 80 total wells of tryptic digested and desalted peptides for downstream LC-MS/MS analysis. Tryptic peptides were analyzed using a Thermo Fisher Scientific Orbitrap Exploris 480 Mass Spectrometer in a DDA mode with a 30-minute LC gradient (40 hours per batch of 16 plasma samples). LC-MS/MS data files were processed using MaxQuant, applying 1% FDR cutoff at the protein and peptide levels.  FIG.  85    shows number of detected protein groups and protein group intensity CVs across the batches. Summary statistics of protein group counts (mean+/−std. dev., % CV) are shown for each automated system used and day ( 8501 ), for each automated system used across days ( 8502 ), and across automated systems and days ( 8503 ). The protein group counts show excellent reproducibility with CVs below 3%. Summary statistics of protein group CVs are shown, where the upper number ( 8504 ) corresponds to the number of protein groups represented in the distributions (present in at least 3 replicates), and the lower number ( 8505 ) the median intensity CV. The median intensity CVs are all below 25%, and below 20% for most nanoparticles. 
     Pooled Nanoparticle (NP) Tandem Mass Tag (IMT) 
     Following nanoparticle separation using the same 5-nanoparticle panel, protein digestion, and desalting, peptides from each fraction of a given biological replicate were pooled together and quantified by nanoDrop. The pooling procedure is schematically illustrated in  FIG.  74   . Two micrograms of each sample (e.g., tryptic peptides) were labeled with one of the TMTpro 16plex reagents (The 16 TMTpro reagents are illustrated in  FIGS.  75 A- 75 B ). The samples were multiplexed, desalted, and fractionated by high pH reverse phase (hpRP) in 48 fractions, and concatenated into final 24 or 12 fractions.  FIG.  76    schematically illustrates the fractions. 
     A total of 250 ng of peptide of each hpRP fraction was separated in a 100 min gradient using a C18 Aurora column (IonOpticks) mounted on a Proxeon EASY nanoLC, coupled to an Orbitrap Fusion Lumos equipped with FAIMS Pro (CV −45, −65, and −80). Data was analyzed with SpectroMine software (Biognosys). 
     Compared to the LFQ method and the Pooled NP LFQ method, the Pooled NP TMT method reached the highest depth of the plasma proteome. When 24 fractions were analyzed over the course of about 48 hours, a total of 2,785 protein groups were identified (NPP-TMT-BEH24). About 78% of the protein groups had 2 or more peptides within the group. The throughput was 8 samples per day. 
     When the throughput was increased to 16 samples per day, approximately 1,784 proteins were identified, with about 74% of the protein groups having 2 or more peptides within the group (NPP-TMT-BEH12). 
     The average number of identifications of single-shot neat plasma samples and Pooled NP samples is 309 and 946 protein groups, respectively.  FIG.  77    shows a plot comparing these methods against the Pooled NP TMT method. 
     The reproducibility of the Pooled NP TMT method was evaluated by calculating CVs across the TMT channels for 4 different preparations (i.e., experiments conducted on different days using different plates) with the same plasma pool. The violin plots shown in  FIG.  78    show TMT channel CV distribution across PSMs for the Pooled NP TMT workflow using 24 fractions. Overall, approximately 86% of the features were detected across all 4 batches. Of these features (about 127,000) 95% showed a CV (%) lower than 37%. 
       FIG.  79 A  shows CV of PSM detected with the Pooled NP TMT method using different plates. The results show that variability of about 16% is attributed to experimental (different plates) plus technical variation (inherent to the assay).  FIG.  79 B  shows CV of PSM detected with the Pooled NP TMT method across different experimental replicates. The results show technical variability (inherent to the assay) of about 12%. 
       FIG.  80    shows the CV of PSM detected with the Pooled NP TMT using two different automated systems for the 5 nanoparticles. CV was computed for protein groups with greater than 3 replicates. 
     A protein accession comparison of the 24 fraction Pooled NP TMT workflow data and the 3,509 PeptideAtlas plasma proteins showed an overlap of 2,072 proteins. 
     Protein concentrations were estimated using the Human Protein Atlas (HPA) by immunoassaying 220 proteins from the 24 fraction Pooled NP TMT workflow data, and ranking them according to their respectively blood concentrations (pg/mL). Plasma proteins spanning 9 orders of magnitudes in the plasma were detected, including 40 cytokine activity proteins and several members of the tumor necrosis factor (TNF) superfamily. The result is illustrated in  FIG.  81   . Among proteins detected in this dataset were numerous low abundance cytokine signaling proteins such as CD4, CD40L, CXCL2, members of TNF superfamily such as TNFSF13, TNFRSF6B, and numerous MHC proteins. 
     Many detected proteins are potential biomarkers for various diseases including 456 cancer-related proteins. Many detected proteins are potential drug targets for various diseases including 168 FDA-approved drug targets.  FIG.  82    shows protein group MS1 intensities, ranked from highest to lowest. Some potential biomarkers identified using HPA are labeled in this plot.  FIG.  83    shows bins for identified protein groups, as classified using HPA. 
     To determine which functional protein classes were identified, functional annotations of Gene Ontology Molecular Function were mapped to Uniprot IDs. The violin plot in  FIG.  84    shows the diversity of functional annotations that were captured, including “cytokine activity”, “integrin activity”, “hormone activity”, and “growth factor receptor binding”. The dots on the violin plot show the MS1 intensity of proteins within each functional category. The colors of the violin plot represent the overlap in percentage between the 24 fraction Pooled NP TMT workflow data and the members of each category. The number of protein groups identified falling within each category is displayed on the right side of each violin plot. 
     In a separate experiment, two different control pooled human plasma samples were processed with an automated system in 4 batches prepared on 4 different days. Tryptic peptides enriched with 5 nanoparticles were pooled together in one fraction and labeled with one of the TMTpro™ 16plex reagents followed by peptide fractionation (24 high pH RP fractions) and LC-MS/MS analysis on a Thermo Fisher Scientific Orbitrap Fusion Lumos Tribrid Mass Spectrometer and a FAIMS Pro Interface. LC-MS analysis were performed with a 48-hour workflow for 16 samples analysis, with 2-hr LC separation and 3 CV (compensation voltage) FAIMS peptide separation.  FIG.  87 A  shows peptide and  FIG.  87 B  shows protein group intensity CVs computed for two different control pooled plasma samples within and between batches (plates) for the TMTpro 16plex runs. Peptide and protein group CVs are below 13% and 10%, respectively, across plates and a few points lower within plates showing an overall high degree of reproducibility across about 16,000 peptides and about 2700 protein groups. 
     It is shown that using an automated workflow, up to 16 biofluid samples can be processed and analyzed with LC-MS/MS with 40-48-hours workflow (LFQ and TMTpro 16plex). Using TMT combined with peptide fractionation results in about 3,000 protein group identifications, of which about 80% of the protein groups comprises 2 or more peptides. Reproducibility experiments show that approximately 86% of features are detected across 4 different batches experimented on 4 different days, with a CV below 15% or 10% (PSM level). Label-free performance across four plates run on two different instrument and two different days were evaluated with protein group intensity for most NPs with CVs &lt;20%, enabling large scale plasma proteomics without compromising depth or precision. Plasma proteins spanning 9 orders of magnitude were detected, including 40 cytokine activity proteins and several members of TNF superfamily. The results illustrate that large-scale plasma proteomics studies are enabled without reducing depth or precision of proteomic assays. 
     While preferred embodiments of the present disclosure have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the disclosure. It should be understood that various alternatives to the embodiments of the disclosure described herein may be employed in practicing the disclosure. It is intended that the following claims define the scope of the disclosure and that methods and structures within the scope of these claims and their equivalents be covered thereby.