Patent Publication Number: US-2023160809-A1

Title: Methods and systems of enhancing optical signals of extracellular vesicles

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of U.S. Patent Application Ser. No. 63/009,495, filed on Apr. 14, 2020. The disclosures of the prior applications are considered part of (and are incorporated by reference in) the disclosure of this application. 
     This invention was made with government support under Grant Nos. R00CA201248 and R21CA217662, awarded by National Cancer Institute at the National Institute of Health. The government has certain rights in the invention. 
    
    
     TECHNICAL FIELD 
     This invention relates to methods of enhancing optical signals of extracellular vesicles, and more particularly to a small numbers of extracellular vesicles. 
     BACKGROUND 
     Extracellular vesicles (EVs) present new opportunities as circulating biomarkers for cancers, cardiovascular, neurodegenerative, and infectious diseases among others. These cell-derived phospholipid vesicles are abundantly present in various bodily fluids (e.g., blood, cerebrospinal fluid, urine, saliva). More importantly, they carry a variety of biomolecules (lipids, proteins, and genetic materials) originating from their parental cells, which can be harnessed as a minimally invasive means to probe the molecular status of their cellular origins. 
     In further exploiting EVs&#39; potential and accelerating their clinical adaptation, a critical unmet need is to develop sensitive, robust, and standardized assays that can determine the composition and molecular profiles of EVs in clinical samples. However, their unique sizes (50-1000 nm) impose technical challenges in conventional analytical methods, which often lead to variable findings. Flow cytometry, for example, often underestimate EV counts; small EVs (i.e., exosomes, typically smaller than 200 nm) could be missed due to their weak light scattering, or a group of vesicles could be counted as a single event. Conventional methods, particularly for protein analyses (e.g., Western blotting, enzyme-linked immunosorbent assay/ELISA), consume large amounts of samples and involve time-consuming and extensive processing steps, making them impractical for clinical scenarios. Developing new molecular platforms for EVs is thus a pivotal mandate to translate EVs as clinically relevant biomarkers. 
     SUMMARY 
     The present disclosure relates to signal amplification strategies to boost optical signals generated from limited amounts of biomolecules present in individual EVs. In particular, the strong optical resonance of metallic nanostructure, e.g., gold or silver nanostructures, can be used to boost an optical signal to significantly amplify optical signals, e.g., fluorescent signals, emitted from EVs. Since the plasmon enhancement is an intrinsic signal amplification method, in certain embodiments, this intrinsic method is combined with other chemical amplification strategies (e.g., branched DNA barcodes) to improve the sensitivity even further. The present disclosure relates to methods and systems for enhancing optical signals of EVs. 
     According to a first aspect, the present disclosure provides nano-plasmonic arrays for detecting target extracellular vesicles (EVs), the arrays including or consisting of a substrate; a plurality of nanostructures arranged to form a periodic array of nanostructures on the substrate, wherein the periodic array of nanostructures is arranged and dimensioned to amplify one or more optical signals of electromagnetic radiation emitted, scattered, or reflected by EVs bound to the nanostructures and/or EVs bound to the substrate near the nanostructures, or to amplify one or more optical signals of electromagnetic radiation emitted, scattered, or reflected by reporter groups attached to the EVs; and one or more affinity ligands fixed on or adjacent to the nanostructures, wherein the affinity ligands selectively bind to target EVs to bind the target EVs to the nanostructures or to the substrate adjacent to the nanostructures. 
     In various embodiments, the optical signal can be one or more of a fluorescent signal, a Raman signal, or dark-field scattering. 
     In certain embodiments, the nanostructures can be or include or consist of a plurality of nanoholes arranged in an array and formed in the substrate or in a metal film disposed on the substrate. In other embodiments, the nanostructures can be or include or consist of a plurality of nanorods, nanodisks, or nanogrooves arranged in an array on a top surface of the substrate. 
     In some embodiments, each of the nanostructures has a maximum size, e.g., diameter, width, or length, of about 30 to 400 nm. For example, the nanostructures can be nanorods or nanosquares and have dimensions of about 50 to about 300 nm in length, about 20 to about 300 nm in width, and about 20 to about 300 nm in height, or the nanostructures can be nanodisks and have dimensions of about 50 to about 200 nm in diameter and about 20 to about 300 nm in height. In certain embodiments, the periodic array of nanostructures has a periodicity of about 400 to 800 nm between nanostructures. 
     In some embodiments, the affinity ligands bind to a capture agent, e.g., an antibody, wherein the capture agent is configured to bind to at least one surface marker on the target EV. In certain embodiments, the affinity ligand is configured to bind to at least one surface marker on the target EV and/or to at least one intravesicular marker inside the target EV. 
     In some embodiments, the nano-plasmonic arrays further include or consist of a metal film disposed on a top surface of the substrate, wherein the metal film comprises a plurality of nanoholes that penetrate the metal film in a periodicity selected to amplify one or more specific wavelengths of electromagnetic radiation, wherein the periodicity of about 400 to 800 nm between nanoholes, wherein the metal film comprises a plurality of affinity ligands fixed on or adjacent to the nanoholes, and wherein the plurality of affinity ligands selectively bind to markers on surfaces of the target EVs. The metal film can be or include, for example, a noble metal, a transition metal, an alkali metal, or any combination thereof. In various implementations, either or both the nanostructures and the metal film are, or include, or consist of gold, silver, aluminum, or platinum. 
     In another aspect, the disclosure includes methods for detecting target extracellular vesicles (EVs) in a liquid sample, the methods including or consisting of obtaining a nano-plasmonic array as described or claimed herein; flowing a liquid sample over the nano-plasmonic array at a flow rate that enables the EVs in the liquid sample, if any, to bind to the affinity ligands thus capturing the EVs on the nano-plasmonic array; labeling target EVs captured on the nano-plasmonic array with one or more reporter groups; projecting a first electromagnetic radiation at one or more specific wavelengths onto the labeled target EVs captured on the nano-plasmonic array, wherein the electromagnetic radiation at the one or more specific wavelengths is selected to cause the reporter groups to emit, scatter, or reflect the first electromagnetic radiation or a second electromagnetic radiation; receiving the first or second electromagnetic radiation emitted, scattered, or reflected by the reporter groups, wherein the nano-plasmonic array of nanostructures is arranged and dimensioned to amplify the first or second electromagnetic radiation emitted, scattered, or reflected by the reporter groups; and capturing an image of the amplified first or second electromagnetic radiation emitted, scattered, or reflected by the reporter groups. 
     In these methods, the number of the target EVs may be less than 1,000. 
     In certain embodiments, the nanostructures include a plurality of nanoholes that penetrate the substrate or a metal film disposed on the substrate, or the nanostructures include or consist of a plurality of nanorods, nanodisks, or nanogrooves arranged on a top surface of the substrate. 
     In some embodiments the methods as described herein further include or consist of identifying EVs by size and discarding large components, e.g., larger than one micron; selecting target EVs from the identified EVs based on positivity for target EV markers; selecting target EVs as originating from specific organs or tissues by positivity for organ- or tissue-specific markers to generate specific target EVs; and analyzing individual specific target EVs based on extravesicular biomarkers on the surface of the specific target EVs and/or based on intravesicular biomarkers within the specific target EVs. 
     In yet another aspect, the disclosure provides systems for detecting target extracellular vesicles (EVs) in a liquid sample, the systems including or consisting of a nano-plasmonic array as described and claimed herein; a sample control unit comprising a pump; at least one fluidic channel configured to flow a liquid sample over the nano-plasmonic array at a flow rate controlled by the pump that enables the EVs in the liquid sample, if any, to bind to the affinity ligands thus capturing the EVs on the nano-plasmonic array; and at least one affinity ligand, e.g., an antibody, to label target EVs captured on the nano-plasmonic array with one or more reporter groups; and an imaging unit comprising a light source configured to project electromagnetic radiation onto the labeled target EVs captured on the nano-plasmonic array; and an electromagnetic radiation detector, e.g., a camera or CCD, configured to receive electromagnetic radiation emitted, scattered, or reflected by the target EVs or reporter groups on the labeled target EVs captured on the nano-plasmonic array, and to capture an image of the labeled target EVs, wherein the electromagnetic radiation emitted, scattered, or reflected by scattered light reflected from the reporter groups has one or more wavelengths amplified by the periodic array of nanostructures. 
     In some embodiments of these systems, the nanostructures include or consist of a plurality of nanoholes arranged in an array and formed in the substrate or in a metal film disposed on the substrate. In other embodiments, the nanostructures include or consist of a plurality of nanorods, nanodisks, or nanogrooves arranged in an array on a top surface of the substrate. 
     In certain embodiments of the systems, each of the nanostructures has a maximum size, e.g., diameter, width, or length, of about 30 to 400 nm, and/or the nanostructures are nanorods or nanosquares and have dimensions of about 50 to about 300 nm in length, about 20 to about 300 nm in width, and about 20 to about 300 nm in height, or wherein the nanostructures are nanodisks and have dimensions of about 50 to about 200 nm in diameter and about 20 to about 300 nm in height. 
     In some embodiments, the affinity ligands bind to a capture agent, wherein the capture agent is configured to bind to at least one surface marker on the target EV. In other embodiments, the affinity ligand is configured to bind to at least one surface marker on the target EV and/or to at least one intravesicular marker inside the target EV. 
     In certain embodiments of the systems, the periodic array of nanostructures has a periodicity of about 400 to 800 nm between nanostructures. 
     Certain terms employed with this document are collected here. Unless stated otherwise, or implicit from context, the following terms and phrases include the meanings provided below. Unless explicitly stated otherwise, or apparent from context, the terms and phrases below do not exclude the meaning that the term or phrase has acquired in the art to which it pertains. The definitions are provided to aid in describing particular embodiments, and are not intended to limit the claimed invention, because the scope of the invention is limited only by the claims. 
     As used herein the term “comprising” or “comprises” is used in reference to compositions, methods, and respective component(s) thereof, that are useful to an embodiment, yet open to the inclusion of unspecified elements, whether useful or not. 
     The singular terms “a,” “an,” and “the” comprise plural referents unless context clearly indicates otherwise. Similarly, the word “or” is intended to include “and” unless the context clearly indicates otherwise. The abbreviation, “e.g.” is used herein to indicate a non-limiting example. 
     The term “periodicity,” as used herein, refers to a recurrence or repetition of a nanostructure at regular intervals by their positioning on the nanostructure. The term “periodic” as used herein therefore refers to the regular predefined pattern of nanostructures with respect to each other, such as a lattice or other repeating unit configuration. A random distribution of nanostructures is a periodic pattern. 
     “Surface plasmon resonance (SPR),” as used herein, refers to the physical phenomenon in which incident light stimulates collective resonant electron oscillations at planar metal surfaces, and in particular at interfaces between negative and positive permittivity materials stimulated by incident light. 
     The term “localized surface plasmon resonance (LSPR)” refers to surface plasmon resonance of nanometer-sized structures, such as a metallic nanoparticle. The oscillating electrons produce strong electromagnetic fields in the (non-conducting) ambient medium near the surface of the metal. 
     As used herein, “surface plasmons,” “surface plasmon polaritons,” or “plasmons” refer to the collective oscillations of free electrons at plasmonic surfaces, such as metals. These oscillations result in self-sustaining, surface electromagnetic waves that propagate in a direction parallel to the metal/dielectric (or metal/vacuum) interface. Because the wave is on the boundary of a metal and the external medium (air or water for example), these oscillations are very sensitive to any refractive index change of this boundary, such as, for example, the adsorption of a molecular target, such as an EV, to the metal surface. Additionally, the electromagnetic field strength decays exponentially from the metal surface to the surrounding environment (e.g., vacuum or dielectric). A maximum value of the electromagnetic field strength can be found at the metal/dielectric or metal/vacuum interface. 
     As used herein, the term “sample” means any biological or other fluids that may contain one or more extracellular vesicles (e.g., exosomes). Such biological fluids include, without limitation, fluids derived from or containing cells, organisms (bacteria, viruses), lysed cells or organisms, cellular extracts, nuclear extracts, components of cells or organisms, extracellular fluid, media in which cells or organisms are cultured in vitro, blood, plasma, serum, gastrointestinal secretions, ascites, homogenates of tissues or tumors, synovial fluid, feces, saliva, sputum, cyst fluid, amniotic fluid, cerebrospinal fluid, peritoneal fluid, lung lavage fluid, semen, lymphatic fluid, tears, pleural fluid, nipple aspirates, breast milk, external sections of the skin, respiratory, intestinal, and 0 genitourinary tracts, and prostatic fluid. A sample can be a viral or bacterial sample, a sample obtained from an environmental source, such as a body of polluted water, an air sample, or a soil sample, as well as a food industry sample. 
     A “biological sample” is derived or obtained from a living organism. The organism can be a whole organism or can be cells or organs grown in culture. In one embodiment, a “biological sample” also refers to a cell or population of cells or a quantity of tissue or fluid from a subject. Most often, a sample has been removed from a subject, but the term “biological sample” can also refer to cells or tissue analyzed in vivo, i.e., without removal from the subject. Often, a “biological sample” will contain cells from a subject, but the term can also refer to non-cellular biological material, such as non-cellular fractions of blood, saliva, or urine. In one embodiment, a biological sample is from a resection, bronchoscopic biopsy, or core needle biopsy of a primary, secondary, or metastatic tumor, or a cell block from pleural fluid. In addition, fine needle aspirate biological samples are also useful. In one embodiment, a biological sample includes primary ascites cells. 
     Biological samples also include explants and primary and/or transformed cell cultures derived from patient tissues. A biological sample can be provided by removing a sample of cells from a subject, but can also be accomplished by using previously isolated cells or cellular extracts (e.g., isolated by another person, at another time, and/or for another purpose). Archival tissues, such as those having treatment or outcome history may also be used. Biological samples include, but are not limited to, tissue biopsies, scrapes (e.g. buccal scrapes), whole blood, plasma, serum, urine, saliva, cell culture, or cerebrospinal fluid. The samples analyzed by the compositions and methods described herein may have been processed for purification or enrichment of EVs contained therein. 
     As used herein, an “extracellular vesicle” (“EV”) refers to a naturally occurring or synthetic vesicle that includes a cavity inside. The EVs comprise a lipid bilayer membrane enclosing contents of the internal cavity. An EV can include, but is not limited to, an ectosome, a microvesicle, a microparticle, an exosome, an oncosome, an apoptotic body, a liposome, a vacuole, a lysosome, a transport vesicle, a secretory vesicle, a gas vesicle, a matrix vesicle, or a multivesicular body. An EV has a dimension of up to about 10 microns, but are typically about 1000 nm or less, about 900 nm or less, about 800 nm or less, about 700 nm or less, about 600 nm or less, about 500 nm or less, about 450 nm or less, about 400 nm or less, about 350 nm or less about 300 nm or less, about 250 nm or less, about 240 nm or less, about 230 nm or less, about 220 nm or less, about 210 nm or less, about 200 nm or less, about 190 nm or less, about 180 nm or less, about 170 nm or less, about 160 nm or less, about 150 nm or less, about 140 nm or less, about 130 nm or less, about 120 nm or less, about 110 nm or less, about 100 nm or less, about 90 nm or less, about 80 nm or less, about 70 nm or less, about 60 nm or less, about 50 nm or less, about 40 nm or less, about 30 nm or less, about 20 nm or less, or about 10 nm or less. 
     Exosomes, or microvesicles, are a type of EV, and can be shed by eukaryotic cells, or budded off of the plasma membrane, to the exterior of the cell. These membrane-bound vesicles are heterogeneous in size with diameters ranging from about 10 nm to about 5000 nm. The methods and compositions described herein are equally applicable for microvesicles of all sizes. 
     In some of the literature, the term “exosome” also refers to protein complexes containing exoribonucleases that are involved in mRNA degradation and the processing of small nucleolar RNAs (snoRNAs), small nuclear RNAs (snRNAs) and ribosomal RNAs (rRNA). Such protein complexes do not have membranes and are not “microvesicles” or “exosomes,” and thus are not EVs, as those terms are used here in. 
     As used herein, the term “patient” and “subject” are used interchangeably to refer to a human or animal, such as a vertebrate, e.g., a mammal. Examples of mammals include, without limitation, primates, rodents, domestic animals, or game animals. Primates include chimpanzees, monkeys, and macaques, e.g., Rhesus. Rodents include mice, rats, rabbits, and hamsters. Domestic and game animals include cows, horses, pigs, deer, buffalo, feline species, e.g., domestic cat, canine species, e.g., dog, fox, avian species, e.g., chicken, duck, and ostrich, and fish, e.g., trout, bass, and salmon. Patient or subject includes any subset of the foregoing, e.g., all of the above, but excluding one or more groups or species such as humans, primates, or rodents. In certain embodiments of the aspects described herein, the subject is a mammal, e.g., a primate, e.g., a human. A subject can be male or female. Additionally, a subject can be any stage of development, e.g., embryo, fetus, infant, child, pre-adolescent, adolescent, young adult, mature adult, and elderly adult. The female subject can be pregnant or not. 
     In one embodiment, the subject can be a patient or other subject in a clinical setting. The subject can be suspected of, or at risk for, having or developing a disease or disorder, or may have already been diagnosed as having a disease or disorder. The subject may be undergoing treatment. 
     As used herein, a “capture agent” refers to any agent having specific binding for an EV (e.g., an exosome). Binding may be to a marker, e.g., biomarker, that is present on all EVs, or to a subset of EVs. Typically the capture agent specifically binds to a biomarker fully or partially present on the external surface of the EVs (referred herein as an extravesicular marker), although in one embodiment, the capture agent specifically binds to a marker that is present on the interior of the EV (referred herein as an intravesicular marker). The capture agent is immobilized on the surface of a plasmonic nanostructure that is contacted to the sample (e.g., the sensing area). Examples of capture agents include, without limitation, nucleic acids, oligonucleotides, peptides, polypeptides, aptamers, antigens, polyclonal antibodies, monoclonal antibodies, single chain antibodies (scFv), antibody portions, F(ab) fragments, F(ab′) 2  fragments, Fv fragments, small organic molecules, polymers, compounds from a combinatorial chemical library, inorganic molecule, or any combination thereof. 
     A “nucleic acid,” as described herein, can be RNA or DNA, and can be single or double stranded, and can be, for example, a nucleic acid encoding a protein of interest, a polynucleotide, an oligonucleotide, a nucleic acid analogue, for example peptide-nucleic acid (PNA), pseudo-complementary PNA (pc-PNA), locked nucleic acid (LNA) etc. Nucleic acid sequences include, for example, but are not limited to, nucleic acid sequences that act as transcriptional repressors, antisense molecules, ribozymes, small inhibitory nucleic acid sequences, for example, but not limited to, RNAi, shRNAi, siRNA, micro RNAi (mRNAi), antisense oligonucleotides etc. 
     As used herein, the term DNA is defined as deoxyribonucleic acid. The term “polynucleotide” is used herein interchangeably with “nucleic acid” to indicate a polymer of nucleosides. Typically a polynucleotide is composed of nucleosides that are naturally found in DNA or RNA (e.g., adenosine, thymidine, guanosine, cytidine, uridine, deoxyadenosine, deoxythymidine, deoxyguanosine, and deoxycytidine) joined by phosphodiester bonds. However the term encompasses molecules including nucleosides or nucleoside analogs containing chemically or biologically modified bases, modified backbones, etc., whether or not found in naturally occurring nucleic acids, and such molecules can be used for certain applications. 
     The term “polypeptide” as used herein refers to a polymer of amino acids. The terms “protein” and “polypeptide” are used interchangeably herein. A peptide is a relatively short polypeptide, typically between about 2 and 60 amino acids in length. Polypeptides used herein typically contain amino acids such as the 20 L-amino acids that are most commonly found in proteins. However, other amino acids and/or amino acid analogs known in the art can be used. One or more of the amino acids in a polypeptide may be modified, for example, by the addition of a chemical entity such as a carbohydrate group, a phosphate group, a fatty acid group, a linker for conjugation, functionalization, etc. A polypeptide that has a nonpolypeptide moiety covalently or noncovalently associated therewith is still considered a “polypeptide.” Examples of modifications include glycosylation and palmitoylation. Polypeptides can be purified from natural sources, produced using recombinant DNA technology, synthesized through chemical means such as conventional solid phase peptide synthesis, etc. An “antigen” is defined herein as a substance inducing an immune response. The antigenic determinant group is termed an epitope, and the epitope in the context of a carrier molecule (that can optionally be part of the same molecule, for example, botulism neurotoxin A, a single molecule, has three different epitopes. Usually antigens are foreign to the animal in which they produce immune reactions. 
     As used herein, “antibodies” can include polyclonal and monoclonal antibodies and antigen-binding derivatives, or portions or fragments thereof. Well-known antigen binding fragments include, for example, single domain antibodies (dAbs; which consist essentially of single VL or VH antibody domains), Fv fragment, including single chain Fv fragment (scFv), Fab fragment, and F(ab′) 2  fragment. Methods for the construction of such antibody molecules are well known in the art. As used herein, the term “antibody” refers to an intact immunoglobulin or to a monoclonal or polyclonal antigen-binding fragment with the Fc (crystallizable fragment) region or FcRn binding fragment of the Fc region. Antigen-binding fragments can be produced by recombinant DNA techniques or by enzymatic or chemical cleavage of intact antibodies. “Antigen-binding fragments” include, inter alia, Fab, Fab′, F(ab′)2, Fv, dAb, and complementarity determining region (CDR) fragments, single-chain antibodies (scFv), single domain antibodies, chimeric antibodies, diabodies and polypeptides that contain at least a portion of an immunoglobulin that is sufficient to confer specific antigen binding to the polypeptide. The terms Fab, Fc, pFc′, F(ab′)2 and Fv are employed with standard immunological meanings (see, e.g., Klein, Immunology (John Wiley, New York, N.Y., 1982); Clark, W. R. (1986) The Experimental Foundations of Modern Immunology (Wiley &amp; Sons, Inc., New York); and Roitt, I. (1991) Essential Immunology, 7th Ed., (Blackwell Scientific Publications, Oxford). 
     The term “reporter group,” as used herein, refers to a composition capable of producing or enhancing a detectable optical signal indicative of the presence of the target in a sample. Examples of reporter groups include fluorescent molecules, such as fluorescein isothiocyanate (FITC), tetramethylrhodamine (TRITC), Alexa Fluor® 488, Cy3, Cy5, Cy5.5, and Cy7; small molecules for Raman signals, such as benzenethiol, 4,4′-bipyridine, and R6G; and nanoparticles made of metal, semiconductor, plastic, polymer, and glass. 
     The terms “label,” as used herein, refer to a composition capable of producing or enhancing a detectable signal indicative of the presence of the target in a sample. “Labeling” is referred herein as a primary antibody conjugated with a biomarker, or a secondary antibody conjugated with a primary antibody. “Labeling” is referred herein as an EV labeling with a capture agent. 
     As used herein, the term “marker” or “biomarker” refers to a molecule that is associated with an EV, and can bind to a capture agent for detecting the EV. A marker can be any components of an EV that can be recognized by a capture agent. Examples of markers include, without limitation, proteins, or nucleic acids or a component of the lipid bilayer that makes up the membrane of the EV. Useful markers include receptors (e.g., extracellular) and channel components. A marker can be either an extravesicular or an intravesicular marker, as defined herein. A marker can be present on all EVs in a sample, or on a subset of EVs in a sample. A marker that is common to all EVs in a sample is referred to herein as a pan-EV marker. 
     As used herein, when one element is “fixed” to another, the two elements are directly connected via bonds, e.g., ionic, covalent, polar, or hydrogen bonds. When two elements are “bound” to each other, the two elements are directly or indirectly connected, via bonds, or via other elements, such as linker groups, e.g., PEG or affinity ligands as described herein. 
     An “affinity ligand” is defined herein as a molecule that is directly attached or fixed to a molecular spacer or to a substrate or nanostructure, and is also directly attached to a capture agent or a molecular spacer. Stated another way, an affinity ligand physically links a molecular spacer (or substrate or nanostructure) and a capture agent (or molecular spacer) together. In one embodiment, the affinity ligand is a first member of a specific binding pair. In such an embodiment, the capture agents may be the second member of the specific binding pair. Examples of such specific binding pairs include, without limitation, antigens, antibodies, haptens, oligonucleotides, polynucleotides, avidin, streptavidin, hormones, receptors, lectins, carbohydrates, IgG protein A, and nucleic acid binding proteins. An affinity ligand can include, but is not limited to, a nucleic acid, oligonucleotide, peptide, polypeptide, antigen, polyclonal antibody, monoclonal antibody, single chain antibody (scFv), an antibody portion, F(ab) fragment, F(ab′) 2  fragment, Fv fragment, small organic molecule, polymer, compounds from a combinatorial chemical library, inorganic molecule, or any combination thereof. 
     Examples of specific binding pairs include antigen-antibody, hapten-antibody or antibody-antibody pairs, complementary oligonucleotides or polynucleotides, avidin-biotin, streptavidin-biotin, hormone-receptor, ligand-receptors, lectin-carbohydrate, IgG-protein A, nucleic acid-nucleic acid binding protein, and nucleic acid-anti-nucleic acid antibody. 
     As used herein, the term “specific binding” refers to a chemical interaction between two molecules, compounds, cells and/or particles wherein the first entity binds to the second, target entity with greater specificity and affinity than it binds to a third entity which is a non-target. In some embodiments, specific binding can refer to an affinity of the first entity for the second target entity that is at least 10 times greater than the affinity for the third non-target entity. A reagent specific for a given target is one that exhibits specific binding for that target under the conditions of the assay being utilized. In certain embodiments, specific binding is indicated by a dissociation constant on the order of ≤10 −8  M, ≤10 −9  M, ≤10 −10  M or below. 
     Polyethylene glycol (PEG) is referred to herein as a possible component of the nano-plasmonic array and is used as a molecular spacer. A variety of forms and combinations of PEG are envisioned for use as such spacers. Polyethylene glycol (PEG) is a polyether compound with many applications from industrial manufacturing to medicine. The structure of PEG is (note the repeated element in parentheses): H—(O—CH 2 —CH 2 ) n —OH. PEG is also known as polyethylene oxide (PEO) or polyoxyethylene (POE), depending on its molecular weight. PEG, PEO, or POE refers to an oligomer or polymer of ethylene oxide. The three names are chemically synonymous, but as used herein, PEG refers to oligomers and polymers with a molecular mass below 20,000 g/mol, PEO refers to polymers with a molecular mass above 20,000 g/mol, and POE refers to a polymer of any molecular mass. PEG and PEO are liquids or low-melting solids, depending on their molecular weights. Different forms of PEG are also available, depending on the initiator used for the polymerization process—the most common initiator is a monofunctional methyl ether PEG, or methoxypoly(ethylene glycol), abbreviated mPEG. Lower-molecular-weight PEGs are also available as purer oligomers, referred to as monodisperse, uniform, or discrete. Branched PEGS have three to ten PEG chains emanating from a central core group. Star PEGS have 10 to 100 PEG chains emanating from a central core group. Comb PEGS have multiple PEG chains normally grafted onto a polymer backbone. 
     A “long-chain polyethylene glycol (PEG)” or “long PEG” is defined herein as a PEG polymer having a molecular weight equal to or higher than 750 Da. 
     A “short-chain PEG” or “short PEG” is defined herein as a PEG polymer having a molecular weight equal to or less than 500 Da. 
     As used herein, “expression level” refers to the number of mRNA molecules and/or polypeptide molecules encoded by a gene of interest that are present in a cell or sample. 
     Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. Methods and materials are described herein for use in the present disclosure; other, suitable methods and materials known in the art can also be used. The materials, methods, and examples are illustrative only and not intended to be limiting. All publications, patent applications, patents, sequences, database entries, and other references mentioned herein are incorporated by reference in their entirety. 
     The details of one or more embodiments of the invention are set forth in the accompanying drawings, description, and the claims. Other features, objects, and advantages of the invention will be apparent from the description and drawings, and from the claims. 
    
    
     
       DESCRIPTION OF DRAWINGS 
         FIG.  1 A  to  FIG.  1 G  illustrate an example of a nano-plasmonic array for multiplexed single EV analysis.  FIG.  1 A  is a diagram of the procedural steps of multiplexed single EV analysis.  FIG.  1 B  is an image of periodic nanoholes in the nano-plasmonic array.  FIG.  1 C  is an image showing the enhanced electromagnetic fields on the nanohole surface of the nano-plasmonic array.  FIG.  1 D  are images of fluorescent nanospheres on glass and the nano-plasmonic substrates.  FIG.  1 E  is a histogram of pixel intensities of a glass substrate and the nano-plasmonic substrate.  FIG.  1 F  is a bar graph showing fluorescence intensity of fluorescent nanostructures on glass and the nano-plasmonic substrate.  FIG.  1 G  is an image showing a magnified view of the nano-plasmonic array. 
         FIG.  2 A  to  FIG.  2 B  illustrate an example of an optical system for the nano-plasmonic array for multiplexed single EV analysis.  FIG.  2 A  is a schematic diagram of an optical system for multiplexed single EV analysis.  FIG.  2 B  is a microscope image of the nano-plasmonic array. 
         FIG.  3 A  to  FIG.  3 J  are images and graphs of fluorescence used to characterize a system for detecting target EVs.  FIG.  3 A  is a series of images of substrates coated different fluorophore-conjugated biotin-binding proteins.  FIG.  3 B  is a bar graph showing intensity profiles of different fluorophore-conjugated biotin-binding proteins.  FIG.  3 C  is a bar graph showing fluorescence intensity of different fluorophore-conjugated biotin-binding proteins.  FIG.  3 D  is a graph showing the absorption/emission spectra of different fluorophore-conjugated biotin-binding proteins.  FIG.  3 E  is a pair of images of EVs captured on glass and substrates of the nano-plasmonic arrays.  FIG.  3 F  is a graph showing intensities of captured EVs on glass and substrates of the nano-plasmonic arrays.  FIG.  3 G  is a bar graph showing the number of EVs on glass and substrates of the nano-plasmonic arrays.  FIG.  3 H  is a pair of images of EVs captured on glass and substrates of the nano-plasmonic arrays with an adhesive layer.  FIG.  3 I  is a graph showing intensities of captured EVs on glass and substrates of the nano-plasmonic arrays with an adhesive layer.  FIG.  3 J  is a bar graph showing the number of EVs on glass and substrates of the nano-plasmonic arrays with an adhesive layer. 
         FIG.  4 A  to  FIG.  4 C  illustrate single EV measurements.  FIG.  4 A  is a series of microscope images of captured EVs with different fluorescent antibodies.  FIG.  4 B  is a series of graphs showing intensities of EVs with different fluorescent antibodies in different positions on the substrate.  FIG.  4 C  is a series of bar graphs showing the number/fraction of EVs with different fluorescent antibodies. 
         FIG.  5 A  to  FIG.  5 E  illustrate measurements of tumor markers of captured EVs.  FIG.  5 A  is a series of microscope images of EVs labeled against tumor marker EGFR.  FIG.  5 B  is a series of microscope images of EVs labeled against tumor marker EGFRvIII.  FIG.  5 C  is a representation of gels showing EGFR expression in different EVs.  FIG.  5 D  is a bar graph showing EGFR fractions in different EVs.  FIG.  5 E  is a bar graph showing EGFRvIII fractions in different EVs. 
         FIG.  6 A  to  FIG.  6 G  illustrate measurements of tumor markers in EV-spiked plasma samples.  FIG.  6 A  is a flow chart showing a decision tree to classify EV populations.  FIG.  6 B  and  FIG.  6 C  are series of graphs showing EV populations positive for different markers.  FIG.  6 D  to  FIG.  6 G  are bar graphs showing EV detection and marker profiling. 
         FIG.  7 A  to  FIG.  7 B  illustrate an example characterization of EVs isolated from Gli36-WT and Gli36-EGFRvIII.  FIG.  7 A  is a graph showing a size distribution of EVs isolated from Gli36-WT and Gli36-EGFRvIII.  FIG.  7 B  is a series of images of gels showing expression levels of EVs isolated from Gli36-WT and Gli36-EGFRvIII. 
         FIG.  8 A  to  FIG.  8 B  illustrate an example characterization of EVs varied in concentrations.  FIG.  8 A  is a series of microscope images of EVs labeled against different markers.  FIG.  8 B  is a series of bar graphs showing the fractions of EVs labeled against different markers under different EV concentrations. 
         FIG.  9 A  to  FIG.  9 B  illustrate negative controls to demonstrate test sensitivity and specificity of the captured EVs.  FIG.  9 A  is a pair of microscope images showing sensitivity and specificity of EVs labeled against EGFR.  FIG.  9 B  is a pair of microscope images showing sensitivity and specificity of EVs labeled against EGFRvIII. 
         FIG.  10 A  to  FIG.  10 D  is a series of schematic diagrams that illustrate an example of a method for fabricating a nano-plasmonic array including gold nanorods.  FIG.  10 A  is a schematic diagram of a step of imprinting nanorods on a resist layer.  FIG.  10 B  is a schematic showing a step of patterning an imprinting mold.  FIG.  10 C  is a schematic diagram of a step of depositing gold nanorods and lifting off of the mask.  FIG.  10 D  is a schematic diagram that shows the use of the nano-plasmonic array to detect EVs that bind to the gold nanorods. 
         FIG.  11 A  to  FIG.  11 D  illustrate an example of a method for detecting target EVs.  FIG.  11 A  is a schematic diagram that illustrates an example of a nano-plasmonic array including nanorods for plasmon-enhanced single EV sensing technology (NEXT).  FIG.  11 B  is a representation of a microscope image of an EV bound to a nanorod.  FIG.  11 C  is a representation of a microscope image of a nanorod array.  FIG.  11 D  is a representation of a microscope image of EVs bound to different locations on nanorod. 
         FIG.  12 A  is a graph showing scattering intensities of nanorods of different sizes. 
         FIG.  12 B  is a graph showing scattering intensities changes in dark field imaging. 
         FIG.  13 A  to  FIG.  13 H  illustrate finite-difference time-domain (FDTD) simulations showing optical resonances of nanorod and nanodisk arrays in different sizes.  FIG.  13 A  is a representation of side view of EV captured on a nanorod.  FIG.  13 B  is a representation of a top view of a nanodisk.  FIG.  13 C  is an image of top view of a nanodisk.  FIG.  13 D  is a graph showing scattering intensities of nanodisks having different diameters.  FIG.  13 E  is a graph showing peak shifts of nanodisks having different diameters.  FIG.  13 F  is a representation of a top view of a nanorod.  FIG.  13 G  is a graph showing scattering intensities of nanorods having different diameters.  FIG.  13 H  is a graph showing peak shifts of nanorods having different diameters. 
         FIG.  14 A  to  FIG.  14 F  illustrate three FDTD simulation scenarios showing spectral shifts of dark-field scattering upon EV binding to nanodisks in different locations and distances to the substrate.  FIG.  14 A  is a representation of Scenario 1 of a first EV binding location and its detected peak wavelength, along with a corresponding graph.  FIG.  14 B  is a representation of Scenario 2 of a second EV binding location and its detected peak wavelength, along with a microscope image showing electromagnetic waves.  FIG.  14 C  is a representation of Scenario 3 of a third EV binding location and its detected peak wavelength, along with a microscope image showing electromagnetic waves.  FIG.  14 D  is a representation of dark-field scattering spectra of nanodisks upon EV binding in varying distances (z) to the surface, showing the different level of spectral shifts depending on the distance between the EV and nanodisk.  FIG.  14 E  is a representation of electromagnetic fields in a cross-section of a nanodisk on a glass substrate, showing the concentrated electromagnetic fields on top of the nanodisk surface.  FIG.  14 F  is a presentation of electromagnetic fields on a nanodisk on a glass substrate in a top view, showing the concentrated electromagnetic fields along the sides of the nanodisk. 
         FIG.  15 A  to  FIG.  15 D  illustrate an example of EV binding detection by measuring dark-field scattering intensity changes.  FIG.  15 A  is a representative dark-field scattering image of nanodisks before EV binding.  FIG.  15 B  is a representative dark-field scattering image of nanodisks after EV binding.  FIG.  15 C  is a representative graph showing the changes in the dark-field scattering intensity over time for EVs on the nanodisk shown in  FIG.  15 A  and  FIG.  15 B .  FIG.  15 D  is a representative graph showing the changes in the dark-field scattering intensity over time for controls on the nanodisk shown in  FIG.  15 A  and  FIG.  15 B . 
         FIG.  16 A  to  FIG.  16 C  illustrate an example of EV binding detection by measuring spectral shifts of dark-field scattering of nanodisks. 
         FIG.  17    illustrates plasmon enhancements of dark-field and three different fluorescent signals (TRITC, Cy5, and Cy5.5) for different diameters of nanodisks. 
         FIG.  18 A  to  FIG.  18 D  illustrate an example plasmon enhancements of dark-field and fluorescence signals in different sizes of nanodisks.  FIG.  18 A  is a plasmon intensity of dark-field corresponding to different diameters of nanodisks.  FIG.  18 B  is a plasmon intensity of TRITC corresponding to different diameters of nanodisks.  FIG.  18 C  is a plasmon intensity of Cy5 corresponding to different diameters of nanodisks.  FIG.  18 D  is a plasmon intensity of Cy5.5 corresponding to different diameters of nanodisks. 
     
    
    
     Like reference symbols in the various drawings indicate like elements. 
     DETAILED DESCRIPTION 
     The present disclosure relates to systems, methods, and devices for detecting target extracellular vesicles (EVs). EVs carry multiple surface biomarkers, which can be used as indicators to monitor or diagnose certain diseases, e.g., cancers, cardiovascular, neurodegenerative, and infectious diseases among others. In particular, the new systems and methods can be used for detecting and diagnosing Alzheimer&#39;s and other neurodegenerative diseases as well as detecting viruses, bacteria, and/or parasites, e.g., by analyzing immune cells that contain materials from the infective agents. 
     However, their unique sizes (50-1000 nm) impose technical challenges in conventional analytical methods, which often lead to variable findings. In addition, EVs often provide weak detection signals, especially when the EV sample does not include a sufficient number of EVs or when there is a low abundance of protein and/or intravesicular markers, which can make it challenging to perform sensitive, robust, and standardized assays that can determine the composition and molecular profiles of EVs in clinical samples. 
     The present disclosure provides a solution to these problems and enables targeting single EVs by amplifying their individual optical signals to achieve an accurate and precise multiplexed analysis of the target EV. Analyzing single EVs can reveal unique molecular profiles of cell-specific EVs, which will further promote clinical use of EVs, e.g., to construct a comprehensive EV “atlas” per different biological parameters (e.g., cellular origin, cell state). 
     The nano-plasmonic systems of the present disclosure enable multiplexed single EV analyses of target membrane and intravesicular markers with improved sensitivities. Specifically, the optical signal, e.g., fluorescence, is amplified using plasmonic metallic nanostructures to provide sensitive, multi-channel EV biomarker profiling. The enhancement can be achieved, for example, by using a substrate with a periodic array of nanostructures, such as nanoholes, nanorods, nanodisks, nanowells, nanosquares, nanogrooves, or any suitable periodic metallic nanostructures. A copper or aluminum film or substrate can be used for UV illumination, and silver and gold can be used for visible wavelength illumination. In general, the substrate, if used under a metal film, is a non-metal, non-conducting substrate such as glass or plastic, but metal, metal oxides, and semiconductors can also be used as substrates. 
     For example, a periodic array of Au nanoholes support surface plasmon resonances extended in a long range (about 100 nm) which is suitable for EVs. Furthermore, the resonance wavelength can be readily tuned by adjusting the nanohole periodicity and size. The same can be done with nanostructures in the form of nanorods or nanodisks. In one embodiment, the nano-plasmonic extracellular vesicle analysis with enhanced fluorescence detection (nPLEX-FL) described herein, along with similar methods using other optical signals, provide a simple, robust signal amplification strategy that improves the detection sensitivity and achieves multiplexed EV analysis. 
     Preparation of Nano-Plasmonic Arrays 
     The nano-plasmonic arrays used herein include a substrate, a plurality of nanostructures on or in the substrate, and a plurality of affinity ligands fixed on or adjacent to the nanostructures. Different surface chemistries (conjugates to affinity ligands) can be used for the metals used to make the nanostructures and the substrates (e.g., glass) to selectively fix the affinity ligands to the nanostructures, e.g., on the surface of nanorods or nanodisks, within nanogrooves, and on walls within the nanoholes or on the substrate or metal film adjacent to the nanoholes. The plurality of nanostructures are arranged to form a periodic array of nanostructures on the substrate, and the periodic array of nanostructures is arranged and dimensioned to amplify one or more specific wavelengths of electromagnetic radiation. 
     In some embodiments, the plurality of affinity ligands is fixed on or adjacent to the nanostructures, and the plurality of affinity ligands selectively bind to target EVs via a capture agent. Different types of affinity ligands can be used in the nano-plasmonic arrays based on a corresponding EV preparation. For example, among high affinity binding pairs, the substrate of the nano-plasmonic arrays selects a biotin-binding protein (e.g., avidin) as their affinity ligands attached on the substrate, then the target EVs are required to comprise a corresponding biotin as the capture agent to be captured by the nano-plasmonic arrays. In some embodiments, the substrate comprises semiconductors, non-conductors, plastics, or any suitable transparent substrates. Methods for attaching a corresponding capture agent to EVs are described below. 
     We developed an advanced nano-plasmonic EV sensing platform for single EV analyses using a new nano-plasmonic sensing platform for single EV detection. Termed NEXT (nanostructure-based extracellular vesicle technology), the system includes arrays of metal, e.g., gold, silver, copper, or aluminum, nanostructures, e.g., nanoholes, nanorods, or nanodisks, in sub-200 nm dimension that can be occupied by single EVs. For example, gold nanorods have a high sensitivity down to single molecule detection and precise tunability of resonant wavelengths by adjusting nanorod dimensions. The arrays of nanostructures can be made using standard nanoimprint lithography techniques with good reproducibility through advanced imprinting and deposition processes. 
     The capture of individual EVs on each nanostructure, e.g., nanorod, induces a spectral shift; those shifts from nanorod arrays will be simultaneously detected by dark-field imaging. Extensive validation studies are performed to benchmark 1) single EV detection sensitivity; 2) specificity for capture a target EV subpopulation; and 3) robustness and reproducibility. 
     Dense arrays (e.g., 10 5  array per cm 2 ) of metal, e.g., gold, nanostructures, e.g., nanorods, can be made using a new nanoimprint lithography method that can pattern gold nanorod arrays in a wafer-scale through simple imprinting and gold deposition processes ( FIGS.  10 A- 10 D ). The technique utilizes a reusable silicon mold with nano-patterns that are transferred to a target substrate coated with a thin resist layer ( FIGS.  10 A- 10 B ). After imprinting, gold is deposited onto the patterned area; subsequent removal of the resist will leave nanorod arrays on a glass substrate ( FIG.  10 C ). We previously made various metallic nanostructures in high densities using a known fabrication method (ACS Nano 2011, 5, 7555-7564, 2011; Anal. Chem., 84, 6031-6039, 2012). These methods can be used to produce 10 chips per each fabrication cycle in 4 hours or 100 chips in a week (10 cycles×10 chips per cycle) using a typical nanofab facility, which is a sufficient chip production rate for subsequent biological experiments. This mass production process is simple, reproducible, and scalable. E-beam lithography can also be used to pattern nanostructure with high precision. 
     Periodic nanoholes are made by patterning a thin (50 to 200 nm thick) gold film on a substrate. Nanoholes can be directly patterned by focused ion-beam milling or through lithography and metal etching. Deep ultraviolet (DUV) lithography is used to make 200 nm periodic circular patterns on a resist spun-coated on the gold film. Furthermore, the underlying gold film is etched by reactive ion etching or ion milling using the resist as an etch mask. Resist removal reveals gold nanohole patterns made in the gold film. 
     Array chips were designed through comprehensive three-dimensional computational calculations, and we found in one example that the nanorod dimension of 80 nm (length)×30 nm (width)×20 nm (height) achieved maximum sensitivity for 100-nm EV (mean diameter) detection, and array sensor dimensions and sensitivities are experimentally tested. The sub-100 nm dimension of gold nanorods also allows single EV capture on each nanorod. Gold nanorod arrays with 3 μm separation between nanorods allow even distribution of EVs on the nanorod arrays; signals from individual EVs are clearly resolved using a 10× or higher objective. The total number of nanorods in a chip is readily scalable with a nanoimprint mold size. For example, one can use a microarray spotter (MicroSys, Digilab Inc.) to functionalize the chip selectively with affinity ligands. With the spotter, 0.1 μL solutions are transferred from a 96-well plate and spotted on designed areas with good reproducibility (&lt;5% variation). Temperature and humidity are controlled inside the spotter chamber for consistent sample spotting and incubation conditions. 
     A metal, e.g., gold, nanostructure, e.g., nanorod, exhibits a unique dark-field light scattering peak at a resonant wavelength. EV binding to the nanorod surface increases a local refractive index, red-shifting the peak wavelength. The spectral shift (i.e. EV binding) can also be detected by measuring a light intensity change at a fixed wavelength, and there is an excellent correlation between spectral and intensity measurements. The intensity measurement method can be used for high throughput parallel signal reading from entire arrays in a field-of-view. This approach is much faster than sequential spectral measurements used in past systems and methods. The dark-field imaging is also compatible with epifluorescence measurements for molecular EV profiling in the same setup. 
     EVs, e.g., tumor-derived EVs, can be captured on nanorods and measured by the number of nanorods exhibiting intensity changes induced by EV binding to the nanorod surface. Computational calculation using a finite difference time-domain (FDTD) solution shows that single 100-nm EV binding induces more than a 10 nm shift, a sufficiently large shift readily detected by dark-field intensity measurements. The signal also correlates with the size of captured EVs, facilitating EV size measurements. Once can use size standard nanospheres for calibration and compare the results with those obtained by a nanoparticle tracking analysis (NTA) system. Combined with molecular profiling, the size information can be used to identify EV subtypes (e.g. exosomes vs microvesicles). 
     By employing dark-field imaging, readout signals from the entire arrays can be measured simultaneously. The intensity measurements provide much higher throughput in readouts of vast arrays than spectral measurements. To avoid signal drift or fluctuation due to changes in light source temperature, one can implement temperature controllers to stabilize the light source temperature and/or increase the number of signal averages to reduce background noises. 
       FIGS.  11 A  to  FIG.  11 D  illustrate an example of a nano-plasmonic array comprising nanorods for plasmon-enhanced single EV sensing technology (NEXT).  FIG.  11 A  is a schematic drawing of NEXT chip sensor consisting of gold nanorod arrays made in grid. The small surface area of nanorods allows for single EV binding on each nanorod.  FIG.  11 B  is a scanning electron micrograph (SEM) of a nanosphere captured on a gold nanorod (scale bar: 50 nm).  FIG.  11 C , is a SEM of a nanorod array made by electron-beam lithography, but can also be made with nanoimprint lithography (scale bar: 500 nm).  FIG.  11 D  is a SEM showing binding of single nanospheres, mimicking EVs, on nanorods (scale bar: 200 nm). Captured EVs are labeled by immunofluorescence probes for high throughput multichannel analyses using plasmon enhanced fluorescence detection. The captured EVs are evenly distributed by a distance between nanorods; this will improve the accuracy of analysis. 
     Isolation and Preparation of EVs 
     Biological samples are obtained, e.g., from a human or other subject, and cells can be cultured in culture media, such as Dulbecco&#39;s modified Eagle&#39;s medium (DMEM, Cellgro). Media can be supplemented with serum, e.g., 10% Fetal Bovine Serum, antibiotics, e.g., penicillin and/or streptomycin, and kept under 5% CO2. See, e.g., Min et al., Plasmon-Enhanced Biosensing for Multiplexed Profiling of Extracellular Vesicles, Advanced Biosystems, 2020, 4, 200003. DOI: 10.1002/adbi.202000003, which is incorporated herein by reference in its entirety, including all figures and reference citations. 
     EVs can be isolated using both standard ultracentrifugation (UC) and size-exclusion chromatography (SEC) methods. Furthermore, EVs are isolated from the medium for the next process. For UC, the filtrates are concentrated, e.g., by 100,000×g for 1 hour. After the supernatant is removed, the EV pellet is washed, e.g., with PBS and centrifuged again, e.g., at 100,000×g for 1 hour. The EV pellet is resuspended in buffer or serum, e.g., in PBS. For SEC, the filtrates are loaded onto filters, e.g., MWCO=10 kDa, and centrifuged, e.g., at 3500×g for 30 minutes at 4° C. After concentration, the volume is adjusted, e.g., to 1 mL with PBS. 
     EVs can be selected using different biomarkers and respective affinity binding pairs and their manufactures directions. 
     EV Labeling and Analysis Protocols 
     The EV analysis is performed based on the nPLEX-FL protocol described herein, which includes using multiple fluorescent labels, Raman signals, and dark-field scattering signals to detect target EVs for EV analysis. For fluorescence detection, EVs are labeled by fluorescence probes conjugated with affinity ligands. For Raman detection, molecules on the surface membrane or inside of EVs can be directly detected or EVs are labeled by Raman probes conjugated with affinity ligands. For dark-field scattering detection, scattering signals from EVs can be directly detected without any labeling. The nanostructures of the nano-plasmonic arrays are labeled with affinity ligands that are bound to capture agents that specifically bind to EVs, and then the substrate is exposed to a biological sample for a sufficient time to ensure that the substrate is bound to a sufficient number of EVs. In one embodiment, biotinylated EVs are captured on neutravidin-coated nanostructures, followed by EV fixation and permeabilization in a fix/perm solution. Surface passivation can be achieved by placing the surface (with or without EVs) in a blocking solution (Superblock PBS, Thermo Fisher) for 20 minutes. This step is important to minimize undesired nonspecific binding. The captured EVs are stained via two-step indirect labeling: first with primary antibodies then with compatible secondary antibodies. Thorough washing is done between steps. 
     The EVs are labeled with capture agents, such as streptavidin. Finally, the labeled EVs are attached to the nanostructures via the capture agents with a mounting solution and covered with a glass coverslip. Antibodies that can be used in the present disclosure are listed in Table 1 below. Primary antibodies are used to specifically bind to a specific biomarker on the surface of the EVs, and secondary antibodies are used to specifically bind to the primary antibodies. Furthermore, the secondary antibodies are conjugated with a reporter group, e.g., a fluorescent probe, to be used in image processing, or a capture agent such as streptavidin. The assay buffer can be, for example, a BD perm/wash buffer solution (BD Biosciences). 
     
       
         
           
               
             
               
                 TABLE 1 
               
             
            
               
                   
               
               
                 Antibodies and Dilution Factors 
               
            
           
           
               
               
               
               
            
               
                   
                   
                   
                 Dilution 
               
               
                 Antibody 
                 Vendor 
                 Cat No. 
                 factor 
               
               
                   
               
               
                 Primary antibodies 
                   
                   
                   
               
               
                 CD9 (mouse) 
                 BioLegend 
                 312102 
                 1:200 
               
               
                 CD63 (mouse) 
                 Ancell 
                 215-820 
                 1:200 
               
               
                 CD81 (mouse) 
                 Santa Cruz 
                 SC-166029 
                 1:100 
               
               
                 EGFR (rabbit) 
                 CST 
                 4267S 
                 1:50  
               
               
                 EGFRvIII (rabbit) 
                 CST 
                 64952S 
                  1:2000 
               
               
                 GAPDH (rabbit) 
                 CST 
                 2118S 
                 1:100 
               
               
                 CD9 (mouse) for WB 
                 Millipore Sigma 
                 CBL162 
                 1:500 
               
               
                 CD63 (mouse) for WB 
                 BD Biosciences 
                 556019 
                 1:500 
               
               
                 CD81 (mouse) for WB 
                 BD Biosciences 
                 555675 
                 1:500 
               
               
                 EGFR (rabbit) for WB 
                 CST 
                 54359S 
                  1:1000 
               
               
                 GAPDH (rabbit) for WB 
                 CST 
                 2118S 
                  1:2000 
               
               
                 Secondary antibodies 
               
               
                 Alexa 488 Goat anti- 
                 CST 
                 4408S 
                  1:1000 
               
               
                 mouse IgG Antibody 
               
               
                 Alexa 555 Goat anti- 
                 CST 
                 4409S 
                  1:1000 
               
               
                 mouse IgG Antibody 
               
               
                 Alexa 647 Goat anti- 
                 CST 
                 4414S 
                  1:1000 
               
               
                 rabbit IgG Antibody 
               
               
                 HRP Goat anti-rabbit 
                 CST 
                 7074S 
                  1:3000 
               
               
                 IgG Antibody for WB 
               
               
                 HRP Goat anti-mouse 
                 CST 
                 7076S 
                  1:3000 
               
               
                 IgG Antibody for WB 
               
               
                 Streptavidin 
               
               
                 Streptavidin Alexa 488 
                 BioLegend 
                 405235 
                 1:400 
               
               
                 Streptavidin cy3 
                 BioLegend 
                 405215 
                 1:400 
               
               
                 Streptavidin cy5 
                 BioLegend 
                 405209 
                 1:400 
               
               
                 Streptavidin cy5.5 
                 Rockland 
                 S000-13 
                 1:400 
               
               
                   
               
            
           
         
       
     
     Any other antibodies can also be used in the present disclosure based on the specific use. Considering different biomarkers of EVs, a corresponding antibody can be selected. EV biomarkers associated with different diseases and purposes are listed in Table 2 below. 
     
       
         
           
               
             
               
                 TABLE 2 
               
             
            
               
                   
               
               
                 EV Biomarkers 
               
            
           
           
               
               
            
               
                   
                 EV Biomarker 
               
               
                   
                   
               
            
           
           
               
               
               
            
               
                   
                 Disease model 
                   
               
               
                   
                 General cancer 
                 EpCAM, EGFR, MUC1, HER2 
               
               
                   
                 Ovarian cancer 
                 EpCAM, CD24 
               
               
                   
                 Glioblastoma 
                 EGFR, EGFRvIII, IDH1 R132H, PDPN 
               
               
                   
                 Pancreatic cancer 
                 EpCAM, EGFR, MUC1, WNT2 
               
               
                   
                 Alzheimer&#39;s diseases 
                 amyloid beta, tau 
               
               
                   
                 Cellular origin 
               
               
                   
                 Neurons 
                 L1CAM, NCAM 
               
               
                   
                 Astrocytes 
                 GLAST, Glutamine synthetase, GFAP, 
               
               
                   
                   
                 EAAT2 
               
               
                   
                 Microglia 
                 CD11b, TMEM119, MHCII 
               
               
                   
                   
               
            
           
         
       
     
     In some embodiments of the present disclosure, the image processing of the captured EVs is performed using image analysis software, such as ImageJ® and CellProfiler®. The streptavidin imaging channel is used to identify location of captured EVs and define regions of interests as masks. For each molecular target (e.g., proteins on the membrane or inside of EVs), the corresponding fluorescent images from target molecules are aligned using ImageJ® plugins (Align slices in the stack). At each mask position, average pixel intensities are obtained. The signal is corrected by subtracting background signal surrounding the mask. 
     Methods of Use 
     The new methods and nano-plasmonic arrays can be used to analyze single EVs in multiple scenarios. For example, tumor-derived EVs contain protein and RNA markers reflective of primary tumor cells, and the new nano-plasmonic array sensors can rapidly and sensitively detect tumor EVs directly from clinical samples. Thus, EV analyses offer compelling clinical potential for diagnosing cancers and monitoring longitudinal tumor response to therapy. 
     Highly sensitive single EV detection platforms as described herein will significantly improve our understanding of EV biology, allow for rapid and reliable screening of EVs from clinical specimens, and enable analysis of subtle phenotypic changes during treatment. Importantly, this would help the field understand how well EVs align with their primary tumor counterparts and whether EV counts and/or molecular profiles offer additional insight into cancer progress or treatment response. In the long-term, achieving successful high-throughput EV profiling in blood will pave the way for other clinically grounded screening studies (e.g. EVs in other body fluids and cancer types). This will render a more accessible tool to significantly accelerate the clinical adoption of EV analyses as routine screening tests for cancer care in clinical settings. The single EV detection platforms as described herein enable to identify individual EVs derived from tumors or specific organs and detect specific target molecules on the membrane or inside of EVs from the target subpopulation, otherwise diluted or undetected by EVs from non-target origins. The molecular profiling of EVs from target-specific tumors or organs can indicate the molecular status of originating cells. 
     Numerical Simulations 
     Furthermore, numerical simulation can be applied to calculate resonance peak wavelengths, determine the choice of labels for maximum optical signal amplification, and optimize nanostructure dimensions and materials. Electrodynamic computation can be performed using the finite-difference time-domain (FDTD) method. For electric field distribution, x-polarized plane wave is illuminated along z direction. 2-nm mesh size is used for the volume of 0.3×0.3×0.2 μm 3  locating at the center of nanohole. Periodic boundary conditions are imposed along the x and y direction and perfect match layers are used for the z direction. A z-polarized dipole source is used for radiative decay rate simulation. The position of the dipole is set to x=100 nm, y=0 nm and z=6 nm to locate it at the edge of nanohole and 6 nm above the Au surface of the nano-plasmonic array. 
     Furthermore, statistical analyses and data plotting can be performed in GraphPad Prism 7®. Group differences are tested using the unpaired t-test. All tests are two-sided, and a P-value of &lt;0.05 is considered statistically significant. 
     General Methodology 
       FIGS.  1 A to  1 G  illustrate an example of a nano-plasmonic array for multiplexed single EV analysis.  FIG.  1 A  illustrates the procedural steps of the multiplexed single EV analysis starting from capturing EVs, labeling the captured EVs, taking images of the labelled EVs, and analyzing the images of EVs. EVs are captured on the nanohole surface and immune-stained by fluorescent detection probes, and then labeled EVs are imaged in different fluorescence channels, and their intensities are analyzed. For example, EVs are captured on the Au nanohole surface via affinity ligands (e.g., capturing biotinylated EVs on avidin-coated Au nanohole surface). The captured EVs are then immune-stained by fluorescently labeled antibodies in different color channels (typically 3 to 4 colors). Depending on the absorption and emission spectra of fluorophores, the fluorescence signals are amplified by surface plasmon resonances (SPR) excited by the underlying Au nanohole structures. 
       FIG.  1 B  illustrates a scanning electron micrograph of periodic nanoholes in the nano-plasmonic array. The diameter of a nanohole is about 200 nm and the periodicity is 500 nm. The scale bar in  FIG.  1 B  is 1 82 m. The nanostructure in  FIG.  1 B  is optimized as a SPR substrate, and the substrate is a 100-nm thick Au film. 
       FIG.  1 C  illustrates a finite-difference time-domain simulation showing the enhanced electromagnetic fields confined on the nanohole surface of the nano-plasmonic array. The strong fields are responsible for plasmon-enhanced fluorescence signals. The periodic nanohole granting on the chip surface concentrates electromagnetic fields with the maximum field intensity up to 300-fold. The resonance fields extend to 110 nm in the z-direction, which mostly covers small EVs (e.g., exosomes with an average diameter of 100 nm). In addition to the localized near field, the fluorescence radiation can be further enhanced by the interaction of the Au nanostructure with proximal fluorophores in the resonance range. 
       FIG.  1 D  illustrates images of fluorescent nanospheres (using Cy5, 200 nm) on glass and the present nano-plasmonic substrates. The scale bar in  FIG.  1 D  is 10 μm. The plasmon-enhanced fluorescence by Au nanohole structures of the nPLEX chip using fluorescent nanospheres (Cy5, 200 nm) is tested in comparison with a glass substrate. Furthermore,  FIG.  1 E  illustrates example histograms of pixel intensities of a glass substrate and the present nano-plasmonic substrate. It shows that the fluorescence intensities of individual nanospheres are significantly higher on the nPLEX-FL substrate (two-tailed t-test, p&lt;0.0001).  FIG.  1 F  illustrates a mean fluorescence intensity of fluorescent nanospheres on glass and the nano-plasmonic substrates. The mean fluorescence intensity is increased by a factor of 18, and the signal-to-noise ratio (given by signal divided by 3-times standard deviation of blank) is increased by a factor of 20, from 17.7 (glass) to 358 (nPLEX-FL). There is no significant difference in the coefficient of variation, given by the ratio of the standard deviation to the mean, for fluorescence intensities between the glass (36.2%) and nPLEX-FL substrates (33.6%), indicating the signal amplification does not increase the intensity variation. 
       FIG.  1 G  illustrates a finite-difference time-domain simulation that shows the enhanced electromagnetic fields around a nanohole. The scanning electron microscopy shows EVs captured by functionalized Au nanohole chip. The dotted circles represent the enhanced electromagnetic field distribution around the nanoholes. 
     Dual-Mode Imaging Device 
       FIG.  2 A  illustrates an example of an optical system for the nano-plasmonic array for multiplexed single EV analysis (NEXT readout system), that integrates dark-field imaging with multi-channel fluorescence imaging.  FIG.  2 A  illustrates an example upright microscope setup  10  for dark-field (transmission) and epifluorescence dual-mode imaging. The charge-coupled device (CCD)  20  of the optical setup can be used to capture electromagnetic radiation emitted, scattered, or reflected by reporter groups (e.g., fluorescent antibodies) on the labeled target EVs captured on the nano-plasmonic array. In some embodiments, the upright microscope setup  10  comprises a filter set  22  to process/filter electromagnetic radiation, a microscope stage  26  on which a substrate with a nano-plasmonic array is placed under an objective  24 , a camera, e.g., CCD,  20  to capture the radiation processed by the filter set  22 . The system also includes a dark field condenser  28 , arranged below the stage  26 , a primary light source  30  arranged to illuminate the microscope stage  26  from above, and a secondary light source  32 , arranged to illuminate the microscope stage  26  from below. In some embodiments, the dark-field scattering imaging is used to illuminate the nano-plasmonic array from below to capture emitted, scattered, or reflected electromagnetic radiation from the nano-plasmonic array through the objective  24 . In some examples, the second light source  32  and the dark-field condenser  28  of the dark-field scattering imaging system can be disposed above the objective  24  to illuminate the microscope stage  26  from above. 
       FIG.  2 B  illustrates an example of a dark-field scatter image of the nanostructure arrays. An inset shows a zoomed image of the nanostructure arrays. In some embodiments, the nanostructure arrays may be nanorod arrays, and each nanorod is separated by 2 μm. 
     EXAMPLES 
     The disclosure is further described in the following examples, which do not limit the scope of the invention described in the claims. 
     Example 1—Characterizing a Nano-Plasmonic Array System 
     We investigated plasmonic enhancements in EVs. We captured biotinylated EVs on glass and nPLEX-FL substrates, and subsequently labeled the captured EVs with streptavidin-conjugated dyes (Cy5,  FIG.  3 E  and AF488). 
     nPLEX-FL chips were prepared using the lithography methods described above. The chip was incubated overnight at room temperature with thiolated biotin polyethylene glycol (PEG) (10×10 −3  m in PBS, PG2-BNTH-1k, Nanocs). After washing with PBS, an equimolar mixture of streptavidin molecules conjugated with either Alexa Fluor 488, Cy3, Cy5, or Cy5.5 (Biolegend) was incubated for 10 min. The concentration of each fluorescence dye was diluted to be 2.5 μg mL−1, except Alexa Fluor 488-conjugated streptavidin (25 μg mL−1 in PBS) due to the weak fluorescence signal compared to other channels. 
     We used a polyphenolic proteins-based bioadhesive layer to capture the same amounts of EVs on different substrates (glass and Au) and investigated fluorescence intensities and detectable EV counts. The averaged signal enhancement factors in terms of fluorescence intensity after background correction were measured to be 1.54 for AF488 and 8.60 for Cy5 ( FIG.  3 F ). The overall signal enhancement in the captured EVs was less prominent than the streptavidin monolayer coating (c.f.  FIGS.  3 C and  3 F ), likely because of the thickness difference between EVs and streptavidin monolayer; the electromagnetic fields are stronger near the surface. Nevertheless, we could detect an order-of-magnitude larger number of Cy5-labeled EVs on the nPLEX-FL chip compared to a glass substrate, indicating higher sensitivity attained by the plasmon-enhanced signal amplification ( FIG.  3 G ). 
     We also observed comparable mean pixel intensities and EV counts for the AF488-labeled EVs on both nanohole chip and glass (see  FIG.  3 H ). This indicates that the plasmon enhancement on Cy5 dyes unveils EVs with weak fluorescence signals otherwise undetected without signal enhancement (glass substrates) or with weak enhancement (AF488). Hence, we assign low abundant or key EV markers in the Cy5 channel in the subsequent validation study for the maximum signal enhancement. 
     In particular,  FIG.  3 A  is a series of fluorescence images of the nano-plasmonic arrays/chips coated with four colors of fluorophore-conjugated streptavidin (AF488, Cy3, Cy5, and Cy5.5). The scale bar in  FIG.  3 A  is 20 μm. Nanohole arrays were made in a 100×100 μm 2  sized square area highlighted by white dashed boxes, e.g., the white dashed box shown in the fluorescence image using AF488. 
       FIG.  3 B  illustrates cross-sectional intensity profiles along the grey horizontal dashed lines in  FIG.  3 A . The plasmon enhancement in different fluorescence channels is tested using a molecular monolayer. The Au nanohole surface is functionalized using thiolated biotin polyethylene glycol derivatives (thiol-PEG-biotin), and fluorophore-conjugated streptavidin molecules are immobilized on the biotinylated Au surface. To prevent fluorescence quenching by underlying Au substrates, the Au surface is functionalized with thiol-PEG-biotin (1 kDa , 6-8 nm) and neutravidin (60 kDa, 4-5 nm), which results in an adhesion layer of 10-13 nm in thickness.  FIG.  3 A  and  FIG.  3 B  show the strong signal enhancements in the 100×100 μm 2  sized square area of nanohole gratings (highlighted by a white dashed box) compared to the flat Au area (outside of the square,  FIG.  3 B ). 
       FIG.  3 C  illustrates an example enhancement factor (EF) of fluorescence intensity in different fluorescence channels. The signal enhancement is most dominated in the Cy5 channel, and the EF of fluorescence intensity in the nanohole area in comparison to flat Au areas is 23 fold. 
       FIG.  3 D  illustrates an example plasmon-supported light transmission spectrum through nanohole arrays overlaid with the absorption/emission spectra of fluorophores. The Cy5.5 and Cy3 intensities are also increased by 17 and 9 folds, respectively, when the AF488 signal is only increased by 3-fold. These EFs in the different channels can be explained by spectral overlaps between plasmon-supported light transmission through nanoholes and absorption/emission spectral of fluorophores. The light transmission peak is measured at 667 nm, which is most overlapped with Cy5 absorption (649 nm) and emission (666 nm) peaks followed by Cy5.5 and Cy3. 
       FIGS.  3 E to  3 I  illustrate example plasmonic enhancements on EVs.  FIG.  3 E  indicates that biotinylated EVs are captured on glass and substrates of the nano-plasmonic arrays, and subsequently labeled the captured EVs with streptavidin-conjugated dyes. The captured EVs are labeled with Cy5-conjugated streptavidin, and then imaged. The scale bar in  FIG.  3 E  is 10 μm.  FIG.  3 H  indicates biotinylated EVs captured on the device surface coated with an L-3,4-dihydroxyphenylalanine (L-DOPA)-based bioadhesive layer, and the captured EVs were labeled with AF488-conjugated streptavidin, and then imaged.  FIG.  3 I  illustrates a comparison of the mean fluorescence intensities and  FIG.  3 J  illustrates the number of captured EVs in  FIG.  3 H  in the region of interest (ROI) between the nanohole chip and glass substrate. An L-DOPA-based bioadhesive layer is used to capture EVs in the same densities on different substrates (glass and Au) and investigated fluorescence intensities and detectable EV counts. 
     Furthermore,  FIG.  3 F  illustrates example histograms of pixel intensities of captured EVs of  FIG.  3 E . The averaged signal enhancement factors in terms of fluorescence intensity after background correction were measured to be 1.54 for AF488 and 8.60 for Cy5. The overall enhancement is less significant than the streptavidin monolayer coating in  FIG.  3 C  likely due to localized electromagnetic fields, which are strongest near the surface shown in  FIG.  3 C . 
       FIG.  3 G  illustrates the number of detected EVs of  FIG.  3 E  between the nanohole chip and glass substrate. The fluorescence intensity is normalized by background signals defined by the sum of the mean fluorescence intensity in the absence of EV and three times the standard deviation. An order-of-magnitude larger number of Cy5 labeled EVs on the nPLEX-FL chip is detected to be compared to a glass substrate, indicating higher sensitivity attained by the plasmon-enhanced signal amplification. In contrast, a comparable mean pixel intensities and EV counts for the AF488-labeled EVs is observed on both nanohole chip and glass. The observed difference is that the plasmon-induced signal amplification of Cy5 unveils the smaller EVs with weak signals, which are undetected on the glass substrates. Hence, for its maximal spectral overlap with the plasmon resonance, Cy5 dye is chosen to label low abundant intravesicular markers. 
     Example 2—Single EV Measurements 
     We applied the nPLEX-FL technology to demonstrate its feasibility on the multiplexed single EV analysis. We used glioblastoma cell lines for testing: Gli36-WT and Gli36-EGFRvIII (overexpressing human EGFRvIII). EGFR and EGFRvIII are biomarkers of interest for glioblastoma as amplification of EGFR and its variant (EGFRvIII) occur frequently in glioblastoma. The presence of protein markers including 1) ubiquitous EV tetraspanin combination named CD-pan (CD9, CD63, and CD81); 2) GAPDH; 3) EGFR; and 4) EGFRvIII was examined by nPLEX-FL and benchmarked against western blotting analysis as a standard method (see  FIGS.  6 A and  6 B ). 
     EVs were isolated from conditioned cell culture media. Nanoparticle tracking analysis showed that the isolated EVs used in this study have a size distribution ranging 50-200 nm with an average diameter of 100 nm, also confirmed by transmission electron micrographs. The isolated EVs were biotinylated, diluted in pure buffer (1-10×10 8  EVs mL−1 phosphate-buffered saline (PBS)), and captured on the neutravidin-coated gold nanohole surface. The captured EVs were immunolabeled against membrane (i.e., CD63, EGFR) and/or intravesicular markers (i.e., GAPDH) and imaged under a fluorescence microscope. Because most EVs are smaller than the diffraction limit, the average blob size of the detected vesicles in fluorescence images was about 500 nm (8 pixels with a pixel size of 63 nm). Single EVs generated detectable fluorescence signals, confirmed by scanning electron micrograph. Some doublet EV showed a higher intensity in the streptavidin channel. Particles imaged larger than 1 μm (or 16 pixels) were considered large aggregates and excluded in our analysis. 
     We chose well-established EV markers for a proof-of-principle demonstration of EV profiling and subpopulation sorting based on marker signals. In consideration of fluorescence signal enhancement, we assigned 1) green dye (AF488) to high abundance/easy-to-detect markers and 2) far-red dye (Cy5) to low abundance/hard-to-detect markers.  FIG.  4 A  shows representative nPLEX-FL images of biotinylated EVs labeled against CD-pan (AF488), streptavidin (Cy3), and GAPDH (Cy5). We chose GAPDH as a representative intravesicular marker, which is commonly used as a control for many other quantitative methods (e.g., western blotting, qPCR). We varied EV concentrations and counted the number of captured EVs. Line scan ( FIG.  4 B ) showed high signal-to-noise ratios and signal heterogeneity for the chosen markers on individual vesicles. We then analyzed the raw intensity data for marker profiling of EVs. For a given marker, we identified two subpopulations—marker-positive and marker-negative, which can be separated by the intensity cutoff (mean+2×standard deviation of negative controls). Roughly 40% of the captured streptavidin-positive vesicles were CD-pan positive, and of the CD-pan-positive EVs, a fraction expressed GAPDH (25%) ( FIG.  3 C ). The false-positive rate in a control sample (no EV) was negligible for both streptavidin staining (&lt;1%) and antibody staining (&lt;0.2%). Based on the negative control data, we set a threshold of 1% for positivity. 
     In particular,  FIG.  4 A  illustrates that EVs from the Gli36-WT cell line are biotinylated and captured on the nanohole surface. Individual EV are detected through staining with fluorescent Cy3-streptavidin (top left). For molecular profiling, EVs are labeled with fluorescent antibodies against transmembrane EV markers (CD63) and intravesicular markers (GAPDH). Multiple EV markers are chosen to detect and classify single EVs based on marker expression levels. AF488 dye to high abundance/easy-to-detect markers and Cy5 to low abundance/hard-to-detect markers are assigned.  FIG.  4 A  shows representative nPLEX-FL images of Gli36-WT derived EVs labeled against CD63 (AF488) and GAPDH (Cy5). GAPDH is chosen as a representative intravesicular marker, which is commonly used as a control for many other quantitative methods (e.g., Western blotting, qPCR). 
       FIG.  4 B  illustrates line scans showing high signal-to-noise for the chosen markers in this example. Gray shading highlights EV positions. Line scan shows high signal-to-noise and heterogeneity for the chosen markers on individual vesicles. 
       FIG.  4 C  illustrates EV subtyping. The raw intensity data shown in  FIG.  4 B  is then analyzed for the marker expression and EV subtyping. For a given marker, we identified two subpopulations: marker-positive and marker-negative, which can be separated by the intensity cutoff of 100. Roughly half the captured vesicles had CD63 (46%), and of the CD63+ EVs, a fraction expressed GAPDH (58%). It is confirmed that strong overlapping (&gt;95%) of GAPDH+ EVs with CD63+ EVs. A higher fraction of Cy5-GAPDH+ EVs on the nanohole chip is observed than other substrates, which could be attributed to the plasmon-derived signal amplification in the Cy5 red channel. 
     Example 3—Demonstration of Tumor Diagnostic Potential 
       FIG.  5 A  to  FIG.  5 E  illustrate an example measurement of tumor markers of captured EVs to demonstrate tumor diagnostic potential of the new systems and methods. EVs from three different cell lines (Gi136-WT, Gli36-EGFRvIII, MCF7) were biotinylated and captured on a nanohole array surface, and EVs were labeled with fluorescent antibodies against the CD-pan marker panel (CD9, CD63, and CD81) as well as tumor markers which comprise EGFR in  FIG.  5 A  and EGFRvIII in  FIG.  5 B . Spots with dotted circles indicate tumor marker-positive EVs in  FIGS.  5 A and  5 B . 
       FIG.  5 C  illustrates a Western blot analysis of EGFR expression in Gli36-WT, Gli36-EGFRvIII, and MCF-7 cell lines. MCF-7 cells served as a negative control for EGFR expression. Blotting antibodies against GAPDH were used for loading control. 
     The bar graphs in  FIGS.  5 D and  5 E  illustrate EV subtyping. Fraction (%)=EV CD-pan+Target +/EV CD-pan+ . As shown in  FIG.  5 D , a significant fraction of Gli36-WT EV were positive for EGFR (54%) whereas a small fraction of Gli36-EGFRvIII are positive for EGFR (7%). As shown in  FIG.  5 E , somewhat over 10% of Gli36-EGFRvIII vesicles were positive for EGFRvIII, while Gli36-WT and MCF7 showed the EGFRvIII-positive fractions (&lt;1%) below the threshold for statistical significance. A negative control was prepared with the same procedure with no EV incubation. 
     To further test the diagnostic potential for clinical applications, we spiked ≈10 10  EVs from Gli36-WT and Gli36-EGFRvIII cell lines into 1 mL human plasma samples. EVs were isolated from the spiked plasma samples using a size exclusion column (Izon column), biotinylated, and then loaded onto the chip (1-5 μL). The captured EVs were labeled against CD-pan (AF488), streptavidin (Cy3), and EGFR or EGFRvIII (Cy5). We implemented a decision tree algorithm with a nested gating strategy to classify EV populations based on EGFR and EGFRvIII signals ( FIG.  6 A ). Briefly, particles labeled with Cy3-conjugated streptavidin were first detected and prescreened by size exclusion (&lt;1 μm) to exclude large aggregates from the analysis. Among particles positive for streptavidin, we defined EVs positive for CD-pan markers (CD9, CD63, and CD81). Then, the prescreened EVs were sub-gated with target glioblastoma markers of EGFR or EGFRvIII. We conducted the power analysis for a Mann-Whitney test using two independent groups (EV positive and negative) to calculate the necessary EV sample size (n&gt;100) given the statistical power of 0.9 and the effect size of 0.43. Given the EV surface coverage of 0.1-0.5 EV μm −2 , the minimum area required is roughly 200-1000 μm 2 . Yet, we used fluorescence images (n=4) in a full field-of-view (FOV) (120 μm×100 μm) and sampled thousands of vesicles per measurement to ensure statistical significance and robust analysis. 
       FIGS.  6 B and  6 C  show biomarker distribution analyses on a single-EV level. We plotted bivariate histograms from three-channel fluorescence images with a FOV of 120 μm×100 μm. On average, we detect about 4200 particles positive for streptavidin in single images (minimum=3604, maximum=5057 EVs,  FIG.  6 D ). We observed 10-15% positivity of streptavidin-positive particles for CD-pan markers ( FIG.  6 E ). The lower fraction of CD-pan+streptavidin+particles in the plasma samples compared to that in the pure-buffer (PBS), could be attributed to the presence of lipoproteins and plasma protein aggregates in human plasma. For marker profiling, the detected EVs positive for CD-pan were screened for target markers of EGFR and EGFRvIII. For plasma samples spiked with EVs from Gli36-WT and Gli36-EGFRvIII cell lines, about 10-20% of detected EVs were positive for EGFR in both samples ( FIG.  6 F ). However, roughly 10% of EVs were positive for EGFRvIII only in the plasma samples with Gli36-EGFRvIII EVs, while the other sample with Gli36-WT EVs showed less than 1% positive EV fraction, which is below the threshold ( FIG.  6 G ). Comparable biomarker positivity for EGFR and EGFRvIII was observed between the plasma samples and the pure-buffer samples. 
     These results show that glioblastoma EVs can be used to detect EGFRvIII mutation proteins. 
     Example 4—Characterization of EVs Isolated from Tumor Cell Lines 
     The nPLEX-FL technology was extended to demonstrate its feasibility on the multiplexed single EV analysis. Glioblastoma (GBM) cell lines were used for testing: Gli36-WT and Gli36-EGFRvIII, a clone of Gli36 EV that is positive for EGFRvIII mutation. EVs were collected from conditioned cell culture media and membrane filtered, biotinylated, immobilized on the nanohole array chip surface, and immunolabeled against membrane (i.e., CD63, EGFR) and/or intravesicular markers (i.e., GAPDH). The isolated EVs used in this study have a size distribution ranging 50-200 nm with an average diameter of 100 nm and the high purity determined by western blotting for ubiquitous EV protein markers (CD9, CD63, and CD81,  FIG.  7 B ). The avidin-functionalized Au chip showed high specificity for biotinylated EV capture, which was confirmed by electron microscopy. 
     In particular,  FIG.  7 A  illustrates a size distribution graph of Gli36-WT and Gli36-EGFRvIII EVs obtained by nanoparticle tracking analysis (NTA).  FIG.  7 B  illustrates a Western blot measurements of Gli36-WT and Gli36-EGFRvIII EVs to determine pan-CD marker expression levels (CD9/CD63/CD81) in bulk. 
     Example 5—Characterization of EVs Varied in Concentrations 
       FIG.  8 A  illustrates EVs from the OVCA429 cell line biotinylated and captured on the nano-plasmonic array device. EVs were collected from conditioned cell culture media and membrane filtered, biotinylated, and immobilized on the nanohole array chip surface. Captured EVs were labeled against the pan-CD marker which is a combination of CD9, CD63, and CD81 (AF488), and EGFR (Cy5). EVs were artificially color-coded for visual aid. The scale bar in  FIG.  8 A  is 10 μm. The three bar graphs in  FIG.  8 B  illustrate various EV concentrations (4-fold difference) and the number of captured EV. Regardless of the EV concentrations, roughly half the CD-pan+ EVs expressed EGFR. Individual vesicles we identified by staining EV with Cy3-streptavidin. 
     Example 6—Negative Controls to Demonstrate Test Sensitivity 
       FIG.  9 A  to  FIG.  9 B  illustrate negative controls to demonstrate test sensitivity and specificity of the captured EVs. EVs from the MCF7 cell line were collected from conditioned cell culture media and membrane filtered, biotinylated, and immobilized on the nanohole array chip surface. EVs were labeled with the CDpan marker which is a combination of CD63/CD81/CD9 (AF488) and EGFR (shown in  FIG.  9 A ) or EGFRvIII (Cy5) (shown in  FIG.  9 B ). 
     The negative control (e.g., no EVs) was prepared with the same procedure with no EV incubation.  FIGS.  9 A  to  FIG.  9 B  indicate a statistical significance of the present nano-plasmonic array sensor system for detecting target EVs. 
     Example 7—Optical Characterization of Nanorod Arrays 
       FIG.  12 A  to  FIG.  12 B  illustrate an example optical characterization of nanorod sensor arrays. The graph of  FIG.  12 A  illustrates the results of finite-difference time-domain (FDTD) simulations showing optical resonance peaks for different sizes of nanorods having lengths of 40, 60, 80, 100, and 120 nm. The optical tuning is important to maximize fluorescence signal enhancement by nanorod&#39;s surface plasmon resonance. The nano-plasmonic array can have a specific size/dimension for each of the nanorods based on a size of the target EVs and/or other requirements (e.g., to detect a specific wavelength of SPR) in analysis steps. 
     The graph in  FIG.  12 B  shows the experimental results of spectral shifts and intensity changes of dark-field scattering as the surface refractive index increases from 1.33 to 1.45. Water and ethanol mixtures in different mix rations were prepared and applied to the nanorod arrays to vary the refractive indices from 1.33 to 1.45. The amount of spectral shifts were measured and plotted against the surface refractive index. The peak wavelength with the surface refractive index of 1.33 was used as a reference to calculate spectral shifts. EV binding to the nanorods increases the surface refractive index, shifting the resonance peak. The EV binding event can be detected by measuring either spectral shifts or scattering light intensity changes. 
     Example 8—Optical Resonances of Nanorods vs Nanodisks 
       FIG.  13 A  is a representation of a side view of an EV captured on a nanorod.  FIG.  13 B  is a representation of top view of a nanodisk in an array.  FIG.  13 C  is a scanning electron microscope image of a top view of an EV on a nanostructure. 
       FIG.  13 D  is a graph showing scattering intensities (a.u) of nanodisks having different diameters of 40, 60, 80, 100, 120, 140, 160, 180, or 200 nm, calculated by FDTD simulations. As shown, the scattering intensity increase with the diameter of the nanodisk, and the wavelength of peak scattering intensity also increases with nanodisk diameter. 
       FIG.  13 E  is a graph showing peak shifts (nm) of nanodisks having different diameters from 40 to 200 nm. As shown, the peak shift is highest for a nanodisk having a diameter of 40 nm, drops sharply from 60 to 80 nm diameters, and then continues to decrease as diameter increases until leveling off at about 180 nm. 
       FIG.  13 F  is a representation of a top view of a nanorod having length L and a width of 30 nm.  FIG.  13 G  is a graph showing scattering intensities (a.u) of nanorods having different lengths of 40, 60, 80, 100, 120, 140, 160, 180, and 200 nm. As shown, the peak scattering intensity increases with wavelength and with length of the nanorods. 
       FIG.  13 H  is a graph showing peak shifts (nm) of nanorods having different lengths from 40 to 120 nm. As shown, the peak shift decreases with nanorod length 
     The nano-plasmonic array can be designed to have nanostructures with specific sizes/dimensions of nanorods or nanodisks based on a size of the target EVs as well as other requirements (e.g., to detect a specific wavelength of SPR) for detecting target EVs. Any shapes similar to nanorods or nanodisks can be also used as plasmonic nanostructures to amplify fluorescent and dark-field signals. 
     Example 9—Finite-Difference Time-Domain (FDTD) Simulations 
       FIG.  14 A  to  FIG.  14 C  illustrate FDTD simulations showing spectral shifts of dark-field scattering upon EV binding to nanodisks in different locations and distances to the substrate. 
       FIG.  14 A  is a representation of Scenario 1 of a first EV binding location and its detected peak wavelength, along with a corresponding graph.  FIG.  14 B  is a representation of Scenario 2 of a second EV binding location and its detected peak wavelength, along with a microscope image showing electromagnetic waves.  FIG.  14 C  is a representation of Scenario 3 of a third EV binding location and its detected peak wavelength, along with a microscope image showing electromagnetic waves concentrated on the nanodisk surface. The results show single EV binding to the nanodisk surface in various binding scenarios can be detected by measuring spectral shifts of dark-field scattering peak wavelength. 
     Example 10—Real Time EV Binding Experiment 
     Real time binding of the EVs to nanodisks was analyzed using the systems and methods described herein. 
       FIG.  15 A  to  FIG.  15 D  illustrate an example of EV binding detection by measuring dark-field scattering intensity changes in real time. Timeline (1), as shown in  FIG.  15 A , is before EV binding to the nanodisk in the center of the array (middle circle). Timeline (2), as shown in  FIG.  15 B , is after EV binding to the nanodisk in the center of the array (middle circle). 
     The graph of  FIG.  15 C  shows real time measurements showing an abrupt intensity change at time point (2) upon EV binding to the center nanodisk. In particular,  FIG.  15 C  shows that the intensity is increased at 100 seconds when an EV binds to the nanodisk, as the signal difference shown in  FIG.  15 A  and  FIG.  15 B  (middle circle). 
     The graph of  FIG.  15 D  shows real time measurements showing no abrupt intensity changes of two controls that show no binding to any of the nanodisks in the array. In particular,  FIG.  15 D  shows the changes in the dark-field scattering intensity over time for the control nanodisks (Control 1 and Control 2 in  FIG.  15 A  and  FIG.  15 B ), which have no affinity ligands. There is no EV binding and thus no intensity change is measured. The vertical dashed lines (1) and (2) in  FIG.  15 C  and  FIG.  15 D  indicate the time points when  FIG.  15 A  and  FIG.  15 B  are taken. 
     Example 11—Spectral Shifts of Dark-Field Scattering of Nanodisks 
     The methods and nanodisk arrays described above we used to analyze spectral shifts of dark-field scattering caused by the nanodisks.  FIG.  16 A  to  FIG.  16 C  illustrate the results of how the EV binding can be detected by measuring spectral shifts of dark-field scattering by the nanodisks. EV binding is confirmed by overlays with EV fluorescence images. EVs were labeled by specific fluorescence probes to locate the nanodisks that have captured EVs. 
       FIG.  16 A  to  FIG.  16 C  show overlaid images of fluorescence channels for EVs and dark-field scattering of nanodisks, which show EVs are captured on the nanodisks (circles in the images). Dark-field scattering spectra before and after EV binding on those nanodisks are shown on the right, demonstrating that EV binding to the nanodisk surface induces spectral shifts. 
     The image in  FIG.  16 A  shows a central nanodisk that shows a fluorescent signal indicating EV binding. The accompanying graph of a single nanodisk spectrum shows a slight spectral shift, where no shift means a perfect overlay, in normalized scattering before or after EV binding. 
     The image of  FIG.  16 B  shows a central nanodisk with a change in fluorescence indicating EV binding. The accompanying graph of a single nanodisk spectrum shows a pronounced rightward (increase in peak wavelength) shift in normalized scattering after EV binding. 
     The image in  FIG.  16 C  shows a nanodisk that shows a fluorescent signal indicating EV binding. The accompanying graph of a single nanodisk spectrum shows no significant shift in normalized scattering before or after EV binding, where no shift means a perfect overlay. 
     Example 12—Plasmon Enhancements of Dark-Field and Fluorescence Signals with Nanodisks of Different Diameters 
     As shown in  FIG.  17    and  FIGS.  18 A-D , the nano-plasmonic arrays can be designed to have nanodisks having specific diameters based on a corresponding dark-field/fluorescent labels for detecting target EVs. A monolayer of fluorescence molecules is formed on the top of the nanodisk arrays, and the fluorescence signals on the nanodisk and substrate were measured. 
       FIG.  17    illustrates plasmon enhancements of dark-field and three different fluorescent signals (TRITC, Cy5, and Cy5.5) for nanodisks having different diameters of 80, 100, 120, 140, 160, 180, and 200 nm. 
       FIGS.  18 A to  18 D  are graphs that illustrate plasmon enhancements of dark-field and fluorescence signals for different sizes of nanodisks.  FIG.  18 A  is a graph of plasmon intensity for dark-field measurements corresponding to different diameters of nanodisks of 80, 100, 120, 140, 160, 180, and 200 nm, and as shown the level increases with diameter up to about 180 nm and then slightly declines at 200 nm. 
       FIG.  18 B  is a graph of plasmon intensity of TRITC corresponding to different diameters of nanodisks of 80, 100, 120, 140, 160, 180, and 200 nm, and as shown the level increases sharply with diameter up to 120 nm and then declines sharply to 140 nm, declines slightly to 180 nm, and then increases again to 200 nm. 
       FIG.  18 C  is a graph of plasmon intensity of Cy5 corresponding to different diameters of nanodisks of 80, 100, 120, 140, 160, 180, and 200, and as shown the level decreases slightly from 80 to 100 nm, then increases sharply with diameter up to 120 nm and then declines to 200 nm. 
       FIG.  18 D  is a graph of plasmon intensity of Cy5.5 corresponding to different diameters of nanodisks of 80, 100, 120, 140, 160, 180, and 200 nm, and as shown the level remains the same from 80 to 100 nm, increases sharply with diameter from 100 up to 140 nm, then declines sharply to 180 nm, and then declines slightly to 200 nm. 
     TRICT and Cy5 show the maximum intensity when they are coated on 120 nm (diameter) nanodisks while Cy5.5 showed the maximum intensity on the 140 nm diameter nanodisk. 
     Example 13—Isolation and Preparation of EVs 
     EVs were isolated from a cell culture of Gli36-WT (ATCC), Gli36-EGFRvIII (generated from Gli36-WT through lentivirus transduction), and MCF-7 cells (ATCC) grown in DMEM (Cellgro), OVCA429 cells (ATCC) cultured in RPMI-1640 medium (Cellgro). Media were supplemented with 10% fetal bovine serum (FBS, Thermo Fisher), 100 U/mL penicillin, and 100 μg/mL streptomycin (Cellgro) at 37° C. in 5% CO 2 . Furthermore, cell lines were tested and were free of mycoplasma contamination (MycoAlert™ mycoplasma detection kit, Lonza). 
     For EV isolation and biotinylation, EVs were incubated in DMEM with 1% exosome-depleted FBS (Thermo Fisher) for 48 hours before EV collection. The conditioned medium was collected and centrifuged, e.g., at 300×g for 5 minutes, and then supernatant was filtered through, e.g., through a 0.2 μm membrane filter (Millipore Sigma). 
     EVs were isolated using both standard ultracentrifugation (UC) and size-exclusion chromatography (SEC) methods: (i) for UC, the filtrates were concentrated by centrifugation at 100,000×g for 1 hour. After the supernatant was removed, the EV pellet was washed with a buffer or saline solution, such as PBS, and centrifuged at 100,000×g for 1 hour. The EV pellet was resuspended in PBS, and (ii) for SEC, the filtrates were loaded onto a centrifugation filter (Centricon® Plus-70 Centrifugal Filter (MWCO=10 kDa, Millipore Sigma), and centrifuged at 3,500×g for 30 minutes at a low temperature, such as 4° C. 
     After concentration, the volume was adjusted to 1 mL with PBS. SEC was performed with modifications. Briefly, 10 mL syringe (BD Biosciences) with a nylon net with 20 μm pore size (Millipore Sigma) at the bottom was prepared and packed with 10 mL of Sepharose CL-4B (GE healthcare). The concentrates were loaded on top and 6 fractions of 1 mL were collected under constant gravitational flow by adding PBS on top of the column. The fractions 4 and 5 were used for EV isolation. These were loaded onto Amicon Ultra-2 Centrifugal Filter (MWCO=10 kDa, Millipore Sigma) and centrifuged at 3,500×g for 30 minutes at 4° C. The isolated EVs were stored at −80° C. until further measurements. 
     The isolated EVs were resuspended in buffer or saline solution, e.g., PBS and incubated with the capture agent, such as EZ-Link Sulfo-NHS-LC-Biotin (Thermo Fisher) for a sufficient time, e.g., 30 minutes, at room temperature. For example, a 20-fold molar excess of sulfo-NHS-biotin to EV protein was used in a 0.5 mL volume. Approximately 4 to 6 biotins are incorporated per molecule. Excess biotin was then removed utilizing the Exosome Spin Columns, MW3000 (Thermo Fisher) per the kit instructions. The prepared EVs were filtered using a 0.22 μm centrifugal filter (Ultrafree®, Millipore). 
     OTHER EMBODIMENTS 
     While this specification contains many specific implementation details, these should not be construed as limitations on the scope of any invention or on the scope of what may be claimed, but rather as descriptions of features that may be specific to particular implementations of particular inventions. Certain features that are described in this specification in the context of separate implementations can also be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations separately or in any suitable sub-combination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can, in some cases, be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination. 
     Particular implementations of the subject matter have been described. Other implementations, alterations, and permutations of the described implementations are within the scope of the following claims as will be apparent to those skilled in the art. While operations are depicted in the drawings or claims in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed (some operations may be considered optional), to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. 
     Moreover, the separation and/or integration of various system modules and components in the implementations described above should not be understood as requiring such separation and/or integration in all implementations, and it should be understood that the described program components and systems can generally be integrated together in a single software product or package into multiple software products. 
     Accordingly, the above description of different embodiments and implementations does not define or constrain this disclosure. Other changes, substitutions, and alterations are also possible without departing from the spirit and scope of this disclosure.