Patent Description:
Embodiments of the disclosure generally relate to methods for biochemical analysis.

Human tissues are often highly heterogeneous, consisting of intermixed cellular populations and morphological substructures. Mass spectrometry (MS)-based proteomic analyses can require samples comprising thousands to millions of cells to provide an in-depth profile of protein expression, which can severely limit the ability to analyze small samples and resolve microheterogeneity within tissues. While MS sensitivity has steadily improved, the inability to effectively prepare and deliver such trace samples to the analytical platform has proven limiting.

Document <CIT> discloses a method for preparing a biological sample, comprising the steps of: obtaining a biological tissue sample via tissue laser-capture microdissection;.

Accordingly, improved methods are needed for proteomic analysis using small sample sizes.

Provided herein, inter alia, are compositions and methods for processing and analysis of small cell populations and biological samples (e.g., a robotically controlled chip-based nanodroplet platform). In particular aspects, the methods described herein can reduce total processing volumes from conventional volumes to nanoliter volumes within a single reactor vessel (e.g., within a single droplet reactor) while minimizing losses, such as due to sample evaporation.

Embodiments described herein can provide advantages over existing methods, which can require samples including a minimum of thousands of cells to provide in-depth proteome profiling. As described herein, embodiments of the disclosure can dramatically enhance the efficiency and recovery of sample processing through downscaling total processing volumes to the nanoliter range, while substantially avoiding sample loss.

The present invention provides a method according to claim <NUM>. Preferred embodiments are provided in the dependent claims.

Described herein are methods for preparing a biological sample, comprising obtaining a biological sample, and providing a platform. The platform includes at least one reactor vessel having one or more hydrophilic surfaces configured for containment of the biological sample, wherein the hydrophilic surfaces have a non-zero, total surface area less than <NUM><NUM>. In other embodiments, the hydrophilic surfaces of the at least one reactor vessel have a total surface area of less than <NUM><NUM>.

The method includes transferring a first volume (a non-zero amount less than <NUM> nL) of the biological sample to a single reactor vessel. Furthermore the methods include processing the biological sample in the single reactor vessel to yield a processed sample, and collecting a second volume of the processed sample (e.g., the second volume is a fraction of the first volume ranging from about <NUM> to about <NUM> %).

The biological sample include tissues and can include at least one of biopsies, cell homogenates, cell fractions, cultured cells, non-cultured cells, whole blood, plasma, and biological fluids. In embodiments, the biological sample is less than <NUM> nL. In other embodiments, the biological sample is less than <NUM> nL.

In various aspects, the methods of obtaining the biological sample may include, for example, dispensing cellular material from suspension and fluorescence-activated cell sorting.

In further aspects, the method may further comprise at least two reactor vessels, wherein the at least two reactor vessels are separated by a hydrophobic surface.

In embodiments, the biological sample for the methods described herein may include a non-zero amount of cells less than <NUM> cells, less than <NUM> cells or less than <NUM> cells.

In other embodiments, the methods described herein further include analyzing the collected second volume (e.g., the second volume is a fraction of the first volume ranging from about <NUM> to about <NUM> %) of the processed biological sample, and the analyzing step is configured to identify at least one unique species within the processed biological sample. In other embodiments, the analyzing step identifies at least <NUM>,<NUM> unique species, at least <NUM>,<NUM> unique species, or at least <NUM>,<NUM> unique species. In various embodiments, analyzing can identify greater than <NUM>,<NUM> unique species from <NUM> or less cells.

In embodiments, the unique species may include at least one of proteins or fragments thereof, lipids, or metabolites.

In other embodiments, the methods described herein further include analyzing the collected second volume, and wherein the analyzing step comprises mass spectrometry or flow cytometry.

In aspects, the platform of the methods described herein includes a glass chip. In other aspects, the glass chip is pre-coated, e.g., with chromium, aluminum, or gold. In other aspects, the glass chip includes a substrate containing the at least one reactor vessel, a spacer containing an aperture positioned on the substrate, and a cover positioned on the spacer, wherein the aperture is dimensioned to surround the at least one reactor vessel when the spacer is positioned on the substrate. In other aspects, the steps involving dispensing and aspiration of sample and processing reagents are performed in a humidity-controlled chamber (e.g., which is maintained from about <NUM>% to about <NUM>%.

The methods described herein include processing the biological sample. Processing the biological sample may include at least one of cell lysis, analyte extraction and solubilization, denaturation, reduction, alkylation, chemical and enzymatic reactions, concentration, and incubation.

In aspects, the methods described herein include collecting the processed sample into a capillary. In other aspects, collecting the processed sample into a capillary includes aspirating the processed sample into the capillary and washing the single reactor vessel with a solvent. Additionally, the capillary may be sealed from the external environment after the processed sample is collected therein. The methods described herein include biological samples comprising of tissues. The tissue includes laser-capture microdissected tissues, e.g., having dimensions less than about <NUM>.

Each of the aspects and embodiments described herein are capable of being used together, unless excluded either explicitly or clearly from the context of the embodiment or aspect.

Those skilled in the art will understand that the systems, devices, and methods specifically described herein and illustrated in the accompanying drawings are non-limiting exemplary embodiments and that the scope of the disclosed embodiments is defined solely by the claims.

Embodiments of the present disclosure relate to methods for preparation and analytical analysis of biological samples. More particularly, embodiments of the present disclosure relate to preparation and analysis of biological samples having nanoscale volumes, interchangeably referred to herein as nanoPOTS: Nanowell-based Preparation in One-pot for Trace Samples. As discussed in detail below, increased efficiency and recovery of proteomic sample processing by downscaling total preparation volumes to the nanoliter range (e.g., from the range of about <NUM>µL to about less than <NUM>µL).

Described herein, proteomic sample preparation and analysis for small cell populations can be improved, for example by reducing the total processing volume to the nanoliter range within a single reactor vessel. The present platform, NanoPOTS, can enable each sample to be processed within a <NUM> nL or smaller droplet that is contained in a wall-less glass reactor having a diameter of approximately <NUM> (e.g., total surface area of about <NUM><NUM>). Compared with a <NUM>µL typical sample preparation volume in <NUM>-centrifuge tubes (<NUM><NUM>), the surface area was reduced by a factor of ~<NUM>, greatly reducing adsorptive losses.

When combined with analysis by ultrasensitive liquid chromatography-mass spectroscopy (LC-MS), biological samples prepared using nanoPOTS can enable deep profiling of greater than about <NUM> proteins from as few as about <NUM> HeLa cells, a level of proteome coverage that has not been previously achieved for fewer than <NUM>,<NUM> mammalian cells. Beneficially, NanoPOTS can enable robust, quantitative and reproducible analyses and provide in-depth characterization of tissue substructures by profiling thin sections of single human islets isolated from clinical pancreatic specimens.

One of the most dramatic technological advances in biological research has been the development of broad "omics-based" molecular profiling capabilities and their scaling to much smaller sample sizes than were previously feasible, including single cells. Highly sensitive genome amplification and sequencing techniques have been developed for the analysis of rare cell populations, interrogation of specific cells and substructures of interest within heterogeneous clinical tissues, and profiling of fine needle aspiration biopsies (Achim, K. , Jaitin, D. , and Shapiro E. However, genomic and transcriptomic technologies can fail to comprehensively inform on cellular state (e.g., phenotype) (Bendall, S. Broad proteome measurements provide more direct characterization of the phenotypes and are crucial for understanding cellular functions and regulatory networks. Flow cytometry (FC) and mass cytometry (MC) (Smith, R. ) approaches can utilize antibody-bound reporter species to enable the detection of up to tens of surface markers and intracellular proteins from single cells. As with other antibody-based technologies, these methods can be fully dependent on the availability, quality and delivery of functional antibody probes. FC and MC are also inherently targeted techniques with limited multiplexing capacity. Mass spectrometry (MS)-based proteomics is capable of broadly revealing protein expression as well as protein post-translational modifications (PTMs) within complex samples, but thousands to millions of cells are typically required to achieve deep proteome coverage.

In the absence of methods for global protein amplification, considerable efforts have been devoted to enhancing the overall analytical sensitivity of MS-based proteomics. <NUM> For example, liquid-phase separations including liquid chromatography (LC) and capillary electrophoresis (CE) have been miniaturized to reduce the total flow rate, leading to enhanced efficiencies at the electrospray ionization (ESI) source (Sun, L et al. , and Kelly, R. Advanced ion focusing approaches and optics such as the electrodynamic ion funnel (Li, S. et al) can minimize ion losses in the transfer from the atmospheric pressure ESI source to the high-vacuum mass analyzer, and are now incorporated into many biological MS platforms. As a result of these and other improvements, mass detection limits as low as <NUM> zmol for MS and <NUM> zmol for tandem MS analysis of peptides can be been achieved (Shen, Y. et al, Sun, X. and Wang, H. This analytical sensitivity can be sufficient to detect many proteins at levels expressed in single mammalian cells (Sun, L. and Kelly, R. However, despite this capability, such performance for `real' application to such small samples remains largely ineffective.

The major gap between demonstrated single-cell analytical sensitivity and the present practical need for orders of magnitude more starting material largely can derive from limitations in required sample preparation, including sample isolation, cell lysis, protein extraction, proteolytic digestion, cleanup and delivery to the analytical platform. As sample sizes decrease without a concomitant reduction in reaction volume (often limited by evaporation and the ∼microliter volumes addressable by pipet), the nonspecific adsorption of proteins and peptides to the surfaces of reactor vessels, along with inefficient digestion kinetics, can become increasingly problematic.

Efforts to improve sample preparation procedures include the use of low-binding sample tubes and `one pot' digestion protocols to limit total surface exposure (Sun, X, et al. , Wisniewski, J et al, Chen, Q et al, Chen W. et al, Waanders, L. et al, Huang, E. et al, and Wang, N. In addition, trifluoroethanol-based protein extraction and denaturation (Wisniewski, J. et al <NUM>), filter-aided sample preparation,<NUM> MS-friendly surfactants (Waanders, L. et al, and Huang E. , et al), high-temperature trypsin digestion (Chen, W. et al), adaptive focused acoustic-assisted protein extraction (Sun, X. et al), and immobilized digestion protocols (Wisniewski, J. et al <NUM>) have further advanced processing of small samples. Using methods such as these, previous work has shown that ~<NUM> to <NUM> proteins can be identified from samples comprising <NUM> to <NUM> cells (Table <NUM> below) (Sun, X, et al, Chen, Q. et al, Chen, W. et al, Waanders, L. et al, Huang, E. and Wang, N.

Recently, single-cell proteomics has been used to explore protein expression heterogeneity in individual blastomeres isolated from Xenopus laevis embryos (Lombard-Banek, C. et al, and Sun, L. These measurements were enabled by the fact each of these large cells contained micrograms of proteins, compared to the ~<NUM> ng (Wisniewski, J, et al. <NUM>) of protein found in typical mammalian cells, and were thus compatible with conventional sample preparation protocols.

While progress has been made in enabling the proteomic analysis of trace samples, it is clear that further reducing sample requirements to biological samples containing low- or subnanogram amounts of protein while maintaining or increasing proteome coverage can enable many new applications.

Samples may be any liquid, semi-solid or solid substance (or material). A sample can be a biological sample or a sample obtained from a biological material. A biological sample can be any solid or fluid sample obtained from, excreted by or secreted by any living organism, including without limitation, single celled organisms, such as bacteria, yeast, protozoans, and amoebas among others, multicellular organisms (such as plants or animals, including samples from a healthy or apparently healthy human subject or a human patient affected by a condition or disease to be diagnosed or investigated, such as cancer). For example, a biological sample can be a biological fluid obtained from, for example, blood, plasma, serum, urine, bile, ascites, saliva, cerebrospinal fluid, aqueous or vitreous humor, or any bodily secretion, a transudate, an exudate (for example, fluid obtained from an abscess or any other site of infection or inflammation), or fluid obtained from a joint (for example, a normal joint or a joint affected by disease).

A biological sample can also be a sample obtained from any organ or tissue (including a biopsy or autopsy specimen, such as a tumor biopsy) or can include a cell (whether a primary cell or cultured cell) or medium conditioned by any cell, tissue or organ. In some examples, a biological sample can be a nuclear extract. In some examples, a biological sample can be bacterial cytoplasm. In other examples, a sample can be a test sample. For example, a test sample can be a cell, a tissue or cell pellet section prepared from a biological sample obtained from a subject. In an example, the subject can be one that is at risk or has acquired a particular condition or disease. In certain embodiments, the sample can be cells isolated from whole blood or cell isolated from histological thin sections. Illustrative biological samples include nanoscale biological samples (e.g., containing low- or subnanogram (e.g., less than about <NUM> ng) amounts of protein which may be processed in a single nanowell or subdivided into multiple nanowells).

In the present invention, the sample is a biological sample and the biological sample is a tissue, and the tissue may be fixed. Tissues may be fixed by either perfusion with or submersion in a fixative, such as an aldehyde (such as formaldehyde, paraformaldehyde, glutaraldehyde, and the like). Other fixatives include oxidizing agents (for example, metallic ions and complexes, such as osmium tetroxide and chromic acid), protein-denaturing agents (for example, acetic acid, methanol, and ethanol), fixatives of unknown mechanism (for example, mercuric chloride, acetone, and picric acid), combination reagents (for example, Carnoy's fixative, methacarn, Bouin's fluid, B5 fixative, Rossman's fluid, and Gendre's fluid), microwaves, and miscellaneous (for example, excluded volume fixation and vapor fixation). Additives also may be included in the fixative, such as buffers, detergents, tannic acid, phenol, metal salts (for example, zinc chloride, zinc sulfate, and lithium salts), and lanthanum.

The method for preparing a biological sample includes displacing a volume of biological sample to a single reactor vessel. The volume of biological sample is a non-zero amount less than <NUM>µL. In exemplary embodiments, the volume of biological sample may be a non-zero amount less than about <NUM> nL, less than about <NUM> nL, less than about <NUM> nL, less than about <NUM> nL, less than about <NUM> nL, less than about <NUM> nL, less than about <NUM> nL, less than about <NUM> nL, less than about <NUM> nL less than about <NUM> nL, less than about <NUM> nL, less than about <NUM> nL, less than about <NUM> nL, less than about <NUM> nL, less than about <NUM> nL, less than about <NUM> nL, less than about <NUM> nL, less than about <NUM> nL, less than about <NUM> nL, less than about <NUM> nL, less than about <NUM> nL, less than about <NUM> nL, less than about <NUM> nL, or less than about <NUM> nL. In particular embodiments, the biological sample comprises about <NUM> nL.

In other embodiments, the biological sample may be measured by their confluence. Confluency refers to cells in contact with one another on a surface (e.g., a tissue culture vessel, a petri dish, a well, and the like). For example, it can be expressed as an estimated (or counted) percentage, e.g., <NUM>% confluency means that <NUM>% of the surface, e.g., of a tissue culture vessel, is covered with cells, <NUM>% means that it is entirely covered. For example, adherent cells grow two dimensionally on the surface of a tissue culture well, plate or flask. Non-adherent cells can be spun down, pulled down by a vacuum, or tissue culture medium aspiration off the top of the cell population, or removed by aspiration or vacuum removal from the bottom of the vessel.

In examples not part of the present invention, the biological sample may include HeLa cells, A549 cells, CHO cells or MCF7 cells, K562 cells, or THP-<NUM> cells, microbial cells, plant cells, or virtually any other biological material.

In other examples not part of the present invention, the biological sample may include of primary or immortalized cells. Examples include but are not limited to, mesenchymal stem cells, lung cells, neuronal cells, fibroblasts, human umbilical vein (HUVEC) cells, and human embryonic kidney (HEK) cells, primary or immortalized hematopoietic stem cell (HSC), T cells, natural killer (NK) cells, cytokineinduced killer (CIK) cells, human cord blood CD34+ cells, B cells. Non limiting examples of T cells may include CD8+ or CD4+ T cells. In some aspects, the CD8+ subpopulation of the CD3+ T cells are used. CD8+ T cells may be purified from the PBMC population by positive isolation using anti-CD8 beads.

The biological sample includes tissues, including but not limited, liver tissue, brain tissue, pancreatic tissue, breast cancer tissue, or plant tissue.

The biological sample is collected and prepared using standard techniques. In examples not part of the present invention, cultured cells are collected and centrifuged. The pellet is then washed and re-suspended. The suspended cells are concentration to obtain desired cell numbers. In embodiments, the desired number of cells can be readily optimized. In certain aspects, the number of cells is <NUM> cell, <NUM> cells, <NUM> cells, <NUM> cells, <NUM> cells, <NUM> cells, <NUM> cells, <NUM> cells, <NUM> cells, <NUM> cells, <NUM> cells, <NUM> cells, <NUM> cells, <NUM> cells, <NUM> cells, <NUM> cells, <NUM> cells. In further embodiments, the sample is then adjusted to obtain a nano liter cell suspension (e.g., a <NUM> nL cell suspension).

In the present invention, the biological sample is a laser microdissected tissue. The tissue is less than about <NUM>, less than about <NUM>, less than about <NUM>, less than about <NUM>, less than about <NUM>, less than about <NUM>, less than about <NUM>, less than about <NUM>, less than about <NUM>, less than about <NUM>, less than about <NUM>, less than about <NUM>, less than about <NUM>, less than about <NUM>, less than about <NUM>, less than about <NUM>.

As described herein, a robotic nanoliter dispensing platform <NUM> can be employed to perform sample processing steps associated with bottom-up proteomics (e.g., robotic platform (Vandermarlier, E et al)). As shown in <FIG>, dispensing platform <NUM> can include a translatable stage <NUM> configured to receive a chip <NUM>. The chip <NUM> can be configured to retain biological samples and reagents dispensed therein for further processing. The robotic platform <NUM> can be configured to provide submicron positioning accuracy and capacity for accurately handling picoliter volumes to dispense cells and reagents into reactor vessels formed in the chip <NUM> for further processing (e.g., to yield a processed sample). and to retrieve samples for subsequent analysis.

Biological samples and/or reagents can be dispensed in the chip <NUM> via a syringe pump <NUM> including a picoliter dispensing tip <NUM> under the control of a controller, which can include one or more user interfaces for receiving commands from a user. The syringe pump <NUM> can be in fluid communication with a source of the biological samples (not shown) and one or more reservoirs <NUM> containing reagents. The platform <NUM> can further include a camera <NUM> or other imaging device for viewing dispensing of the biological samples and/or reagents.

In embodiments, the total volume of biological samples and/or reagents can be less than <NUM> nL (in particular embodiments, a non-zero amount of less than <NUM> nL). Embodiments of the method can dramatically reduce surface contact to minimize sample loss while also enhancing reaction kinetics.

In certain embodiments, the nanoPOTS platform described herein can reduce the total processing volumes (for example, the volume of the biological sample plus the total volume of all the reagents for processing) from the conventional tens or hundreds of microliters to less than <NUM>,<NUM> nL, less than <NUM>,<NUM> nL, less than <NUM>,<NUM> nL, less than <NUM>,<NUM> nL, less than <NUM> nL, less than <NUM> nL, less than <NUM> nL, less than <NUM> nL, less than <NUM> nL, less than <NUM> nL, less than <NUM> nL, less than <NUM> nL, less than 5nL.

As described herein, the biological sample is processed in a single reactor vessel to yield a processed sample. The single reactor vessel avoids the need to transfer samples to multiple reactor vessels for processing and therefore avoids the corresponding sample losses that such steps incur.

In embodiment, and as described herein the biological sample is processed in a single reactor vessel, a cocktail containing a reducing agent (e.g., dithiothreitol) is added and the sample is incubated. This allows for lysing, extraction, and denaturation of the proteins, and to reduce disulfide bonds in a single step.

In certain aspects, the pH is between <NUM> and <NUM>, preferably <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM> or <NUM>. More preferably, a solution pH value of <NUM> may be used.

In an exemplary processing step, a protease is then added to the single reactor vessel (e.g., trypsin or LysC). The addition of a protease allows digestion of the polypeptides.

In some examples, the process may be performed in a humidity-controlled chamber. In some examples, the humidity-controlled chamber is maintained at a relative humidity within the range from about <NUM>% to about <NUM>%, e.g., at about <NUM>% humidity. For chemical or enzymatic processing steps that require extended reaction times at room temperature or elevated temperatures, a cover plate may be employed to minimize evaporation.

In some aspects, the single reactor vessel is sealed during incubation times (e.g., after the addition of a reducing agent). The sealed single reactor vessel aids in minimizing evaporation and therefore sample loss.

Optionally, methods disclosed herein may include steps such as washing steps to maximize recovery (e.g., into a capillary). In aspects, the capillary can be fused or sealed from the external environment and stored. In an example, the processed biological sample is washed with a buffer (e.g., with water containing formic acid), and in some examples, multiple washing steps are performed, for example, <NUM> washing steps. Storage of the processed biological sample in the capillary may be short term (e.g., at about -<NUM> for less than <NUM> months) or long term (e.g., at about -<NUM> for greater than <NUM> months).

In certain aspects, the chip can be inverted during sample incubation to prevent the sample from settling on the reactor vessel surface (see <FIG>). For example, droplets containing the biological sample may hang below the reactor vessel surface.

In a further embodiment of the disclosed methods, the processed biological sample can be subjected to mass spectrometry for identification, characterization, quantification, purification, concentration and/or separation of polypeptides without further steps of sample preparation. Since embodiments of the disclosed sample preparation methods can be performed in a single reactor vessel without filtering, precipitation or resolubilization steps, it can facilitate efficient analysis of cell-limited samples.

Embodiments of the disclosed methods, as described herein, can have broad application in the fields of proteomics, metabolomics, and lipidomics, as such robust analysis from small samples have not been achievable using previously developed procedures. However this description of potential applications is non-limiting and one skilled in the art will appreciate that embodiments of the disclosure can be employed in other applications without limit.

As described herein, biological samples processed according to embodiments of the disclosed methods may be analyzed using a variety of methods. In particular examples, the methods used to analyze the processed biological sample can include, but are not limited to, quantitative proteomic analysis methods. In embodiments, the processed biological sample may be analyzed mass spectrometry.

Mass spectrometry can utilize matrix-assisted laser desorption/ionization (MALDI), electrospray ionization (ESI), and other specialized mass spectrometry techniques. For example, MALDI mass spectrometry is a technique for the analysis of peptide mixtures resulting from proteolysis (e.g., digestion of proteins by trypsin). Embodiments of the methods disclosed herein can be used for top-down or bottom-up proteomics.

Chromatography can also be employed for peptide separation. Liquid chromatography or capillary electrophoresis can be coupled to mass spectrometry, particularly with an electrospray ionization source. In the case of proteomic analysis using liquid chromatography/mass spectrometry, a transfer device (e.g., a transfer capillary) can be directly coupled to a solid-phase microextraction column. The microextraction column can, in turn, be coupled to the head of the liquid chromatography column. Alternatively, the transfer capillary may also be directly coupled to the head of the liquid chromatography column.

In embodiments, the analyzing the processed biological sample can identify unique species, including but not limited to proteins or fragments thereof, lipids, or metabolites.

In embodiments, analyzing the processed biological sample can identify at least about <NUM>,<NUM> unique species (e.g., proteins or fragments thereof, lipids, and/or metabolites). In additional embodiments, the processed biological sample can identify at least <NUM>,<NUM> unique species, at least <NUM>,<NUM> unique species, at least <NUM>,<NUM> unique species, at least <NUM>,<NUM> unique species, at least <NUM>,<NUM> unique species. In other embodiments, the number of unique species identified can be at least <NUM> or more proteins and/or <NUM> or more metabolites or lipids.

In embodiments, the methods described herein can allow for the identification and quantitative measurements from less than about <NUM> cells (e.g., from the range of about <NUM> to about <NUM> mammalian cells). In particular embodiments, method described herein enables for identification of over <NUM>,<NUM> unique species from about <NUM>-<NUM> mammalian cells.

In another embodiment, nanowell sample processing can be coupled with laser-capture microdissection (LCM) for deep proteome analysis of heterogeneous tissue thin sections with <<NUM> resolution. Deciphering the cellular interactions that drive disease within tissue microenvironment can be beneficial for understanding tumor formation and propagation, developing drug targets, and designing personalized treatment regimens.

While LCM can differentiate and isolate subsections of tissue with high specificity, sample requirements for proteomics can limit the resolution of LCM to large or pooled thin sections comprising thousands or tens of thousands of cells and millimeter or larger dimensions. Such heterogeneous tissues can confound molecular analysis due to a blurring of cellular constituents and their respective contributions. In contrast, embodiments of the presently disclosed methods can provide proteomic analysis of LCM-isolated tissues by reducing sample size by approximately <NUM> orders of magnitude, to less than about <NUM> cells, which can enable both high resolution proteomic imaging (e.g., less than about <NUM>) as well as isolation of specific tissues from much smaller samples, such as smears from fine needle aspiration biopsies.

LCM can be used to excise and transfer select tissue from thin section to the nanowell. As an example, an LCM (e.g., Zeiss PALM Microbeam LCM®) can be used to excise selected tissue from fresh frozen or archived formalin-fixed, paraffin embedded (FFPE) thin sections (e.g., obtained from Conversant Bio, Inc. The Zeiss system can provide submicron resolution and it can be equipped with laser-pressure catapulting to eject excised samples to a variety of substrates, including centrifuge tube lids and slides (e.g., <NUM> × <NUM>). The Zeiss LCM can be compatible with standard glass slides for archived specimens as well as LCM-dedicated polymer membrane-coated slides.

The nanowells can be configured for compatibility with the <NUM> × <NUM> form factor. This can allow for direct coupling and facilitate transfer from thin sections to the nanowells. As discussed in greater detail below, the nanowells can have a diameter of about <NUM> to about <NUM>. The spacing between the nanowell slide and the thin section slide may be adjusted to achieve the requisite transfer accuracy. Nanowell surface treatments may be implemented as needed to ensure adhesion of the catapulted tissue upon contact. As an alternative approach, excised samples can be catapulted into centrifuge tube caps and micromanipulationbased strategies can be used to transfer the sample to the nanowell.

In the present invention, sample processing is seamlessly integrated with LCM by providing a capture liquid in or on a reactor vessel. This method can avoid manual transfer of dissected tissues to the nanowells that is required in a conventional LCM system. In a conventional LCM system, after being dissected, tissue pieces may be collected into microtubes by gravity or catapulted into tube caps prefilled with extraction solution or adhesive coating, depending on the instrument vendor and configuration. However, these collection approaches cannot be automatically integrated with a nanoPOTS system because the rapid evaporation of nanoliter-scale extraction solution and the prohibitive absorptive losses of proteins on the adhesive silicone coating. Utilizing a sacrificial capture medium in the nanowells addresses this challenge.

The capture liquid has an ultra-low vapor pressure ( less than or equal to <NUM> mbar at room temperature), and evaporates very slowly under ambient conditions, which allows for long working times and uninterrupted sample collection. For example, as shown in <FIG>, the evaporation times of <NUM> nL to <NUM> nL dimethyl sulfoxide (DMSO) droplets were <NUM> to <NUM>, which were ><NUM> times longer than for water droplets. Such prolonged times are sufficient to collect up to hundreds of tissue samples in each chip. The capture liquid can be completely removed by gentle heating or vacuum, eliminating any possible interference during subsequent sample processing and analysis steps. Compared with other low-vapor-pressure solvents such as dimethylformamide, the capture liquid should have a lower toxicity, thus enabling its use as a storage solvent for cells. An illustrative capture liquid is dimethyl sulfoxide (DMSO). In addition to having an ultra-low vapor pressure and lower toxicity, the freezing point of DMSO is <NUM>, which should facilitate chip and sample transfer between histology and analytical labs without the risk of sample mixing or losses during shipping. In addition, it has been presently found that DMSO significantly increases the sensitivity of protein identification of brain tissues, which may be ascribed to improved protein extraction efficiency as explained in more detail below. The amount of capture liquid provided in each nanowell may be sufficient to cover a portion of, or the entire surface, of the nanowell. For example, the capture liquid may be present in an amount of at least <NUM> nL to <NUM> nL.

Embodiments of the methods described herein can be used for molecular characterization of tissue cellular heterogeneity or pathology in a variety of diseases. Exemplary diseases can include, but are not limited to, inflammatory diseases, metabolic diseases, cancers, neoplasias, and the like.

As used herein, metabolic disease can include its customary and ordinary meaning and can refer to diabetes, including type II diabetes, insulin-deficiency, insulin-resistance, insulin-resistance related disorders, glucose intolerance, syndrome X, inflammatory and immune disorders, osteoarthritis, dyslipidemia, metabolic syndrome, non-alcoholic fatty liver, abnormal lipid metabolism, neurodegenerative disorders, sleep apnea, hypertension, high cholesterol, atherogenic dyslipidemia, hyperlipidemic conditions such as atherosclerosis, hypercholesterolemia, and other coronary artery diseases in mammals, and other disorders of metabolism. For example, the methods as used herein can be used in characterizing type <NUM> or type <NUM> diabetes.

As used herein, neoplasia can include its customary and ordinary meaning and can refer to a disease or disorder characterized by excess proliferation or reduced apoptosis. Illustrative neoplasms for which the embodiment may be used include, but are not limited to pancreatic cancer, leukemias (e.g., acute leukemia, acute lymphocytic leukemia, acute myelocytic leukemia, acute myeloblastic leukemia, acute promyelocytic leukemia, acute myelomonocytic leukemia, acute monocytic leukemia, acute erythroleukemia, chronic leukemia, chronic myelocytic leukemia, chronic lymphocytic leukemia), polycythemia vera, lymphoma (Hodgkin's disease, non-Hodgkin's disease), Waldenstrom's macroglobulinemia, heavy chain disease, and solid tumors such as sarcomas and carcinomas (e.g., fibrosarcoma, myxosarcoma, liposarcoma, chondrosarcoma, osteogenic sarcoma, chordoma, angiosarcoma, endotheliosarcoma, lymphangiosarcoma, lymphangioendotheliosarcoma, synovioma, mesothelioma, Ewing's tumor, leiomyosarcoma, rhabdomyosarcoma, colon carcinoma, breast cancer, ovarian cancer, prostate cancer, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinomas, cystadenocarcinoma, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatoma, nile duct carcinoma, choriocarcinoma, seminoma, embryonal carcinoma, Wilm's tumor, cervical cancer, uterine cancer, testicular cancer, lung carcinoma, small cell lung carcinoma, bladder carcinoma, epithelial carcinoma, glioma, glioblastoma multiforme, astrocytoma, medulloblastoma, craniopharyngioma, ependymoma, pinealoma, hemangioblastoma, acoustic neuroma, oligodenroglioma, schwannoma, meningioma, melanoma, neuroblastoma, and retinoblastoma).

While various embodiments and aspects of the present disclosure are shown and described herein, it will be obvious to those skilled in the art that such embodiments and aspects are provided by way of example only. Numerous variations, changes, and substitutions can occur to those skilled in the art without departing from the disclosed embodiments. It should be understood that various alternatives to the embodiments described herein may be employed.

The section headings used herein are for organizational purposes only and are not to be construed as limiting the subject matter described. All documents, or portions of documents, cited in the application including, without limitation, patents, patent applications, articles, books, manuals, and treatises are hereby expressly incorporated by reference in their entirety for any purpose.

Unless defined otherwise herein, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which embodiments of the disclosure pertains.

As used herein, the term "biological sample" can include its customary and ordinary meaning and can refers to a sample obtained from a biological subject, including sample of biological tissue or fluid origin obtained in vivo or in vitro. Such samples can be, but are not limited to, body fluid (e.g., blood, blood plasma, serum, or urine), organs, tissues, fractions, and cells isolated from mammals including, humans. Biological samples also may include sections of the biological sample including tissues (e.g., sectional portions of an organ or tissue). Biological samples may also include extracts from a biological sample, for example, an antigen from a biological fluid (e.g., blood or urine).

A biological sample may be of prokaryotic origin or eukaryotic origin (e.g., insects, protozoa, birds, fish, or reptiles). In some embodiments, the biological sample can be mammalian (e.g., rat, mouse, cow, dog, donkey, guinea pig, or rabbit). In certain embodiments, the biological sample can be of primate origin (e.g., example, chimpanzee, or human).

The transitional term "comprising," which is synonymous with "including," "containing," or "characterized by," is inclusive or open-ended and does not exclude additional, unrecited elements or method steps. By contrast, the transitional phrase "consisting of' excludes any element, step, or ingredient not specified in the claim. The transitional phrase "consisting essentially of" limits the scope of a claim to the specified materials or steps "and those that do not materially affect the basic and novel characteristic(s)" of the claimed embodiments.

"Detectable moiety" or a "label" can include its customary and ordinary meaning and it can refer to a composition detectable by spectroscopic, photochemical, biochemical, immunochemical, or chemical means. For example, useful labels include <NUM>P, <NUM>S, fluorescent dyes, electron-dense reagents, enzymes (e.g., as commonly used in an ELISA), biotin-streptavidin, dioxigenin, haptens and proteins for which antisera or monoclonal antibodies are available, or nucleic acid molecules with a sequence complementary to a target. The detectable moiety can generate a measurable signal, such as a radioactive, chromogenic, or fluorescent signal, that can be used to quantify the amount of bound detectable moiety in a sample. Quantitation of the signal can be achieved by, e.g., scintillation counting, densitometry, mass spectrometry, and/or flow cytometry.

By "FFPE" can refer to formalin fixed paraffin embedded tissue. FFPE samples can be derived from tissues (often suspected tumor samples) that are fixed with formalin to preserve structural-spatial and biomolecule characteristics (e.g., cytoskeletal and protein structure) and then embedded in a type of paraffin wax so the tissue can be sliced. Formalin can irreversibly cross-link proteins via the amino groups, thus preserving the structural integrity of the cells so they can be stained with dyes or with immunostains used to analyze for abnormalities in the tissue that indicate altered cellular conditions, e.g., cancer. However, the effect of these cross-linking fixatives on the RNA and DNA nucleic acids within the sample can be detrimental to the sensitivity and specificity achievable in current molecular assays e.g., molecular assays which use DNA or RNA derived from FFPE samples. Additionally, samples may be prepared using non-formalin reagents, including, for example, glutaraldehyde, mercurial, oxidizing agents, alcohols, and picrates.

The term "hydrophilic surface" can include its customary and ordinary meaning and it can refer to a surface to have native hydrophilic property such as glass or fused silica, or which either hydrophilic compounds are covalently or non-covalently attached or which is formed of a polymer that has hydrophilic properties. In embodiments, the polymer with hydrophilic properties can be an organic polymer, (e.g., polyacrylamide, polyacrylic acid, polyacrylimide, polyelectrolytes, polyethylenimin, polyethylenglycol, polyethylenoxid, polyvinylalcohol, polyvinylpyrrolidon polystyrenesulfonic acid, copolymers of styrene and maleic acid, vinyl methyl ether malic acid copolymer, and polyvinylsulfonic acid.

As used herein, the singular terms "a," "an," and "the" include the plural reference unless the context clearly indicates otherwise.

An exemplary nanoPOTS chip, also referred to as a platform or chip <NUM>, is illustrated in <FIG>. The chip <NUM> can include a substrate <NUM>, a spacer <NUM>, one or more sealing membrane <NUM>, and a cover <NUM>. When the chip is assembled, the spacer <NUM> can overlie the substrate <NUM>, the sealing membrane <NUM> can overlie the spacer <NUM>, and the cover <NUM> can overlie the sealing membrane <NUM>.

The substrate <NUM>, the spacer <NUM>, and the cover <NUM> can be formed from a material that is transparent to optical light (e.g., glass). Forming the substrate <NUM> from glass can facilitate microscopic imaging of samples and minimize protein and peptide adsorption relative to many other materials due to its hydrophilicity and reduced surface charge at low pH (Zhu, Y, et al.

As discussed in greater detail below, the substrate <NUM> includes a physical and/or chemical pattern <NUM> that defines at least one reactor vessel having one or more hydrophilic and optionally hydrophobic surfaces configured for containment of a biological sample. The hydrophilic surfaces have a non-zero total surface area less than <NUM><NUM>.

The spacer <NUM> can contain a first aperture 204a and the sealing membrane <NUM> can include a second aperture 206a. The first and second apertures 204a, 206a can be dimensioned to accommodate the pattern <NUM> of reactor vessels when the chip <NUM> is assembled. As an example, at least the first aperture 204a of the spacer can surround the pattern <NUM> of reactor vessels.

The sealing membrane <NUM> can be interposed between the spacer <NUM> and the cover <NUM> and it can be configured to form a fluid-tight seal between the spacer <NUM> and the cover <NUM>. In other embodiments, not shown, the sealing membrane can be interposed between the substrate and the spacer. Formation of fluid-tight seals using the sealing membrane can minimize evaporation of reactor vessel contents when performing incubation during sample preparation, as discussed below. Optionally, other sealing mechanisms can be employed and the sealing membrane can be omitted. For example, the cover <NUM> can be pre-coated with a layer of sealing membrane such as PDMS (polydimethylsiloxane).

<FIG> illustrate an exemplary forming the pattern <NUM> by photolithography. In <FIG>, a substrate <NUM> coated with an anti-reflective coating <NUM> and photoresist <NUM> is illustrated. A photomask <NUM> can be used in conjunction with light <NUM> (e.g., ultraviolet light) to transfer a geometric pattern of the photomask <NUM> to the photoresist <NUM>. The anti-reflective coating <NUM> can be configured to control reflection and absorption of the light <NUM>. As shown in <FIG>, the portions of the photomask <NUM> and anti-reflective coating <NUM> outside the transferred pattern can be removed by a chemical etching to yield a patterned substrate <NUM> that includes pillars <NUM> defining wells <NUM> therebetween of predetermined depth within the substrate <NUM>. The photomask <NUM> and anti-reflective coating <NUM> remaining on the upper surface of the pillars <NUM> are removed with further chemical etching, as shown in <FIG>.

<FIG> illustrate examples of the patterned substrate <NUM> defining reactor vessels configured for multiple-step proteomic sample processing. As shown in <FIG>, a hydrophobic coating can be deposited on the patterned substrate <NUM>, adjacent to the pillars <NUM>, to form a hydrophobic surface <NUM>. A hydrophilic coating can be deposited on the patterned substrate <NUM> on the upper surface of the pillars <NUM> to form a hydrophilic surface <NUM>. Alternatively, when the substrate is formed from a hydrophilic material, a hydrophobic coating can be omitted and the bare surface of the substrate can form the hydrophilic surface <NUM>. So configured, the upper surface of each pillar <NUM> with the hydrophilic surface <NUM> can define the lateral boundary of respective reactor vessels <NUM>. In certain examples, the patterned pillars <NUM> can reduce surface area contact relative to the use of concave wells.

Conversely, as shown in <FIG>, the locations of the hydrophobic and hydrophilic coatings can be reversed. That is, the hydrophilic coating can be deposited on the patterned substrate <NUM> adjacent to the pillars <NUM> (e.g., within the wells <NUM>) to form the hydrophilic surface <NUM>. Alternatively, as discussed above, when the substrate is formed from a hydrophilic material, a hydrophobic coating can be omitted and the bare surface of the substrate can form the hydrophilic surface <NUM>. Likewise, the hydrophobic coating can be deposited on the patterned substrate <NUM> on the upper surface of the pillars <NUM> to form the hydrophobic surface <NUM>. So configured, the wells <NUM> with the hydrophilic surface <NUM> can define the lateral boundary of respective reactor vessels <NUM>.

In another example, as illustrated in <FIG>, a patterned substrate <NUM> can be formed by a pattern of hydrophobic surfaces <NUM> and hydrophilic surfaces <NUM> alone, without pillars <NUM> or wells <NUM>. The hydrophobic surfaces <NUM> and the hydrophilic surfaces can be provided as discussed above and they can define the lateral extent of the one or more reactor vessels <NUM>.

In certain embodiments, as illustrated in <FIG>, a chip <NUM> that includes a substrate <NUM> and reactor vessel pillars <NUM> is inverted during processing of a biological sample. A droplet <NUM> of a capture liquid suspends from the hydrophilic surface of the reactor vessel pillar <NUM>. The capture liquid droplet <NUM> contains a biological sample <NUM> that can be subjected to processing.

In an example not part of the present invention, the RapiGest-based one-pot protocol (Waters, Milford, USA) was adapted for proteomic sample preparation with minimal modification (<FIG>). Briefly, after cells or other tissue samples were deposited into each chamber of the array, microscopic imaging was used for sample size quantification (cell number, tissue dimensions, etc.). A cocktail containing RapiGest and dithiothreitol was added and incubated at <NUM> to lyse cells, extract and denature proteins, as well as reduce disulfide bonds in a single step. The proteins were alkylated and digested using a two-step enzymatic hydrolysis. Finally, the solution was acidified to cleave and inactivate the RapiGest surfactant. Manipulations were conducted in a humidified chamber, and the cover plate was sealed to the nanowell chip during extended incubation steps to minimize evaporation of the nanoliter droplets. The prepared sample was collected into a fused-silica capillary, followed by a two-step wash of the nanowell to maximize recovery (<FIG>). The collector capillary can be fully sealed and stored in a freezer for months without observable sample loss. The capillary also simplified downstream solid-phase extraction-based cleanup and LC-MS analysis by enabling direct coupling with standard fittings.

The sensitivity by processing <NUM>-<NUM> cultured HeLa cells with nanoPOTS (<FIG>) was evaluated. Three different blank controls were used to confirm negligible carryover and contamination from the SPE and LC columns, reagents, and cell supernatant, respectively (<FIG>). In contrast to the control samples, all cell-containing samples showed feature-rich base peak chromatogram profiles, and the number of peaks and their intensities increased with the number of cells (<FIG>). The percentage of peptides having tryptic cleavage sites ranged from <NUM>% to <NUM>%, while the percentage of peptides having tryptic missed cleavage sites ranged from <NUM>% to <NUM>% (<FIG>), indicating a digestion efficiency that is on par with conventional bulk processing (Wang, N. The average peptide coverage based on MS/MS identification ranged from <NUM>,<NUM> to <NUM>,<NUM>, and protein coverage ranged from <NUM>,<NUM> to <NUM>,<NUM> for triplicate groups comprising <NUM>-<NUM>, <NUM>-<NUM> and <NUM>-<NUM> cells, respectively (<FIG>). When the Match Between Runs (MBR) algorithm of Maxquant (Shen, Y et al. ) was used, <NUM>% of the identified proteins were found to be common to all samples (<FIG>), indicating more proteins could likely be identified and quantified from the smaller samples if a larger reference library were used, or an appropriate accurate mass and time (AMT) tag database (Tyanova S, et al. <NUM>) were available. The ability to identify an average of <NUM>,<NUM> proteins in as small as ~<NUM> cells (<FIG>) represents a ><NUM>-fold decrease in sample size to achieve similar proteome coverage relative to previously reported methods (Sun, X et al, Chen, W et al. , Wannders, L. et al, Huang, E. et al, and Wang, N. et al) (Table <NUM>, below).

To understand the absolute sensitivity of the nanoPOTS-LC-MS platform, the proteins were matched identified from <NUM>-<NUM> cells to the reported databases containing protein copy numbers per HeLa cell (Wisniewski, J. et al <NUM>, and Volpe, P. In the first database, the absolute copy numbers of <NUM> proteins in HeLa cell were precisely quantified using spiked-in protein epitope signature tags (PrEST) in combination with SILAC-based isotopic labeling (Volpe, P. Thirty-four of the <NUM> proteins were identified, and the <NUM> missed proteins were low in abundance. The corresponding protein copy number per cell ranged from about <NUM>×<NUM><NUM> to about <NUM>×<NUM><NUM> (Table <NUM>), with <NUM> expressed at <<NUM><NUM> copies/cell. Considering the highly reliable values obtained using the PrEST-SILAC method, the detection limit of nanoPOTS for protein is <<NUM>×<NUM><NUM> copies, or < <NUM> zmol.

In the second database, a total of ><NUM>,<NUM> proteins in HeLa cells were quantified using a histone-based 'proteomic ruler' and label-free quantitation based on MS intensities (Wisniewski, J. <NUM>,<NUM> of these proteins matched the proteins identified in the <NUM>-<NUM>-cell samples, and the distribution of copy number per cell are shown in <FIG>. The results are biased to high-abundance proteins due to the use of only ~<NUM> cells. The median copy number within our samples was ~<NUM>×<NUM><NUM>, which is approximately <NUM> times higher than the reference value (Wisniewski, J. et al <NUM>). Importantly, a number of low-abundance proteins were identified, including <NUM> proteins with copy numbers below <NUM>,<NUM> and <NUM> proteins below <NUM>,<NUM> (<FIG>). These results indicate that the detection limit of the nanoPOTS-LC-MS platform may below <NUM> zmol. The results also show the great potential of the nanoPOTS platform for single cell proteomics with further improvement in sensitivity by optimizing processing volumes, miniaturizing the LC separation, and improving MS instrumentation.

The reproducibility of nanoPOTS processing was evaluated using MS<NUM> intensity-based label-free quantification at both the peptide and the protein levels. The MBR analysis produced over <NUM>,<NUM> quantifiable peptides and <NUM>,<NUM> protein groups (<FIG>). Median coefficients of variance (CVs) were ≤<NUM>% (peptide level) and <NUM>% (protein level) for all the three cell loading groups (<FIG>). Peptide and protein intensities spanning more than <NUM> orders of magnitude were observed (<FIG>), indicating that dynamic range and proteome depth were substantially retained relative to bulk analyses. Pairwise analysis of any two samples with similar cell loadings showed Pearson correlation coefficients from <NUM> to <NUM> (<FIG>) at the peptide level. Protein LFQ intensity revealed higher correlations with coefficients of <NUM> to <NUM> (<FIG>). These data suggest that label-free quantification is feasible for far smaller proteomic samples than have been previously accessible.

Deionized water (<NUM> MΩ) was purified using a Barnstead Nanopure Infinity system (Los Angeles, USA). Dithiothreitol (DTT) and iodoacetamide (IAA) were purchased from Thermo Scientific (St. Louis, USA) and freshly prepared in <NUM> ammonium bicarbonate buffer each day before use. RapiGest SF surfactant (Waters, Milford, USA) was dissolved in <NUM> ammonium bicarbonate buffer with a concentration <NUM>% (m/m), aliquoted, and stored at -<NUM> until use. Trypsin (MS grade) and Lys-C (MS grade) were products of Promega (Madison, USA). Other unmentioned reagents were obtained from Sigma-Aldrich (St. Louis, USA).

The photomask was designed with AutoCAD and printed with a direct-write lithography system (SF-<NUM>, Intelligent Micro Patterning LLC, St. Petersburg, USA). An array of <NUM> × <NUM> spots with diameters of <NUM> and a spacing of <NUM> was designed on a <NUM> × <NUM> glass slide (soda lime) that was pre-coated with chromium and photoresist (Telic Company, Valencia, USA). After photoresist exposure (<FIG>), development, and chromium etching (Transene, Danvers, USA; <FIG>), the glass slide was hard baked at <NUM> for <NUM>. The back side of the slide was protected with packing tape and the glass substrate surface was etched around the patterned photoresist/Cr features using wet etching solution containing <NUM> HF, <NUM> NH<NUM>F, and <NUM> HNO<NUM> at <NUM> for <NUM> to reach a depth of <NUM> (<FIG>). The remaining photoresist was removed using AZ 400T stripper. The glass slide was thoroughly rinsed with water, dried using compressed nitrogen, and further dried in an oven at <NUM> for <NUM>. The chip surface was then cleaned and activated with oxygen plasma treatment for <NUM> minutes using a March Plasma Systems PX250 (Concord, USA). The glass surface that was not protected with Cr was rendered hydrophobic with a fluorosilane solution containing <NUM>% (v/v) heptadecafluoro-<NUM>,<NUM>,<NUM>,<NUM>-tetrahydrodecyl)dimethylchlorosilane (PFDS) in <NUM>,<NUM>,<NUM>-trimethylpentane (<FIG>) for <NUM>. The residual silane solution was removed by immersing the chip in <NUM>,<NUM>,<NUM>-trimethylpentane followed by ethanol. Remaining chromium was removed using chromium etchant (Transene), leaving elevated hydrophilic nanowells on a hydrophobic background (<FIG>).

The glass spacer was fabricated by milling a standard microscope slide (<NUM> × <NUM> × <NUM>) with a CNC machine (Minitech Machinery Corporation, Norcross, USA). Epoxy was used to glue the patterned chip and the glass spacer together. The glass cover was fabricated by spin coating a thin layer of polydimethylsiloxane (PDMS) membrane (<NUM>-µm thickness) onto a standard glass microscope slide of the same dimensions. Briefly, Dow Coming Sylgard <NUM> silicone base was mixed with its curing reagent at a ratio of <NUM>:<NUM> (w/w) and degassed for <NUM>. The mixture was coated on the slide by spinning at <NUM> rpm for <NUM> followed by <NUM> rpm for <NUM> (WS-<NUM>, Laurell Technologies, North Wales, USA). Finally, the PDMS membrane was cured at <NUM> for <NUM> hours. A piece of Parafilm (Bemis Company, Oshkosh, USA) was precisely cut to serve as moisture barrier between the glass spacer and the glass cover.

All sample and reagent solutions were delivered to the nanowells using a home-built liquid handling system with a metering precision of <NUM> nL. The liquid handling system is similar to those described previously (Zhu, Y. et al <NUM>, Zhu Y. et al <NUM>, and Zhu, Y. <NUM>) and was composed of four parts including a 3D translation stage (SKR series, THK, Japan) for automated position control, a home-built high-precision syringe pump (KR series, THK, Japan) for liquid metering, a microscopic camera system (MQ013MG-ON, XIMEA Corp. , Lakewood, USA) for monitoring the liquid handling process, and a tapered capillary probe for liquid dispensing. The capillary probe was fabricated by heating pulling a fused silica capillary (<NUM> i. , Polymicro Technologies, Phoenix, USA) to generate a tapered tip (<NUM> i. A home-built program with LabView (Version <NUM>, National Instruments, Austin, USA) was used to synchronously control the movement of the 3D stages and the liquid dispensing of the syringe pump. To minimize evaporation during the liquid handling procedure, the whole system was enclosed in a Lexan chamber maintained at <NUM>% relative humidity.

The syringe pump was set at a withdraw rate of <NUM>µL/min and an infusion rate of <NUM>µL/min. The translation stages were operated at a start speed of <NUM>/s, a maximum speed of <NUM>/s, and an acceleration time of <NUM>. In the typical setup, it took total ~ <NUM> to dispense one reagent to all the <NUM> droplets in single chip including the time for withdrawing reagent into the capillary probe, moving of the robotic stages, and dispensing <NUM> nL reagent into each droplet.

To meet the requirement of processing large number of samples in single experiment, the nanowells can be scaled up with the present photolithography-based microfabrication technique. Up to <NUM> nanowells can be fabricated on a <NUM> × <NUM> microscope slide and further scale-up is possible with larger substrates. The robot can be simply configured to fit different formats of nanowell array. Because of the high liquid handling speed, <NUM> droplets could be addressed in <<NUM>.

All cells were cultured at <NUM> and <NUM>% CO<NUM> and split every <NUM> days following standard protocol. HeLa was grown in Eagle's Minimum Essential Medium (EMEM) supplemented with <NUM>% fetal bovine serum (FBS) and <NUM>× penicillin streptomycin.

Ten-pm-thick pancreatic tissue slices were cut from OCT blocks using a cryo-microtome and mounted on PEN slides for islet dissection. Slides were briefly fixed with methanol, rinsed with H<NUM>O to remove OCT, and dehydrated using an alcohol gradient before placing in a desiccator to dry (<NUM> minutes). Dehydrated and dried slides were placed on the stage of a laser microdissection microscope (Leica LMD7000). Islets were identified based on autofluorescence and morphology. Dissections were performed under a <NUM>× objective. Laser dissected islets were collected in the cap of a <NUM>-mL tube mounted underneath the slides. After dissection, samples were stored at -<NUM> until further analysis.

HeLa cells were collected in a <NUM> tube and centrifuged at <NUM> rpm for <NUM> minutes to remove culture media. The cell pellet was further washed three times with <NUM> of <NUM>× PBS buffer. The cells were then suspended in <NUM> PBS buffer and counted to obtain cell concentration. Eppendorf protein low-binding vials (<NUM>) were used throughout the process. Cells were lysed at a concentration of <NUM>×<NUM><NUM>/mL in <NUM>% RapiGest and <NUM> DTT in <NUM> ammonium bicarbonate (ABC). After heating at <NUM> for <NUM>, the cell lysate was diluted in <NUM> ABC buffer and aliquoted to different vails with a volume of <NUM>µL. <NUM>µL of IAA solution (<NUM> in <NUM> ABC) was dispensed to alkylate sulfhydryl groups by incubating the vials in the dark for <NUM> minutes at room temperature. <NUM>µL of Lys-C (<NUM> ng in <NUM> ABC) was added and incubated at <NUM> for <NUM>. <NUM>µL of Trypsin (<NUM> ng in <NUM> ABC) was added and incubated overnight at <NUM>. Finally, <NUM>µL of formic acid solution (<NUM>%, v/v) were dispensed and allowed to incubate for <NUM> at room temperature to cleave RapiGest surfactant for downstream analysis.

Before use, the chip was washed with isopropanol and water to minimize contamination. The liquid handling system was configured to minimize cross contamination by adjusting the vertical distance between the probe tip and the nanowell surface, which was previously termed semi-contact dispensing (Zhu, Y. et al <NUM>).

For cultured cell samples, cells were collected in a <NUM> tube and centrifuged at <NUM> rpm for <NUM> minutes to remove culture media. The cell pellet was further washed three times with <NUM> of <NUM>× PBS buffer. The cells were then suspended in <NUM> PBS buffer and counted to obtain cell concentration. Cell concentrations were adjusted by serially diluting them in PBS to obtain different cell numbers in nanowells. After dispensing <NUM> nL of cell suspension into each nanowell, we observed that the distribution of cell numbers in nanowell was stochastic, especially for lowconcentration cell suspensions. Thus, the accurate cell number in each nanowell was counted using an inverted microscope and indexed to the two-dimensional spatial position of the corresponding nanowell. For LCM tissues, a high precision tweezer with a tip of <NUM> (TerraUniversal, Buellton, USA) was used to transfer tissue pieces from collection tubes into individual nanowells under a stereomicroscope (SMZ1270, Nikon, Japan). ImageJ software<NUM> was used to measure the area of LCM islets to calculate islet equivalents (IEQ) and cell numbers.

For sample preparation of cultured cells, <NUM>-nL RapiGest (Yu et al. <NUM>) (<NUM>%) solution with <NUM> DTT in <NUM> ammonium bicarbonate (ABC) was added into the nanodroplets that had been preloaded with cells. For LCM tissue samples, <NUM> nL of RapiGest solution (<NUM>% in <NUM> ABC) containing <NUM> DTT was added. The cover was then sealed to the nanodroplet chip, which was incubated in <NUM> for <NUM> to achieve cell lysis, protein denaturation, and disulfide reduction. In the second step, <NUM> nL of IAA solution (<NUM> in <NUM> ABC) was dispensed to alkylate sulfhydryl groups by incubating the chip in the dark for <NUM> minutes at room temperature. In the third step, <NUM> nL enzyme solution containing <NUM> ng Lys-C in <NUM> ABC was added and incubated at <NUM> for <NUM> for predigestion. In the fourth step, <NUM> nL of enzyme solution containing <NUM> ng trypsin in <NUM> ABC was added into each droplet and incubated overnight at <NUM> for tryptic digestion. Finally, <NUM> nL of formic acid solution (<NUM>%, v/v) was dispensed and allowed to incubate for <NUM> at room temperature to cleave RapiGest surfactant for downstream analysis. To minimize liquid evaporation in nanowells, the chip was completely sealed during cell counting, incubation, and transfer procedures. During each dispensing step, the chip was opened and closed within the humidity chamber to minimize droplet evaporation. However, as the total dispensed volume in each droplet was <NUM> nL, and the final volume was typically <<NUM> nL, some evaporative losses clearly occurred. Some of these water losses were observed as condensation on the contactless cover upon cooling from the <NUM> protein extraction step, and the extended digestions at <NUM> also resulted in minor volume reductions. Such water losses have no negative effect on the performance of nanoPOTS platform, but could become limiting when further downscaling processing volumes.

Digested peptide samples in each nanowell were collected and stored in a section of fused silica capillary (<NUM> long, <NUM> i. Before sample collection, the capillary was connected to the syringe pump and filled with water containing <NUM>% formic acid (LC Buffer A) as carrier. A plug of air (<NUM> nL, <NUM> in length) was aspirated into the front end of the capillary to separate sample from carrier. The capillary-to-nanowell distance was adjusted to ~<NUM> to allow majority of sample to be aspirated into the capillary. To achieve highest sample recovery, the nanowell was twice washed with <NUM>-nL buffer A and the wash solutions were also collected in the same capillary. A section of capillary containing a train of plugs consisting of carrier, air bubble, sample, and wash solutions was then cut from the syringe pump. The capillary section was sealed with Parafilm at both ends and stored at -<NUM> for short-term storage or -<NUM> for long-term storage.

The SPE precolumn and LC column were slurry-packed with <NUM>-µm C18 packing material (<NUM>-Å pore size, Phenomenex, Terrence, USA) as described previously (Shen, Y. et al <NUM>, and Shen, Y. The SPE column was prepared from a <NUM>-cm-long fused silica capillary (<NUM> i. , Polymicro Technologies, Phoenix, AZ). The LC column was prepared from a <NUM>-cm Self-Pack PicoFrit column with an i. of <NUM> and a tip size of <NUM> (New Objective, Woburn, USA). The sample storage capillary was connected to the SPE column with a PEEK union (Valco instruments, Houston, USA). Sample was loaded and desalted in the SPE precolumn by infusing buffer A (<NUM>% formic acid in water) at a flow rate of <NUM> nL/min for <NUM> minutes with an nanoACQUITY UPLC pump (Waters, Milford, USA). The SPE precolumn was reconnected to the LC column with a low-dead-volume PEEK union (Valco, Houston, USA). The LC separation flow rate was <NUM> nL/min, which was split from <NUM> nL/min with a nanoACQUITY UPLC pump (Waters, Milford, USA). A linear <NUM>-min gradient of <NUM>-<NUM>% buffer B (<NUM>% formic acid in acetonitrile) was used for separation. The LC column was washed by ramping buffer B to <NUM>% in <NUM> minutes, and finally re-equilibrated with buffer A for another <NUM> minutes.

An Obitrap Fusion Lumos Tribrid MS (ThermoFisher) was employed for all data collection. Electrospray voltage of <NUM> kV was applied at the source. The ion transfer tube was set at <NUM> for desolvation. S-lens RF level was set at <NUM>. A full MS scan range of <NUM>-<NUM> and Obitrap resolution of <NUM>,<NUM> (at m/z <NUM>) was used for all samples. The AGC target and maximum injection time were set as 1E6 and <NUM>. Data-dependent acquisition (DDA) mode was used to trigger precursor isolation and sequencing. Precursor ions with charges of +<NUM> to +<NUM> were isolated with an m/z window of <NUM> and fragmented by high energy dissociation (HCD) with a collision energy of <NUM>%. The signal intensity threshold was set at <NUM>. To minimize repeated sequencing, dynamic exclusion with duration of <NUM> and mass tolerance of ±<NUM> ppm was utilized. MS/MS scans were performed in the Obitrap. The AGC target was fixed at 1E5. For different sample inputs, different scan resolutions and injection times were used to maximize sensitivity (<NUM> and <NUM> for blank control and ~<NUM>-cell samples; <NUM> and <NUM> for ~<NUM>-cell samples; <NUM> and <NUM> for ∼<NUM>-cell samples).

All raw files were processed using Maxquant (version <NUM>. <NUM>) for feature detection, database searching and protein/peptide quantification (Tyanova, S. et al <NUM>). MS/MS spectra were searched against the UniProtKB/Swiss-Prot human database (Downloaded in <NUM>/<NUM>/<NUM> containing <NUM>,<NUM> reviewed sequences). N-terminal protein acetylation and methionine oxidation were selected as variable modifications. Carbamidomethylation of cysteine residues was set as a fixed modification. The peptide mass tolerances of the first search and main search (recalibrated) were < <NUM> and <NUM> ppm, respectively. The match tolerance, de novo tolerance, and deisotoping tolerance for MS/MS search were <NUM>, <NUM>, and <NUM> ppm, respectively. The minimum peptide length was <NUM> amino acids and maximum peptide mass was <NUM> Da. The allowed missed cleavages for each peptide was <NUM>. The second peptide search was activated to identify co-eluting and cofragmented peptides from one MS/MS spectrum. Both peptides and proteins were filtered with a maximum false discovery rate (FDR) of <NUM>. The Match Between Runs feature, with a match window of <NUM> and alignment window of <NUM>, was activated to increase peptide/protein identification of low-cell-number samples. LFQ calculations were performed separately in each parameter group that containing similar cell loading. Both unique and razor peptides were selected for protein quantification. Requiring MS/MS for LFQ comparisons was not activated to increase the quantifiable proteins in low-cell-number samples. Other unmentioned parameters were the default settings of the Maxquant software.

Perseus (Tyanova, S. <NUM>) was used to perform data analysis and extraction. To identify the significantly changed proteins from a non-diabetic donor and a T1D donor, the datasets were filtered to contain <NUM> valid LFQ intensity values in at least one group. The missing values were imputed from normal distribution with a width of <NUM> and a down shift of <NUM>. Two sample T-test with a minimal fold change of <NUM> and a FDR of <NUM> was performed for statistical analysis. The extracted data were further processed and visualized with OriginLab <NUM>. Global scaling normalization was achieved using scaling coefficients calculated as the ratio of peptide abundance to the median peptide abundance measured for each loading set. Coefficients of variation were calculated by dividing the standard deviation of normalized intensities by the mean intensity across the datasets of similar loading. The Violin plot was generated with an online tool (BoxPlotR, http://shiny. org/boxplotr/) (Spitzer, M.

The nanoPOTS platform provided a robust, semi-automated nanodroplet-based proteomic processing system for handling extremely small biological samples down to as few as <NUM> cells with high processing efficiency and minimal sample loss. This capability opens up many potential biomedical applications from small cell populations and clinical specimens such as tissue sections for characterizing tissue or cellular heterogeneity. Reproducible quantitative proteome measurements with coverage of <NUM>-<NUM>,<NUM> protein groups from as few as <NUM> mammalian cells or single human islet cross sections (~<NUM> cells) from clinical specimens were demonstrated. While several previous efforts have pursued the analysis of <<NUM> cells, most of these methods lacked the robustness and reproducibility for biological applications because of the highly manual processes involved (Li, S. et al <NUM>, Chen, Q. et al <NUM>, Chen, W. <NUM>, and Waanders, L. The nanoPOTS platform not only provided unparalleled proteome coverage for analyzing <NUM>-<NUM> cells, but also offered a number of technical advantages for achieving a high degree of robustness and reproducibility for high throughput processing and quantitative measurements when coupled with LC-MS. First, the platform effectively addressed the bottleneck of sample losses during proteomics sample preparation by performing all of the multi-step reactions within a single nanodroplet of <<NUM> nL volume, while all previous methods still suffer from a significant degree of protein/peptide losses during processing. Second, the nanodroplet processing mechanism allowed us to perform each reaction at optimal concentrations. For example, by preserving the <NUM>-<NUM>:<NUM> ratio (Vandermarlier, E. et al) of protein to protease within the nanodroplet, the digestion rate and efficiency is potentially increased by orders of magnitude relative to a standard-volume preparation for the same number of cells. Finally, in addition to label-free quantification, other stable isotopebased quantification methods are readily adaptable to the workflow.

Compared with other microfluidic platforms having closed microchannels and chambers (White, A. <NUM>, and Zhu, Y. <NUM>), the nanoPOTS has an open structure, which is inherently suitable for integration with upstream and downstream proteomic workflows, including sample isolation for processing and transfer for LC-MS analysis.

To further explore potential applications involving characterization of substructures or molecular phenotyping of heterogeneous tissues such as human pancreas, the method was used to analyze cross-sections of individual human islets having a thickness of <NUM> (<FIG>) that were isolated by laser microdissection from clinical pancreatic specimens (<FIG>). The islet equivalents (IEQ) were calculated to be from <NUM> to <NUM>, corresponding to approximately <NUM> to <NUM> cells based on their volumes and a previous quantitative study (Zeiler, M. ) (Table <NUM>).

An average of <NUM>,<NUM> and a total of <NUM>,<NUM> protein groups were identified for the nine single islet slices; <NUM>,<NUM> protein groups were quantifiable with valid intensities and ><NUM> unique peptides in <NUM> out of <NUM> samples. The protein group identifications exceed those of previously reported single intact islets (Huang, E. et al <NUM>). Pairwise correlation analysis of protein LFQ intensity resulted in coefficients ranging from <NUM> to <NUM> (<FIG> and <FIG>), indicating a degree of islet heterogeneity. Gene Ontology analysis indicated that the proteome data provided coverage of cellular compartments similar to bulk analyses (<FIG>), demonstrating the nanoPOTS avoid biases in protein extraction from different compartments. <FIG> further illustrated the coverage of a network of proteins involved in vesicular transport, including the SNARE and Coatomer complex (Clair, G. ), an important function for secreting islet cells. This initial study indicates nanoPOTS will specifically enable studies of single islet heterogeneity using clinical specimens to explore islet pathology of type <NUM> or type <NUM> diabetes (Pisania, A. ), and more broadly enable clinical analysis of many otherwise inaccessible samples.

Currently, there is no residue/adhesive-free method available to transfer samples from LMD to small-volume reactor vessel for effective sample preparation of small samples. This method is broadly applicable and transferrable in the fields of proteomics, metabolomics, lipidomics, peptidomics, genomics, transcriptomics, etc., as analysis of small samples isolated by LMD is limited by interference of the adhesive capture material and by the large volumes require by the process.

Additionally, experiments showed that the nanoPOTS chip directly interfaced with fluorescence-activated cell sorting (FACS) for cell isolation. With the photolithography-based microfabrication technique, the nanodroplet array size and density can be easily scaled for increased preparation throughput.

While the current demonstrated limit is to analyze of as few as <NUM> cells, nanoPOTS represented a highly promising platform towards single mammalian cell proteomics with optimized processing volumes and further refinements to the LC-MS platform. To maximize the overall sensitivity of nanoPOTS for single cells, the total processing volume could be reduced to the lownanoliter range to further minimize sample loss. FACS or other cell isolation techniques should be used to isolate single cells into nanowells without the minimal exogenous contamination from, e.g., secreted proteins or lysed cells. NanoLC columns with narrower bore (Shen, Y. <NUM>, and Shen, Y. <NUM>), and ESI emitter technology accommodating the lower resulting flow rates (Smit, R. ) could be employed to improve the detection sensitivity of the LC-MS system. Finally, in addition to single cell analysis, nanoPOTS should also provide a viable path towards tissue imaging at the proteome level by performing in-depth spatially resolved proteome measurements for specific cellular regions.

Nanowells are prepopulated with DMSO droplets to serve as a sacrificial capture medium for small tissue samples in the nanoPOTS chip (<FIG>) as described below in detail.

Reagents and chemicals. Deionized water (<NUM> MΩ) generated from a Barnstead Nanopure Infinity system (Los Angeles, CA) was used throughout. Dithiothreitol (DTT) and iodoacetamide (IAA) were from ThermoFisher Scientific (St. Louis, MO), and their working solutions were freshly prepared in <NUM> ammonium bicarbonate buffer before use. n-dodecyl-β-D-maltoside (DDM), Mayer's hematoxylin, eosin Y (alcoholic solution), Scott's Tap Water Substitute, DMSO were purchased from Sigma-Aldrich. Trypsin (MS grade) and Lys-C (MS grade) were from Promega (Madison, WI). Other unmentioned reagents were obtained from ThermoFisher.

Nanowell chip fabrication. The nanowell chip consisted of three parts including a nanowell-containing substrate, a spacer, and a cover plate. The nanowell substrate was fabricated with the similar procedures described previously. (<NPL>; <NPL>) Briefly, a glass slide (<NUM> × <NUM>) with pre-coated chromium and photoresist (Telic company, Valencia, CA) was used as starting material. Standard photolithography and wet etching procedures were employed to generate an array of pedestals with a diameter of <NUM>, a height of <NUM>, and a spacing of <NUM> between adjacent pedestals on the slide. The exposed surfaces surrounding the pedestals were treated to be hydrophobic with <NUM>% (v/v) heptadecafluoro-<NUM>,<NUM>,<NUM>,<NUM>-tetrahydrodecyl)dimethylchlorosilane (PFDS) (Sigma Aldrich) in <NUM>,<NUM>,<NUM>-trimethylpentane. After removing the chromium layer, the pedestals maintained the hydrophilicity of untreated glass and served as nanoliter-scale wells for tissue collection and proteomic sample processing. The glass spacer was laser-machined (Coherent Inc. , Santa Clara, CA) on a standard <NUM>-mm-thick microscope slide. The machining process removed the center region of the slide, leaving a thin frame of ~ <NUM> in width. The machined slide was glued to the nanowell substrate using a silicone adhesive, and served as a spacer to limit the headspace of the nanowells after reversibly sealing to a cover plate to minimize evaporation during incubation steps, while prevent contact of the droplet reactors with the cover plate. The cover plate was produced by spin coating of a thin layer of Sylgard <NUM> and its curing reagent (<NUM>/<NUM>, v/v) (Dow Coming) at a spin speed of <NUM> rpm for <NUM> followed by <NUM> rpm for <NUM>. The cover plate was baked at <NUM> for <NUM> hours to generate a ~<NUM>-µm-thick polydimethylsiloxane (PDMS) layer.

Tissue preparation. Rats were anesthetized by intra-peritoneal injection of chloral hydrate. Rat brain was dissected and snap frozen in liquid nitrogen. The brains were stored at -<NUM> until use. A cryostat (NX-<NUM>, Thermo Scientific, St. Louis, MO) was used to cut tissues to a thickness of <NUM>. The chuck and blade temperatures were set as -<NUM> and -<NUM>, respectively. The tissue sections were deposited on PEN membrane slides (Carl Zeiss Microscopy, Germany) and stored at -<NUM>.

Before the hematoxylin and eosin (H&E) staining procedures, the tissue section was removed from the freezer or dry ice box and immediately immersed into <NUM>% ethanol to fix proteins. The tissue was then rehydrated in deionized water for <NUM> and stained in Mayer's hematoxylin solution for <NUM>. Excess dye was rinsed with water and the tissue was blued in Scott's Tap Water Substitute for <NUM>. Next, <NUM>% ethanol was used to dehydrate the tissue and a <NUM>% dilution of eosin Y solution (v/v in ethanol) was applied for <NUM>-<NUM> by a quick dip. The tissue sample was further dehydrated by immersion twice in <NUM>% ethanol for <NUM>, twice in <NUM>% ethanol for <NUM>, and finally in xylene for <NUM>. All the procedures were performed in a fume hood and the slide was blotted on absorbent paper between different solutions to minimize carry over. The processed tissue could be directly used for LCM or stored at -<NUM> until use.

Laser capture microdissection. Unless mentioned otherwise, an array of DMSO droplets with a volume of <NUM> nL were deposited on nanowells using a nanoliter-dispensing robotic system (<FIG>). A PALM microbeam laser capture microdissection system (Carl Zeiss MicroImaging, Munich, Germany) was employed. The nanowell chip was fixed on a standard adapter for microscope slide (SlideCollector <NUM>, Carl Zeiss MicroImaging) and then mounted on the robotic arm of the LCM system (<FIG>). The brain tissues were cut at an energy level of <NUM>, and catapulted into DMSO droplet using the "CenterRoboLPC" function with an energy level of delta <NUM> and a focus level of delta <NUM>. Tissue samples in the nanowell chip could be processed directly or stored at -<NUM>.

NanoPOTS proteomic sample processing. Before processing, DMSO droplets were evaporated to dryness by keeping the nanowell chip in a vacuum desiccator for <NUM> to <NUM> (<FIG>). Reagent dispensing was performed using the robotic system as described previously. (<NPL>; <NPL>; <NPL>) Briefly, <NUM> nL <NUM>× PBS buffer containing <NUM>% DDM surfactant and <NUM> DTT was added into each nanowell. The chip was incubated at <NUM> for <NUM> for protein extraction and denaturation. Proteins were then alkylated by adding <NUM> nL of <NUM> IAA in <NUM> ammonium bicarbonate (ABC) in each reaction and then incubating for <NUM> in the dark. A two-step digestion was performed at <NUM> with Lys-C and trypsin for <NUM> and <NUM>, respectively. Finally, the digested peptide samples were collected and stored in a fused silica capillary (<NUM> long, <NUM> i. Each nanowell was washed twice with <NUM> nL, <NUM>% formic acid aqueous buffer and the wash solution was also collected into the same capillary to maximize sample recovery. To prevent residual PEN membrane pieces be drawn into the collection capillary, the distance between the capillary distal end and the nanowell surface was kept at <NUM> during the sample aspiration process. The capillary was sealed with Parafilm at both ends and stored at -<NUM> until analyzed.

NanoLC-MS/MS for protein identification. Samples in the collection capillary were desalted and concentrated on a solid phase extraction (SPE) column (<NUM>-pm-i. fused silica capillary packed with <NUM>, <NUM>Å pore size C18 particles, Phenomenex, Terrence, CA). Peptides were separated using a <NUM>-cm-long, <NUM>-pm-i. nanoLC column (<NUM>, <NUM>Å pore size C18 particles, Phenomenex) with an integrated electrospray emitter (Self-Pack PicoFrit column, New Objective, Woburn, MA). A nanoUPLC pump (Dionex UltiMate NCP-3200RS, Thermo Scientific, Waltham, MI) was used to deliver mobile phase to the LC column. To obtain reproducible and smooth gradient profiles, a tee interface was used to split the LC flow rate from <NUM> nL/min to <NUM> nl/min for the <NUM>-pm-i. A linear <NUM>-min gradient starting from <NUM>% buffer B (<NUM>% formic acid in acetonitrile; buffer A: <NUM>% formic acid in water) to <NUM>%, followed by a <NUM>-min linear increase to <NUM>% buffer B. The column was washed with <NUM>% buffer B for <NUM> and re-equilibrated with <NUM>% buffer B for <NUM> prior to the subsequent analysis.

Peptides were ionized at the nanospray source using a potential of <NUM> kV. An Obitrap Fusion Lumos Tribrid MS (ThermoFisher) operated in data dependent mode to automatically switch between full scan MS and MS/MS acquisition with a cycle time of <NUM>. The ion transfer capillary was heated to <NUM> to accelerate desolvation, and the S lens was set at <NUM>. Full-scan MS spectra (m/z <NUM>-<NUM>) were acquired in the Orbitrap analyzer with <NUM>,<NUM> resolution (m/z <NUM>), and AGC target of <NUM> × <NUM><NUM>, and a maximum ion accumulation time of <NUM>. Precursor ions with charges from +<NUM> to +<NUM> were isolated with an m/z window of <NUM> and were sequentially fragmented by high energy dissociation (HCD) with a collision energy of <NUM>%. The AGC target was set at <NUM> × <NUM><NUM>. MS/MS scan spectra were acquired in the Orbitrap with an ion accumulation time of <NUM> and resolution of <NUM>,<NUM> for <NUM>-µm-diameter tissue sample, an ion accumulation time of <NUM> and <NUM>,<NUM> resolution for <NUM>-µm-diameter tissue sample, or an ion accumulation time of <NUM> and <NUM>,<NUM> resolution for <NUM>-µm-diameter tissue samples, respectively.

Data Analysis. Raw data were analyzed by MaxQuant <NUM>. <NUM> as previously described. Briefly, Andromeda engine was used to search MS/MS spectra against a UniProtKB/Swiss-Prot mouse database containing <NUM>,<NUM> reviewed entries. Carbamidomethylation was set as a fixed modification, and n-terminal protein acetylation and methionine oxidation were set as variable modifications. Recalibrated MS/MS spectra were matched with a tolerance of <NUM> ppm on precursor mass and <NUM> ppm on fragment mass. The minimum peptide length was set at <NUM> amino acids, and maximum peptide mass was <NUM> Da. Two missed cleavages were allowed for each peptide. A false discovery rate (FDR) of <NUM>% was applied for both peptide and protein filtering. For the spatially resolved study of brain tissue samples, Match Between Runs (MBR) was activated to enhance identification sensitivity. The time widows for feature alignment and match were <NUM>, and <NUM>, respectively. Label-free relative protein quantification (LFQ) was performed in each parameter group containing tissue samples of similar size.

Contamination and reverse identification was filtered with Perseus (version <NUM>. For relative quantification, the LFQ intensities were transformed with log2 function, and then filtered to contain > <NUM>% valid values in at least one group. The missing values were imputed by normal distribution in each column with a width of <NUM> and a down shift of <NUM>. To identify significant differences, ANOVA multiple sample test with permutation-based FDR control approach was used. P-value <<NUM>, q-value <<NUM>, and fold change ><NUM> (S0 = <NUM>) were required to obtain significant proteins. The results were exported to a table and visualized with OriginPro <NUM> and an online tool powered by R language.

The capture efficiency with square tissues having side lengths of <NUM>, <NUM>, <NUM>, and <NUM> using a <NUM>-µm-thick breast cancer tissue section from a previous study was evaluated. For smaller tissue samples with square side lengths from <NUM> to <NUM>, a total of <NUM> cuts were collected into three droplets for each size. For the largest tissue samples (<NUM>), a total of <NUM> cuts were collected. The "CenterRoboLPC" function, in which the catapult laser pulse was applied at the centroid of pre-cut tissue piece, was used instead of commonly-used "RoboLPC". The "CenterRoboLPC" function provided better control on the catapult trajectory of tissue pieces from slide to DMSO droplets. Under the optimized condition, the capture efficiencies ranged from <NUM>% to <NUM>% for smaller tissue samples (<NUM> to <NUM>), indicating the majority of LCM tissues can be collected (<FIG>). When tissue diameters were equal to or larger than <NUM>, all were successfully collected. With the increase of tissue sizes, the dissection time increased from <NUM> to <NUM> for each tissue sample. The high-speed dissection and high capture efficiencies, along with batch sample processing, should enable many applications requiring high-throughput proteomic studies such as large-scale mapping of heterogeneous tissues. It should also be noted that tissue pieces with a diameter of <NUM> correspond to single cells in most of mammalian tissues, demonstrating the potential of the present approach for single-cell isolation and analysis.

Proteomic analysis of LCM isolated rat brain tissues. To determine whether DMSO adversely affected tissue analysis, rat cortex tissue samples collected with DMSO droplets were analyzed, and compared with that obtained using manual transfer without DMSO. Surprisingly, a <NUM>% and <NUM>% increase in average and total unique peptide identifications was observed, respectively, resulting in the corresponding <NUM>% and <NUM>% increase in protein identifications, when DMSO was used for tissue collection (<FIG>). A Venn diagram of total protein identifications indicates that most of the proteins obtained from DMSO-free samples were included in that of DMSO-collected samples (<FIG>). This demonstrates that the use of DMSO droplets did not generate any negative effects on the proteomic analysis. On the contrary, proteome coverage significantly increased for small tissue samples. A possible explanation for this result is that protein extraction efficiency was improved after hydrophobic lipids were removed by DMSO in the brain tissue. Protein extraction from tissue samples was found to be more challenging than for cultured cells, especially for tissue containing high lipid content such as brain. Various approaches have been developed to address this challenge by employing strong detergents or organic solvent in the extraction buffer. As a type of organic solvent, DMSO is expected to have high solubility for most lipids, and thus could dissolve them prior to protein extraction. Compared with commonly used detergent approaches, sample losses in detergent removing steps including buffer exchange and spin columns was avoided using the inventive approach described herein. These merits of DMSO have thus provided an added benefit in the workflow of spatially-resolved proteomic analysis.

The sensitivity of the LCM-DMSO-nanoPOTS system on proteomic analysis of small tissue samples was tested. Rat cortex tissue with diameters of <NUM>, <NUM>, and <NUM> were used as model samples. Based on hematoxylin staining of cell nuclei provided by Allen brain atlas project, the corresponding cell numbers were ~<NUM>-<NUM>, <NUM>-<NUM>, and <NUM>-<NUM>, for the different tissue diameters, respectively. <FIG> and <FIG> show the linear increase of unique peptide and protein identifications with tissue size. As expected, nearly all peptides and proteins identified in the smaller tissues were also identified in larger tissues (<FIG>), demonstrating analytical sensitivity dominated the proteome coverage. The present system is capable of identifying an average of <NUM> ± <NUM>, <NUM> ± <NUM>, and <NUM> ± <NUM> protein groups (n=<NUM>) from cortex tissues with diameters of <NUM>, <NUM>, and <NUM>, respectively. Compared with previous spatially-resolved proteomic studies, in which at least millimeter-sized tissues were required to obtain a depth ><NUM> proteins, the LCM-DMSO-nanoPOTS system provided ><NUM> times better spatial resolution with higher proteome coverage.

The <NUM> total proteins identified from <NUM>-µm-diameter cortex tissues were submitted for Gene Ontology Cellular Component (GOCC) analysis. As shown in <FIG>, we observed a high percentage (<NUM>%) of membrane proteins and half of them (<NUM>%) were localized in plasma membrane, although no specific sample preparation procedures were used for membrane proteins. <NUM>% synapse proteins and <NUM>% axon proteins (not shown in <FIG>), which are vital for brain function, were also observed. In brain, the major neurotransmitters are glutamate and GABA, which play excitatory and inhibitory functions, respectively. In the plasma protein category, we identified three types of GABA receptors (GABRA1, GABRA2, GABRB1, GABRB2, GABRB2, and GABRG2), and a large family of glutamate receptors including DRIA1, DRIA2, DRIA3, DRIA4, GRM2, GRM3, GRM5, GPR158, GRIK3, GRIN1, GRIN2a, and GRIN2b.

Quantitative, spatially-resolved proteomic study of rat brain tissues. The performance of the LCM-DMSO-nanoPOTS system was evaluated for quantitative and spatially-resolved proteomic studies, we dissected and analyzed three different rat brain regions (cerebral cortex (CTX), corpus callosum (CC), and caudoputamen (CP)) from a <NUM>-µm-thick coronal section (<FIG>). Tissue samples were dissected with a diameter of <NUM>, corresponding to an area of ~<NUM><NUM>. The spatial distances (center to center) were from <NUM> to <NUM> between the same regions, and from <NUM> to <NUM>,<NUM> between different regions (<FIG>), showing the high spatial resolution of the present measurement. For each region, six samples were processed and four of them were submitted for LC-MS analysis (<FIG>).

To increase the quantifiable proteins, the Match Between Runs (MBR) algorithm of Maxquant was used, wherein the peptides were identified based on accurate intact masses and LC retention times (AMTs). A total <NUM> protein groups were identified and <NUM> (<NUM>%) were common across all the three brain regions. After stringent filtering for valid log2-transformed LFQ values, <NUM>,<NUM> protein groups were quantifiable. A high correlation with Pearson's correlation coefficients from <NUM> to <NUM> was observed between biological replicates of the same tissue regions, demonstrating excellent technical and biological reproducibility of the present system for quantification (<FIG>). Between different tissue regions, CTX and CP shows lower in correlation coefficients from <NUM> to <NUM>, while CC has lowest correlations (from <NUM> to <NUM>) with the other two regions. Such differences are also indicated in the morphology of the brain tissue (<FIG>).

The LCM-DMSO-nanoPOTS system was tested to see if it could be applied to distinguish different tissue types. Unsupervised principal component analysis (PCA) was used to process the LFQ intensity data from the <NUM> tissue samples. As shown in <FIG>, the three tissue regions were segregated based on component <NUM> and component <NUM>, which accounted for <NUM>% and <NUM>%, respectively. All four biological replicates were well clustered within the corresponding tissue region without overlap with other regions, suggesting the present system can efficiently distinguish tissue types based on their protein expressions.

To identify significant differences in protein expression among the three tissue regions, a multiple sample ANOVA test was employed with a permutation-based FDR algorithm, which is embedded in Perseus data analysis platform. Using a difference (S0) of <NUM>, p-value of <NUM>, and a FDR level of <NUM>, <NUM> out of total <NUM> quantifiable protein groups were identified to have significant differences. The most abundant proteins, such as Tuba1b, Tubb2a, Actb, Sptan1, Cltc, and Atp5b, were found to have no difference in LFQ intensity, which agrees well with previous report. For the <NUM> significant proteins, <NUM>, <NUM>, <NUM> proteins groups enriched in CTX, CC, and CP regions with fold change ><NUM> over their mean values were observed, respectively. To visualize the difference, we used unsupervised hierarchical clustering analysis (HCA) of the significant proteins (<FIG>). Similar to PCA plot, each four replicates from the same regions were clustered together. In addition, each region has distinct hot spots in protein abundance relative to other regions, indicating different biological functions existed in these regions.

The results described herein demonstrate that the LCM- capture liquid-nanoPOTS platform significantly advances spatially-resolved proteomics by improving the resolution and increasing the sensitivity. The use of DMSO droplets not only served to efficiently capture dissected tissue pieces as small as <NUM>-µm diameter (single-cell scale) into nanowells, but also significantly improved the proteome coverage. The whole workflow can be fully automated without manual transfer, and thus sample loss and protein contamination is minimized. This platform may play an important role in proteomic analyses and may be applied to various fields including biomedical research, clinical diagnosis, microbial community, and plant science. Finally, the LCM- capture liquid-nanoPOTS platform should be readily extended to other omics studies requiring tissue isolation and nanoscale processing, such as transcriptomics, lipidomics, and metabolomics.

Claim 1:
A method for preparing a biological sample (<NUM>), comprising the steps of
obtaining a biological tissue sample via tissue laser-capture microdissection;
providing a platform (<NUM>, <NUM>) including at least one reactor vessel (<NUM>, <NUM>, <NUM>) having one or more hydrophilic surfaces (<NUM>) configured for containment of the biological tissue sample, wherein the hydrophilic surfaces have a non-zero, total surface area less than <NUM><NUM>;
introducing a capture liquid (<NUM>) into or onto the at least one reactor vessel, wherein the capture liquid has a vapor pressure of less than or equal to <NUM> mbar at room temperature;
transferring a first volume of the biological tissue sample to the at least one reactor vessel that includes the capture liquid, wherein the first volume is a non-zero amount less than <NUM> nL;
fully or partly evaporating the capture liquid after transferring the first volume of the biological tissue sample to the at least one reactor vessel; subsequently
processing the biological tissue sample in the at least one reactor vessel to yield a processed sample, and
collecting a second volume of the processed sample.