Systems and methods for sample preparation, data generation, and protein corona analysis

Abstract

To provide systems and methods for sample preparation, data generation, and protein corona analysis.SOLUTION: Systems and methods for automated sample preparation and processing of protein corona are described herein, as well as its application in the discovery of advanced diagnostic tools as well as therapeutic agents. In some aspects, the present disclosure provides an automated apparatus for generating a subset of biomolecules from a complex biological sample, the automated apparatus comprising: (i) a substrate comprising a plurality of partitions, (where the plurality of partitions comprises a plurality of particles); (ii) a loading unit comprising the complex biological sample; and (iii) a loading unit that is movable at least across the substrate.SELECTED DRAWING: None

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to and benefits from U.S. Provisional Application No. 62 / 883,107, filed August 5, 2019, the entire contents of which are incorporated herein by reference. [Background technology]

[0002] background The large-scale compilation of proteomic information in science and medicine has lagged behind genomics, primarily due to the inherent complexity of the protein molecules themselves, which necessitates complex workflows that limit the large-scale scaling of such analyses. Disclosed herein are systems, methods, and kits for rapid and automated sample preparation, processing of proteomic data, and identification of key biomarkers associated with disease states. Summary of the Invention [Means for solving the problem]

[0003] Abstract The present disclosure provides automated systems, methods, and kits for preparing and analyzing protein coronas. In some embodiments, the present disclosure provides an automated device for generating a subset of biomolecules from a complex biological sample, the automated device comprising: (i) a substrate including a plurality of partitions, wherein the plurality of partitions includes a plurality of particles; (ii) a sample storage unit containing the complex biological sample; and (iii) a loading unit movable across at least the substrate, wherein the loading unit transfers one or more volumes of the complex biological sample in the sample storage unit to the plurality of partitions on the substrate, thereby contacting the plurality of particles in the plurality of partitions with the biomolecules of the complex biological sample to form a biomolecular corona, thereby generating the subset of biomolecules of the complex biological sample, and wherein the dynamic range of the subset of biomolecules is compressed compared to the dynamic range of the biomolecules present in the complex biological sample. In some embodiments, the substrate is a multiwell plate. In some embodiments, the biomolecule subset comprises at least 20% to at least 60% of the biomolecule species from the complex biological sample within a six-order concentration range. In some embodiments, the biomolecule subset comprises at least 20% to at least 60% of the protein species from the complex biological sample within a six-order concentration range. In some embodiments, the automated instrument generates the biomolecule subset from a complex biological sample in less than seven hours.

[0004] In some embodiments, the automated instrument includes an incubation element that agitates or heats the plurality of particle volumes within the complex biological sample volume within the plurality of partitions. In some embodiments, the incubation element is configured to shake, mix, stir, spin, vibrate, static, or any combination thereof. In some embodiments, the incubation element is configured to heat and / or incubate the substrate to a temperature of about 20°C to about 100°C.

[0005] In some embodiments, the plurality of partitions are at least partially covered or sealed. In some embodiments, one partition among the plurality of partitions is covered or sealed. In some embodiments, the automated equipment has the capability to add or remove a lid onto the substrate, wherein the lid covers at least one partition among the plurality of partitions.

[0006] In some embodiments, the automated instrument comprises a unit containing a resuspension solution, in some embodiments, the resuspension solution is Tris EDTA 150 mM KCl In some embodiments, the resuspension solution comprises 10 mM Tris-HCl pH 7.4, 1 mM EDTA.

[0007] In some embodiments, the instrument includes a unit containing a denaturing solution. In some embodiments, the denaturing solution includes a protease. In some embodiments, the denaturing solution includes a reducing agent, a methylating agent, guanidine, urea, sodium deoxycholate, acetonitrile, or any combination thereof. In some embodiments, the denaturing solution produces peptide fragments with an average mass of less than 4600 daltons.

[0008] In some embodiments, the loading unit includes a plurality of pipettes. In some embodiments, the loading unit is configured to dispense 10 μL to 400 μL of solution into one or more of the plurality of partitions. In some embodiments, the loading unit is configured to dispense 5 μL to 150 μL of solution into one or more of the plurality of partitions. In some embodiments, the loading unit is configured to dispense 35 μL to 80 μL of solution into one or more of the plurality of partitions. In some embodiments, the solution is selected from the group consisting of a wash solution, the resuspension solution, the denaturing solution, a buffer, and a reagent. In some embodiments, the loading unit is configured to dispense 10 μL to 400 μL of the complex biological sample into one or more of the plurality of partitions. In some embodiments, the loading unit is configured to dispense 5 μL to 150 μL of the complex biological sample into one or more of the plurality of partitions. In some embodiments, the loading unit is configured to dispense between 35 μL and 80 μL of the complex biological sample into one or more of the plurality of partitions.

[0009] In some embodiments, the complex biological sample comprises a bodily fluid from a subject, ie, plasma, serum, urine, cerebrospinal fluid, synovial fluid, tears, saliva, whole blood, milk, nipple aspirate, ductal lavage, vaginal fluid, nasal secretion, inner ear fluid, gastric juice, pancreatic juice, trabecular fluid, pulmonary lavage, sweat, gingival crevicular fluid, semen, prostatic fluid, sputum, feces, bronchial lavage, fluid from a swab, bronchial aspirate, mobilized solids, fine needle aspirate, tissue homogenate, lymph, cell culture sample, or any combination thereof.

[0010] In some embodiments, the automated instrument further comprises a magnet, hi some embodiments, one or more particles of the plurality of particles are magnetic particles, and the substrate and the magnet are in close proximity such that the one or more magnetic particles are immobilized on the substrate.

[0011] In some embodiments, the automated instrument further comprises a housing, wherein the substrate and the loading unit are disposed within the housing and are at least partially enclosed.

[0012] In some embodiments, the compressed dynamic range includes an increase in the number of biomolecule species whose concentrations are within a six-order of magnitude relative to the most abundant biomolecule in the sample. In some embodiments, the compressed dynamic range includes an increase in the number of biomolecule species whose concentrations are within a five-order of magnitude relative to the most abundant biomolecule in the sample. In some embodiments, the compressed dynamic range includes an increase in the number of biomolecule species whose concentrations are within a four-order of magnitude relative to the most abundant biomolecule in the sample. In some embodiments, the compressed dynamic range includes an increase in the number of protein species whose concentrations are within a six-order of magnitude relative to the most abundant protein in the sample. In some embodiments, the increase in the number of biomolecule species whose concentrations are within a six-order of magnitude relative to the most concentrated biomolecule in the sample is at least 25%, 50%, 100%, 200%, 300%, 500%, or 1000%. In some embodiments, the compressed dynamic range includes an increase in the number of protein species whose concentrations are within a six-order of magnitude relative to the most abundant protein in the sample. In some embodiments, the increase in the number of protein species whose concentrations are within six orders of magnitude of the most abundant protein in the sample is at least 25%, 50%, 100%, 200%, 300%, 500%, or 1000%.

[0013] In some embodiments, the dynamic range of the biomolecules in the biomolecular corona is a first ratio of top decile biomolecules to bottom decile biomolecules in the plurality of biomolecular coronas, hi some embodiments, the dynamic range of the biomolecules in the biomolecular corona is a first ratio that includes an interquartile range of biomolecules in the plurality of biomolecular coronas.

[0014] In some embodiments, the purification enriches low-abundance biomolecules from the complex biological sample. In some embodiments, the low-abundance biomolecules are biomolecules at a concentration of 10 ng / mL or less in the complex biological sample. In some embodiments, the subset of biomolecules from the complex biological sample comprises proteins.

[0015] In some embodiments, a change of up to 10 mg / mL in the lipid concentration of the complex biological sample results in less than a 10%, 5%, 2%, or 1% change in the protein composition in the subset of biomolecules generated from the complex biological sample.

[0016] In some embodiments, at least two particles in the plurality of particles differ in at least one physicochemical property. In some embodiments, the at least one physicochemical property is selected from the group consisting of composition, size, surface charge, hydrophobicity, hydrophilicity, surface functionality, surface topography, surface curvature, porosity, core material, shell material, shape, and any combination thereof. In some embodiments, the surface functionality comprises aminopropyl functionalization, amine functionalization, boronic acid functionalization, carboxylic acid functionalization, methyl functionalization, N-succinimidyl ester functionalization, PEG functionalization, streptavidin functionalization, methyl ether functionalization, triethoxypropylaminosilane functionalization, thiol functionalization, PCP functionalization, citrate functionalization, lipoic acid functionalization, or BPEI functionalization. In some embodiments, a particle in the plurality of particles is a micelle, a liposome, an iron oxide particle, a silver particle, a gold particle, a palladium particle, a quantum dot, a platinum particle, a titanium particle, a silica particle, a metal or inorganic oxide particle, a synthetic polymer particle, a copolymer particle, a terpolymer particle, a polymer particle having a metal core, a polymer particle having a metal oxide core, a polystyrene sulfonate particle, a polyethylene oxide particle, a polyoxyethylene glycol particle, a polyethyleneimine particle, a polylactic acid particle, a polycaprolactone particle, a polyglycolic acid particle, a poly(lactide-co-glycolide) polymer particle, a cellulose ether polymer particle, a polyvinylpyrrolidone particle, a polyvinyl acetate particle, a polyvinylpyrrolidone-vinyl acetate copolymer particle, a polyvinyl alcohol particle, an acrylate particle, a polyacrylic acid particle, a crotonic acid copolymer particle, a polyethylene phosphonate (polyethylene phosphonate particles, polyalkylene particles, carboxyvinyl polymer particles, sodium alginate particles, carrageenan particles, xanthan gum particles, gum arabic particles, gum arabic particles, guar gum particles, pullulan particles, agar particles, chitin particles, chitosan particles, pectin particles, karaya gum particles, locust bean gum particles, maltodextrin particles, amylose particles, corn starch particles, potato starch particles, rice starch particles, tapioca starch particles, pea starch particles,Sweet potato starch particles, barley starch particles, wheat starch particles, hydroxypropylated high amylose starch particles, dextrin particles, levan particles, elsinan particles, gluten particles, collagen particles, whey protein isolate particles, casein particles, milk protein particles, soy protein particles, keratin particles, polyethylene particles, polycarbonate particles, polyanhydride particles, polyhydroxy acid particles, polypropyl fumarate particles, polycaprolactone particles, polyamine particles, polyacetal particles, polyether particles, polyester particles, poly(orthoester) particles, polycyanoacrylate particles, polyurethane particles In some embodiments, the plurality of particles is selected from the group consisting of polyphosphazene particles, polyacrylate particles, polymethacrylate particles, polycyanoacrylate particles, polyurea particles, polyamine particles, polystyrene particles, poly(lysine) particles, chitosan particles, dextran particles, poly(acrylamide) particles, derivatized poly(acrylamide) particles, gelatin particles, starch particles, chitosan particles, dextran particles, gelatin particles, starch particles, poly-β-amino-ester particles, poly(amidoamine) particles, polylactic-glycolic acid particles, polyanhydride particles, bioreducible polymer particles, and 2-(3-aminopropylamino)ethanol particles, and any combination thereof. In some embodiments, one or more particles of the plurality of particles adsorb at least 100 different proteins upon contact with the complex biological sample. In some embodiments, the plurality of particles comprises at least two unique particle species, at least three unique particle species, at least four unique particle species, at least five unique particle species, at least six unique particle species, at least seven unique particle species, at least eight unique particle species, at least nine unique particle species, at least ten unique particle species, at least eleven unique particle species, at least 12 unique particle species, at least thirteen unique particle species, at least fourteen unique particle species, at least fifteen unique particle species, at least twenty unique particle species, at least twenty-five particle species, or at least thirty unique particle species.

[0017] In some embodiments, the biomolecules of the biomolecular corona comprise a large number of protein groups. In some embodiments, the large number of protein groups comprises 1 to 20,000 protein groups. In some embodiments, the large number of protein groups comprises 100 to 10,000 protein groups. In some embodiments, the large number of protein groups comprises 100 to 5,000 protein groups. In some embodiments, the large number of protein groups comprises 300 to 2,200 protein groups. In some embodiments, the large number of protein groups comprises 1,200 to 2,200 protein groups.

[0018] In some embodiments, at least two partitions of the plurality of partitions comprise different buffers. In some embodiments, the different buffers differ in pH, salinity, osmolality, viscosity, dielectric constant, or any combination thereof. In some embodiments, at least two partitions of the plurality of partitions comprise different ratios of buffer to the complex biological sample. In some embodiments, one or more partitions of the plurality of partitions comprise nanoparticles at 1 pM to 100 nM. In some embodiments, at least two partitions of the plurality of partitions comprise different concentrations of nanoparticles.

[0019] In some embodiments, the automated instrument further comprises a purification unit, hi some embodiments, the purification unit comprises a solid phase extraction (SPE) plate.

[0020] Various aspects of the present disclosure provide an automated system including: (i) an automated instrument configured to isolate the subset of biomolecules from the biological sample; (ii) a mass spectrometer configured to receive the subset of biomolecules and generate data comprising a mass spectrometry signal or a tandem mass spectrometry signal; and (iii) a computer including one or more computer processors, and a computer-readable medium including machine-executable code that, when executed by the one or more computer processors, performs a method including generating a biomolecular fingerprint and assigning a biological state based on the biomolecular fingerprint.

[0021] In some embodiments, the biomolecular fingerprint comprises a plurality of unique biomolecular corona signatures. In some embodiments, the biomolecular fingerprint comprises at least 5, 10, 20, 40, or 80, 150, or 200 unique biomolecular corona signatures. In some embodiments, the computer is configured to process data comprising mass spectrometry or tandem mass spectrometry signal intensities, APEX, peptide spectral counts or numbers, or ion mobility behaviors among the plurality of unique biomolecular corona signatures. In some embodiments, the computer is configured to process data from 100 to 2000 mass spectrometry or tandem mass spectrometry signals among the plurality of unique biomolecular corona signatures. In some embodiments, the computer is configured to process data comprising 10,000 to 5,000,000 mass spectrometry or tandem mass spectrometry signal intensities among the plurality of unique biomolecular corona signatures. In some embodiments, the biomolecular fingerprint is generated from data from a single mass spectrometry or tandem mass spectrometry run. In some embodiments, the single mass spectrometry or tandem mass spectrometry run is performed in less than one hour. In some embodiments, the computer is configured to identify biomolecules or characterize unidentified molecular features based on mass spectrometry or tandem mass spectrometry signals and / or ion mobility and chromatographic behavior, and wherein the computer provides at least a 95% confidence threshold for identifying features or characterizing unidentified features. In some embodiments, the automated system is configured to generate the biomolecular fingerprint from the complex biological sample in less than about 10 hours. In some embodiments, the determining includes comparing the abundances of two biomolecules whose concentrations in the complex biological sample range from at least 7 to at least 12 orders of magnitude.

[0022] In some embodiments, the computer can distinguish between two or more biological states associated with biomolecular fingerprints that differ by less than 10%, 5%, 2%, or 1%. In some embodiments, the biological state is a disease, disorder, or tissue abnormality. In some embodiments, the disease is an early-phase or intermediate disease state. In some embodiments, the disease is cancer. In some embodiments, the cancer is stage 0 cancer or stage 1 cancer.In some embodiments, the cancer is lung cancer, pancreatic cancer, myeloma, myeloid leukemia, meningioma, glioblastoma, breast cancer, esophageal squamous cell carcinoma, gastric adenocarcinoma, prostate cancer, bladder cancer, ovarian cancer, thyroid cancer, neuroendocrine cancer, colon cancer, ovarian cancer, head and neck cancer, Hodgkin's disease, non-Hodgkin's lymphoma, rectum cancer, urinary tract cancer, uterine cancer, oral cancer. cancer), skin cancer, stomach cancer, brain tumor, liver cancer, laryngeal cancer, esophageal cancer, breast tumor, fibrosarcoma, myxosarcoma, liposarcoma, chondrosarcoma, osteosarcoma, chordoma, angiosarcoma, endothelial sarcoma, Ewing's sarcoma, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinoma, cystadenocarcinoma, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatocellular carcinoma, bile duct carcinoma, choriocarcinoma, seminoma, embryonal carcinoma, Wilms' tumor, cervical cancer, testicular tumor, endometrial cancer, lung cancer carcinoma, small cell lung cancer, bladder cancer, epithelial carcinoma, glioblastoma, neuronomas, craniopharingioma, schwannoma, glioma, astrocytoma, meningioma, melanoma, neuroblastoma, retinoblastoma, leukemia and lymphoma, acute lymphocytic leukemia and acute myelocytic polycythemia vera, multiple myeloma, Waldenstrom's macroglobulinemia and heavy chain disease, acute nonlymphocytic leukemia, chronic myeloid leukemia, In some embodiments, the biological condition is selected from the group consisting of myeloid leukemia, chronic myeloid leukemia, childhood null-cell acute lymphocytic leukemia (ALL), thymic ALL (acute lymphocytic leukemia), B-cell ALL (acute lymphocytic leukemia), acute megakaryocytic leukemia, Burkitt's lymphoma, and T-cell leukemia, small cell and large cell (non-small cell) lung cancer, acute granulocytic leukemia, germ cell tumor, endometrial cancer, gastric cancer, hairy cell leukemia, thyroid cancer, and other cancers known in the art. In some embodiments, the biological condition is a pre-disease state.

[0023] Various aspects of the present disclosure provide methods for identifying a biological state of a complex biological sample, the method including: feeding the complex biological sample to an automated instrument to generate a subset of biomolecules; assaying the subset of biomolecules to generate a biomolecular fingerprint; and identifying the biological state of the complex biological sample using the biomolecular fingerprint.

[0024] In some embodiments, the biomolecular fingerprint comprises proteins. In some embodiments, the subset of biomolecules from the complex biological sample has a smaller ratio of albumin to non-albumin peptides than the complex biological sample. In some embodiments, the subset of biomolecules comprises biomolecules whose concentration range in the complex biological sample spans at least 6 to at least 12 orders of magnitude. In some embodiments, the subset of biomolecules comprises proteins whose concentration range in the complex biological sample spans at least 6 to at least 12 orders of magnitude. In some embodiments, the biomolecular fingerprint comprises 1 to 74,000 protein groups.

[0025] In some embodiments, the assay comprises desorbing a plurality of biomolecules from a biomolecular corona in the plurality of biomolecular coronas. In some embodiments, the assay comprises chemically modifying biomolecules in the plurality of adsorbed biomolecules. In some embodiments, the assay comprises fragmenting biomolecules in the plurality of adsorbed biomolecules. In some embodiments, the fragmentation comprises protease digestion. In some embodiments, the fragmentation comprises chemical peptide cleavage.

[0026] In some embodiments, the assay includes collecting the plurality of adsorbed biomolecules. In some embodiments, the assay includes purifying the collected plurality of adsorbed biomolecules. In some embodiments, the purification includes solid phase extraction. In some embodiments, the purification includes depleting non-protein biomolecules from the collected plurality of adsorbed biomolecules. In some embodiments, the assay includes discarding the plurality of adsorbed biomolecules. In some embodiments, the assay includes desorbing a first subset of biomolecules and a second set of biomolecules from a biomolecular corona in the plurality of biomolecular coronas, analyzing the biomolecules in the first subset of biomolecules, and analyzing the biomolecules in the second subset of biomolecules.

[0027] In some embodiments, the assay comprises analyzing biomolecular coronas from the plurality of biomolecular coronas by mass spectrometry, tandem mass spectrometry, mass cytometry, potentiometry, fluorimetry, absorption spectroscopy, Raman spectroscopy, chromatography, electrophoresis, immunohistochemistry, PCR, next-generation sequencing (NGS), or any combination thereof. In some embodiments, the assay comprises mass spectrometry or tandem mass spectrometry. In some embodiments, the assay comprises identifying a three-dimensional structure of a protein from the subset of biomolecules. In some embodiments, the assay comprises identifying a post-translational modification on a protein from the subset of biomolecules. In some embodiments, the identification comprises comparing the relative abundance of at least 200 to at least 1000 biomolecules from the subset of biomolecules. In some embodiments, the assay identifies biomolecules at a concentration of less than 10 ng / mL in the complex biological sample.

[0028] Various aspects of the present disclosure provide an automated instrument for generating a subset of biomolecules from a complex biological sample, the automated instrument comprising a plurality of particles and the complex biological sample, wherein the automated instrument is configured to generate the subset of biomolecules by contacting the plurality of particles with the complex biological sample to form a plurality of biomolecular coronas comprising the subset of biomolecules, and wherein the dynamic range of the subset of biomolecules is compressed compared to the dynamic range of biomolecules present in the complex biological sample. In some embodiments, the automated instrument comprises a substrate. In some embodiments, the substrate comprises a multiwell plate. In some embodiments, the substrate is a multiwell plate. In some embodiments, the automated instrument generates the subset of biomolecules from the complex biological sample in less than 7 hours.

[0029] In some embodiments, the automated instrument includes an incubation element configured to heat and / or incubate the plurality of particles and the complex biological sample to a temperature between 4°C and 40°C.

[0030] In some embodiments, the automated instrument comprises at least one solution selected from the group consisting of a wash solution, a resuspension solution, a denaturing solution, a buffer, and a reagent. In some embodiments, the resuspension solution comprises Tris-EDTA buffer, phosphate buffer, and / or water. In some embodiments, the denaturing solution comprises a protease. In some embodiments, the denaturing solution comprises a small molecule capable of peptide cleavage.

[0031] In some embodiments, the automated device includes a loading unit including a plurality of pipettes. In some embodiments, each pipette of the plurality of pipettes is configured to dispense between about 5 μL and 150 μL of the solution, the complex biological sample, and / or the plurality of particles. In some embodiments, the complex biological sample includes plasma, serum, urine, cerebrospinal fluid, synovial fluid, tears, saliva, whole blood, milk, nipple aspirate, ductal lavage, vaginal fluid, nasal secretion, inner ear fluid, gastric juice, pancreatic juice, trabecular fluid, pulmonary lavage, sweat, gingival crevicular fluid, semen, prostatic fluid, sputum, feces, bronchial lavage, fluid from a swab, bronchial aspirate, mobilized solids, fine needle aspirate, tissue homogenate, lymph, cell culture sample, or any combination thereof. In some embodiments, the automated device includes a magnet. In some embodiments, the automated device includes a filter.

[0032] In some embodiments, the compressed dynamic range includes an increase in the number of biomolecule types whose concentrations are within 4-6 orders of magnitude of the most abundant biomolecule in the sample. In some embodiments, the biomolecule types include proteins. In some embodiments, the dynamic range of the biomolecules in the biomolecular corona is a first ratio of top decile biomolecules to bottom decile biomolecules in the plurality of biomolecule coronas. In some embodiments, the generation enriches low-abundance biomolecules from the complex biological sample. In some embodiments, the low-abundance biomolecules are biomolecules at a concentration of 10 ng / mL or less in the complex biological sample.

[0033] In some embodiments, at least two particles in the plurality of particles differ in at least one physicochemical property selected from the group consisting of composition, size, surface charge, hydrophobicity, hydrophilicity, surface functionality, surface topography, surface curvature, porosity, core material, shell material, shape, and any combination thereof. In some embodiments, a particle in the plurality of particles is a micelle, a liposome, an iron oxide particle, a silver particle, a gold particle, a palladium particle, a quantum dot, a platinum particle, a titanium particle, a silica particle, a metal or inorganic oxide particle, a synthetic polymer particle, a copolymer particle, a terpolymer particle, a polymer particle having a metal core, a polymer particle having a metal oxide core, a polystyrene sulfonate particle, a polyethylene oxide particle, a polyoxyethylene glycol particle, a polyethyleneimine particle, a polylactic acid particle, a polycaprolactone particle, a polyglycolic acid particle, a poly(lactide-co-glycolide) polymer particle, a cellulose ether polymer particle, a polyvinylpyrrolidone particle, a polyvinyl acetate particle, a polyvinylpyrrolidone-vinyl acetate copolymer particle, a polyvinyl alcohol particle, an acrylate particle, a polyacrylic acid particle, a crotonic acid copolymer particle, a polyethylene phosphonate (polyethylene phosphonate particles, polyalkylene particles, carboxyvinyl polymer particles, sodium alginate particles, carrageenan particles, xanthan gum particles, gum arabic particles, gum arabic particles, guar gum particles, pullulan particles, agar particles, chitin particles, chitosan particles, pectin particles, karaya gumtum particles, locust bean gum particles, maltodextrin particles, amylose particles, corn starch particles, potato starch particles, rice starch particles, tapioca starch particles, pea starch particles, sweet potato starch particles, barley starch particles, wheat starch particles, hydroxypropylated high amylose starch particles, dextrin particles, levan particles, elsinan particles, gluten particles, collagen particles, whey protein isolate particles, casein particles, milk protein particles, soy protein particles, keratin particles, polyethylene particles, polycarbonate particles, polyanhydride particles, polyhydroxy acid particles, polypropyl fumarate particles, polycaprolactone particles, polyamine particles, polyacetal particles, polyacetal particles, the polymer particles are selected from the group consisting of ether particles, polyester particles, poly(orthoester) particles, polycyanoacrylate particles, polyurethane particles, polyphosphazene particles, polyacrylate particles, polymethacrylate particles, polycyanoacrylate particles, polyurea particles, polyamine particles, polystyrene particles, poly(lysine) particles, chitosan particles, dextran particles, poly(acrylamide) particles, derivatized poly(acrylamide) particles, gelatin particles, starch particles, chitosan particles, dextran particles, gelatin particles, starch particles, poly-β-amino-ester particles, poly(amidoamine) particles, polylactic acid / glycolic acid particles, polyanhydride particles, bioreducible polymer particles, and 2-(3-aminopropylamino)ethanol particles, and any combination thereof.

[0034] In some embodiments, the biomolecules of the biomolecular corona comprise a large number of protein groups. In some embodiments, the large number of protein groups comprises 1 to 20,000 protein groups. In some embodiments, the large number of protein groups comprises 100 to 10,000 protein groups. In some embodiments, the large number of protein groups comprises 100 to 5,000 protein groups. In some embodiments, the large number of protein groups comprises 300 to 2,200 protein groups. In some embodiments, the large number of protein groups comprises 1,200 to 2,200 protein groups.

[0035] In some embodiments, the automated instrument comprises a purification unit, hi some embodiments, the purification unit comprises a solid phase extraction (SPE) plate.

[0036] Various aspects of the present disclosure provide methods for generating a subset of biomolecules from a complex biological sample, the method comprising providing the complex biological sample to an automated instrument, wherein the automated instrument contacts the complex biological sample with a plurality of particles to generate a biomolecular corona, wherein the automated instrument processes the biomolecular corona to generate the subset of biomolecules, and wherein the dynamic range of the subset of biomolecules is compressed compared to the dynamic range of biomolecules present in the complex biological sample.

[0037] In some embodiments, the method includes assaying a subset of the biomolecules to generate a biomolecular fingerprint. In some embodiments, the assay identifies biomolecules at concentrations less than 10 ng / mL in the complex biological sample. In some embodiments, the assay includes analyzing the biomolecular corona by mass spectrometry, tandem mass spectrometry, mass cytometry, potentiometry, fluorimetry, absorption spectroscopy, Raman spectroscopy, chromatography, electrophoresis, immunohistochemistry, or a combination thereof. In some embodiments, the assay includes mass spectrometry or tandem mass spectrometry.

[0038] In some embodiments, the method includes identifying a biological state of the complex biological sample by the biomolecular fingerprint. In some embodiments, the biomolecular fingerprint comprises a plurality of unique biomolecular corona signatures. In some embodiments, the biomolecular fingerprint comprises at least 5, 10, 20, 40, or 80, 150, or 200 unique biomolecular corona signatures. In some embodiments, the biological state is a disease, disorder, or tissue abnormality. In some embodiments, the disease is an early-phase or intermediate disease state. In some embodiments, the disease is cancer. In some embodiments, the cancer is stage 0 cancer or stage 1 cancer.In some embodiments, the cancer is lung cancer, pancreatic cancer, myeloma, myeloid leukemia, meningioma, glioblastoma, breast cancer, esophageal squamous cell carcinoma, gastric adenocarcinoma, prostate cancer, bladder cancer, ovarian cancer, thyroid cancer, neuroendocrine cancer, colon cancer, ovarian cancer, head and neck cancer, Hodgkin's disease, non-Hodgkin's lymphoma, rectum cancer, urinary tract cancer, uterine cancer, oral cancer. cancer), skin cancer, stomach cancer, brain tumor, liver cancer, laryngeal cancer, esophageal cancer, breast tumor, fibrosarcoma, myxosarcoma, liposarcoma, chondrosarcoma, osteosarcoma, chordoma, angiosarcoma, endothelial sarcoma, Ewing's sarcoma, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinoma, cystadenocarcinoma, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatocellular carcinoma, bile duct carcinoma, choriocarcinoma, seminoma, embryonal carcinoma, Wilms' tumor, cervical cancer, testicular tumor, endometrial cancer, lung cancer carcinoma), small cell lung cancer, bladder cancer, epithelial carcinoma, glioblastoma, neuronomas, craniopharingioma, schwannoma, glioma, astrocytoma, meningioma, melanoma, neuroblastoma, retinoblastoma, leukemia and lymphoma, acute lymphocytic leukemia and acute myelocytic polycythemia vera, multiple myeloma, Waldenstrom's macroglobulinemia, and heavy chain disease, acute The biological condition is selected from the group consisting of non-lymphocytic leukemia, chronic lymphocytic leukemia, chronic myeloid leukemia, childhood null cell acute lymphocytic leukemia (ALL), thymic ALL (acute lymphocytic leukemia), B-cell ALL (acute lymphocytic leukemia), acute megakaryocytic leukemia, Burkitt's lymphoma, and T-cell leukemia, small cell and large cell (non-small cell) lung cancer, acute granulocytic leukemia, germ cell tumor, endometrial cancer, gastric cancer, hairy cell leukemia, or thyroid cancer. In some embodiments, the biological condition is a pre-disease state.

[0039] Various aspects of the present disclosure provide an automated instrument for identifying proteins in a biological sample, the automated instrument comprising: a sample preparation unit; a substrate including a plurality of channels; a plurality of pipettes; a plurality of solutions; and a plurality of nanoparticles, and wherein the automated instrument is configured to form a protein corona and digest the protein corona.

[0040] Various aspects of the present disclosure provide an automated instrument for identifying proteins in a biological sample, the automated instrument comprising: a sample preparation unit; a substrate including a plurality of channels; a plurality of pipettes; a plurality of solutions; and a plurality of nanoparticles, wherein the automated instrument is configured to form a protein corona and digest the protein corona, and wherein at least one of the solutions is TE 150 mM KCl 0.05% CHAPS buffer.

[0041] In some embodiments, the sample preparation unit is configured to add the nanoparticles to the substrate with the pipettes. In some embodiments, the sample preparation unit is configured to add the biological sample to the substrate with the pipettes. In some embodiments, the sample preparation unit is configured to incubate the nanoparticles with the biological sample to form the protein corona. In some embodiments, the sample preparation unit is configured to separate the protein corona from the supernatant to form a protein corona pellet. In some embodiments, the sample preparation unit is configured to reconstitute the protein corona pellet with TE 150 mM KCl 0.05% CHAPS buffer.

[0042] In some embodiments, the automated instrument includes a magnetic source. In some embodiments, the automated instrument is configured for trypsin digestion of BCA, gel, or protein corona. In some embodiments, the automated instrument is enclosed. In some embodiments, the automated instrument is sterilized prior to use. In some embodiments, the automated instrument is adapted for mass spectrometry. In some embodiments, the automated instrument is temperature controlled.

[0043] Various aspects of the present disclosure provide methods for identifying proteins in a biological sample, the method comprising: adding the biological sample to an automated instrument; generating proteomic data using the automated instrument; and quantifying the proteomic data. In some embodiments, the method further comprises incubating a plurality of nanoparticles with the biological sample in the automated instrument to form a protein corona. In some embodiments, the method further comprises separating the protein corona from a supernatant in the automated instrument. In some embodiments, the method further comprises digesting the protein corona in the automated instrument to form a digested sample. In some embodiments, the method further comprises washing the digested sample in the automated instrument. In some embodiments, quantifying the proteomic data comprises providing the proteomic data to mass spectrometry. In some embodiments, the biological sample is a bodily fluid. In some embodiments, the bodily fluid is serum or plasma.

[0044] In some embodiments, the present disclosure provides an automated system comprising a network of units with differentiated functions in identifying the state of a complex biological sample using a plurality of particles having surfaces with different physicochemical properties, wherein a first unit comprises a multi-channel fluid transfer device for transferring fluid from unit to unit in the system; a second unit comprises a support for storing a plurality of biological samples; a third unit comprises a support for a sensor array plate having partitions comprising the plurality of particles having surfaces with different physicochemical properties for binding a population of analytes in the complex biological sample; and a fourth unit comprises a support for storing a plurality of reagents. a fifth unit includes a support for storing reagents to be discarded; a sixth unit includes a support for storing consumables used by the multi-channel fluid transfer device; and wherein the system is programmed to perform a series of steps including contacting the complex biological sample with a specific partition of the sensor array; incubating the complex biological sample with the plurality of particles contained within the partition of the sensor array plate; removing all components from the partition except for the plurality of particles and a population of analytes that interact with the particles; and preparing a sample for mass spectrometry.

[0045] In some embodiments, the first unit is mobile enough to allow access to all other units in the system, hi some embodiments, the first unit is capable of performing pipetting functions.

[0046] In some embodiments, the support of the second and / or third unit comprises a support for a single plate, a 6-well plate, a 12-well plate, a 96-well plate, or a rack of microtubes. In some embodiments, the second and / or third unit comprises a thermal unit capable of regulating the temperature of the support and the sample. In some embodiments, the second and / or third unit comprises a rotation unit capable of physically stirring and / or mixing the sample.

[0047] In some embodiments, the plurality of particles having surfaces with different physicochemical properties for binding a population of analytes in the complex biological sample are immobilized on a surface within a partition of the sensor array. In some embodiments, the plurality of particles comprises a plurality of magnetic nanoparticles having different physicochemical properties for binding a population of analytes in the complex biological sample. In some embodiments, the system includes a step of transferring the sensor array plate to an additional seventh unit including a magnetizable support and a thermal unit capable of adjusting the temperature of the support and the sample, and incubating for an additional period of time.

[0048] In some embodiments, the fourth unit includes a set of reagents for generating the sensor array plate; washing unbound sample; and / or preparing the sample for mass spectrometry. In some embodiments, contacting the biological sample with a specific partition of the sensor array includes pipetting a specific volume of the biological sample into the specific partition of the sensor array. In some embodiments, contacting the biological sample with the specific partition of the sensor array includes pipetting a volume corresponding to a ratio of a plurality of particles in solution to the biological sample of 1:1, 1:2:1:3, 1:4, 1:5, 1:6, 1:7, 1:8, 1:9, 1:10, 1:15, or 1:20.

[0049] In some embodiments, contacting the biological sample with a particular partition of the sensor array comprises pipetting a volume of at least 10 microliters, at least 50 microliters, at least 100 microliters, at least 250 microliters, at least 500 microliters, or at least 1000 microliters of the biological sample into the particular partition of the sensor array.

[0050] In some embodiments, incubating the biological sample with the plurality of particles contained within the partition of the sensor array plate is performed for at least about 10 seconds, at least about 15 seconds, at least about 20 seconds, at least about 25 seconds, at least about 30 seconds, at least about 40 seconds, at least about 50 seconds, at least about 60 seconds, at least about 90 seconds, at least about 2 minutes, at least about 3 minutes, at least about 4 minutes, at least about 5 minutes, at least about 6 minutes, at least about 7 minutes, at least about 8 minutes, at least about 9 minutes, at least about 10 minutes, at least about 15 minutes, at least about 20 minutes, Including incubation times of at least about 25 minutes, at least about 30 minutes, at least about 45 minutes, at least about 50 minutes, at least about 60 minutes, at least about 90 minutes, at least about 2 hours, at least about 3 hours, at least about 4 hours, at least about 5 hours, at least about 6 hours, at least about 7 hours, at least about 8 hours, at least about 9 hours, at least about 10 hours, at least about 12 hours, at least about 14 hours, at least about 15 hours, at least about 16 hours, at least about 17 hours, at least about 18 hours, at least about 19 hours, at least about 20 hours, or at least about 24 hours.

[0051] In some embodiments, incubating the biological sample with the plurality of particles contained within the partition of the substrate comprises an incubation temperature of about 4° C. to about 40° C. Incubating the biological sample with the plurality of particles contained within the partition of the substrate may comprise an incubation temperature of about 4° C. to about 37° C. Incubating the biological sample with the plurality of particles contained within the partition of the substrate may comprise an incubation temperature of about 4° C. to about 100° C.

[0052] In some embodiments, removing all components from the partition except for the plurality of particles and the population of analytes that interact with the particles comprises a series of washing steps.

[0053] In some embodiments, the second unit can facilitate the transfer of the sample to a mass analysis unit for mass analysis.

[0054] In some embodiments, the present disclosure provides an automated instrument for identifying proteins in a biological sample, the automated instrument comprising: a sample preparation unit; a substrate including a plurality of channels; a plurality of pipettes; a plurality of solutions; and a plurality of nanoparticles, and wherein the automated instrument is configured to form a protein corona and digest the protein corona.

[0055] In some embodiments, the present disclosure provides an automated instrument for identifying proteins in a biological sample, the automated instrument comprising: a sample preparation unit; a substrate including a plurality of channels; a plurality of pipettes; a plurality of solutions; and a plurality of nanoparticles, wherein the automated instrument is configured to form a protein corona and digest the protein corona, and wherein at least one of the solutions is TE 150 mM KCl 0.05% CHAPS buffer.

[0056] In some embodiments, the sample preparation unit is configured to add the nanoparticles to the substrate with the pipettes. In some embodiments, the sample preparation unit is configured to add the biological sample to the substrate with the pipettes. In some embodiments, the sample preparation unit is configured to incubate the nanoparticles with the biological sample to form the protein corona.

[0057] In some embodiments, the sample preparation unit is configured to separate the protein corona from the supernatant to form a protein corona pellet, hi some embodiments, the sample preparation unit is configured to reconstitute the protein corona pellet with TE 150 mM KCl 0.05% CHAPS buffer.

[0058] In some embodiments, the automated instrument further comprises a magnetic source. In some embodiments, the automated instrument is configured for trypsin digestion of BCA, gel, or protein corona.

[0059] In some embodiments, the automated instrument is enclosed. In some embodiments, the automated instrument is sterilized prior to use. In some embodiments, the automated instrument is adapted for mass spectrometry. In some embodiments, the automated instrument is temperature controlled.

[0060] In some embodiments, the present disclosure provides a method for identifying proteins in a biological sample, the method comprising: adding the biological sample to an automated instrument disclosed herein; generating proteomic data by the automated instrument; and quantifying the proteomic data.

[0061] In some embodiments, the method further comprises incubating a plurality of nanoparticles with the biological sample in the automated instrument to form a protein corona. In some embodiments, the method further comprises separating the protein corona from a supernatant in the automated instrument. In some embodiments, the method further comprises digesting the protein corona in the automated instrument to form a digested sample.

[0062] In some embodiments, the method further comprises washing the digested sample in the automated instrument. In some embodiments, quantifying the proteomic data comprises submitting the proteomic data to mass spectrometry.

[0063] In some embodiments, the biological sample is a bodily fluid, hi some embodiments, the bodily fluid is serum or plasma.

[0064] Another aspect of the present disclosure provides a non-transitory computer-readable medium containing machine-executable code that, when executed by one or more computer processors, performs any of the methods described above or elsewhere herein.

[0065] Another aspect of the present disclosure provides a system including one or more computer processors and a computer memory coupled thereto, the computer memory including machine-executable code that, when executed by the one or more computer processors, performs any of the methods described above or elsewhere herein.

[0066] Additional aspects and advantages of the present disclosure will become readily apparent to those skilled in the art from the following detailed description, wherein only illustrative embodiments of the present disclosure are shown and described. As will be realized, the present disclosure is capable of other and different embodiments, and its several details are capable of modifications in various obvious respects, all without departing from the present disclosure. Accordingly, the drawings and description are to be regarded as illustrative in nature, and not as restrictive. Incorporation by Reference

[0067] All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference. [Brief explanation of the drawings]

[0068] The novel features of the invention are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the invention are utilized, and the accompanying drawings (also referred to herein as "Figures" and "FIGs.").

[0069] [Figure 1] FIG. 1 shows a schematic diagram of the steps for generating data using the nanoparticle or protein corona method.

[0070] [Figure 2] FIG. 2 shows an exemplary schematic diagram of the steps for generating data using nanoparticle or protein corona methods and the units of an automated system in which they can be performed.

[0071] [Figure 3] FIG. 3 shows an exemplary layout of the system and its connection to sequential MS for high-throughput applications.

[0072] [Figure 4] FIG. 4 shows an exemplary schematic of a sensor array analyte capture method.

[0073] [Figure 5] FIG. 5 shows a step-by-step schematic of automated sample processing for magnetic sensor array particles.

[0074] [Figure 6] FIG. 6 shows a step-by-step schematic of automated sample processing for immobilized sensor array particles.

[0075] [Figure 7] FIG. 7 shows the surface chemistry for the magnetic nanoparticle sensor array.

[0076] [Figure 8] FIG. 8 shows an example of a protein corona-based method for detecting disease biomarkers in cancer patients (see US20180172694A1, incorporated herein by reference in its entirety).

[0077] [Figure 9] Figure 9 shows the process for proteomics analysis. This process is adapted for high-throughput automation, allowing it to be run in parallel across multiple samples in a few hours. The process involves particle-matrix assembly, particle washing (three times), protein corona formation, in-plate digestion, and mass spectrometry. Using this process, a batch of 96 samples would take only 4-6 hours. One or more nanoparticles can be incubated with a sample at a time.

[0078] [Figure 10]Figure 10 shows protein counts (number of proteins identified by corona analysis) collected on multiple particles containing 1 to 12 particle species. Each particle in the multiple particles may have a unique material, surface functionalization, and / or physical characteristics (e.g., size or shape). Pooled plasma from a group of healthy subjects was used. Counts are the number of unique proteins collected from multiple particles and observed in a roughly 2-hour mass spectrometry (MS) run. 1,318 proteins were identified from a sample contacted with multiple particles containing 12 particle species.

[0079] [Figure 11] Figure 11 shows the distribution of median-normalized MS feature intensities, filtered by presence and filtered by cluster quality, for a 56-sample NSCLC comparative study. Each line represents the density of log2 feature intensities for either disease or control samples. Density is plotted from 0.00 to 0.15 on the y-axis, and log2 feature intensity is plotted from 15 to 35 on the x-axis. At the largest peak (density range of approximately 0.13 to approximately 0.17), located near a log2 feature intensity of approximately 28, the two largest trajectories correspond to control samples, while the smallest trajectory corresponds to a disease sample. The remaining control and disease trajectories are distributed between the largest and smallest trajectories. At the two shoulder peaks (located at approximately 20 and 23 log2 feature intensities), the two largest trajectories at 20 log2 feature intensity are control trajectories, the two smallest trajectories are control trajectories, and the largest trajectory at 23 log2 feature intensity is a disease trajectory.

[0080] [Figure 12]Figure 12 shows altered features in a non-small cell lung cancer (NSCLC) pilot study using poly(N-(3-(dimethylamino)propyl)methacrylamide) (PDMAPMA)-coated SPION particles. Seven MS features were identified as statistically significantly different between 28 subjects with stage IV NSCLC (with associated comorbidities and treatment effects) and 28 age- and sex-matched apparently healthy subjects. The table at the bottom lists the seven significantly different proteins, including five known proteins and two unknown proteins. If a peptide-spectrum match was found for the MS2 data associated with the feature, the peptide sequence (and charge) and potential parent protein are shown; if no MS2 match was associated with the feature, the peptide and protein are both described as "unknown."

[0081] [Figure 13] Figure 13 shows the correlation between the maximum intensity of particle corona proteins and plasma proteins and the published concentrations of the same proteins. The blue line is the linear regression model for the data, and the shaded area indicates the standard error of the model fit. The dynamic range of samples assayed with particles ("S-003," "S-007," and "S-011," listed in Table 1) exhibited a compressed dynamic range compared to a plasma sample not assayed with particles ("Plasma"), as indicated by the reduced slope of the linear fit. The slopes of each plot are 0.47, 0.19, 0.22, and 0.18 for plasma without particles, plasma with S-003 particles, plasma with S-007 particles, and plasma with S-011 particles, respectively.

[0082] [Figure 14]Figure 14 shows the dynamic range compression of the mass spectrometry protein corona analysis assay compared to mass spectrometry without particle corona formation. The protein intensities of common proteins identified in the particle corona in the plasma sample assayed in Figure 13 ("Nanoparticle MS(Intensity)") are plotted against the protein intensities identified by mass spectrometry of plasma without particles ("Plasma MS(Intensity)"). The maximum dotted line indicates a slope of 1 and represents the dynamic range of mass spectrometry without particles. The slopes of the linear fits to the protein intensities are 0.12, 0.36, and 0.093 for S-003, S-007, and S-011 particles, respectively. The gray areas indicate the standard error regions of the regression fits. DETAILED DESCRIPTION OF THE INVENTION

[0083] While various embodiments of the present invention have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions may occur to those skilled in the art without departing from the invention. It should be understood that various alternatives to the embodiments of the present invention may be employed.

[0084] The term "at least" is used before the first number in a series of two or more numbers. Whenever the terms "at least," "greater than," or "greater than or equal to" appear, the terms "at least," "greater than," or "greater than or equal to" apply to each individual number in the series. For example, greater than or equal to 1, 2, or 3 is synonymous with greater than or equal to 1, greater than or equal to 2, or greater than or equal to 3.

[0085] Whenever the terms "no more than," "less than," or "less than or equal to" are preceded by the first number in a series of two or more numbers, the terms "no more than," "less than," or "less than or equal to" apply to each and every number in the series. For example, less than or equal to 1, 2, or 3 is synonymous with less than or equal to 1, less than or equal to 2, or less than or equal to 3.

[0086] As used herein, a "feature" identified by mass spectrometry includes a signal at a particular combination of retention time and m / z (mass-to-charge ratio), where each feature has an associated intensity. Some features are further fragmented in a second mass spectrometry (MS2) for identification.

[0087] As used herein, the term "sensor element" refers to an element that can bind to multiple biomolecules upon contact with a sample, and encompasses the term "nanoscale sensor element." A sensor element can be a particle (e.g., a nanoparticle or a microparticle). A sensor element can be a surface or a portion of a surface. A sensor element can include a single particle or multiple particles. A sensor element can include multiple surfaces that can adsorb or bind biomolecules. A sensor element can include a porous material (e.g., a material into which biomolecules can penetrate).

[0088] As used herein, a "sensor array" can include a plurality of sensor elements, where the plurality of sensor elements (e.g., particles) includes various types of sensor elements. The sensor elements can be different types that differ from each other in at least one physicochemical property. A sensor array can be a substrate having multiple partitions containing multiple sensor elements (e.g., particles). For example, a sensor array can include a multiwell plate having multiple particles distributed among multiple wells. A sensor array can be a substrate having multiple partitions, where the multiple partitions include multiple particles. In some embodiments, each sensor element or particle can bind to multiple biomolecules in a sample and generate a biomolecular corona signature. In some embodiments, each sensor element (e.g., particle species) has a unique biomolecular corona signature.

[0089] As used herein, the term "biomolecular corona" refers to the plurality of different biomolecules that bind to the sensor element. The term "biomolecular corona" can refer to proteins, lipids, and other plasma components that bind to particles (e.g., nanoparticles) when they come into contact with a biological sample or biological system. For purposes of use herein, the term "biomolecular corona" also refers to the "biomolecular corona" described by Milani et al., "Reversible versus Irreversible Binding of Transferring to Polystyrene Nanoparticles: Soft and Hard This term also encompasses both soft and hard protein coronas, as referred to in "Corona" by Mirshafiee et al., "Impact of protein pre-coating on the protein corona composition and nanoparticle cellular uptake," BiomateriALS vol. 75, January 2016, pp. 295-304; Mahmoudi et al., "Emerging understanding of the protein corona at the nano-bio interfaces," Nanotoday 11(6), December 2016, pp. 817-832; and Mahmoudi et al., "Protein-Nanoparticle Interactions: Opportunities and Challenges," Chem. Rev., 2011, 111(9), pp. 5610-5637 (the entire contents of which are incorporated herein by reference). As described therein, adsorption curves show the accumulation of a strongly bound monolayer up to a monolayer saturation point (at a geometrically defined protein-to-NP ratio), beyond which a second, weakly bound layer forms. The first layer is irreversibly bound (hard corona), whereas the second layer (soft corona) may exhibit dynamic exchange. Proteins that adsorb with high affinity may form a "hard" corona containing strongly bound proteins that do not readily desorb, while proteins that adsorb with low affinity may form a "soft" corona containing loosely bound proteins. Soft and hard coronas can also be characterized based on their exchange times. Hard coronas can exhibit much longer exchange times, on the order of several hours. See, for example, M. Rahman et al., Protein-Nanoparticle Interactions, Spring Series in BiopHysics 15, 2013 (incorporated by reference in its entirety).

[0090] The term "biomolecule" refers to a biological component that may form a corona, including, but not limited to, for example, a protein, a polypeptide, a polysaccharide, a sugar, a lipid, a lipoprotein, a metabolite, an oligonucleotide, a metabolome, or a combination thereof. It is contemplated that the biomolecular corona of a unique particle may include some of the same biomolecules, may include biomolecules that are unique to other sensor elements, and / or may differ in the level, quantity, type, or conformation of the biomolecules bound to each sensor element. In one embodiment, the biomolecule is selected from the group of a protein, a nucleic acid, a lipid, and a metabolome.

[0091] The term "biomolecular corona signature" refers to the composition, signature, or pattern of different biomolecules bound to each type of particle or separate sensor element. The signature may refer not only to the different biomolecules, but also to differences in the amount, level, or quantity of the biomolecules bound to the sensor element, or differences in the conformation of the biomolecules bound to the particle or sensor element. It is contemplated that the biomolecular corona signature of each unique type of sensor element may include some of the same biomolecules, may include biomolecules unique to other sensor elements, and / or may differ in the level or quantity, type, or conformation of various biomolecules. The biomolecular corona signature may depend not only on the physicochemical properties of the sensor element (e.g., particle), but also on the nature of the sample and the duration of exposure to a biological sample.

[0092] Disclosed herein are compositions and methods for multi-omic analysis. "Multi-omic" or "multi-omic" can refer to an analytical approach for large-scale analysis of biomolecules, where the data set is a diverse ome (e.g., proteome, genome, transcriptome, lipidome, and metabolome). Non-limiting examples of multi-omic data include proteomics data, genome data, lipidomics data, glycomics data, transcriptomics data, or metabolomics data.

[0093] The "biomolecule" in "biomolecular corona" can refer to any molecule or biological component that can be produced by or present in an organism. Non-limiting examples of biomolecules include proteins (protein corona), polypeptides, oligopeptides, polyketides, polysaccharides, sugars, lipids, lipoproteins, metabolites, oligonucleotides, nucleic acids (DNA, RNA, microRNA, plasmids, single-stranded nucleic acids, double-stranded nucleic acids), metabolomes, and small molecules such as primary metabolites, secondary metabolites, and other natural products, or combinations thereof. In some embodiments, the biomolecule is selected from the group consisting of proteins, nucleic acids, lipids, and metabolomes.

[0094] Currently, only a small number of protein-based biomarkers are used for clinical diagnosis, and despite extensive attempts to expand the number of markers in plasma proteome analysis, relatively few new candidates have been recognized as clinically useful indicators. The plasma proteome contains >10,000 proteins and potentially contains an additional order of magnitude of protein isoforms spanning a concentration range of over 10 orders of magnitude (mg / mL to pg / mL). These attributes, combined with the lack of convenient molecular tools for proteome analysis, make comprehensive study of the plasma proteome extremely challenging. Approaches to overcome the wide dynamic range of proteins in biological samples must be able to identify and quantify them against a background of thousands of unique proteins and even more protein variants. However, no existing technology can simultaneously measure proteins across the entire plasma concentration range in a format with sufficient throughput and a realistic cost profile to enable studies of appropriate scale with robust validation and replication. These challenges have not only limited the discovery of novel disease biomarkers but also hindered the adoption of proteomic and protein annotation of genomic variants. Advances in mass spectrometry (MS) and the development of improved data analytics have provided tools for deep and broad proteomic analyses. Several attempts have been made to significantly improve the detection of low-abundance proteins (e.g., depletion of highly abundant proteins, plasma fractionation, and peptide fractionation). Currently, the identification of over 4,500 proteins in plasma is possible. However, current approaches are quite complex and time-consuming (days to weeks), thus requiring a trade-off between protein coverage and sample throughput. Therefore, a simple and robust strategy for comprehensive and rapid analysis of the information available in the proteome remains an unmet need.

[0095] Furthermore, the earlier a disease is diagnosed, the greater the chance that it can be cured or successfully managed, thereby resulting in a better prognosis for the patient. If a disease is treated early, problems caused by the disease may be prevented or delayed, which may improve the patient's outcome (including extending the patient's life and / or quality of life).

[0096] Early diagnosis of cancer is crucial because many types of cancer can be successfully treated in the early stages. For example, the 5-year survival rates for breast cancer, ovarian cancer, and lung cancer are 15%, 5%, and 10%, respectively, for patients diagnosed at the most advanced stage of the disease, compared with 90%, 90%, and 70%, respectively, after early diagnosis and treatment. Once cancer cells leave the tissue of origin, the chances of successful treatment using available, established therapeutic agents are slim. While early diagnosis can be achieved by recognizing warning signs of cancer and taking prompt action, the majority of cancers (e.g., lung cancer) only show symptoms after cancer cells have infiltrated surrounding tissues and metastasized throughout the body. For example, more than 60% of patients with breast cancer, lung cancer, colon cancer, and ovarian cancer may have occult or even metastatic colonies by the time their cancer is discovered. Therefore, there is an urgent need to develop effective approaches for early cancer detection. Such an approach should have the sensitivity to identify cancer at various stages and the specificity to produce a negative result if the person being tested does not have cancer. Although extensive attempts have been made to develop methods for the early detection of cancer, and a vast number of risk factors and biomarkers have been proposed, a broadly valid platform for the early detection of a wide range of cancers remains elusive.

[0097] Because various types of cancer can alter the composition of plasma even in the early stages, one promising approach for early detection is molecular blood analysis for biomarkers. While this strategy has already worked for some cancers (such as PSA for prostate cancer), specific biomarkers for early detection of the majority of cancers do not yet exist. For such cancers (e.g., lung cancer), none of the identified candidate circulating biomarkers have been clinically validated, and only a few have reached the later stages of clinical development. Therefore, there is an urgent need for novel approaches to improve our ability to detect cancer (as well as other diseases) at very early stages. Automated Sample Preparation

[0098] The present disclosure provides systems and methods for automated sample preparation, data generation, and protein corona analysis. As illustrated in FIG. 1 , the systems and methods can include: (1) contacting a sample with particles (e.g., in a particle mixture) on a sensor array, substrate, plate, or in any of the aforementioned partitions; (2) binding biomolecules in the sample to the particles; (3) removing unbound sample from the particles; and (4) preparing the sample for analysis (e.g., using mass spectrometry (“MS”). For example, in (1), the disclosed method can include contacting a biological sample with a plurality of particles. In (2), the sample can be incubated with the plurality of particles to promote adsorption of biomolecules to the particles. In (3), unbound sample can be removed, retaining the particles and the biomolecules adsorbed thereto. In (4), the adsorbed biomolecules can be desorbed from the particles and prepared for mass spectrometry (which can generate illustrative data).

[0099] The present disclosure provides automated systems, methods, and kits for biomolecular corona preparation and analysis. The automated instrument can perform the aforementioned data generation steps outlined in FIG. 1 using at least the various units illustrated in FIG. 2. The automated instrument can include a substrate having multiple partitions containing sensor elements 205 and biological samples 210. A loading unit 215 on the instrument can transfer portions of the biological sample 210 into partitions on the substrate 205, causing adsorption of biomolecules from the biological sample onto sensor elements in the partitions on the substrate. The automated instrument can then remove unbound biomolecules from the partitions and, optionally, transfer the unbound sample to a waste reservoir 220. Remaining biomolecules (e.g., biomolecules adsorbed to the sensor elements) can be desorbed, collected, and prepared for mass spectrometry analysis. The reagents 225 can include buffers (e.g., a resuspension buffer capable of desorbing biomolecules from the biomolecular corona, or a denaturing buffer capable of denaturing or fragmenting biomolecules). Reagents (eg, buffer proteases) 225 may also be loaded using the loading unit 215 to facilitate any of the foregoing.

[0100] In some embodiments, the present disclosure provides an automated system including a network of units having differentiated functions in identifying the state of a complex biological sample using a plurality of particles having surfaces with different physicochemical properties, wherein a first unit includes a multi-channel fluid transfer device for transferring fluid from unit to unit within the system; a second unit includes a support for storing a plurality of biological samples; and a third unit includes a sensor array plate (e.g., a substrate (e.g., a 96-well plate including nanoparticles) including a plurality of partitions containing sensor elements) having partitions containing the plurality of particles having surfaces with different physicochemical properties for detecting binding interactions between a population of analytes in the complex biological sample and the plurality of particles. a fourth unit includes a support for storing a plurality of reagents; a fifth unit includes a support for storing reagents to be discarded; and a sixth unit includes a support for storing consumables used by the multi-channel fluid transfer device; and wherein the system is programmed to perform a series of steps including contacting the biological sample with a particular partition of the sensor array; incubating the biological sample with the plurality of particles contained within the partition of the sensor array plate; removing all components from the partition except for the plurality of particles and a population of analytes that interact with the particles; and preparing the sample for mass spectrometry.

[0101] An example of such an instrument is shown in Figure 3. The instrument includes an automated pipette capable of transferring volumes between a biological sample storage unit, a substrate including multiple partitions containing multiple sensor elements, a waste collection unit, a unit containing a denaturing solution, and a unit containing a resuspension solution. The automated instrument can perform a biomolecular corona assay that includes transferring a portion of the biological sample into a partition in the substrate containing a sensor element, incubating the portion of the sample with the sensor element to bind biomolecules from the biological sample to the sensor element, removing contents from the partition containing biomolecules not bound to the sensor element, and then preparing the biomolecules remaining in the partition for mass spectrometry (MS) analysis (e.g., LC-MS).

[0102] The loading may be mobile to the extent that it allows access to all other units in the system. The loading may be capable of performing pipetting functions.

[0103] The disclosed systems or instruments may include supports for single plates, 6-well plates, 12-well plates, 96-well plates, 192-well plates, 384-well plates, or racks of microtubes. In some embodiments, the disclosed systems or instruments may include a thermal unit capable of regulating the temperature of the supports and samples. In some embodiments, the disclosed systems or instruments may include a rotation unit capable of physically stirring and / or mixing samples.

[0104] In some embodiments, the plurality of particles, including surfaces with different physicochemical properties for detecting binding interactions between a population of analytes in the complex biological sample and the plurality of particles, are immobilized on a surface within a partition of the sensor array. In some embodiments, the plurality of particles include a plurality of magnetic nanoparticles in solution with different physicochemical properties for detecting binding interactions between a population of analytes in the complex biological sample and the plurality of particles. In some embodiments, the system includes a step of transferring the sensor array plate to an additional seventh unit including a magnetizable support and a thermal unit capable of adjusting the temperature of the support and the sample, and incubating for an additional period of time.

[0105] In some embodiments, the fourth unit includes a set of reagents for generating the sensor array plate; washing unbound sample; and / or preparing the sample for mass spectrometry. In some embodiments, contacting the biological sample with a specific partition of the sensor array includes pipetting a specific volume of the biological sample into the specific partition of the sensor array. In some embodiments, contacting the biological sample with the specific partition of the sensor array includes pipetting a volume corresponding to a ratio of a plurality of particles in solution to the biological sample of 1:1, 1:2:1:3, 1:4, 1:5, 1:6, 1:7, 1:8, 1:9, 1:10, 1:15, or 1:20.

[0106] In some embodiments, contacting the biological sample with a particular partition of the sensor array comprises pipetting a volume of at least 10 microliters, at least 50 microliters, at least 100 microliters, at least 250 microliters, at least 500 microliters, or at least 1000 microliters of the biological sample into the particular partition of the sensor array. automation equipment

[0107] In some embodiments, the present disclosure provides an automated instrument for generating a subset of biomolecules from a biological sample, the instrument comprising: a substrate comprising a plurality of partitions; a first unit comprising the biological sample; and a loading unit that is movable across the substrate and can transfer volumes (e.g., volumes of buffer) between different units of the instrument. In some cases, the substrate is a multi-well plate.

[0108] The plurality of partitions may include a plurality of sensor elements. The plurality of sensor elements may include particles. The plurality of sensor elements may be particles (e.g., nanoparticles or microparticles).

[0109] A partition within the plurality of partitions may contain 1 to 100 different sensor elements (e.g., specific particle species). A partition within the plurality of partitions may contain 2 to 50 different sensor elements. A partition within the plurality of partitions may contain 2 to 5 different sensor elements. A partition within the plurality of partitions may contain 3 to 8 different sensor elements. A partition within the plurality of partitions may contain 4 to 10 different sensor elements. A partition within the plurality of partitions may contain 5 to 12 different sensor elements. A partition within the plurality of partitions may contain 6 to 15 different sensor elements. A partition within the plurality of partitions may contain 8 to 20 different sensor elements.

[0110] Two or more partitions in the plurality of partitions may include different numbers of sensor elements. Two or more partitions in the plurality of partitions may include different types of sensor elements. A partition in the plurality of partitions may include a different combination of types and / or numbers of sensor element(s) than other partitions in the plurality. A subset of partitions in the plurality of partitions may each include a unique combination of sensor elements that is unique to other partitions in the plurality.

[0111] The sensor element can be stored in a dry form inside or within the partition. The dried sensor element can be reconstituted or rehydrated before use. The sensor element can also be stored in a solution. For example, the substrate partition can contain a solution containing a high concentration of particles.

[0112] Partitions within the plurality of partitions may contain different concentrations or amounts (e.g., mass / molar amount per unit volume of sample) of sensor elements. A partition within the plurality of partitions may contain between 1 pM and 100 nM of sensor elements. A partition within the plurality of partitions may contain between 1 pM and 500 pM of sensor elements. A partition within the plurality of partitions may contain between 10 pM and 1 nM of sensor elements. A partition within the plurality of partitions may contain between 100 pM and 10 nM of sensor elements. A partition within the plurality of partitions may contain between 500 pM and 100 nM of sensor elements. A partition within the plurality of partitions may contain between 50 μg / ml and 300 μg / ml of sensor elements. A partition within the plurality of partitions may contain between 100 μg / ml and 500 μg / ml of sensor elements. A partition within the plurality of partitions may contain between 250 μg / ml and 750 μg / ml of sensor elements. A partition of the plurality of partitions may contain between 400 μg / ml and 1 mg / ml of sensor element. A partition of the plurality of partitions may contain between 600 μg / ml and 1.5 mg / ml of sensor element. A partition of the plurality of partitions may contain between 800 μg / ml and 2 mg / ml of sensor element. A partition of the plurality of partitions may contain between 1 mg / ml and 3 mg / ml of sensor element. A partition of the plurality of partitions may contain between 2 mg / ml and 5 mg / ml of sensor element. A partition of the plurality of partitions may contain greater than 5 mg / ml of sensor element.

[0113] The loading unit can be configured to move and transfer volumes (e.g., volumes of solution or powder) between any units, compartments, or partitions within the device. The loading unit can be configured to move precise volumes (e.g., within 0.1%, 0.01%, 0.001% of a particular volume). The loading unit can be configured to collect a volume from the substrate or a compartment or partition within the substrate and dispense the volume back into the substrate or a compartment or partition within the substrate, or to dispense the volume or portions of the volume into a different unit, compartment, or partition. The loading unit can be configured to simultaneously move multiple volumes (e.g., 2 to 400 separate volumes). The loading unit can include multiple pipette tips.

[0114] The loading unit can be configured to transfer a volume of lipid, which can be about 0.1 μl, 0.2 μl, 0.3 μl, 0.4 μl, 0.5 μl, 0.6 μl, 0.7 μl, 0.8 μl, 0.9 μl, 1 μl, 2 μl, 3 μl, 4 μl, 5 μl, 6 μl, 7 μl, 8 μl, 9 μl, 10 μl, 12 μl, 15 μl, 20 μl, 25 μl, 30 μl, 40 μl, 50 μl, 60 μl, 70 μl, 80 μl, 90 μl, 100 μl, 120 μl, 150 μl, 180 μl, 200 μl, 250 μl, 300 μl, 400 μl, 500 μl, 600 μl, 800 μl, 1 ml, or greater than 1 ml. The lipid may be a biological sample or a solution.

[0115] In some cases, the solution comprises a wash solution, a resuspension solution, a denaturing solution, a buffer, a reagent (e.g., a reducing reagent), or any combination thereof. In some cases, the solution comprises a biological sample.

[0116] In part due to these functionalities, the loading unit may be capable of dividing a sample. In some embodiments, this involves distributing the sample into multiple partitions. A sample may be distributed into at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100, 120, 150, 180, 200, 250, 300, 350, 400, 500, or more partitions. A sample may be distributed into 96, 192, or 384 partitions. The automated device may include multiple substrates comprising partitions. The automated device may include 1, 2, 3, 4, 5, or more substrates comprising partitions. In some cases, the loading unit loads different volumes of the biological sample into different partitions. In some cases, the loading unit loads the same volume into two or more partitions. The volume of biological sample loaded into a partition can be about 0.1 μl, 0.2 μl, 0.3 μl, 0.4 μl, 0.5 μl, 0.6 μl, 0.7 μl, 0.8 μl, 0.9 μl, 1 μl, 2 μl, 3 μl, 4 μl, 5 μl, 6 μl, 7 μl, 8 μl, 9 μl, 10 μl, 12 μl, 15 μl, 20 μl, 25 μl, 30 μl, 40 μl, 50 μl, 60 μl, 70 μl, 80 μl, 90 μl, 100 μl, 120 μl, 150 μl, 180 μl, 200 μl, 250 μl, 300 μl, 400 μl, 500 μl, 600 μl, 800 μl, 1 ml, or more than 1 ml. The volume of biological sample loaded into a partition can be between about 10 μl and 400 μl. The volume of biological sample loaded into a partition can be between about 5 μl and 150 μl. The volume of biological sample loaded into a partition can be between about 35 μl and 80 μl. In some cases, the loading unit can dispense two or more biological samples. For example, a sample storage unit can contain two biological samples that the system dispenses into a single-well plate.In some embodiments, the loading unit may facilitate the transfer of the sample to a mass analysis unit for mass analysis.

[0117] The system can be configured to dilute the sample or sample partition. The sample or sample partition can be diluted with a buffer, water (e.g., purified water), a non-aqueous solvent, or any combination thereof. The diluent can be stored in the automated instrument before being dispensed into the substrate partition. The automated instrument can store multiple diluents that vary in pH, salinity, osmolality, viscosity, dielectric constant, or any combination thereof. The diluent can be used to adjust the chemical properties of the sample or sample partition. The automated instrument can dilute the sample or sample partition by 2x, 3x, 4x, 5x, 6x, 8x, 10x, 15x, 20x, 30x, 40x, 50x, 75x, 100x, 150x, 200x, 300x, 400x, 500x, or 500x. Ultra-dilution may be performed. The automated instrument may perform different dilutions for two samples or sample partitions. The system may perform different dilutions for each partition in a plurality of partitions. For example, the system may perform different dilutions for each of 96 sample partitions in a 96-well plate. In some cases, the different dilutions include dilutions of different degrees (e.g., 2x vs. 4x). In some cases, the different dilutions include dilutions with different solutions (e.g., different buffers). In some cases, the two sample partitions may be made to differ in one or more chemical properties (e.g., pH, salinity, or viscosity).

[0118] In some cases, the system may modify the chemical composition of the sample or sample partition. The system may modify or adjust the pH, salinity, osmolality, dielectric constant, viscosity, buffer type, salt type, sugar type, surfactant type, or any combination thereof of the sample or sample partition. Such modification or adjustment may include mixing the sample or sample partition with a reagent from the fourth unit. The system may modify the chemical composition of two samples or sample partitions differently.

[0119] The system or automated device of the present disclosure may also include an incubation element. The incubation element may contact, support, or hold another component of the automated device (e.g., the substrate or unit). The incubation unit may contact, support, or hold various components of the automated device. The incubation element may contact the substrate and facilitate heat transfer between the incubation element and the substrate. The incubation unit may be configured to regulate (e.g., by heating or cooling) the temperature of the one or more components of the automated device. The incubation element may be capable of cooling the device components to between 20°C and 1°C. The incubation element may be capable of heating the device components to between 25°C and 100°C. The incubation element may be capable of adjusting the temperature of the device components to between 4°C and 37°C. The incubation element may be configured to heat or cool different portions of the automated instrument components to different temperatures. For example, the incubation element may maintain a first partition in the substrate at 30°C and a second partition in the substrate at 35°C. The incubation element may control the temperature of a sample or partition. The incubation element may include a temperature sensor (e.g., a thermocouple) to detect the temperature within a partition or container. The incubation element may calibrate its heating or cooling to readings from the temperature sensor.

[0120] The incubation element may be configured to physically agitate components of the automated instrument. The agitation may be in the form of shaking or rotation, vibration, rocking, sonication, or any combination thereof. The incubation element may be capable of providing various agitation intensities and / or frequencies. For example, the incubation element may include various settings for shaking at different frequencies and amplitudes. The incubation element may also be capable of stirring and / or mixing a volume (e.g., a portion of the biological sample).

[0121] The automated instrument may include a unit containing a resuspension solution. The loading unit may be capable of transferring a volume of the resuspension solution to a partition among the plurality of partitions of the substrate. In some cases, this may result in dilution of the sample present in the partition and may also result in desorption of multiple biomolecules from the biomolecular corona disposed on the sensor element within the partition. The quantity of biomolecules desorbed from the biomolecular corona may depend on the volume of the resuspension solution added to the partition, the temperature of the partition, the composition of the resuspension solution (e.g., salinity, osmolality, viscosity, dielectric constant, or pH), the volume of the biological sample within the partition, and the type and composition of biomolecules in the sensor element. Transferring a volume of the resuspension solution into a partition may result in desorption of less than 5% of the biomolecules from the biomolecular corona. Transferring a volume of the resuspension solution into a partition may result in desorption of 10% to 20% of the biomolecules from the biomolecular corona. Transferring a volume of the resuspension solution into a partition can result in desorption of 20% to 30% of the biomolecules from the biomolecular corona. Transferring a volume of the resuspension solution into a partition can result in desorption of 30% to 40% of the biomolecules from the biomolecular corona. Transferring a volume of the resuspension solution into a partition can result in desorption of 40% to 50% of the biomolecules from the biomolecular corona. Transferring a volume of the resuspension solution into a partition can result in desorption of 50% to 60% of the biomolecules from the biomolecular corona. Transferring a volume of the resuspension solution into a partition can result in desorption of 60% to 70% of the biomolecules from the biomolecular corona. Transferring a volume of the resuspension solution into a partition can result in desorption of 70% to 80% of the biomolecules from the biomolecular corona. Transferring a volume of the resuspension solution into a partition can result in desorption of 80% to 90% of the biomolecules from the biomolecular corona.Transfer of the volume of the resuspension solution into the partition can result in desorption of greater than 90% of the biomolecules from the biomolecular corona.

[0122] In some cases, multiple rounds of desorption are performed. In each round, the supernatant containing the desorbed biomolecules can be collected, analyzed, or discarded. The type and abundance of biomolecules in the supernatant can vary between desorption rounds. The automated instrument can perform one or more desorption and discard cycles (i.e., washes), followed by one or more desorption cycles including sample collection and / or analysis.

[0123] The resuspension solution can be tailored to optimize enrichment of specific biomarkers. The resuspension solution can include a buffer (e.g., Tris-EDTA (TE), CHAPS, PBS, citric acid, HEPES, MES, CHES, or another biobuffer). The resuspension solution can include Tris-EDTA (TE) 150 mM KCl 0.05% CHAPS buffer. The resuspension solution can include 10 mM Tris-HCl pH 7.4, 1 mM EDTA. The resuspension solution can also include highly purified water (e.g., distilled or deionized water) or can be highly purified water (e.g., distilled or deionized water). Desorption of biomolecules can be enhanced by heating or agitation with an incubation element. The supernatant can be transferred to a new partition following desorption. The resuspension solution can be used to dilute the sample.

[0124] The automated instrument may include a unit containing a denaturing solution. The denaturing solution may include a protease. The denaturing solution may include a chemical capable of peptide cleavage (e.g., cyanogen bromide, formic acid, or hydroxylamine, 2-nitro-5-thiocyanatobenzoic acid). The denaturing solution may include a chemical denaturant such as guanidine, urea, sodium deoxycholate, acetonitrile, trichloroacetic acid, acetic acid, sulfosalicylic acid, sodium bicarbonate, ethanol, perchlorate, dodecyl sulfate, or any combination thereof. The denaturing solution may include a reducing agent (e.g., 2-mercaptoethanol, dithiothreitol, or tris(2-carboxyethyl)phosphine). The protease may be trypsin. The denaturing solution may be added to the partition following desorption. The denaturing solution may be added to the partition containing the biomolecule corona.

[0125] The automated device may include a magnet or an array of magnets. The automated device may be capable of moving the substrate onto and away from the magnet or array of magnets. The array of magnets may be constructed so that multiple magnets in the array of magnets can rest directly beneath multiple partitions of the substrate. The magnets may be capable of immobilizing magnetic sensor elements (e.g., magnetic particles such as coated or uncoated superparamagnetic iron oxide nanoparticles) within partitions on the substrate. For example, the magnets may prevent magnetic nanoparticles from being removed from the partitions during a washing step. The magnets may also generate pellets from a collection of magnetic particles. The magnets may generate particle pellets in less than 10 minutes. The magnets may generate particle pellets in less than 5 minutes. The particle pellets may include particles having a biomolecular corona.

[0126] The automated instrument may include a purification unit. The purification unit may include multiple partitions containing adsorbents or resins. The purification unit may include a solid-phase extraction array or plate. The solid-phase extraction array or plate may include a polar stationary phase material. The solid-phase extraction array or plate may include a non-polar stationary phase material. The solid-phase extraction array or plate may include a C18 stationary phase material (e.g., octadecyl silica gel). The automated instrument may include a unit containing a conditioning solution for the purification unit (e.g., a conditioning solution for the solid-phase extraction material). The automated instrument may include a unit containing an elution solution for removing biomolecules from the purification unit.

[0127] In some embodiments, components are removed from the partition except for the plurality of sensor elements and a population of analytes that interact with the plurality of sensor elements (i.e., a washing step). In some examples, the automated instrument may perform a series of washing steps. The washing steps may remove biomolecules that are not bound to the sensor elements in the partition. The washing steps may desorb a subset of biomolecules bound to the sensor elements in the partition. For example, the washing steps may result in the desorption and removal of a subset of soft corona analytes while leaving a majority of hard corona analytes bound to the sensor elements.

[0128] In some embodiments, the present disclosure provides an automated instrument for identifying proteins in a biological sample, the automated instrument comprising: a sample preparation unit; a substrate including a plurality of channels; a plurality of pipettes; a plurality of solutions; and a plurality of particles, and wherein the automated instrument is configured to form a protein corona and digest the protein corona.

[0129] In some embodiments, the present disclosure provides an automated instrument for identifying proteins in a biological sample, the automated instrument comprising: a sample preparation unit; a substrate including a plurality of channels; a plurality of pipettes; a plurality of solutions; and a plurality of nanoparticles, wherein the automated instrument is configured to form a protein corona and digest the protein corona, and wherein at least one of the solutions is TE 150 mM KCl 0.05% CHAPS buffer.

[0130] In some embodiments, the sample preparation unit is configured to add the nanoparticles to the substrate with the pipettes. In some embodiments, the sample preparation unit is configured to add the biological sample to the substrate with the pipettes. In some embodiments, the sample preparation unit is configured to incubate the nanoparticles with the biological sample to form the protein corona.

[0131] In some embodiments, the sample preparation unit is configured to separate the protein corona from the supernatant to form a protein corona pellet, hi some embodiments, the sample preparation unit is configured to reconstitute the protein corona pellet with TE 150 mM KCl 0.05% CHAPS buffer.

[0132] In some embodiments, the automated instrument further comprises a magnetic source. In some embodiments, the automated instrument is configured for trypsin digestion of BCA, gel, or protein corona.

[0133] In some embodiments, the automated instrument is enclosed. In some embodiments, the automated instrument is sterilized prior to use. In some embodiments, the automated instrument is adapted for mass spectrometry. In some embodiments, the automated instrument is temperature controlled. Assay Method

[0134] In some embodiments, the present disclosure provides a method for identifying proteins in a biological sample. In some cases, the method includes: adding the biological sample to an automated instrument disclosed herein; generating proteomic data by the automated instrument; and quantifying the proteomic data.

[0135] In some embodiments, the method includes incubating a plurality of biomolecules with the biological sample in the automated instrument to form a biomolecular corona. In some embodiments, incubating the biological sample with the plurality of sensor elements (e.g., particles) contained within the partition of the substrate is for at least about 10 seconds, at least about 15 seconds, at least about 20 seconds, at least about 25 seconds, at least about 30 seconds, at least about 40 seconds, at least about 50 seconds, at least about 60 seconds, at least about 90 seconds, at least about 2 minutes, at least about 3 minutes, at least about 4 minutes, at least about 5 minutes, at least about 6 minutes, at least about 7 minutes, at least about 8 minutes, at least about 9 minutes, at least about 10 minutes, at least about 15 minutes ... In some embodiments, the incubation time may be at least about 0 minutes, at least about 25 minutes, at least about 30 minutes, at least about 45 minutes, at least about 50 minutes, at least about 60 minutes, at least about 90 minutes, at least about 2 hours, at least about 3 hours, at least about 4 hours, at least about 5 hours, at least about 6 hours, at least about 7 hours, at least about 8 hours, at least about 9 hours, at least about 10 hours, at least about 12 hours, at least about 14 hours, at least about 15 hours, at least about 16 hours, at least about 17 hours, at least about 18 hours, at least about 19 hours, at least about 20 hours, or at least about 24 hours. In some cases, two wells will have two different incubation times. In some embodiments, incubating the biological sample with the plurality of particles contained within the partition of the substrate comprises an incubation temperature of about 4°C to about 37°C. In some embodiments, incubating the biological sample with the plurality of particles contained within the partition of the substrate comprises an incubation temperature of about 4°C to about 100°C.

[0136] The disclosed methods, systems, and devices may include covering or sealing a partition on the substrate. This may include covering a surface of the device with a lid or seal. The lid or seal may prevent a solution or species from leaving the partition (e.g., evaporating from the partition). The automated device may be configured to add and / or remove the lid or seal. The lid or seal may be pierceable (e.g., may include a septum), thereby allowing a syringe or needle to enter the substrate partition without removing the lid or seal.

[0137] In some cases, the disclosed systems, devices, and methods further include preparing an analyte from the biomolecular corona for analysis (e.g., mass spectrometry). This may include separating the biomolecular corona from the supernatant in the automated device. The biomolecular corona may be separated from the supernatant by removing the supernatant and then desorbing multiple proteins from the biomolecular corona into a desorbent solution (e.g., a resuspension solution). In some cases, a first portion of biomolecules from the biomolecular corona are desorbed from the biomolecular corona and discarded, and a second portion of biomolecules from the biomolecular corona are desorbed from the biomolecular corona and collected (e.g., for analysis). Multiple portions of biomolecules from the biomolecular corona may be desorbed, collected, and analyzed separately.

[0138] In some cases, biomolecules in the biomolecular corona are denatured, fragmented, chemically modified, or any combination thereof. These treatments can be performed on the desorbed biomolecules or on the biomolecular corona. The biomolecules desorbed from the biomolecular corona can comprise 1%, 2%, 3%, 4%, 5%, 6%, 8%, 10%, 12%, 15%, 20%, 25%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 98%, 99%, or more than 99% of the biomolecules from the biomolecular corona. The desorption can be carried out for different lengths of time, including 5 seconds, 15 seconds, 30 seconds, 1 minute, 2 minutes, 3 minutes, 4 minutes, 5 minutes, 6 minutes, 8 minutes, 10 minutes, 12 minutes, 15 minutes, 20 minutes, 30 minutes, 40 minutes, 50 minutes, 1 hour, 1.5 hours, 2 hours, 3 hours, 4 hours, 5 hours, 6 hours, 8 hours, 12 hours, or more than 12 hours. In some cases, the desorption includes physical agitation (e.g., shaking or sonication). The percentage of protein desorbed from the particle corona can depend on the desorption time, the chemical composition (e.g., pH or buffer species) of the desorbent solution into which the protein is desorbed, the desorption temperature, the form and strength of the physical agitation applied, or any combination thereof. Furthermore, the type of protein desorbed from the protein corona can be responsive to the desorption conditions and method. The types of proteins desorbed from the protein corona can differ by 1%, 2%, 3%, 4%, 5%, 6%, 8%, 10%, 12%, 15%, 20%, 25%, 30%, 40%, 50%, 60%, or more than 60% between the two desorption conditions or methods.

[0139] In some cases, preparing an analyte from the biomolecular corona for analysis includes digesting the biomolecular corona, a subset of biomolecules in the protein corona, or biomolecules desorbed from the biomolecular corona to form a digested sample within the automated instrument. Preparing an analyte from the biomolecular corona for analysis can also include chemically modifying a biomolecule from the biomolecular corona (e.g., methylating or reducing the biomolecule).

[0140] The desorbed biomolecules can be collected for further analysis (e.g., mass spectrometry). The automated instrument can perform the collection, for example, by collecting a sample volume from the substrate partition containing the biomolecules desorbed from the biomolecular corona. The method includes placing a partition or multiple partitions (e.g., a well plate), which can be placed directly into the instrument for performing the analysis.

[0141] The method can include multiple rounds of preparation of analytes from the biomolecular corona for analysis. The method can include 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more rounds of preparation. In some cases, each round generates a separate sample for analysis (e.g., desorbed biomolecules can be collected after each round and subjected to mass spectrometry). Two rounds can include desorbing different proteins from the biomolecular corona. The two rounds can also include different desorption methods or conditions (e.g., different desorbent solution volumes, different desorbent solution types (e.g., desorbent solutions with different buffers or osmolalities), different temperatures, or different types or degrees of physical agitation). Two or more consecutive rounds of preparation from one biomolecular corona (e.g., desorption and collection of a first subset of biomolecules from the biomolecular corona, followed by desorption and collection of a second subset of biomolecules from the biomolecular corona) can generate two sets of biomolecules. This can provide information for the detection or analysis of biomolecular interactions within the protein corona. Thus, multiple rounds of preparation from a single biomolecular corona can be used to generate biomolecular subsets in numbers that exceed the number of partitions or sensor types. For example, a method utilizing a substrate with 96 partitions (e.g., a 96-well plate) can generate as many as 960 unique biomolecular subsets, where each partition contains a unique combination of particles and solution conditions, and 10 rounds of analyte preparation are performed for each partition.

[0142] Different rounds of analyte preparation can be performed in different partitions. The partitions can also be exposed to different analyte preparation conditions. Performing more rounds of analyte preparation can increase the number or variety of proteins collected for analysis (e.g., making more proteins within a concentration range available for simultaneous mass spectrometric detection). The number or variety of proteins detected when multiple rounds of analyte preparation are performed can be 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%, 150%, 200%, or more than 200% greater than when a single round of analyte preparation is performed.

[0143] In some cases, the method includes immobilizing the sensor element within the partition. The immobilization can prevent the sensor element from being removed from the partition when a volume is removed from the partition (e.g., the loading unit removes 95% of the solution from the partition). The immobilization can be performed, for example, chemically (e.g., by covalent or non-covalent attachment to a substrate). Chemical immobilization can include reacting the sensor element with the surface of the partition. Chemical immobilization can also include non-covalently binding the sensor element to the surface of the partition. For example, the sensor element can include a biotin moiety that binds to streptavidin bound to the surface of the partition. Immobilization can be achieved by applying a magnetic field to hold the magnetic sensor element within the partition. For example, multiple sensor elements can include multiple magnetic particles, and the substrate and the magnet can be in close proximity such that the one or more magnetic particles are immobilized within a partition in the substrate. Immobilization can be achieved by providing a substrate with the sensor element formed or embedded within a partition on the substrate. For example, the sensor elements may be semi-particles formed on the surface of a partition within the substrate.

[0144] In some cases, sensor element immobilization allows the biomolecular corona to separate from the sensor element, which may include desorbing multiple biomolecules from a biomolecular corona bound to a sensor element, immobilizing the sensor element within the partition, and then collecting the solution with the multiple biomolecules from the biomolecular corona, thereby separating at least a portion of the biomolecular corona from the sensor element.

[0145] 4 shows examples of methods involving immobilization of sensor elements that can be performed by the automated instruments of the present disclosure. These methods utilize particles 402 and 411 to capture subsets 403 and 404 of biomolecules in a sample.

[0146] Panel 400 shows a partition 401 containing particles 402 and biomolecules. The particles are suspended within the partition and adsorb biomolecules 403 from the sample, thereby forming a biomolecular corona. Many biomolecules 404 may not adsorb to the particles and instead will be suspended within the partition. Panel 410 shows an alternative method, including particles 411 formed on the surface of the partition.

[0147] Panels 420 and 430 show two methods for immobilizing the particles. In panel 420, the particles are collected at the bottom of the partition by a magnet 421. In panel 430, the particles are cross-linked to the partition by a linker 431. Both of these methods immobilize the particles to the partition. Throughout the immobilization process, particle-adsorbed biomolecules 403 remain adsorbed to the particles, while unbound biomolecules 404 remain unbound to the particles.

[0148] Panel 440 shows the results of a washing step on the partitions from panels 410, 420, and 430. In all three cases, the washing removes unbound biomolecules from the partitions, leaving behind the immobilized particles and the biomolecules adsorbed thereto. Panel 450 then shows desorption from the biomolecular corona, where a first plurality of biomolecules 451 elutes from the particles and a second plurality of biomolecules 403 remains adsorbed to the particles. The ratio of eluted biomolecules to adsorbed biomolecules and the types of biomolecules eluted from the particles depend on the elution conditions (e.g., temperature, degree and type of physical agitation, solution conditions such as pH, etc.). The eluted biomolecules can be collected (e.g., by a loading unit) for further processing (e.g., fragmentation) or direct analysis.

[0149] FIG. 5 illustrates an example of a sample preparation method that may be performed by an automated instrument of the present disclosure. The method utilizes sensor elements 512 to generate a subset of biomolecules from a biological sample 502. The biological sample (shown in panel 500) (stored in sample container 501) contains a large number of biomolecules. A volume of the sample may optionally be processed 504 (e.g., cells within the sample may be lysed, nucleic acids and proteins may be fragmented, the sample may be filtered to remove large biomolecules, etc.) and then added to a partition 511 containing sensor elements 512. As illustrated in panel 520, a portion of the biomolecules 521 may bind to the sensor elements, thereby separating them from a portion of the biomolecules 522 that do not bind to the sensor elements. As shown in panel 530, the sensor elements may then be immobilized within the partition by contacting the partition with a magnet 531. The partition may then be subjected to a wash cycle (e.g., adding buffer to the partition followed by removal of sample from the partition), which removes the portion of biomolecule 522 that is not bound to the sensor element (shown in panel 540). Bound biomolecule 521 may be eluted from the sensor element and collected for further processing or analysis.

[0150] FIG. 6 illustrates a sample preparation method that may be performed by an automated instrument of the present disclosure. This method utilizes a sensor element 512 formed on the surface of a substrate partition 511 to collect biomolecules 503 from a sample 502. The biological sample is transferred 504 from the sample retention unit 501 to the substrate partition 511. As shown in panel 520, the sensor element will adsorb a first portion 521 of the biomolecules from the sample, while a second portion 522 will remain unbound. Panel 530 illustrates the removal of unbound biomolecules, leaving the sensor element 512 and the sensor element-bound biomolecules 521 within the partition. These biomolecules may then be desorbed from the sensor element and collected (e.g., by the loading unit) for further processing or analysis.

[0151] The methods disclosed herein can include filtering the sensor element from a solution. For example, the method can include desorbing multiple biomolecules from a biomolecular corona bound to the sensor element and filtering the solution so that the sensor element is collected on a filter and the multiple biomolecules remain in solution. The filtration can be performed after denaturation (e.g., digestion). The filtration can also remove multiple biomolecules or biological species, such as intact proteins (e.g., undigested proteins or proteases from the biological sample).

[0152] In some cases, the method includes a purification step. The purification step may precede or follow preparation of an analyte from the biomolecular corona. The purification step may include transferring a biological sample (e.g., biomolecules eluted or collected from the biomolecular corona) to a purification unit (e.g., a chromatography column) or a partition within a purification unit. Purification may include transferring multiple sample partitions from the substrate into separate partitions in the purification unit. The purification unit may include a solid-phase extraction plate. The purification step may remove reagents (e.g., chemicals and enzymes) from the denaturing solution. Following purification, the biological sample may be recollected within the substrate or purification unit for further enrichment or chemical treatment, or may be collected for direct analysis (e.g., mass spectrometry).

[0153] In summary, the method of the present disclosure allows for high profiling depth for biological samples.The subset of biomolecules collected in the method of the present disclosure can allow mass spectrometric detection of at least 2%, at least 3%, at least 4%, at least 5%, at least 6%, at least 7%, at least 8%, at least 9%, at least 10%, at least 12%, at least 15%, at least 20%, at least 25%, at least 30%, at least 40%, at least 50%, at least 60% or more than 60% of the types of biomolecules in the biological sample from which the subset of biomolecules is collected, without further manipulation or modification of the subset of biomolecules. The subset of biomolecules may allow for mass spectrometric detection of at least 2%, at least 3%, at least 4%, at least 5%, at least 6%, at least 7%, at least 8%, at least 9%, at least 10%, at least 12%, at least 15%, at least 20%, at least 25%, at least 30%, at least 40%, at least 50%, or more than 50% of the proteins in a sample without further manipulation or modification of the subset of biomolecules. The subset of biomolecules collected on a sensor element or prepared for analysis may allow for simultaneous mass spectrometric detection of two biomolecules (e.g., proteins) spanning six, seven, eight, nine, ten, eleven, twelve, or more orders of magnitude in a sample without further manipulation or modification of the subset of biomolecules. For example, the two biomolecules may be desorbed and collected at concentrations within a six-order range in a single sample, fragmented, and then subjected to mass analysis.

[0154] In some cases, a type of sensor element (e.g., all sensor elements of a given type in contact with a single sample) adsorbs at least 100-300 different proteins when contacted with a biological sample. A type of sensor element adsorbs at least 200-500 different proteins when contacted with a biological sample. A type of sensor element adsorbs at least 300-800 different proteins when contacted with a biological sample. A type of sensor element adsorbs at least 400-1000 different proteins when contacted with a biological sample. A type of sensor element adsorbs at least 500-1200 different proteins when contacted with a biological sample.

[0155] In some cases, the proteins collected from multiple sensor elements may be identified at the protein group level. The multiple protein groups collected on sensor elements in a partition may include 1 to 20,000 protein groups. The multiple protein groups collected on sensor elements in a partition may include 100 to 10,000 protein groups. The multiple protein groups collected on sensor elements in a partition may include 100 to 5,000 protein groups. The multiple protein groups collected on sensor elements in a partition may include 300 to 2,200 protein groups. The multiple protein groups collected on sensor elements in a partition may include 1,200 to 2,200 protein groups. The multiple protein groups collected on sensor elements in a partition may include 20,000 to 25,000 protein groups. The multiple protein groups collected on sensor elements in a partition may include 25,000 to 30,000 protein groups. The plurality of protein groups collected on the sensor elements in a partition may include 30,000 to 50,000 protein groups.

[0156] The disclosed methods can result in enrichment of low-abundance biomolecules (e.g., proteins) from a biological sample. A low-abundance biomolecule can be a biomolecule having a concentration of 10 ng / mL or less in a biological sample.

[0157] The disclosed methods can result in enrichment of biomolecules (e.g., proteins) present at concentrations at least six orders of magnitude lower than the concentration of the most abundant biomolecule of the same type in the same sample (e.g., a low-abundance protein can be a protein whose concentration is at least six orders of magnitude lower than the most abundant protein in the sample). Databases (e.g., the Carr database characterizing the plasma proteome (Keshishian et al., Mol. Cell Proteomics 14, 2375-2393 (2015)), Plasma Proteome Database (plasmaproteomedatabase.org)) can provide a basis for comparison to determine whether a detected protein or biomolecule is enriched relative to other biomolecules present in the plasma sample. Similar databases can be used for other types of biological samples.

[0158] In certain cases, the biological sample comprises blood, plasma, or serum, and the biomolecular corona has a lower ratio of albumin to non-albumin proteins than the biological sample, which may be 20%, 30%, 40%, 50%, 60%, or 70% lower in the biomolecular corona than in the sample from which proteins were desorbed.

[0159] The concentration range of multiple biomolecules can be compressed during biomolecular corona formation. For example, the automated instrument can increase the number of biomolecule species whose concentrations are within a six-order of magnitude relative to the most enriched biomolecule in the sample by at least 25%, 50%, 100%, 200%, 300%, 500%, or 1000%. Similarly, the compressed dynamic range can include an increase in the number of protein species whose concentrations are within a six-order of magnitude relative to the most abundant biomolecule in the sample. The automated instrument can increase the number of protein species whose concentrations are within a six-order of magnitude relative to the most enriched protein in the sample by at least 25%, 50%, 100%, 200%, 300%, 500%, or 1000%. The automated instrument can enrich a subset of biomolecules from a biological sample, and the subset of biomolecules can include at least 10% of the biomolecule species from the biological sample over a six-order concentration range. The automated instrument may enrich a subset of biomolecules from a biological sample, the subset of biomolecules comprising at least 20% of the biomolecular species from the biological sample over a six-order concentration range. The automated instrument may enrich a subset of biomolecules from a biological sample, the subset of biomolecules comprising at least 30% of the biomolecular species from the biological sample over a six-order concentration range. The automated instrument may enrich a subset of biomolecules from a biological sample, the subset of biomolecules comprising at least 40% of the biomolecular species from the biological sample over a six-order concentration range. The automated instrument may enrich a subset of biomolecules from a biological sample, the subset of biomolecules comprising at least 50% of the biomolecular species from the biological sample over a six-order concentration range. The automated instrument can enrich a subset of biomolecules from a biological sample, and the subset of biomolecules can include at least 60% of the species of biomolecules from the biological sample over a six-order concentration range.The automated instrument may enrich a subset of biomolecules from a biological sample, wherein the subset of biomolecules comprises at least 70% of the types of biomolecules from the biological sample over a six-order concentration range. The automated instrument may enrich a subset of biomolecules from a biological sample, wherein the subset of biomolecules comprises at least 10% of the types of proteins from the biological sample over a six-order concentration range. The automated instrument may enrich a subset of biomolecules from a biological sample, wherein the subset of biomolecules comprises at least 20% of the types of proteins from the biological sample over a six-order concentration range. The automated instrument may enrich a subset of biomolecules from a biological sample, wherein the subset of biomolecules comprises at least 30% of the types of proteins from the biological sample over a six-order concentration range. The automated instrument may enrich a subset of biomolecules from a biological sample, wherein the subset of biomolecules comprises at least 40% of the protein species from the biological sample over a six-order concentration range. The automated instrument may enrich a subset of biomolecules from a biological sample, wherein the subset of biomolecules comprises at least 50% of the protein species from the biological sample over a six-order concentration range. The automated instrument may enrich a subset of biomolecules from a biological sample, wherein the subset of biomolecules comprises at least 60% of the protein species from the biological sample over a six-order concentration range. The automated instrument may enrich a subset of biomolecules from a biological sample, wherein the subset of biomolecules comprises at least 70% of the protein species from the biological sample over a six-order concentration range.

[0160] The disclosed methods and sensor elements can be adapted so that the composition of the biomolecular corona is invariant to the sample lipid concentration. A maximum 10% change in lipid concentration in a biological sample can result in less than a 5%, 2%, 1%, or 0.1% change in the composition of proteins in the biomolecular corona. A maximum 10% change in lipid concentration in a biological sample can result in less than a 5%, 2%, 1%, or 0.1% change in the number of protein species in the biomolecular corona. A maximum 10% change in lipid concentration in a biological sample can result in less than a 5%, 2%, 1%, or 0.1% change in the total number of proteins in the biomolecular corona.

[0161] In some embodiments, the method further comprises washing the digested sample in the automated instrument. In some embodiments, quantifying the proteomic data comprises providing the proteomic data to a mass spectrometer. In some embodiments, the biological sample is a bodily fluid. In some embodiments, the bodily fluid is serum or plasma.

[0162] In some cases, the total assay time (including sample preparation and LC-MS) for a single sample (e.g., a pooled plasma sample) can be about 8 hours. The total assay time (including sample preparation and LC-MS) for a single sample (e.g., a pooled plasma sample) can be about at least 1 hour, at least 2 hours, at least 3 hours, at least 4 hours, at least 5 hours, at least 6 hours, at least 7 hours, at least 8 hours, at least 9 hours, at least 10 hours, less than 20 hours, less than 19 hours, less than 18 hours, less than 17 hours, less than 16 hours, less than 15 hours, less than 14 hours, less than 13 hours, less than 12 hours, less than 11 hours, less than 10 hours, less than 9 hours, less than 8 hours, less than 7 hours, less than 6 hours, less than 5 hours, less than 4 hours, less than 3 hours, less than 2 hours, less than 1 hour, at least 5-10 minutes, at least 10-20 minutes, at least 20-30 minutes. minutes, at least 30 to 40 minutes, at least 40 to 50 minutes, at least 50 to 60 minutes, at least 1 to 1.5 hours, at least 1.5 to 2 hours, at least 2 to 2.5 hours, at least 2.5 to 3 hours, at least 3 to 3.5 hours, at least 3.5 to 4 hours, at least 4 to 4.5 hours, at least 4.5 to 5 hours, at least 5 to 5.5 hours, at least 5.5 to 6 hours, at least 6 to 6.5 hours, at least 6.5 to 7 hours, at least 7 to 7.5 hours, at least 7.5 to 8 hours, at least 8 to 8.5 hours, at least 8.5 to 9 hours, at least 9 to 9.5 hours, or at least 9.5 to 10 hours. Dynamic Range

[0163] The biomolecular corona analysis methods described herein can include assaying biomolecules in a sample of the present disclosure over a wide dynamic range. The dynamic range of a biomolecule assayed in a sample can be the range of measured signals of biomolecule abundance as measured by an assay method (e.g., mass spectrometry, chromatography, gel electrophoresis, spectroscopy, or immunoassay) for the biomolecule contained in the sample. For example, an assay capable of detecting proteins over a wide dynamic range can be capable of detecting proteins ranging from very low abundance to very high abundance. The dynamic range of an assay can be directly related to the slope of the assay signal intensity as a function of biomolecule abundance. For example, an assay with a low dynamic range can have a small (but positive) slope of the assay signal intensity as a function of biomolecule abundance; for example, the ratio of the signal detected for a high-abundance biomolecule to the signal detected for a low-abundance biomolecule can be smaller in an assay with a low dynamic range than in an assay with a high dynamic range. In certain cases, dynamic range can refer to the dynamic range of proteins in a sample or assay method.

[0164] The biomolecular corona analysis methods described herein can compress the dynamic range of an assay. The dynamic range of an assay can be compressed compared to another assay if the slope of the assay signal intensity as a function of biomolecule abundance is smaller than that of another assay. For example, as shown in Figures 13 and 14, a plasma sample assayed using protein corona analysis in conjunction with mass spectrometry can have a compressed dynamic range compared to a plasma sample assayed using mass spectrometry alone (the assay is performed either directly on the sample or by comparison with plasma protein abundance values ​​provided in a database (e.g., Keshishian et al., Mol. Cell Proteomics 14, 2375-2393 (2015), also referred to herein as the "Carr database"). The compressed dynamic range can enable the detection of lower abundance biomolecules in a biological sample when using biomolecular corona analysis in conjunction with mass spectrometry than when using mass spectrometry alone.

[0165] In some embodiments, the dynamic range of a proteomic analysis assay can be the ratio of the signal generated by the most abundant protein (e.g., the top 10% of abundant proteins) to the least abundant protein (e.g., the bottom 10% of abundant proteins). Compressing the dynamic range of a proteomic analysis can include reducing the ratio of the signal generated by the most abundant protein to the least abundant protein in a first proteomic analysis assay compared to that in a second proteomic analysis assay. The protein corona analysis assays disclosed herein can compress the dynamic range compared to the dynamic range of total protein analysis methods (e.g., mass spectrometry, gel electrophoresis, or liquid chromatography).

[0166] Provided herein are several methods for compressing the dynamic range of biomolecular analytical assays to facilitate the detection of low-abundance biomolecules relative to high-abundance biomolecules. For example, the particle species of the present disclosure can be used to sequentially interrogate samples. Incubating the particle species in the sample creates a biomolecular corona on the surface of the particle species. If biomolecules are detected directly in the sample without the use of the particle species (e.g., by direct mass spectrometry of the sample), the dynamic range may span a wider concentration range, or more orders of magnitude, than if the biomolecules are detected on the surface of the particle species. Thus, the use of the particle species disclosed herein can be used to compress the dynamic range of biomolecules in a sample. Without being bound by theory, this effect may be observed due to the greater capture of higher-affinity, lower-abundance biomolecules in the biomolecular corona of the particle species, and the less capture of lower-affinity, higher-abundance biomolecules in the biomolecular corona of the particle species.

[0167] The dynamic range of a proteomic analysis assay can be the slope of a plot of the protein signal measured by the proteomic analysis assay as a function of the total abundance of protein in the sample. Compressing the dynamic range can include reducing the slope of a plot of the protein signal measured by a proteomic analysis assay as a function of the total abundance of protein in the sample compared to the slope of a plot of the protein signal measured by a second proteomic analysis assay as a function of the total abundance of protein in the sample. The protein corona analysis assays disclosed herein can compress the dynamic range compared to the dynamic range of total protein analysis methods (e.g., mass spectrometry, gel electrophoresis, or liquid chromatography). Automation Systems

[0168] Various aspects of the present disclosure provide an automated system including an automated instrument configured to isolate a subset of biomolecules from a biological sample, a mass spectrometer configured to receive the subset of biomolecules and generate data including a mass spectrometry signal or a tandem mass spectrometry signal, and a computer including one or more computer processors, and a computer-readable medium including machine-executable code that (when executing the code) generates a biological fingerprint and assigns a biological state based on the biological fingerprint.

[0169] In many cases, the automated instrument includes a sensor element or multiple sensor elements that adsorb biomolecules from a biological solution, thereby forming a biomolecular corona. The types, amounts, and categories of biomolecules that comprise these biomolecular coronas are strongly related to the physicochemical properties of the sensor element itself and the complex interactions between the different biomolecules themselves and the sensor element. These interactions result in the generation of a unique biomolecular corona signature for each sensor element. In other words, which biomolecules interact with a sensor element not only affects the composition of the biomolecular corona, but may also change which other different biomolecules may similarly interact with that particular sensor element.

[0170] Different sensor elements, each with a unique biomolecular corona signature, can contact a sample and generate a unique biomolecular fingerprint for the sample. This fingerprint can then be used to determine the disease state of the subject. Multiple sensor elements can bind to multiple biomolecules in a sample and generate biomolecular corona signatures. Multiple sensor elements can have unique biomolecular corona signatures. In certain cases, each type of sensor element has a unique biomolecular corona signature. For example, a plurality of particles containing 5 pM of each of five types of particles can have one biomolecular corona signature for each particle type.

[0171] The plurality of sensor elements, when contacted with a sample, generate a plurality of biomolecular corona signatures, which together form a biomolecular fingerprint. The "biomolecular fingerprint" is a composite composition or pattern of biomolecules having at least two biomolecular corona signatures for the plurality of sensor elements. The biomolecular fingerprint may include at least 5, 10, 20, 40, 80, 150, or 200 unique biomolecular corona signatures.

[0172] In some cases, the automated system is configured so that the biomolecular corona can be assayed separately for each sensor element, thereby enabling the biomolecular corona signature to be determined for each element. More generally, the automated system can be configured so that each sample partition (each well in the substrate) can be assayed separately, thereby allowing a combined set of biomolecular corona signatures to be determined for each partition.

[0173] Similarly, the computer can be configured to compare data from diverse biomolecular corona signatures, partitions, or separate subsets of biomolecules collected from individual partitions (e.g., by multiple rounds of desorption). This can achieve profiling sensitivity not possible with conventional methods. Many biological conditions (such as many pre-disease states) produce subtle changes in patient biological samples (e.g., blood, urine, etc.) that often cannot be identified by biomarker analysis alone. The power of the present devices, systems, methods, and sensor elements derives, in part, from the interdependence of the sensor element's properties and the biological sample's composition on the composition of the biomolecular corona; therefore, even small changes in the population of low-abundance biomolecules, small changes in their chemical state (e.g., post-translational modification state), or small structural changes can have a significant impact on the biomolecular corona signature for a particular sensor element. Furthermore, biological conditions that may not be evident from a single set of data can be clearly elucidated by correlations between different biomolecule abundances across diverse biomolecular corona signatures or sample partition measurements. Thus, a combination of nearly identical biomolecular corona signatures can distinguish healthy subjects from those affected by cancer with high accuracy.

[0174] In some cases, the computer is configured to process data including mass spectrometry or tandem mass spectrometry signal intensities, APEX, peptide spectral counts or numbers, and ion mobility behaviors between a plurality of the unique biomolecular corona signatures. The computer may be configured to process 5,000 to 5,000,000 signals between a plurality of the unique biomolecular corona signatures or sample partitions. The computer may be configured to process 10,000 to 5,000,000 signals between a plurality of the unique biomolecular corona signatures or sample partitions. The computer may be configured to compare 20,000 to 200,000 signals between a plurality of the unique biomolecular corona signatures or sample partitions. The computer may be configured to compare 400,000 to 1,000,000 signals between a plurality of the unique biomolecular corona signatures or sample partitions. The computer can be configured to compare 600,000 to 2,000,000 signals between a plurality of the unique biomolecular corona signatures or sample partitions. The computer can be configured to compare 1,000,000 to 5,000,000 signals between a plurality of the unique biomolecular corona signatures or sample partitions. In some cases, the signals include mass spectrometry or tandem mass spectrometry signals.

[0175] Embodiments of the present disclosure provide methods for generating biomolecular fingerprints from one or more sets of mass spectrometry data, tandem mass spectrometry data, chromatographic data, ion mobility data, or any combination thereof. In some cases, mass spectrometry data, tandem mass spectrometry data, chromatographic data, or ion mobility data can be used to determine the concentration of biomolecules from a biological sample. Multiple sample partitions can be subjected to a single mass spectrometry or tandem mass spectrometry run. Multiple sample partitions can also be pooled and analyzed together in a single mass spectrometry or tandem mass spectrometry run. Multiple mass spectrometry runs can be coupled with a variety of different chromatographic methods (e.g., different columns, buffers, or gradients). A single mass spectrometry or tandem mass spectrometry run can take less than two hours, less than one hour, or less than half an hour.

[0176] Embodiments of the present disclosure provide a method for identifying biological states and biomolecules with high certainty and accuracy. The computer can be configured to identify biomolecules or characterize unidentified molecular properties based on mass spectrometry signals or tandem mass spectrometry signals and / or ion mobility and chromatographic behavior with a probability or certainty threshold of at least 95%. The computer can associate biomolecular fingerprints with biological states with at least 70% accuracy, at least 75% accuracy, at least 80% accuracy, at least 85% accuracy, at least 90% accuracy, at least 92% accuracy, at least 95% accuracy, at least 96% accuracy, at least 97% accuracy, at least 98% accuracy, at least 99% accuracy, or 100% accuracy. The computer can associate the biomolecular fingerprint with a biological state with at least 70% sensitivity, at least 75% sensitivity, at least 80% sensitivity, at least 85% sensitivity, at least 90% sensitivity, at least 92% sensitivity, at least 95% sensitivity, at least 96% sensitivity, at least 97% sensitivity, at least 98% sensitivity, at least 99% sensitivity, or 100% sensitivity. The computer can distinguish between two biological states related to the biological fingerprint with a difference of less than 20%, 15%, 10%, 8%, 5%, 3%, 2%, or 1%. In some embodiments, the identification of a biomolecule is confirmed when a threshold level of diagnostic signal is detected. For example, in a mass spectrometry assay, if a threshold number of three uniquely assignable peptide fragment signals is provided for protein group identification, two peptides corresponding to a specific protein group will not be counted. Sensor Element

[0177] As used herein, the term "sensor element" refers to an element capable of binding multiple biomolecules when contacted with a sample, and also encompasses the term "particle." The sensor element may be an element measuring from about 5 nanometers (nm) to about 50,000 nm in at least one direction.Suitable sensor elements include, but are not limited to, sensor elements of about 5 nm to about 50,000 nm in at least one direction, such as about 5 nm to about 40,000 nm, alternatively about 5 nm to about 30,000 nm, alternatively about 5 nm to about 20,000 nm, alternatively about 5 nm to about 10,000 nm, alternatively about 5 nm to about 5,000 nm, alternatively about 5 nm to about 1,000 nm, alternatively about 5 nm to about 500 nm, alternatively about 5 nm to 50 nm, alternatively about 10 nm to 100 nm, alternatively about 20 nm to 200 nm, alternatively about 30 nm to 300 nm, or about 40 nm to 400 nm, or about 50 nm to 500 nm, or about 60 nm to 600 nm, or about 70 nm to 700 nm, or about 80 nm to 800 nm, or about 90 nm to 900 nm, or about 100 nm to 1000 nm, or about 1000 nm to 10,000 nm, or about 10,000 nm to 50,000 nm, and combinations thereof, or intermediate values ​​thereof (e.g., 5 nm, 10 nm, 15 nm, 20 nm, 25 nm, 30 nm, 35 nm, 40 nm, 45 nm, 50 nm, 55nm, 60nm, 65nm, 70nm, 80nm, 90nm, 100nm, 125nm, 150nm, 175nm, 200nm, 225nm, 250nm, 275nm, 300nm, 350nm, 400nm, 450nm, 500nm, 550nm, 600nm, 650nm, 700nm, 750nm, 800nm, 850nm, 900nm, 1000nm, 1200nm, 1300nm, 140 0nm, 1500nm, 1600nm, 1700nm, 1800nm, 1900nm, 2000nm, 2500nm, 3000nm, These wavelengths include: 3500nm, 4000nm, 4500nm, 5000nm, 5500nm, 6000nm, 6500nm, 7000nm, 7500nm, 8000nm, 8500nm, 9000nm, 10000nm, 11000nm, 12000nm, 13000nm, 14000nm, 15000nm, 16000nm, 17000nm, 18000nm, 19000nm, 20000nm, 25000nm, 30000nm, 35000nm, 40000nm, 45000nm, 50000nm, and any number in between.Nanoscale sensor elements refer to sensor elements that are less than 1 micron in at least one direction. Suitable examples of ranges for nanoscale sensor elements include, but are not limited to, elements that are about 5 nm to about 1000 nm in one direction, such as, for example, about 5 nm to about 500 nm, alternatively about 5 nm to about 400 nm, alternatively about 5 nm to about 300 nm, alternatively about 5 nm to about 200 nm, alternatively about 5 nm to about 100 nm, alternatively about 5 nm to about 50 nm, alternatively about 10 nm to about 1000 nm, alternatively about 10 nm to about 750 nm, alternatively about 10 nm to about 500 nm, alternatively about 10 nm to about 250 nm, alternatively about 10 nm to about 200 nm, alternatively about 10 nm to about 100 nm, alternatively about 50 nm to about 1000 nm, alternatively about 50 nm to about 500 nm. Alternatively, the range may be from about 50 nm to about 250 nm, or from about 50 nm to about 200 nm, or from about 50 nm to about 100 nm, and any combination thereof, ranges or values ​​therebetween (e.g., 5 nm, 10 nm, 15 nm, 20 nm, 25 nm, 30 nm, 35 nm, 40 nm, 45 nm, 55 nm, 60 nm, 65 nm, 70 nm, 80 nm, 90 nm, 100 nm, 125 nm, 150 nm, 175 nm, 200 nm, 225 nm, 250 nm, 275 nm, 300 nm, 350 nm, 400 nm, 450 nm, 500 nm, 550 nm, 600 nm, 650 nm, 700 nm, 750 nm, 800 nm, 850 nm, 900 nm, 1000 nm, etc.). With respect to the sensor arrays described herein, the use of the term sensor element includes the use of nanoscale sensor elements for said sensor element and related methods.

[0178] The term "plurality of sensor elements" refers to more than one, e.g., at least two, sensor elements. In some embodiments, the plurality of sensor elements ranges from at least two sensor elements to at least 10 15 In some embodiments, the plurality of sensor elements comprises 10 6 ~10 7 , 10 6 ~108 、10 6 ~10 9 、10 6 ~10 10 、10 6 ~10 11 、10 6 ~10 12 、10 6 ~10 13 、10 6 ~10 14 、10 6 ~10 15 、10 7 ~10 8 、10 7 ~10 9 、10 7 ~10 10 、10 7 ~10 11 、10 7 ~10 12 、10 7 ~10 13 、10 7 ~10 14 、10 7 ~10 15 、10 8 ~10 9 、10 8 ~10 10 、10 8 ~10 11 、10 8 ~10 12 、10 8 ~10 13 、10 8 ~10 14 、10 8 ~10 15 、10 9 ~10 10 、10 9 ~10 11 、10 9 ~10 12 、10 9 ~10 13 、10 9 ~10 14 、10 9 ~10 15 、10 10 ~10 11 、10 10 ~10 12 、10 10 ~10 13, 10 10 ~10 14 , 10 10 ~10 15 , 10 11 ~10 12 , 10 11 ~10 13 , 10 11 ~10 14 , 10 11 ~10 15 , 10 12 ~10 13 , 10 12 ~10 14 , 10 12 ~10 15 , 10 13 ~10 14 , 10 13 ~10 15 , or 10 14 ~10 15 The sensor element includes different

[0179] In some embodiments, the plurality of sensor elements includes a plurality of types of sensor elements, such as at least 2 to at least 1000 types of sensor elements, or at least 2 to at least 50 types of sensor elements, or at least 2 to at least 30 types of sensor elements, or at least 2 to at least 20 types of sensor elements, or at least 2 to at least 10 types of sensor elements, or at least 3 to at least 50 types of sensor elements, or at least 3 to at least 30 types of sensor elements, or at least 3 to at least 20 types of sensor elements, or at least 3 to at least 10 types of sensor elements, or at least 4 to at least 50 types of sensor elements, or at least 4 to at least 30 types of sensor elements, or at least 4 At least 20 different sensor elements, or at least 4 to at least 10 different sensor elements, and any number of different sensor elements in between (e.g., at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 225, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, etc.). The plurality of sensor elements may include at least 6 types of sensor elements to at least 20 types of sensor elements, or at least 6 types of sensor elements to at least 10 types of sensor elements.

[0180] In some cases, increasing the number of sensor elements can be a way to increase the number of biomolecules (e.g., proteins) that can be identified in a given sample. Figure 10 illustrates how expanding the panel size can increase the number of identified proteins. This figure shows the number of proteins identified by corona analysis in assays utilizing panels containing 1 to 12 particle species. In these assays, unique proteins, as opposed to protein groups, were identified by mass spectrometry. The number of identified protein types increased with increasing number of particle species, ranging from 419 uniquely identified proteins (when proteins were collected using one particle species) to 1,318 uniquely identified proteins (when proteins were collected using 12 particle species).

[0181] The sensor element can be functionalized to have a wide range of physicochemical properties. Suitable methods for functionalizing the sensor element are known in the art and depend on the composition of the sensor element (e.g., gold, iron oxide, silica, silver, etc.), and include, but are not limited to, aminopropyl functionalization, amine functionalization, boronic acid functionalization, carboxylic acid functionalization, methyl functionalization, succinimidyl ester functionalization, PEG functionalization, streptavidin functionalization, methyl ether functionalization, triethoxypropylaminosilane functionalization, thiol functionalization, PCP functionalization, citrate functionalization, lipoic acid functionalization, BPEI functionalization, carboxyl functionalization, hydroxyl functionalization, etc. In one embodiment, the sensor element can be functionalized with an amine group (-NH2) or a carboxyl group (COOH). In some embodiments, the nanoscale sensor element is functionalized with a polar functional group. Non-limiting examples of polar functional groups include a carboxyl group, a hydroxyl group, a thiol group, a cyano group, a nitro group, an ammonium group, an imidazolium group, a sulfonium group, a pyridinium group, a pyrrolidinium group, a phosphonium group, or any combination thereof. In some embodiments, the functional group is an acidic functional group (e.g., a sulfonic acid group, a carboxyl group, etc.), a basic functional group (e.g., an amino group, a cyclic secondary amino group (e.g., a pyrrolidyl group and a piperidyl group), a pyridyl group, an imidazole group, etc.). , guanidine group, etc.), carbamoyl group, hydroxyl group, aldehyde group, etc. In some embodiments, the polar functional group is an ionic functional group. Non-limiting examples of ionic functional groups include ammonium groups, imidazolium groups, sulfonium groups, pyridinium groups, pyrrolidinium groups, and phosphonium groups. In some embodiments, the sensor element is functionalized with a polymerizable functional group. Non-limiting examples of polymerizable functional groups include vinyl groups and (meth)acrylic groups. In some embodiments, the functional group is pyrrolidyl acrylate, acrylic acid, methacrylic acid, acrylamide, 2-(dimethylamino)ethyl methacrylate, hydroxyethyl methacrylate, etc.

[0182] The physicochemical properties of the sensor element can be modified by altering the surface charge. For example, the surface can be modified to produce a neutral net charge, a positive net surface charge, a negative net surface charge, or a zwitterionic charge. The surface charge can be controlled during the synthesis of the element or by post-synthetic modification of charge through surface functionalization. For polymeric sensor elements (e.g., polymer particles), charge differences can be achieved during synthesis by using different synthesis procedures, using different charged comonomers, or by having mixed oxidation states in inorganic materials.

[0183] Non-limiting examples of the plurality of sensor elements include, but are not limited to, (a) multiple sensor elements made of the same material but with different physiochemical properties; (b) multiple sensor elements in which one or more sensor elements are made of different materials with the same or different physiochemical properties; (c) multiple sensor elements made of the same material but with different sizes; (d) multiple sensor elements made of different materials but with approximately the same size; (e) multiple sensor elements made of different materials but with different sizes; (f) multiple sensor elements in which each element is made of a different material; and (g) multiple sensor elements with different electrical charges. The plurality of sensor elements can be any suitable combination of two or more sensor elements, each of which provides a unique biomolecular corona signature. For example, the plurality of sensor elements can include one or more liposomes and one or more particles described herein. In one embodiment, the plurality of sensor elements can be multiple liposomes with different lipid content and / or different electrical charges (cationic / anionic / neutral). In another embodiment, the plurality of sensors may include one or more nanoparticles made of the same material but differing in size and physiochemical properties. In another embodiment, the plurality of sensors may include one or more particles made of different materials (e.g., silica and polystyrene) with similar or different sizes and / or physiochemical properties (e.g., modifications, e.g., -NH2, -COOH functionalization). These combinations are provided purely by way of example and are not intended to limit the scope of the present disclosure.

[0184] The sensor element may comprise a particle (e.g., a nanoparticle or a microparticle). The sensor element may be a particle (e.g., a nanoparticle or a microparticle). The sensor element may comprise a surface of a material, or a portion of a surface. The sensor element may comprise a porous material (e.g., a polymer matrix) into which biomolecules can penetrate. The sensor element may comprise a material with protrusions (e.g., a polymer, oligomer, or metallic dendrites). The sensor element may comprise an aggregate of particles (e.g., a nanoworm). particle material

[0185] The particles disclosed herein may be made from a variety of different materials and may include specific types of nanoparticles to identify a broad range of proteins in the sample or to selectively assay for a particular protein or set of proteins of interest.

[0186] The plurality of particles may be at least one unique particle species, at least two unique particle species, at least three unique particle species, at least four unique particle species, at least five unique particle species, at least six unique particle species, at least seven unique particle species, at least eight unique particle species, at least nine unique particle species, at least ten unique particle species, at least eleven unique particle species, at least twelve unique particle species, at least thirteen unique particle species, at least fourteen unique particle species, at least fifteen unique particle species, at least 16 unique particle species, at least 17 unique particle species, at least 18 unique particle species, at least 19 unique particle species, at least 20 unique particle species, at least 25 unique particle species, at least 30 unique particle species, at least 35 unique particle species, at least 40 unique particle species, at least 45 unique particle species, at least 50 unique particle species, at least 55 unique particle species, at least 60 unique particle species, at least 65 unique particle species, at least 70 unique particle species, at least 75 unique particle species, at least 80 unique particle species, at least 85 unique particle species, at least 90 unique particle species, at least 95 unique particle species, at least 100 unique particle species, 1-5 unique particle species, 5-10 unique particle species, 10-15 unique particle species, 15-20 unique particle species, 20-25 unique particle species, 25-30 unique particle species, 30-35 unique particle species, 35-40 unique particle species, 40-45 unique particle species, 45-50 unique particle species, The particles may include 50-55 unique particle species, 55-60 unique particle species, 60-65 unique particle species, 65-70 unique particle species, 70-75 unique particle species, 75-80 unique particle species, 80-85 unique particle species, 85-90 unique particle species, 90-95 unique particle species, 95-100 unique particle species, 1-100 unique particle species, 20-40 unique particle species, 5-10 unique particle species, 3-7 unique particle species, 2-10 unique particle species, 6-15 unique particle species, or 10-20 unique particle species. The plurality of particles may include 3-10 particle species. The plurality of particles may include 4-11 unique particle species. The plurality of particles may include 5 to 15 unique particle species.The plurality of particles may include 5 to 15 unique particle species.The plurality of particles may include 8 to 12 unique particle species. The plurality of particles may include 9 to 13 unique particle species. The plurality of particles may include 10 unique particle species. The particle species may include nanoparticles.

[0187] For example, the present disclosure provides for the production of at least two unique particle species, at least three different surface chemistries, at least four different surface chemistries, at least five different surface chemistries, at least six different surface chemistries, at least seven different surface chemistries, at least eight different surface chemistries, at least nine different surface chemistries, at least ten different surface chemistries, at least eleven different surface chemistries, at least thirteen different surface chemistries, at least fourteen different surface chemistries, at least fifteen different surface chemistries, at least twenty different surface chemistries, at least twenty-five different surface chemistries, at least thirty-five different surface chemistries, at least thirty-five different surface chemistries, at least forty-five different surface chemistries, at least forty-five different surface chemistries, at least fifteen different surface chemistries, at least twenty-five different surface chemistries, at least thirty ... twenty-five different surface chemistries, at least thirty-five different surface chemistries, at least thirty-five different surface chemistries, at least forty-five different surface chemistries, at least fifteen different surface chemistries, at least twenty-five different surface chemistries, at least twenty-five different surface chemistries, at least twenty-five different surface chemistries, at least twenty-five different surface chemistries, at least twenty-five different surface chemistries, at least twenty-five different surface chemistries, at least twenty-five different surface chemistries, at least twenty a plurality of particles having surface chemistries, at least 100 different surface chemistries, at least 150 different surface chemistries, at least 200 different surface chemistries, at least 250 different surface chemistries, at least 300 different surface chemistries, at least 350 different surface chemistries, at least 400 different surface chemistries, at least 450 different surface chemistries, at least 500 different surface chemistries, 2 to 500 different surface chemistries, 2 to 5 different surface chemistries, 5 to 10 different surface chemistries, 10 to 15 different surface chemistries, 15 to 20 different surface chemistries, 20 to 40 different surface chemistries, 40 to 60 different surface chemistries, 60 to 80 different surface chemistries, 80 to 100 different surface chemistries, 100 to 500 different surface chemistries, 4 to 15 different surface chemistries, or 2 to 20 different surface chemistries.

[0188] The present disclosure provides for a method for producing a polymeric composition having at least 2 different physical properties, at least 3 different physical properties, at least 4 different physical properties, at least 5 different physical properties, at least 6 different physical properties, at least 7 different physical properties, at least 8 different physical properties, at least 9 different physical properties, at least 10 different physical properties, at least 11 different physical properties, at least 12 different physical properties, at least 13 different physical properties, at least 14 different physical properties, at least 15 different physical properties, at least 20 different physical properties, at least 25 different physical properties, at least 30 different physical properties, at least 35 different physical properties, at least 40 different physical properties, at least 45 different physical properties, at least 50 different physical properties, at least 60 different physical properties, at least 70 different physical properties, at least 80 different physical properties, at least 90 different physical properties, at least 100 different physical properties, at least 110 different physical properties, at least 120 different physical properties, at least 130 different physical properties, at least 140 different physical properties, at least 150 different physical properties, at least 200 different physical properties, at least 250 different physical properties, at least 300 different physical properties, at least 350 different physical properties, at least 400 different physical properties, at least 450 different physical properties, at least 500 different physical properties, at least 6 ... A plurality of particles having at least 100 different physical properties, at least 150 different physical properties, at least 200 different physical properties, at least 250 different physical properties, at least 300 different physical properties, at least 350 different physical properties, at least 400 different physical properties, at least 450 different physical properties, at least 500 different physical properties, 2 to 500 different physical properties, 2 to 5 different physical properties, 5 to 10 different physical properties, 10 to 15 different physical properties, 15 to 20 different physical properties, 20 to 40 different physical properties, 40 to 60 different physical properties, 60 to 80 different physical properties, 80 to 100 different physical properties, 100 to 500 different physical properties, 4 to 15 different physical properties, or 2 to 20 different physical properties.

[0189] The particles may be made of various materials. For example, nanoparticle materials consistent with the present disclosure include metals, polymers, magnetic materials, and lipids. The magnetic nanoparticles may be iron oxide nanoparticles. Metallic materials include, for example, gold, silver, copper, nickel, cobalt, palladium, platinum, iridium, osmium, rhodium, ruthenium, rhenium, vanadium, chromium, manganese, niobium, molybdenum, tungsten, tantalum, iron, and cadmium, or any combination thereof, or any other material described in U.S. Pat. No. 7,749,299.

[0190] The polymer can be any one of polyethylene, polycarbonate, polyanhydride, polyhydroxy acid, polypropyl fumarate, polycaprolactone, polyamide, polyacetal, polyether, polyester, poly(orthoester), polycyanoacrylate, polyvinyl alcohol, polyurethane, polyphosphazene, polyacrylate, polymethacrylate, polycyanoacrylate, polyurea, polystyrene, or polyamine, polyalkylene glycol (e.g., polyethylene glycol (PEG)), polyester (e.g., poly(lactide-co-glycolide) (PLGA), polylactic acid, or polycaprolactone), or copolymers of two or more polymers (e.g., copolymers of polyalkylene glycol (e.g., PEG) and polyester (e.g., PLGA)), or any combination thereof. In some embodiments, the polymer is a lipid-terminated polyalkylene glycol and polyester, or any other material disclosed in U.S. Pat. No. 9,549,901. The polymer may also be a liposome.

[0191] Lipids that can be used to form the nanoparticles of the present disclosure include, for example, cationic, anionic, and neutrally charged lipids. For example, nanoparticles can be formed using lipids such as dioleoylphosphatidylglycerol (DOPG), diacylphosphatidylcholine, diacylphosphatidylethanolamine, ceramide, sphingomyelin, cephalin, cholesterol, cerebrosides, and diacylglycerol, dioleoylphosphatidylcholine (DOPC), dimyristoylphosphatidylcholine (DMPC), and dioleoylphosphatidylserine (DOPS), phosphatidylglycerol, cardiolipin ... dioleoylphosphatidylcholine (DOPS), dioleoylphosphatidylglycerol, dioleoylphosphatidylcholine (DOPG), diacylphosphatidylethanolamine, dioleoylphosphatidylcholine (DOPG), diacylphosphatidylethanolamine, dioleoylphosphatidylcholine (DOPG), diacylphosphatidylethanolamine, dioleoylphosphatidylcholine (DOPG), diacylphosphatidylethanolamine, dioleoylphosphatidylcholine (DOPG), diacylphosphatidylethanolamine, dioleoylphosphatidylcholine (DOPG), diacylphosphatidyl Tidylserine, diacylphosphatidic acid, N-dodecanoylphosphatidylethanolamine, N-succinylphosphatidylethanolamine, N-glutarylphosphatidylethanolamine, lysylphosphatidylglycerol, palmitoyloleoylphosphatidylglycerol (POPG), lecithin, lysolecithin, phosphatidylethanolamine, lysophosphatidylethanolamine, dioleoylphosphatidylethanolamine Dipalmitoylphosphatidylethanolamine (DOPE), dipalmitoylphosphatidylethanolamine (DPPE), dimyristoylphosphoethanolamine (DMPE), distearoylphosphatidylethanolamine (DSPE), palmitoyloleoylphosphatidylethanolamine (POPE), palmitoyloleoylphosphatidylcholine (POPC), egg phosphatidylcholine (EPC), distearoylphosphatidylcholine (DSPC), dioleoylphosphatidylcholine Dipalmitoylphosphatidylcholine (DPPC), dioleoylphosphatidylglycerol (DOPG), dipalmitoylphosphatidylglycerol (DPPG), palmitoyloleoylphosphatidylglycerol (POPG), 16-O-monomethyl PE, 16-O-dimethyl PE, 18-1-trans PE, palmitoyloleoyl-phosphatidylethanolamine (POPE),It may be made from any one of 1-stearoyl-2-oleoyl-phosphatidyethanolamine (SOPE), phosphatidylserine, phosphatidylinositol, sphingomyelin, cephalin, cardiolipin, phosphatidic acid, cerebrosides, dicetyl phosphate, and cholesterol, or any other material disclosed in US9445994, or any combination thereof.

[0192] In various cases, the core of the nanoparticle can comprise an organic particle, an inorganic particle, or a particle comprising both organic and inorganic materials. For example, the particle can have a core structure that is or includes a metal particle, a quantum dot particle, a metal oxide particle, or a core-shell particle. For example, the core structure can be or include a polymer particle or a lipid-based particle, and the linker can include a lipid, a surfactant, a polymer, a hydrocarbon chain, or an amphiphilic polymer. For example, the linker can include polyethylene glycol or polyalkylene glycol, e.g., a first end of the linker can include a lipid bound to polyethylene glycol (PEG) and a second end can include a functional group bound to the PEG. The particle can have a core-shell structure. In some cases, the particle has a core comprising a first material or composite and multiple shells comprising different materials or composites. In some cases, the particle has a magnetic core surrounded by one or more non-magnetic shells. For example, the particles may include a magnetic iron oxide core surrounded by a non-magnetic polymer shell. In some cases, the magnetic core has a diameter of 10 nm to 500 nm, and the shell has a thickness of 5 nm to 100 nm.

[0193] Examples of particle types consistent with the present disclosure are shown below in Table 1. Additional examples of particles (e.g., magnetic core nanoparticles (MNPs)) and corresponding surface chemistries are shown in Figure 7. [Table 1] Particle characteristics

[0194] Nanoparticles consistent with the present disclosure can be made and used in methods to form a protein corona after incubation in a body fluid in a wide range of sizes, for example, the nanoparticles disclosed herein can be at least 10 nm, at least 100 nm, at least 200 nm, at least 300 nm, at least 400 nm, at least 500 nm, at least 600 nm, at least 700 nm, at least 800 nm, at least 900 nm, 10 nm to 50 nm, 50 nm to 100 nm, 100 nm to 150 nm, 150 nm to 200 nm, 200 nm to 250 nm, 250 nm to 300 nm, 300 nm to 350 nm, 350 nm to 400 nm, 400 nm to 450 nm, 450 nm to 500 nm, 500 nm to 550 nm. m, 550nm to 600nm, 600nm to 650nm, 650nm to 700nm, 700nm to 750nm, 750nm to 800nm, 800nm ​​to 850nm, 850nm to 900nm, 100nm to 300nm, 150nm to 350nm, 200nm to 400nm, 250nm to 450nm, 300nm to 500nm, 350nm to 550nm, 400nm to 600nm, 450nm to 650nm, 500nm to 700nm, 550nm to 750nm, 600nm to 800nm, 650nm to 850nm, 700nm to 900nm, or 10nm to 900nm.

[0195] Furthermore, the particles can have a uniform or non-uniform particle size distribution. The polydispersity index (PDI), which can be measured by techniques such as dynamic light scattering, is a measure of particle size distribution. A lower PDI indicates a more uniform particle size distribution, while a higher PDI indicates a more non-uniform particle size distribution. In some cases, the PDI of the particles can be 0.01-0.1, 0.1-0.5, 0.5-1, 1-5, 5-20, or greater than 20.

[0196] The particles disclosed herein can have a range of different surface charges. The particles can be negatively charged, positively charged, or neutrally charged. In some embodiments, the surface charge of the particles can range from -500 mV to -450 mV, -450 mV to -400 mV, -400 mV to -350 mV, -350 mV to -300 mV, -300 mV to -250 mV, -250 mV to -200 mV, -200 mV to -150 mV, -150 mV to -100 mV, -100 mV to -90 mV, -90 mV to -80 mV, or mV, -80mV~-70mV, -70mV~-60mV, -60mV~-50mV, -50mV~-40mV, -40mV~-30mV, -30mV~-20mV, -20m V~-10mV, -10mV~0mV, 0mV~10mV, 10mV~20mV, 20mV~30mV, 30mV~40mV, 40mV~50mV, 50mV~60mV, 60 mV~70mV, 70mV~80mV, 80mV~90mV, 90mV~100mV, 100mV~110mV, 110mV~120mV, 120mV~130mV, 130m V~140mV, 140mV~150mV, 150mV~200mV, 200mV~250mV, 250mV~300mV, 300mV~350mV, 350mV~400mV , 400mV to 450mV, 450mV to 500mV, -500mV to -400mV, -400mV to -300mV, -300mV to -200mV, -200mV to -100mV, -100mV to 0mV, 0mV to 100mV, 100mV to 200mV, 200mV to 300mV, 300mV to 400mV, or 400mV to 500mV.

[0197] Various particle morphologies are consistent with the particle species in the panels of this disclosure. For example, particles can be spherical, colloidal, cubic, rod-like, wire-like, cone-like, pyramidal, or ellipsoidal. Biomolecular Corona

[0198] Provided herein are automated instruments, systems, methods, and sensor elements capable of generating biomolecular coronas comprising, consisting essentially of, or consisting of a plurality of sensor elements, wherein the plurality of sensor elements differ from one another in at least one physicochemical property. The plurality of sensor elements may comprise a plurality of particles (e.g., nanoparticles). The plurality of sensor elements may be a plurality of particles. The plurality of sensor elements may be capable of binding to a plurality of biomolecules in a complex biological sample and generating a biomolecular corona signature. The plurality of sensor elements may comprise a plurality of unique biomolecular corona signatures.

[0199] A biomolecule of interest (e.g., a low-abundance protein) can be enriched in the biomolecular corona compared to an untreated sample (e.g., a sample not assayed using particles). The biomolecule of interest can be a protein. The biomolecular corona can be a protein corona. The level of enrichment can be a percentage increase or a fold increase in the relative abundance (e.g., copy number of the biomolecule of interest relative to the total number of biomolecules) of the biomolecule of interest in the biomolecular corona compared to the biological sample from which the biomolecule corona was collected. A biomolecule of interest can be enriched in the biomolecular corona by increasing the abundance of the biomolecule of interest in the biomolecular corona compared to a sample not contacted with the sensor element. A biomolecule of interest can be enriched by decreasing the abundance of a biomolecule in a high-abundance biological sample.

[0200] Biomolecular corona analysis assays can be used to rapidly identify low-abundance biomolecules in a biological sample (e.g., a bodily fluid). Biomolecular corona analysis can be used to identify at least about 500 low-abundance biomolecules in a biological sample within about 8 hours of first contacting the biological sample with a sensor element (e.g., a particle). Biomolecular corona analysis can identify at least about 1,000 low-abundance biomolecules in a biological sample within about 8 hours of first contacting the biological sample with a sensor element (e.g., a particle). Biomolecular corona analysis can identify at least about 500 low-abundance biomolecules in a biological sample within about 4 hours of first contacting the biological sample with a sensor element (e.g., a particle). Biomolecular corona analysis can identify at least about 1,000 low-abundance biomolecules in a biological sample within about 4 hours of first contacting the biological sample with a sensor element (e.g., a particle).

[0201] The biomolecular corona signature may include proteins, peptides, polysaccharides, oligosaccharides, monosaccharides, metabolites, lipids, nucleic acids, or any combination thereof. The biomolecular corona signature may be a protein corona signature. The biomolecular corona signature may be a polysaccharide corona signature. The biomolecular corona signature may be a metabolite corona signature. The biomolecular corona signature may be a lipidomic corona signature. The biomolecular corona signature may include the biomolecules found in soft and hard coronas. The soft corona may be a soft protein corona. The hard corona may be a hard protein corona.

[0202] The biomolecular corona signature refers to the composition, signature, or pattern of different biomolecules bound to each separate sensor element or nanoparticle. In some cases, the biomolecular corona signature is a protein corona signature. In other cases, the biomolecular corona signature is a polysaccharide corona signature. In yet other cases, the biomolecular corona signature is a metabolite corona signature. In some cases, the biomolecular corona signature is a lipidomic corona signature. The signature can refer to the different biomolecules. The signature can also refer to differences in the amount, level, or quantity of the biomolecules bound to the sensor element or nanoparticle, or differences in the conformation of the biomolecules bound to the sensor element or nanoparticle. It is contemplated that the biomolecular corona signature of each sensor element may include some of the same biomolecules, may include biomolecules unique to other sensor elements or nanoparticles, and / or may differ in the level or quantity, type, or conformation of the biomolecules. The biomolecular corona signature may depend not only on the physicochemical properties of the sensor element or particle, but also on the nature of the sample and duration of exposure. In some embodiments, the biomolecular corona signature includes the biomolecules found in the soft corona and the hard corona.

[0203] In some embodiments, the plurality of sensor elements includes a first sensor element that generates a first biomolecular corona signature and at least one second sensor element (e.g., at least one nanoparticle) that generates at least one second biomolecular corona signature when the sensor array contacts a complex biological sample. In some cases, each type of sensor element in the plurality of sensor elements generates a unique biomolecular corona signature.

[0204] The plurality of sensor elements, when contacted with a sample, generate a plurality of biomolecular corona signatures that can combine to form a biomolecular fingerprint. A "biomolecular fingerprint" refers to the combined composition or pattern of biomolecules in at least two biomolecular corona signatures for the plurality of sensor elements. It is contemplated that the biomolecular fingerprint can be formed from at least two biomolecular corona signatures, e.g., at least 1,000 unique biomolecular corona signatures, when many different biomolecular signatures are assayed. The biomolecular coronas can be assayed separately for each sensor element (e.g., each nanoparticle or each liposome) to determine the biomolecular corona signature for each sensor element and combined to form the biomolecular fingerprint. In some cases, the biomolecular fingerprint can be generated by simultaneously assaying two or more biomolecular coronas. Identified proteins

[0205] The automated instruments, systems, methods, and sensor elements (e.g., particles) disclosed herein can be used to identify many biomolecules, proteins, peptides, or protein groups. Characteristic intensity, as disclosed herein, refers to the intensity of a signal from an analytical measurement (e.g., the intensity of the mass-to-charge ratio from a mass spectrometry run of a sample). Using the data analysis methods described herein, the characteristic intensities of peptides and peptide fragments can be sorted into protein groups. A protein group refers to two or more proteins identified by a common peptide sequence. Alternatively, a protein group can refer to a single protein identified using a unique identifying sequence. For example, if a common peptide sequence is assayed between two proteins (protein 1: XYZZX and protein 2: XYZYZ) in a sample, the protein group can be an "XYZ protein group" with two members (protein 1 and protein 2). Alternatively, if the peptide sequence is unique to only one protein (protein 1), the protein group can be a "ZZX" protein group with one member (protein 1). Each protein group can be supported by more than one peptide sequence. A protein detected or identified according to the present disclosure can refer to a unique protein detected in the sample (e.g., unique relative to other proteins detected using mass spectrometry). Thus, analysis of proteins present in a unique corona corresponding to the unique sensor element type will yield a number of characteristic intensities. This number decreases as the characteristic intensities are processed into unique peptides, further decreases as the unique peptides are processed into unique proteins, and further decreases as the peptides are grouped into protein groups (two or more proteins that share a unique peptide sequence).

[0206] The automated instruments, systems, methods, and sensor elements (e.g., particles) disclosed herein may detect at least at least 100 protein groups, at least 200 protein groups, at least 300 protein groups, at least 400 protein groups, at least 500 protein groups, at least 600 protein groups, at least 700 protein groups, at least 800 protein groups, at least 900 protein groups, at least 1000 protein groups, at least 1100 protein groups, at least 1200 protein groups, at least 1300 protein groups, at least 1400 protein groups, at least 1500 protein groups, at least 1600 protein groups, at least 1700 protein groups, at least 1800 protein groups, at least 1900 protein groups, at least 2000 protein groups, at least 2100 protein groups, at least 2200 protein groups, at least 230 0 protein groups, at least 2400 protein groups, at least 2500 protein groups, at least 2600 protein groups, at least 2700 protein groups, at least 2800 protein groups, at least 2900 protein groups, at least 3000 protein groups, at least 3100 protein groups, at least 3200 protein groups, at least 3300 protein groups, at least 3400 protein groups, at least 3500 protein groups, at least 3600 protein groups, at least 3700 protein groups, at least 3800 protein groups, at least 3900 protein groups, at least 4000 protein groups, at least 4100 protein groups, at least 4200 protein groups, at least 4300 protein groups, at least 4400 protein groups, at least 4500 protein groups, at least 4600 protein groups, at least 4700 protein groups,At least 4800 protein groups, at least 4900 protein groups, at least 5000 protein groups, at least 10000 protein groups, at least 20000 protein groups, at least 100000 protein groups, 100-5000 protein groups, 200-4700 protein groups, 300-4400 protein groups, 400-4100 protein groups, 500-3800 protein groups, 600 ~3500 protein groups, 700-3200 protein groups, 800-2900 protein groups, 900-2600 protein groups, 1000-2300 protein groups, 1000-3000 protein groups, 3000-4000 protein groups, 4000-5000 protein groups, 5000-6000 protein groups, 6000-7000 protein groups, 7000-8000 protein groups, 8000-9000 Protein groups of 9000-10000, protein groups of 10000-11000, protein groups of 11000-12000, protein groups of 12000-13000, protein groups of 13000-14000, protein groups of 14000-15000, protein groups of 15000-16000, protein groups of 16000-17000, protein groups of 17000-18000, protein groups of 18000-19000 It can be used to identify protein groups, 19,000-20,000 protein groups, 20,000-25,000 protein groups, 25,000-30,000 protein groups, 10,000-20,000 protein groups, 10,000-50,000 protein groups, 20,000-100,000 protein groups, 2,000-20,000 protein groups, 1800-20,000 protein groups, or 10,000-100,000 protein groups.

[0207] The automated instruments, systems, methods, and sensor elements (e.g., particles) disclosed herein are capable of detecting at least at least 100 proteins, at least 200 proteins, at least 300 proteins, at least 400 proteins, at least 500 proteins, at least 600 proteins, at least 700 proteins, at least 800 proteins, at least 900 proteins, at least 1000 proteins, at least 1100 proteins, at least 1200 proteins, at least 1300 proteins, at least 1400 proteins, at least 1500 proteins, at least 1600 proteins, at least 1700 proteins, at least 1800 proteins, at least 1900 proteins, at least 2000 proteins, at least 2100 proteins, at least 2200 proteins, at least 2300 proteins, at least 2400 proteins, at least 2500 proteins, at least 2600 proteins, at least 2700 proteins, at least 2800 proteins, at least 2900 proteins, at least 3000 proteins, at least 3100 proteins, at least 3200 proteins, at least 3300 proteins, at least 3400 proteins, at least 3500 proteins, at least 3600 proteins, at least 3700 proteins, at least 3800 proteins, at least 3900 proteins, at least 4000 proteins, at least 4100 proteins, at least 4200 proteins, at least 4300 proteins, at least 4400 proteins Quality, at least 4500 proteins, at least 4600 proteins, at least 4700 proteins, at least 4800 proteins, at least 4900 proteins, at least 5000 proteins, 100-5000 proteins, 200-4700 proteins, 300-4400 proteins, 400-4100 proteins, 500-3800 proteins, 600-3500 proteins, 700-3200 proteins, 800-2900 proteins, 900-2600 proteins, 1000-2300 proteins,1000-3000 proteins, 3000-4000 proteins, 4000-5000 proteins, 5000-6000 proteins, 6000-7000 proteins, 7000-8000 proteins, 8000-9000 proteins, 9000-10000 proteins, 10000-11000 proteins, 11000-12000 proteins, 12000-13000 proteins, 13000-14000 proteins It can be used to identify 4,000 proteins, 14,000 to 15,000 proteins, 15,000 to 16,000 proteins, 16,000 to 17,000 proteins, 17,000 to 18,000 proteins, 18,000 to 19,000 proteins, 19,000 to 20,000 proteins, 20,000 to 25,000 proteins, 25,000 to 30,000 proteins, or 10,000 to 20,000 proteins.

[0208] The sensor elements disclosed herein can be used to identify any of the multiple unique proteins disclosed herein and / or any of the specific proteins disclosed herein across a wide dynamic range. For example, a plurality of particles comprising the unique particle species disclosed herein can enrich for proteins in a sample (which can be identified using the methods of the present disclosure) across the entire dynamic range over which proteins can be present in the sample (e.g., a plasma sample). A panel of particles can include any number of the unique particle species disclosed herein and can enrich and identify biomolecules in a sample across a concentration range of at least 2 to at least 12 orders of magnitude. Disease detection

[0209] The systems and methods disclosed herein can be used to detect markers in a sample from a subject that correspond to a particular biological (e.g., disease) state. The biological state can be a disease, disorder, or tissue abnormality. The disease state can be an early-phase or intermediate-phase disease state.

[0210] The disclosed systems and methods can be used to detect a wide range of disease states in a given sample. For example, the disclosed systems and methods can be used to detect cancer. The cancer can be brain cancer, lung cancer, pancreatic cancer, glioblastoma, meningioma, myeloma, or pancreatic cancer.

[0211] In some cases, biomolecular fingerprints can be used to determine a subject's disease state, diagnose or predict a disease in a subject, or identify a unique pattern of biomarkers associated with a disease state or disease or disorder. For example, changes in the biomolecular fingerprint over time (days, months, years) in a subject (which can be broadly applied to determining biomolecular fingerprints that may be associated with a disease or any other disease state) enable the ability to track a disease or disorder (e.g., a disease state) in a subject. As disclosed herein, the ability to detect a disease (e.g., cancer) at an early stage (even before the disease has fully developed or metastasized) can significantly improve positive outcomes for the patient, as well as the ability to extend life expectancy and reduce mortality associated with the disease.

[0212] The automated instruments, systems, methods, and sensor elements (e.g., particles) disclosed herein may provide a unique opportunity to generate biomolecular fingerprints associated with pre- or prodromal stages of the disease in a high-throughput manner. The present disclosure provides large-scale, rapid sample processing to generate biomolecular fingerprints in a high-throughput manner, thereby enabling large-scale determination of a subject's disease state, diagnosis or prognosis of a disease in a subject, or identification of unique patterns of biomarkers associated with a disease state or disease or disorder across many subjects.

[0213] In some embodiments, a method for detecting a disease or disorder in a subject is provided, the method comprising: (a) obtaining a sample from the subject; (b) contacting the sample with a sensor array described herein; and (c) determining a biomolecular fingerprint associated with the sample, wherein the biomolecular fingerprint distinguishes a subject's health state in a disease state from, for example, a health state without the disease or disorder, a health state having a precursor to the disease or disorder, and a health state having the disease or disorder.

[0214] Determining whether a biomolecular fingerprint is associated with the sample can include detecting the biomolecular corona signature for at least two sensor elements, where a combination of the at least two biomolecular corona signatures generates the biomolecular fingerprint. In some embodiments, the biomolecular corona signatures of the at least two sensor elements are assayed separately and the results are combined to determine the biomolecular fingerprint. In some embodiments, the biomolecular corona signatures of the at least two elements are assayed simultaneously or in the same sample.

[0215] The automated instruments, systems, sensor arrays, and methods described herein can be used to determine disease states and / or to predict or diagnose diseases or disorders, including, but not limited to, cancer, cardiovascular disease, endocrine disease, inflammatory disease, neurological disease, and the like.

[0216] In one embodiment, the disease or disorder is cancer. The term "cancer" is intended to encompass any cancer, neoplastic disease, and preneoplastic disease characterized by abnormal cell growth, including tumors and benign growths. The cancer may be, for example, lung cancer, pancreatic cancer, or skin cancer. In appropriate embodiments, the automated instruments, systems, sensor arrays, and methods described herein can not only diagnose cancer (e.g., determine whether a subject (a) does not have cancer, (b) is in a precancerous stage, (c) is in an early stage of cancer, or (d) is in a late stage of cancer), but also, in some embodiments, determine the type of cancer. As demonstrated in the examples below, a sensor array including six sensor elements was able to accurately determine the disease state of cancer or its absence. Furthermore, by example, a sensor array including six sensor elements was able to distinguish between different cancer types (e.g., lung cancer, glioblastoma, meningioma, myeloma, and pancreatic cancer).

[0217] The automated instruments, systems, sensor arrays, and methods of the present disclosure may also be used to detect other cancers, such as acute lymphocytic leukemia (ALL); acute myeloid leukemia (AML); cancer in young people; adrenocortical carcinoma; pediatric adrenocortical carcinoma; rare cancers of childhood; AIDS-related cancers; Kaposi's sarcoma (soft tissue sarcoma); AIDS-related lymphoma; primary central nervous system lymphoma; anal cancer; appendix cancer (see gastrointestinal carcinoid tumors); astrocytoma, pediatric (brain cancer); atypical teratoid / rhabdoid tumor, pediatric, central nervous system (brain cancer); basal cell carcinoma of the skin (skin cancer). see); Bile duct cancer; Bladder cancer; Pediatric bladder cancer; Bone cancer (Ewing's sarcoma and osteosarcoma and malignant fibrous histiocytoma); Brain tumors; Breast cancer; Pediatric breast cancer; Bronchial tumors, children; Burkitt's lymphoma (see non-Hodgkin's lymphoma); Carcinoid tumors (gastrointestinal); Pediatric carcinoid tumors; Carcinoma of unknown primary; Carcinoma of unknown primary in children; Cardiac (heart) tumors, children; Central nervous system; Atypical teratoid / rhabdoid tumors, children (brain cancer); Embryonal tumors, children (brain cancer); Germ cell tumors, children (brain cancer); Primary CNS lymphoma; Cervical cancer; Pediatric cervical cancer; Pediatric cancer; Small intestine Childhood Cancers, Rare; Cholangiocarcinoma (see Cholangiocarcinoma); Chordoma, Childhood; Chronic Lymphocytic Leukemia (CLL); Chronic Myeloid Leukemia (CML); Chronic Myeloproliferative Neoplasms; Colorectal Cancer; Childhood Colorectal Cancer; Craniopharyngioma, Childhood (Brain Cancer); Cutaneous T-Cell Lymphoma (see Lymphoma) (Mycosis Fungoides and Sézary Syndrome); Ductal Carcinoma in Situ (DCIS) (see Breast Cancer); Embryonal Tumors, Central Nervous System, Childhood (Brain Cancer); Endometrial Carcinoma (Uterine Cancer); Ependymoma, Childhood (Brain Cancer); Esophageal Cancer; Pediatric Esophageal Cancer; Nasal Neuroblastoma (Head and Neck Cancer); Ewing's Sarcoma (Bone Cancer); Extracranial Germ Cell Tumors, Childhood ;Extragonadal germ cell tumor;Eye cancer;Pediatric intraocular melanoma;Intraocular melanoma;Retinoblastoma;Fallen tube cancer;Fibrous histiocytoma, malignant, and osteosarcoma of bone;Gallbladder cancer;Gastric (stomach) cancer;Pediatric gastric (stomach) cancer;Gastrointestinal carcinoid tumor;Gastrointestinal stromal tumor (GIST) (soft tissue sarcoma);Pediatric gastrointestinal stromal tumor;Germ cell tumor;Pediatric central nervous system germ cell tumor (brain cancer);Pediatric extracranial germ cell tumor;Extragonadal germ cell tumor;Ovarian germ cell tumor;Testicular cancer;Gestational trophoblastic disease;Hairy cell leukemia;Head and neck cancer;Heart tumors, pediatric;Hepatocellular (liver) carcinoma;Histiocytosis, Langerhans cell; Hodgkin's lymphoma; Hypopharyngeal carcinoma (head and neck cancer); Intraocular melanoma; Pediatric intraocular melanoma; Pancreatic islet tumors, pancreatic neuroendocrine tumors; Kaposi's sarcoma (soft tissue sarcoma); Renal (renal cell) carcinoma; Langerhans cell histiocytosis; Laryngeal cancer (head and neck cancer); Leukemia; Oral cavity cancer (head and neck cancer); Liver cancer; Lung cancer (non-small cell and small cell); Pediatric lung cancer; Lymphoma; Male breast cancer; Malignant fibrous histiocytoma and osteosarcoma of bone; Melanoma; Pediatric melanoma; Melanoma, intraocular (eye); Pediatric intraocular melanoma; Merkel cell carcinoma (skin cancer); Mesothelioma, malignant; Pediatric mesothelioma; Metastatic cancer; Metastatic squamous cell carcinoma of the neck of unknown primary (head and neck cancer); Midline carcinoma with nut gene mutations; Oral cancer cancer) (head and neck cancer); multiple endocrine neoplasia syndrome; multiple myeloma / plasma cell neoplasm; mycosis fungoides (lymphoma); myelodysplastic syndrome, myelodysplastic / myeloproliferative neoplasm; myeloid leukemia, chronic (cml); myeloid leukemia, acute (aml); myeloproliferative neoplasm, chronic; nasal cavity and sinus cancer (head and neck cancer); nasopharyngeal carcinoma (head and neck cancer); neuroblastoma; non-Hodgkin's lymphoma; non-small cell lung cancer; oral cancer, lip and oral cavity cancer cancer) and oropharyngeal cancer (head and neck cancer); osteosarcoma and malignant fibrous histiocytoma of bone; ovarian cancer; pediatric ovarian cancer; pancreatic cancer; pediatric pancreatic cancer; pancreatic neuroendocrine tumors (pancreatic islet tumors); papilloma (pediatric larynx); paraganglioma; pediatric paraganglioma; paranasal sinus and nasal cancer (head and neck cancer); parathyroid cancer; penile cancer; pharyngeal cancer (head and neck cancer); pheochromocytoma; pediatric pheochromocytoma; pituitary tumor; plasma cell neoplasm / multiple myeloma; pleuropulmonary blastoma; gestational breast cancer; primary central nervous system (CNS) lymphoma; primary peritoneal cancer; prostate cancer; rectal cancer cancer);recurrent cancer;renal cell (kidney) cancer;retinoblastoma;rhabdomyosarcoma, childhood (soft tissue sarcoma);salivary gland carcinoma (head and neck cancer);sarcoma;pediatric rhabdomyosarcoma (soft tissue sarcoma);pediatric vascular tumor (soft tissue sarcoma);Ewing's sarcoma (bone cancer);Kaposi's sarcoma (soft tissue sarcoma);osteosarcoma (bone cancer);soft tissue sarcoma;uterine sarcoma;Sezary syndrome (lymphoma);skin cancer;pediatric skin cancer;small cell lung cancer;small intestine cancer;soft tissue sarcoma;squamous cell carcinoma of the skin (see skin cancer);squamous cell carcinoma of the neck of unknown primary, metastatic (head and neck cancer);It may be used to detect stomach (gastric) cancer; childhood stomach (gastric) cancer; T-cell lymphoma, skin (see lymphoma) (mycosis fungoides and Sézary syndrome); testicular cancer; childhood testicular cancer; pharyngeal cancer (head and neck cancer); nasopharyngeal cancer; oropharyngeal cancer; hypopharyngeal cancer; thymoma and thymic carcinoma; thyroid cancer; transitional cell carcinoma of the renal pelvis and ureter (kidney (renal cell) carcinoma); carcinoma of unknown primary; childhood cancer of unknown primary; rare cancers of childhood; transitional cell carcinoma of the ureter and renal pelvis (kidney (renal cell) carcinoma); urethral cancer; uterine cancer, endometrium; uterine sarcoma; vaginal cancer; childhood vaginal cancer; vascular tumors (soft tissue sarcomas); vulvar cancer; Wilms' tumor and other childhood kidney tumors; or cancer in young adults.

[0218] In some cases, the disease or disorder is cardiovascular disease. As used herein, the term "cardiovascular disease" (CVD) or "cardiovascular disease" is used to classify a wide range of conditions that affect the heart, heart valves, and the body's vasculature (e.g., veins and arteries), and includes, but is not limited to, atherosclerosis, myocardial infarction, acute coronary syndrome, angina, congestive heart failure, aortic aneurysm, aortic dissection, iliac or femoral aneurysm, pulmonary embolism, atrial fibrillation, stroke, transient ischemic attack, systolic dysfunction, diastolic dysfunction, myocarditis, atrial tachycardia, ventricular fibrillation, endocarditis, peripheral vascular disease, and coronary artery disease (CAD). Furthermore, the term cardiovascular disease refers to causes that ultimately result in a cardiovascular event or cardiovascular complication (which refers to a manifestation of an adverse condition in a subject caused by cardiovascular disease), including, but not limited to, myocardial infarction, unstable angina, aneurysm, stroke, heart failure, non-fatal myocardial infarction, stroke, angina pectoris), transient ischemic attack, aortic aneurysm, aortic dissection, cardiomyopathy, abnormal cardiac catheterization, abnormal cardiac imaging, stent or graft revascularization, risk of experiencing an abnormal stress test, risk of experiencing abnormal myocardial perfusion, and death.

[0219] As used herein, the ability to detect, diagnose, or predict cardiovascular disease (e.g., atherosclerosis) can include determining whether a patient is in a pre-stage of cardiovascular disease, has developed an early, moderate, or severe form of cardiovascular disease, or has experienced one or more cardiovascular events or complications associated with cardiovascular disease.

[0220] Atherosclerosis (also known as arteriosclerotic vascular disease or ASVD) is a cardiovascular disease characterized by thickening of arterial walls resulting from the infiltration and accumulation and deposition of arterial plaque containing white blood cells into the innermost layer of the arterial wall, resulting in narrowing and hardening of the artery. Arterial plaque is a buildup of macrophage cells or debris, containing lipids (cholesterol and fatty acids), calcium, and varying amounts of fibrous connective tissue. Diseases associated with atherosclerosis include, but are not limited to, atherothrombosis, coronary heart disease, deep vein thrombosis, carotid artery disease, angina pectoris, peripheral arterial disease, chronic kidney disease, acute coronary syndrome, vascular stenosis, myocardial infarction, aneurysm, or stroke. In one embodiment, the automated devices, compositions, and methods disclosed herein can distinguish between different stages of atherosclerosis (including, but not limited to, different degrees of stenosis) in a subject.

[0221] In some cases, the disease or disorder is an endocrine disease.The term " endocrine disease " refers to the disorder related to the dysregulation of the endocrine system of the subject.Endocrine disease can be caused by the gland that produces too much or too little endocrine hormone, which causes hormonal imbalance, or can be caused by the occurrence of lesions (such as nodules or tumors) in the endocrine system, which can affect or not affect hormone levels. Suitable endocrine disorders that may be treated include, but are not limited to, acromegaly, Addison's disease, adrenal cancer, adrenal gland disorders, aplastic thyroid cancer, Cushing's syndrome, de Quervain's thyroiditis, diabetes, follicular thyroid cancer, gestational diabetes, goiter, Graves' disease, growth failure, growth hormone deficiency, Hashimoto's thyroiditis, Hurthle cell thyroid carcinoma, hyperglycemia, hyperparathyroidism, hyperthyroidism, hypoglycemia, hypoparathyroidism, hypothyroidism, low testosterone, medullary thyroid carcinoma, MEN 1, MEN 2A, MEN 2B, menopause, metabolic syndrome, obesity, osteoporosis, papillary thyroid carcinoma, parathyroid disease, pheochromocytoma, pituitary disorders, pituitary tumors, polycystic ovary syndrome, prediabetes, silent thyroiditis, thyroid cancer, thyroid disease, thyroid nodules, thyroiditis, Turner's syndrome, type 1 diabetes, type 2 diabetes, and the like.

[0222] In some cases, the disease or disorder is an inflammatory disease. As used herein, inflammatory disease refers to a disease caused by uncontrolled inflammation in the subject's body. Inflammation is a biological response of a subject to harmful stimuli (such as pathogens, necrotic cells and tissues, irritants, etc.) that can be external or internal. However, if the inflammatory response becomes abnormal, it can lead to tissue damage and cause various diseases and disorders. Inflammatory diseases include, but are not limited to, asthma, glomerulonephritis, inflammatory bowel disease, rheumatoid arthritis, hypersensitivity, pelvic inflammatory disease, autoimmune disease, arthritis; necrotizing enterocolitis (NEC), gastroenteritis, pelvic inflammatory disease (PID), emphysema, pleurisy, pyelitis, pharyngitis, angina, acne vulgaris, urinary tract infection, appendicitis, bursitis, colitis, cystitis, skin The arthritis may include inflammation, phlebitis, rhinitis, tendonitis, tonsillitis, vasculitis, autoimmune diseases; celiac disease; chronic prostatitis, hypersensitivity, reperfusion injury; sarcoidosis, graft rejection, vasculitis, interstitial cystitis, hay fever, periodontitis, atherosclerosis, psoriasis, ankylosing spondylitis, juvenile idiopathic arthritis, Behcet's disease, spondyloarthritis, uveitis, systemic lupus erythematosus, and cancer. For example, the arthritis may include rheumatoid arthritis, psoriatic arthritis, osteoarthritis, or juvenile idiopathic arthritis.

[0223] The disease or disorder may be a neurological disease. Neuropathy and neurological disease are used interchangeably and refer to the brain, spinal cord, and the nerves connecting them. Neurological diseases include, but are not limited to, brain tumors, epilepsy, Parkinson's disease, Alzheimer's disease, ALS, arteriovenous malformations, cerebrovascular disease, cerebral aneurysms, epilepsy, multiple sclerosis, peripheral neuropathy, postherpetic neuralgia, stroke, frontotemporal dementia, demyelinating diseases (including, but not limited to, multiple sclerosis, Devic's disease (i.e., neuromyelitis optica), central pontine myelinolysis, progressive multifocal leukoencephalopathy, leukodystrophy, Guillain-Barré syndrome, progressive inflammatory polyneuropathy, Charcot-Marie-Tooth disease, chronic inflammatory demyelinating polyneuropathy, and anti-MAG peripheral neuropathy). Neurological disorders also include immune-mediated neurological disorders (IMNDs), which include diseases in which at least one component of the immune system reacts to host proteins present in the central or peripheral nervous system and contributes to the pathology of the disease. IMNDs can include, but are not limited to, demyelinating diseases, paraneoplastic neurological syndromes, immune-mediated encephalomyelitis, immune-mediated autonomic neuropathies, myasthenia gravis, autoimmune encephalopathy, and acute disseminated encephalomyelitis.

[0224] The disclosed methods, systems, and / or devices may be able to accurately distinguish between patients with Alzheimer's disease and those without. They may be able to detect patients who may develop Alzheimer's disease years after pre-symptomatic screening. This provides the advantage of being able to treat patients at a very early stage, even before the onset of the disease.

[0225] The disclosed methods, systems, and devices can detect pre-disease stages of diseases or disorders. A pre-disease stage is a stage in which a patient does not exhibit any signs or symptoms of the disease. A pre-cancerous stage would be a stage in which cancer, tumors, or cancerous cells have not been identified in the patient's body. A pre-neurological disease stage would be a stage in which a person does not exhibit one or more symptoms of the neurological disease. The ability to diagnose a disease before one or more signs or symptoms of the disease appear allows for close monitoring of the subject and the ability to treat the disease at a very early stage, improving the likelihood of stopping the progression of the disease or reducing its severity.

[0226] In some embodiments, the automated devices, systems, sensor arrays, and methods of the present disclosure are capable of detecting the early stages of a disease or disorder. The early stage of a disease may refer to the time when the first signs or symptoms of the disease may become apparent within a subject. The early stage of a disease may be a stage in which no outward signs or symptoms are present. For example, in Alzheimer's disease, the early stage may be the pre-Alzheimer's stage, in which symptoms are not yet detectable, but the patient will develop Alzheimer's disease after several months or years.

[0227] Identifying disease either before the onset of disease or at an early stage can often increase the patient's chances of a positive outcome. For example, diagnosing cancer at an early stage (stage 0 or stage 1) can improve survival rates by more than 80%. Stage 0 cancer may refer to cancer before it begins to metastasize to nearby tissues. Cancer at this stage is often curable (usually by surgically removing the entire tumor). Stage 1 cancer may usually be a small cancer or tumor that has not grown deep into nearby tissues and has not metastasized to lymph nodes or other parts of the body.

[0228] Figure 8 shows a schematic overview of a cancer detection method that can be performed using the automated instrument of the present disclosure. Whole blood samples can be collected from a range of patients, including healthy patients and patients with different types and stages of cancer. The whole blood can be fractionated into plasma samples and then contacted with multiple types of particles, including positively charged particles, negatively charged particles, and neutral particles. Each particle type collects a different type of protein from the plasma sample, resulting in a unique biomolecular fingerprint for each patient. The biomolecular fingerprint not only includes the relative abundance of proteins for each particle type, but also the relative abundance of proteins across particle types. For example, increased abundance of fibronectin for one particle type may be an appropriate indicator only if the abundance of complement component 4 for a second particle type is low. The biomolecular fingerprint can be used not only to determine which patients have cancer, but also to determine the stage and type of cancer.

[0229] In some embodiments, the automated devices, systems, sensor arrays, and methods can detect intermediate stages of the disease. An intermediate stage of the disease refers to a stage of the disease that has passed the initial signs and symptoms, and the patient has one or more symptoms of the disease. For example, for cancer, stage II or III cancer is considered intermediate and refers to a larger cancer or tumor that has grown deep into nearby tissue. In some instances, stage II or III cancer may also have metastasis to lymph nodes but not to other parts of the body.

[0230] Furthermore, the automated devices, systems, sensor arrays, and methods can detect late or advanced stages of the disease. Late or advanced stages of the disease are also called "severe" or "advanced," and usually refer to the subject experiencing various symptoms and effects of the disease. For example, severe cancer includes stage IV, in which the cancer has metastasized to other organs or parts of the body, and may be referred to as advanced or metastatic cancer.

[0231] The disclosed methods can include processing the biomolecular fingerprint of a sample against a collection of biomolecular fingerprints associated with multiple diseases and / or multiple disease states to determine whether the sample indicates a disease and / or disease state. For example, samples can be collected from a population of subjects over time. When the subjects develop a disease or disorder, the present disclosure enables the ability to characterize and detect changes in the biomolecular fingerprint in the subject over time by computationally analyzing the biomolecular fingerprint of the sample from the same subject before the disease develops against the biomolecular fingerprint of the subject after the disease develops. Samples can also be taken from a cohort of patients who all develop the same disease, allowing for analysis and characterization of the biomolecular fingerprints associated with different stages of the disease (e.g., from pre-symptomatic to diseased) for these patients.

[0232] In some cases, the disclosed devices, systems, compositions, and methods may not only distinguish between different types of disease, but also between different stages of the disease (e.g., early stages of cancer). This may include distinguishing between healthy subjects and subjects with a pre-disease state. The pre-disease state may be stage 0 or stage 1 cancer, neurodegenerative disease, dementia, coronary disease, kidney disease, cardiovascular disease (e.g., coronary artery disease), diabetes, or liver disease. Distinguishing between different stages of the disease may include distinguishing between two stages of cancer (e.g., stage 0 and stage 1, or stage 1 and stage 3). sample

[0233] The panels of the present disclosure can be used to generate proteomic data from protein coronas, which can then be associated with any of the biological conditions described herein. Samples consistent with the present disclosure include biological samples from subjects. The subjects can be human or non-human animals. The biological sample can be a bodily fluid. For example, the bodily fluid can be plasma, serum, CSF, urine, tears, or saliva. The biological sample can contain multiple proteins or proteomic data, which can be analyzed after adsorption of various sensor element (e.g., particle) species in a panel of proteins to the surface, followed by digestion of the protein corona. The proteomic data can include nucleic acids, peptides, or proteins.

[0234] A wide range of biological samples are suitable for use in the automated device of the present disclosure. The biological samples may include plasma, serum, urine, cerebrospinal fluid, synovial fluid, tears, saliva, whole blood, milk, nipple aspirate, ductal lavage, vaginal fluid, nasal secretion, inner ear fluid, gastric juice, pancreatic juice, trabecular fluid, pulmonary lavage, sweat, gingival crevicular fluid, semen, prostatic fluid, sputum, feces, bronchial lavage, fluid from a swab, bronchial aspirate, mobilized solids, fine needle aspiration samples, tissue homogenates, lymph, cell culture samples, or any combination thereof. The biological samples may include multiple biological samples (e.g., pooled plasma from multiple subjects or multiple tissue samples from a single source). The biological samples may include a single type of bodily fluid or biomaterial from a single source.

[0235] The biological sample may be diluted or pretreated. The biological sample (e.g., the biological sample includes serum) may be depleted before use in the automated device. The biological sample may also be subjected to physical (e.g., homogenization or sonication) or chemical treatment before use in the automated device. The biological sample may be diluted before use in the automated device. The dilution medium may contain a buffer or salt, or may be purified water (e.g., distilled water). Different partitions of a biological sample may be subjected to different degrees of dilution. The biological sample or sample partition may be subjected to 1.1-fold, 1.2-fold, 1.3-fold, 1.4-fold, 1.5-fold, 2-fold, 3-fold, 4-fold, 5-fold, 6-fold, 8-fold, 10-fold, 12-fold, 15-fold, 20-fold, 30-fold, 40-fold, 50-fold, 75-fold, 100-fold, 200-fold, 500-fold, or 1000-fold dilution.

[0236] In some embodiments, the disclosed panel provides for the identification and measurement of specific proteins in the biological sample by processing the proteomic data from digestion of the corona formed on the sensor element. Proteins that can be identified and measured include, for example, high-abundance proteins, medium-abundance proteins, and low-abundance proteins. High-abundance proteins include, for example, albumin and IgG.

[0237] In some embodiments, proteins that may be measured and identified include, for example, albumin, immunoglobulin G (IgG), lysozyme, carcinoembryonic antigen (CEA), receptor tyrosine protein kinase erbB-2 (HER-2 / neu), bladder tumor antigen, thyroglobulin, alpha-fetoprotein, prostate-specific antigen (PSA), mucin 16 (CA125), carbohydrate antigen 19-9 (CA19.9), cancer antigen 15-3 (CA15.3), leptin, prolactin, osteopontin, insulin-like growth factor 2 (IGF-II), 4F2 cell surface antigen heavy chain (CD98), fascin, sPigR, 14-3-3 eta, troponin I, B-type natriuretic peptide, BRCA1 (breast cancer type 1 susceptibility protein, c-Myc proto-oncogene protein (c-Myc), interleukin-6 (IL-6), fibrinogen, epidermal growth factor receptor (EGFR), gastrin, PH, granulocyte colony-stimulating factor (G-CSF), desmin, enolase 1 (NSE), follicle-stimulating hormone (FSH), FSH, vascular endothelial growth factor (VEGF), p21, proliferating cell nuclear antigen (PCNA), calcitonin, infection-specific protein (PR), luteinizing hormone (LH), somatostatin, S100, insulin, alpha-prolactin, adrenocorticotropic hormone (ACTH), B-cell lymphoma 2 (Bcl2), estrogen receptor alpha (ERα), antigen k (Ki-67), oncoprotein (p53), cathepsin D, beta-catenin, von Willebrand factor (VWF), CD15, k-ras, caspase 3, ENTH domain-containing protein (EPN), CD10, FAS, BRCA2 (breast cancer type 2 susceptibility protein, CD30L, CD30, CGA, CRP, prothrombin, CD44, APEX, transferrin, GM-CSF, E-cadherin, interleukin-2 (IL-2), Bax, IFN-γ, β-2-MG, tumor necrosis factor α (TNF-α), cluster of differentiation antigen 340, trypsin, cyclin D1, MG These include B, XBP-1, HG-1, YKL-40, S-gamma, NESP-55, netrin-1, geminin, GADD45A, CDK-6, CCL21, breast cancer metastasis suppressor 1 (BrMS1), 17βHDI, platelet-derived growth factor receptor A (PDGRFA), P300 / CBP-associated factor (Pcaf), chemokine ligand 5 (CCL5), matrix metalloproteinase-3 (MMP3), claudin-4, and claudin-3. Analysis method

[0238] The proteomic data of the sample can be identified, measured, and quantified using a number of different analytical techniques. For example, proteomic data can be analyzed using sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) or any gel-based separation technique. Peptides and proteins can be identified, measured, and quantified using immunoassays (e.g., enzyme-linked immunosorbent assays (ELISAs)). Alternatively, proteomic data can be identified, measured, and quantified using mass spectrometry, high-performance liquid chromatography, LC-MS / MS, and other protein separation techniques.

[0239] In some cases, the method for determining the biomolecular fingerprint includes detecting and determining the biomolecular corona signature of the at least two sensor elements, which may be performed by separating the plurality of biomolecules bound to each sensor element (e.g., separating the biomolecular corona from the sensor element) and assaying the plurality of biomolecules to determine the composition of the plurality of biomolecular coronas and determine a biomolecular fingerprint. In some cases, the composition of each biomolecular corona signature of each sensor element is assayed independently, and the results are combined to generate the biomolecular fingerprint (e.g., each sensor element is in a separate channel or compartment, where the specific composition of the biomolecular corona for that particular sensor element can be analyzed separately (e.g., by either desorbing the biomolecules and analyzing them by mass spectrometry and / or chromatography, or by detecting the biomolecules still bound to the sensor element by fluorescence, luminescence, or other means). The at least two sensor elements may also be in the same partition, and the composition of the biomolecular corona for the at least two sensor elements is assayed simultaneously by separating the biomolecular coronas from both sensor elements into a solution and assaying the solution to determine the biomolecular signatures.

[0240] Methods for assaying the plurality of biomolecules that make up the biomolecular corona signature or biomolecular fingerprint can include, but are not limited to, gel electrophoresis, liquid chromatography, mass spectrometry, nuclear magnetic resonance spectroscopy (NMR), Fourier transform infrared spectroscopy (FTIR), circular dichroism, Raman spectroscopy, and combinations thereof. In some cases, the assay involves an analyte-specific identification technique, such as ELISA, immunostaining, or nucleic acid capture by hybridization. In preferred embodiments, the assay involves liquid chromatography, mass spectrometry, or a combination thereof.

[0241] As used herein, nucleic acids can be processed by standard molecular biology techniques for downstream applications. Embodiments of the methods and compositions disclosed herein relate to nucleic acid (polynucleotide) sequencing. In some methods and compositions described herein, the nucleotide sequence of a portion of a target nucleic acid or its fragments can be determined using various methods and devices. Examples of sequencing methods include electrophoretic methods, sequencing-by-synthesis methods, sequencing-by-ligation methods, sequencing-by-hybridization methods, single-molecule sequencing methods, and real-time sequencing methods. In some embodiments, the method for determining the nucleotide sequence of a target nucleic acid or its fragments can be an automated process. In some embodiments, a capture probe can function as a primer that enables the initiation of a nucleotide synthesis reaction using a polynucleotide from a nucleic acid sample as a template. In this way, information about the sequence of the polynucleotide provided to the array can be obtained. In some embodiments, if a primer that hybridizes to the polynucleotide bound to the capture probe and sequencing reagents are further provided to the array, the polynucleotide hybridized to the capture probe on the array can serve as a sequencing template. Array-based sequencing methods have been previously described in the art.

[0242] In some embodiments, for sequencing on a substrate such as an array, paired-end reads can be obtained on nucleic acid clusters. Methods for obtaining paired-end reads are described in WO / 07010252 and WO / 07091077 (each of which is incorporated herein by reference in its entirety). Paired-end sequencing facilitates reading both the forward and reverse template strands of each cluster during a single paired-end read. Generally, template clusters can be amplified on the surface of a substrate (e.g., a flow cell) by bridge amplification and sequentially sequenced using paired primers. During amplification of the template strands, a bridged double-stranded structure can be generated, which can be treated to release one strand of each double strand from the surface. The single-stranded nucleic acid can be used for cycles of sequencing, primer hybridization, and primer extension. After the first sequencing run, the end of the first single-stranded template can be hybridized to the immobilized primer remaining from the initial cluster amplification procedure. The immobilized primer can be extended using the hybridized first single strand as a template to resynthesize the original double-stranded structure. The double-stranded structure can be treated to remove at least a portion of the first template strand to release the immobilized resynthesized strand in single-stranded form. The resynthesized strand can be sequenced to determine a second read, the position of which begins at the opposite end of the original template fragment obtained from the fragmentation process.

[0243] Nucleic acid sequencing can be single-molecule sequencing or sequencing-by-synthesis. Sequencing can be massively parallel array sequencing, which can be performed using template nucleic acid molecules immobilized on a support (e.g., a flow cell). For example, sequencing can include first-generation sequencing methods (e.g., Maxam-Gilbert sequencing or Sanger sequencing) or high-throughput sequencing (e.g., next-generation sequencing or NGS). High-throughput sequencing methods can simultaneously (or substantially simultaneously) sequence at least about 10,000, 100,000, 1 million, 100 million, 100 million, 1 billion, or more polynucleotide molecules. Sequencing methods may include, but are not limited to, pyrosequencing, sequencing by synthesis, single molecule sequencing, nanopore sequencing, semiconductor sequencing, sequencing by ligation, sequencing by hybridization, Digital Gene Expression (Helicos), massively parallel sequencing (e.g., sequencing using the Helicos platform, Clonal Single Molecule Array platform (Solexa / Illumina), PacBio platform, SOLiD platform, Ion Torrent platform, or Nanopore platform).

[0244] A sensor element can include a complex of a first component and a polymeric fluorophore or other quencher component that is chemically complementary to the first component (where such complex has an initial background or reference fluorescence). When the first component contacts a biomolecule (e.g., upon formation of a biomolecular corona), it can affect quenching of the fluorophore, and this change in fluorescence can be measured. After the sensor is illuminated and / or excited by a laser, the effect and / or change in fluorescence for each sensor element can be measured and compared to or processed against the background fluorescence to generate the biomolecular fingerprint. Computer System

[0245] The present disclosure provides a computerized system programmed to execute the disclosed methods. This determination, analysis, or statistical classification is performed by methods known in the art, including, but not limited to, a wide variety of supervised or unsupervised data analysis and clustering approaches, such as hierarchical cluster analysis (HCA), principal component analysis (PCA), partial least squares discriminant analysis (PLSDA), machine learning (also known as random forests), logistic regression, decision trees, support vector machines (SVM), k-nearest neighbors, naive Bayes, linear regression, polynomial regression, SVM (support vector machine) for regression, k-means, and hidden Markov models, among others. The computerized system can perform various aspects of analyzing protein sets or protein coronas of the present disclosure, such as comparing / analyzing the biomolecular coronas of several samples to determine, with statistical significance, common patterns among individual biomolecular coronas and determining protein sets associated with the biological state. The computerized system can be used to generate classifiers (e.g., protein corona compositional features) for detecting and distinguishing distinct protein sets or protein coronas. Data collected from the sensor arrays disclosed herein can be used to train machine learning algorithms, particularly algorithms that receive array measurements from patients and output the specific biomolecular corona composition from each patient. Prior to training the algorithm, the raw data from the array can first be denoised to reduce the variability of each variable.

[0246] Machine learning can be generalized as the ability of a learning machine to perform accurately on new, unseen examples / tasks after going through a training dataset. Machine learning can include the following concepts and methods: supervised concepts such as AODE; artificial neural networks, such as backpropagation, autoencoders, Hopfield networks, Boltzmann machines, restricted Boltzmann machines, and spiking neural networks; Bayesian statistics, such as Bayesian networks and Bayesian knowledge bases; case-based reasoning; Gaussian process regression; gene expression programming; group method of data handling (GMDH); inductive logic programming; example-based learning; lazy learning;

[0247] These may include learning automata; learning vector quantization; logistic model trees; minimum message length (decision trees, decision graphs, etc.), such as nearest neighbor algorithms and analogy modeling; probabilistic and approximately correct (PAC) learning; ripple-down rules, knowledge acquisition techniques; symbolic machine learning algorithms; support vector machines; random forests; ensembles of classifiers, such as bootstrap aggregating (bagging) and boosting (meta-algorithms); ordinal classification; information fuzzy networks (IFNs); conditional random fields; ANOVA; linear classifiers, such as Fisher's linear discriminant, linear regression, logistic regression, multinomial logistic regression, naive Bayes classifier, perceptron, support vector machine, etc.; quadratic classifiers; k-nearest neighbors; boosting; decision trees, such as C4.5, random forests, ID3, CART, SLIQ SPRINT, etc.; Bayesian networks, such as naive Bayes; and hidden Markov models. Unsupervised learning concepts may include expectation maximization algorithms; vector quantization; generative topograpHic maps (GTMs); information bottleneck methods; artificial neural networks, such as self-organizing maps; association rule learning, such as the Apriori algorithm, the Eclat algorithm, and the FP-growth algorithm; hierarchical clustering, such as single-link clustering and conceptual clustering; cluster analysis, such as the K-means algorithm, fuzzy clustering, DBSCAN, and the OPTICS algorithm; and outlier detection, such as local outlier factor methods. Semi-supervised learning concepts may include generative models; sparse separation; graph-based methods; and co-training.

[0248] Reinforcement learning concepts may include temporal difference learning; Q-learning; learning automata; and SARSA. Deep learning concepts may include deep belief networks; deep Boltzmann machines; deep convolutional neural networks; deep recurrent neural networks; and hierarchical temporal memory (HTM). A computer system may be adapted to perform the methods described herein. The system includes a central computer server programmed to perform the methods described herein. The server includes a central processing unit (CPU, also referred to as a "processor"), which may be a single-core processor, a multi-core processor, or multiple processors for parallel processing. The server also includes memory (e.g., random access memory, read-only memory, flash memory); an electronic storage unit (e.g., a hard disk); a communication interface (e.g., a network adapter) for communicating with one or more other systems; and peripherals (which may include cache, other memory, data storage devices, and / or electronic display adapters). The memory, storage unit, interface, and peripherals communicate with the processor through a communication bus (solid line) (e.g., a motherboard, etc.). The storage unit may be a data storage unit for storing data. The server is operably coupled to a computer network ("network") with the aid of the communication interface. The network may be the Internet, an intranet and / or an extranet, an intranet and / or an extranet in communication with the Internet, a telecommunications or data network. The network may, in some cases, implement a peer-to-peer network with the aid of the server, which may enable devices coupled to the server to act as clients or servers.

[0249] The storage unit may store files, such as subject reports, and / or correspondence with data about individuals, any aspect of data relevant to the present disclosure.

[0250] The computer server may communicate with one or more remote computer systems over the network, which may be, for example, a personal computer, a laptop, a tablet, a phone, a smartphone, or a personal digital assistant.

[0251] In some applications, the computer system includes a single server. In other situations, the system includes multiple servers communicating with each other over an intranet, an extranet, and / or the Internet.

[0252] The server may be adapted to store the measurement data or databases provided herein, patient data from the subject (e.g., medical history, etc.), family history, demographic data, and / or other clinical or personal information potentially relevant to a particular application. Such information may be stored in the storage unit or the server, and such data may be transmitted over a network.

[0253] The methods described herein may be performed by machine (or computer processor) executable code (or software) stored in the server's electronic storage area (e.g., in memory or an electronic storage unit). During use, the code may be executed by the processor. In some cases, the code may be retrieved from the storage unit and stored in the memory for ready access by the processor. In some situations, the electronic storage unit may be eliminated and machine-executable instructions may be stored in memory.

[0254] Alternatively, the code may run on a second computer system.

[0255] Aspects of the systems and methods provided herein (e.g., the servers, etc.) may be embodied in programming. Various aspects of this technology may be considered "products" or "articles of manufacture," typically in the form of machine (or processor) executable code and / or associated data carried on or embodied in some type of machine-readable medium. The machine-executable code may be stored in an electronic storage unit (e.g., a memory (e.g., read-only memory, random access memory, flash memory), or a hard disk. A "storage" type medium may include any or all tangible memory (e.g., various semiconductor memories, tape drives, disk drives, etc.) of the computer, processor, etc., or its associated modules, which may provide non-transitory storage of the software programming at any time. All or portions of the software may at times be communicated over the Internet or various other telecommunications networks. Such communication may, for example, enable loading of software from one computer or processor to another, e.g., from a management server to a host computer to an application server computer platform. Thus, other types of media that may carry software elements include light waves, radio waves, or electromagnetic waves (e.g., those used to traverse physical interfaces between local devices, through wired and optical landline networks, and by various wireless connections). The physical elements (e.g., wired or wireless connections) that carry such waves may be transmitted over the Internet or various other telecommunications networks. Other media, such as computer or machine "readable medium," may also be considered media that carry the software. As used herein, unless limited to non-transitory tangible "storage" media, terms such as computer or machine "readable medium" may refer to any medium that participates in providing instructions to a processor for execution.

[0256] The computer systems described herein may include computer-executable code for executing the algorithms or algorithm-based methods described herein. In some applications, the algorithms described herein will utilize a memory unit consisting of at least one database.

[0257] Data related to the present disclosure may be transmitted over a network or connection for receipt and / or review by a recipient. The recipient may be, but is not limited to, the subject to whom the report pertains; or their caregiver, such as a healthcare provider, administrator, other healthcare professional, or other caretaker; or the person or entity that performed and / or directed the analysis. The recipient may also be a local or remote system (e.g., a server or other system in a "cloud computing" architecture) for storing such reports. In one embodiment, the computer-readable medium comprises a medium suitable for transmitting the results of an analysis of a biological sample using the methods described herein.

[0258] Aspects of the systems and methods provided herein may be embodied in programming. Various aspects of this technology may be considered "products" or "articles of manufacture," typically in the form of machine (or processor) executable code and / or associated data carried on or embodied in some type of machine-readable medium. The machine-executable code may be stored in an electronic storage unit (e.g., memory (e.g., read-only memory, random access memory, flash memory) or hard disk). "Storage" type media may include any or all tangible memory (e.g., various semiconductor memories, tape drives, disk drives, etc.) of the computer, processor, etc., or its associated modules, which may provide non-transitory storage of software programming at any time. All or portions of the software may at times be communicated over the Internet or various other telecommunications networks. Such communication may occur, for example, from one computer or processor to another, e.g., for management purposes. The server may enable the software to be loaded from a host computer into the computer platform of the application server. Thus, another type of medium that may carry software elements includes optical waves, radio waves, or electromagnetic waves (e.g., those used across physical interfaces between local devices through wired and optical landline networks and by various wireless connections). The physical elements that carry such waves (e.g., wired or wireless connections, optical connections, etc.) may also be considered media that carry the software. As used herein, unless limited to non-transitory tangible "storage" media, terms such as computer or machine "readable medium" refer to any medium that participates in providing instructions to a processor for execution.

[0259] Thus, machine-readable media (e.g., computer-executable code) may take many forms, including, but not limited to, tangible storage media, carrier wave media, or physical transmission media. Non-volatile storage media include, for example, optical or magnetic disks, such as those that may be used to implement a database, such as any storage device in any computer, as illustrated in the drawings. Volatile storage media include dynamic memory (such as the main memory of such a computer platform). Tangible transmission media include coaxial cables; copper wire and fiber optics, including the wires that comprise a bus within a computer system. Carrier-wave transmission media may take the form of electric or electromagnetic signals, or acoustic or light waves, such as those generated during radio frequency (RF) and infrared (IR) data communications. Common forms of computer-readable media thus include, for example, floppy disks, flexible disks, hard disks, magnetic tape, any other magnetic media, CD-ROMs, DVDs or DVD-ROMs, any other optical media, punch cards, paper tape, any other physical storage media with patterns of holes, RAM, ROM, PROMs and EPROMs, Flash EPROMs, any other memory chip or cartridge, a carrier wave carrying data or instructions, a cable or link which transmits such a carrier wave, or any other medium from which a computer can read code and / or data. Many of these forms of computer-readable media may be involved in carrying one or more sequences of one or more instructions to a processor for execution. [Example]

[0260] The following examples are included to further illustrate some aspects of the disclosure and should not be used to limit the scope of the disclosure. Example 1: Formation of a protein corona with magnetic nanoparticles and biological fluids by complete resuspension

[0261] This exemplary procedure applies to the manual generation of protein coronas in bodily fluid samples using a panel of magnetic nanoparticles with thorough resuspension of the nanoparticles. The systems and methods of the present disclosure can be applied to the procedures described herein.

[0262] material:

[0263] The materials used in the production of the protein corona are listed in Table 2. [Table 2]

[0264] Storage and Handling:

[0265] The following reagents were stored at room temperature as shown in Table 3: [Table 3]

[0266] As shown in Table 4, the following reagents were stored at approximately 2-8°C. [Table 4]

[0267] Preparation:

[0268] The body fluid samples were removed from the freezer and allowed to thaw completely. The nanoparticles were sonicated and vortexed approximately 10 minutes before use. Before starting the assay, TE 150 mM KCl 0.05% CHAPS buffer was prepared.

[0269] Preparation of TE 150mM KCl 0.05% CHAPS buffer: 11.18g Potassium chloride and 500 mg CHAPS were added to a Corning 1 L bottle. 998.3 g of 1x TE pH 7.4 buffer was added. Using house vacuum, the buffer was filtered through a 0.1 μm or 0.2 μm 1000 mL filter set. The buffer can be stored at room temperature (approximately 1 month) or at 2-8°C (more than 1 month). The buffer was thoroughly mixed before use.

[0270] Nanoparticle Preparation: The nanoparticles (aqueous) were diluted in reagent grade water to the appropriate specified concentration. For dry powder nanoparticles, dry powder nanoparticles were weighed on a balance before adding the appropriate volume of water to the required concentration.

[0271] Sample Preparation: The samples were removed from the freezer. The samples were allowed to thaw completely and then centrifuged at 16,000G for approximately 2 minutes. The samples were either diluted (1:5) with TE 150 mM KCl 0.05% CHAPS buffer or left undiluted.

[0272] Figure 9 illustrates a sample preparation method consistent with the present disclosure. This method involves four steps: generating a subset of biomolecules from a biological sample and then using the subset of biomolecules to generate a biomolecular fingerprint. The first step involves transferring a plasma sample into multiple partitions (wells in a well plate) containing multiple sensor elements (e.g., magnetic nanoparticles). The sample is incubated in the partitions with shaking at 37°C for 1 hour, thereby generating a biomolecular corona on the sensor elements. The partitions are then exposed to a strong magnetic field sufficient to immobilize the sensor elements within the partitions. The partitions are then washed three times (e.g., by sequentially adding and removing resuspension buffer) to remove biomolecules that did not adsorb to the sensor elements. After the third wash, the particles are resuspended in buffer, thereby desorbing the biomolecular subset from the biomolecular corona. The biomolecular subset is then subjected to a set of denaturation and chemical treatment steps (including heating to 95°C, reduction and alkylation, protease digestion, and further washing). A subset of the biomolecules is then subjected to mass spectrometry, which generates a biomolecular fingerprint for the sample.

[0273] procedure:

[0274] Reagents and equipment were prepared as described in the previous section (see "Preparation"). 100 μL of diluted nanoparticles was loaded into each well using a multichannel pipette. 100 μL of diluted sample per nanoparticle well was added using a pipette. The wells were agitated approximately 10 times by aspiration using a pipette. The plate was covered with an adhesive plate sealer and incubated at 37°C for approximately 1 hour on a plate shaker set at 300 rpm. After this approximately 1 hour incubation, the adhesive plate sealer was removed, and the plate was placed on a magnet for approximately 5 minutes to allow the formation of a nanoparticle corona pellet at the bottom of the well. For washing, the supernatant was removed using a multichannel pipette. Approximately 200 μL of TE 150 mM KCl 0.05% CHAPS buffer was added using a pipette, and the nanoparticles were thoroughly resuspended. The solution was again placed on the magnet for approximately 5 minutes. The washing step was repeated three times. The nanoparticle pellet was resuspended in the appropriate reagent for BCA, gel, or trypsin digestion. Example 2: Trypsin Gold digestion

[0275] material:

[0276] The materials used in the Trypsin Gold digestion are listed in Table 5. [Table 5-1]

[0277] Preparation:

[0278] 50 mM ABC (ammonium bicarbonate). 0.25 mL of 2 M ABC was added to 9.75 mL of water to give 10 mL of 50 mM ABC. This solution was vortexed and stored at 4°C for up to 1 week.

[0279] 8M Urea. Weigh out 4.8g of urea and add 50mM ABC to close to the 10mL mark. Vortex the solution and rotate in a 37°C incubator as needed to aid dissolution. Add 50mM ABC to the 10mL mark and vortex.

[0280] 200mM DTT. 0.031g of DTT was weighed out and 1mL of 50mM ABC was added. The solution was vortexed and stored at 4°C, away from light.

[0281] 200 mM IAA. 400 μL of 50 mM ABC was added to a pre-weighed 0.015 g of IAA. The solution was vortexed and stored at 4° C. The solution should be reconstituted before use.

[0282] Reconstitution of Trypsin Gold. The solution was prepared according to the manufacturer's instructions. 100 μL of 50 mM acetic acid was added to 100 μg of trypsin and vortexed. The final concentration was 1 μg / μL trypsin.

[0283] Sample / trypsin preparation

[0284] 40 μL of 8M urea was added to each sample. The solution was vortexed and sonicated for approximately 1 minute. 2 μL of 200 mM DTT was added to each sample and vortexed. The solution was incubated in the dark at room temperature for approximately 30 minutes. 8 μL of 200 mM IAA was added to each sample and vortexed. The solution was incubated in the dark at room temperature for approximately 30 minutes. 8 μL of 200 mM DTT was added to each sample and vortexed. The solution was incubated in the dark at room temperature for approximately 30 minutes. 50 mM ABC was added so that the added urea was less than 2M. 110 μL of 50 mM ABC was added to 58 μL of sample. An appropriate amount of trypsin was added to the sample. 3 μL of reconstituted trypsin was added to each tube. Protein:trypsin ratio = approximately 30 μg protein: 1 μg trypsin. The solution was incubated overnight at 37° C. The digestion was stopped by adding 17 μL of 10% FA. Example 3: Proteomic analysis of NSCLC samples and healthy controls

[0285] This example demonstrates the proteomic analysis of NSCLC samples and healthy controls. To demonstrate the utility of the corona analysis platform, we evaluated the platform's capabilities using a single particle species (poly(N-(3-(dimethylamino)propyl)methacrylamide) (PDMAPMA)-coated SPIONs) and serum samples from 56 subjects (28 stage IV NSCLC and 28 age- and sex-matched controls) to observe differences between these groups. The selected subject samples provided a reasonably balanced study to identify potential MS features that differ between these groups. All data for subject annotation, including disease status and comorbidities, are summarized in Table 5. [Table 5-2]

[0286] After collecting and filtering MS1 features and subsequently log2-transforming their intensities, the datasets were median-scaled independently of classification. Figure 11 shows the normalized intensity distributions for all 56 subject datasets. All 56 sample MS raw data files from the NSCLC versus control study were processed by an OpenMS pipeline script to extract MS1 features and their intensities and cluster them into feature groups based on overlapping mz and RT values ​​within a specified tolerance. Only feature groups with 1) at least 50% of the features present in the group from at least one arm of the comparison and 2) feature group cluster quality above the 25th percentile were retained. The retained features were median-normalized independently of classification and used for subsequent univariate comparisons. Inspection of the distribution revealed no outliers, and all datasets were retained for univariate analysis.

[0287] Upon inspection, no outliers appeared to be present. Univariate comparisons of trait group strengths between classifications were performed with nonparametric Wilcoxon tests (two-tailed). The p-values ​​obtained for this comparison were corrected for multiple testing using the Benjamini-Hochberg method. Using a corrected p-value cutoff of 0.05, a total of seven trait groups demonstrated statistical significance, as summarized in Figure 12.

[0288] All five proteins identified as differentially abundant between NSCLC disease and control groups have previously been implicated in cancer, if not NSCLC itself. PON1 (also known as paraoxonase-1) has a complex pattern in lung cancer, including the involvement of a relatively common minor allelic variant (Q192R) as a risk factor. At the protein level, PON1 is moderately reduced in lung adenocarcinoma. SAA1 is an acute-phase protein shown to be overexpressed in NSCLC in MS-related studies, and the identified peptide was found to be 5.4-fold increased in diseased subjects. The matrisome factor tenascin-C (TENA) has been shown to be elevated in primary lung tumors and associated lymph node metastases compared with normal tissue, and in this study, associated MS features were found to be two-fold increased. Neural cell adhesion molecule 1 (NCAM1) serves as a marker for diagnosing pulmonary neuroendocrine tumors. FIBA peptides were identified by MS analysis at elevated levels correlated with progressive lung cancer progression. Of particular note were two unknown features (groups 2 and 7) that showed differences between control and disease groups. Group 2 was found in 54 of 56 subjects and was moderately (33%) reduced in disease subjects. In contrast, group 7 was found only in disease subjects (14 of 28 members of this class). These results demonstrate the potential utility of particle coronas to aid in the identification of known and unknown markers for different disease states. Example 4: Dynamic range compression of plasma using protein corona analysis

[0289] This example describes dynamic range compression using particles that collect proteins from plasma samples.

[0290] To evaluate the ability of particles to compress the measured dynamic range, the feature intensities of measured and identified proteins were compared to published values ​​for the same protein concentrations. First, for each protein, the resulting peptide feature was selected by the maximum MS-determined intensity of all possible features for the protein (using the OpenMS mass data processing tool to extract single-isotope peaks). The intensity was then modeled against published values ​​for the abundance level of the same protein. Figure 13 shows the correlation between the maximum intensity of particle corona proteins and plasma proteins and the published concentrations of the same proteins. The blue line is the linear regression model for the data, and the shaded area indicates the standard error of the model fit. The dynamic range of samples assayed with particles ("S-003," "S-007," and "S-011," listed in Table 1) exhibited a compressed dynamic range, as indicated by the reduced slope of the linear fit, compared to a plasma sample not assayed with particles ("Plasma"). The slopes of each plot are 0.47, 0.19, 0.22, and 0.18 for plasma without particles, plasma with S-003 particles, plasma with S-007 particles, and plasma with S-011 particles, respectively. Figure 14 shows the dynamic range compression of the mass spectrometry protein corona analysis assay compared to mass spectrometry without particle corona formation. The protein intensities of common proteins identified in the particle corona in the plasma samples assayed in Figure 13 ("Nanoparticle MS(Intensity)") are plotted against the protein intensities identified by mass spectrometry of plasma without particles ("Plasma MS(Intensity)"). The maximum dotted line indicates a slope of 1, which represents the dynamic range of mass spectrometry without particles. The slopes of the linear fits to the protein intensities are 0.12, 0.36, and 0.093 for S-003, S-007, and S-011 particles, respectively. The gray areas indicate the standard error regions of the regression fits.

[0291] Comparison of the slope of the regression model with the intensity range of the measured data revealed that the biomolecular corona contains more proteins at lower abundances (measured or reported) than plasma. The dynamic range of these measurements was compressed (the slope of the regression model was reduced) for particle measurements compared to plasma measurements. This is consistent with previous observations that particles can effectively compress the measured dynamic range of abundance in the resulting corona compared to the original dynamic range in plasma, which may be due to a combination of the protein's absolute concentration, binding affinity for particles, and interactions with neighboring proteins. This result indicates that the biomolecular corona strategy facilitates the identification of a broad spectrum of plasma proteins, especially at low abundances that are difficult to rapidly detect using traditional proteomics.

[0292] While preferred embodiments of the present invention have been shown and described herein, it will be apparent to those skilled in the art that these embodiments are provided by way of example only. Numerous variations, changes, and substitutions will be readily apparent to those skilled in the art without departing from the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in practicing the invention. It is intended that the following claims define the scope of the invention, and that methods and structures within the scope of these claims and their equivalents be covered thereby. The present invention provides, for example, the following items. (Item 1) 1. An automated instrument for generating a subset of biomolecules from a complex biological sample, comprising: (a) a substrate comprising a plurality of partitions, the plurality of partitions comprising a plurality of particles; (b) a sample storage unit containing the composite biological sample; and (c) a loading unit movable across at least the substrate; Including, the loading unit transfers one or more volumes of the complex biological sample in the sample storage unit to the plurality of partitions on the substrate, thereby contacting the plurality of particles in the plurality of partitions with biomolecules of the complex biological sample to form a biomolecular corona, thereby generating a subset of the biomolecules of the complex biological sample; The dynamic range of the subset of biomolecules is compressed compared to the dynamic range of biomolecules present in the complex biological sample. (Item 2) 2. The automated instrument of item 1, further comprising an incubation element that agitates or heats the plurality of particle volumes within the complex biological sample volume within the plurality of partitions. (Item 3) 3. The automated instrument of item 2, wherein the incubation element is configured to shake, mix, agitate, spin, vibrate, be static, or any combination thereof. (Item 4) 4. The automated instrument of item 2 or 3, wherein the incubation element is configured to heat and / or incubate the substrate to a temperature of about 20°C to about 100°C. (Item 5) 5. The automated instrument of any one of items 1 to 4, wherein the plurality of partitions are at least partially covered or sealed. (Item 6) 6. The automated equipment of item 5, wherein the automated equipment has the ability to add or remove a lid on the substrate, wherein the lid covers at least one partition in the plurality of partitions. (Item 7) 7. The automated instrument of any one of items 1 to 6, further comprising a unit containing a resuspension solution. (Item 8) 8. The automated instrument of item 7, wherein the resuspension solution comprises Tris EDTA 150 mM KCl 0.05% CHAPS buffer. (Item 9) 8. The automated instrument of item 7, wherein the resuspension solution comprises 10 mM Tris-HCl pH 7.4, 1 mM EDTA. (Item 10) 10. The automated instrument of any one of items 1 to 9, further comprising a unit containing a denaturing solution. (Item 11) Item 11. The automated instrument of item 10, wherein the denaturing solution comprises a protease. (Item 12) 12. The automated instrument of item 10 or 11, wherein the denaturing solution comprises a reducing agent, a methylating agent, guanidine, urea, sodium deoxycholate, acetonitrile, or any combination thereof. (Item 13) 13. The automated instrument of any one of items 10 to 12, wherein the denaturing solution produces peptide fragments with an average mass of less than 4600 daltons. (Item 14) 14. The automated instrument of any one of items 1 to 13, wherein the substrate is a multiwell plate. (Item 15) 15. The automated instrument of any one of items 1 to 14, wherein the loading unit comprises a plurality of pipettes. (Item 16) 16. The automated instrument of any one of items 1 to 15, wherein the loading unit is configured to dispense between 10 μL and 400 μL of solution into one or more of the plurality of partitions. (Item 17) Item 17. The automated instrument of item 16, wherein the loading unit is configured to dispense between 5 μL and 150 μL of solution into one or more of the plurality of partitions. (Item 18) Item 17. The automated instrument of item 16, wherein the loading unit is configured to dispense 35 μL to 80 μL of solution into one or more of the plurality of partitions. (Item 19) 19. The automated instrument of any one of items 16 to 18, wherein the solution is selected from the group consisting of a wash solution, the resuspension solution, the denaturing solution, a buffer, and a reagent. (Item 20) 20. The automated instrument of any one of items 1 to 19, wherein the loading unit is configured to dispense between 10 μL and 400 μL of the complex biological sample into one or more of the plurality of partitions. (Item 21) 21. The automated instrument of item 20, wherein the loading unit is configured to dispense between 5 μL and 150 μL of the complex biological sample into one or more of the plurality of partitions. (Item 22) 21. The automated instrument of item 20, wherein the loading unit is configured to dispense 35 μL to 80 μL of the complex biological sample into one or more of the plurality of partitions. (Item 23) 23. The automated instrument of any one of items 1 to 22, wherein the complex biological sample comprises a bodily fluid from a subject. (Item 24) 24. The automated instrument of item 23, wherein the complex biological sample comprises plasma, serum, urine, cerebrospinal fluid, synovial fluid, tears, saliva, whole blood, milk, nipple aspirate, ductal lavage, vaginal fluid, nasal secretion, inner ear fluid, gastric juice, pancreatic juice, trabecular fluid, pulmonary lavage, sweat, gingival crevicular fluid, semen, prostatic fluid, sputum, feces, bronchial lavage, fluid from a swab, bronchial aspirate, mobilized solids, fine needle aspiration sample, tissue homogenate, lymph, cell culture sample, or any combination thereof. (Item 25) 19. The automated instrument of any one of items 1 to 18, further comprising a magnet. (Item 26) 26. The automated instrument of item 25, wherein one or more particles of the plurality of particles are magnetic particles, and wherein the substrate and the magnet are in close proximity such that the one or more magnetic particles are immobilized on the substrate. (Item 27) 27. The automated instrument of any one of items 1 to 26, further comprising a housing, wherein the substrate and the loading unit are disposed within the housing, and wherein the housing is at least partially enclosed. (Item 28) 28. The automated instrument of any one of items 1 to 27, wherein the compressed dynamic range comprises an increased number of biomolecule species whose concentrations are within six orders of magnitude relative to the most abundant biomolecule in the sample. (Item 29) 29. The automated instrument of item 28, wherein the compressed dynamic range comprises an increased number of species of biomolecules whose concentrations are within five orders of magnitude relative to the most abundant biomolecule in the sample. (Item 30) 30. The automated instrument of item 29, wherein the compressed dynamic range comprises an increased number of biomolecule species whose concentrations are within four orders of magnitude relative to the most abundant biomolecule in the sample. (Item 31) 31. The automated instrument of item 30, wherein the compressed dynamic range comprises an increased number of protein species whose concentrations are within six orders of magnitude relative to the most abundant protein in the sample. (Item 32) 32. The automated instrument of item 31, wherein the increase in the number of biomolecule species whose concentrations are within six orders of magnitude relative to the most enriched biomolecule in the sample is at least 25%, 50%, 100%, 200%, 300%, 500%, or 1000%. (Item 33) 33. The automated instrument of any one of items 1 to 32, wherein the compressed dynamic range comprises an increased number of protein species whose concentrations are within six orders of magnitude relative to the most abundant protein in the sample. (Item 34) 34. The automated instrument of item 33, wherein the increase in the number of protein species whose concentrations are within six orders of magnitude of the most abundant protein in the sample is at least 25%, 50%, 100%, 200%, 300%, 500%, or 1000%. (Item 35) 35. The automated instrument of any one of items 1 to 34, wherein the subset of biomolecules comprises at least 20% to at least 60% of the biomolecule species from the complex biological sample within a six-digit concentration range. (Item 36) 36. The automated instrument of item 35, wherein the subset of biomolecules comprises at least 20% to at least 60% of the protein species from the complex biological sample within a six-order concentration range. (Item 37) 37. The automated instrument of any one of items 1 to 36, wherein the dynamic range of the biomolecules of the biomolecular corona is a first ratio of top decile biomolecules to bottom decile biomolecules in the plurality of biomolecular coronas. (Item 38) 37. The automated instrument of any one of items 1 to 36, wherein the dynamic range of the biomolecules in the biomolecular corona is a first ratio that includes an interquartile range of biomolecules in the plurality of biomolecular coronas. (Item 39) 39. The automated instrument of any one of items 1 to 38, wherein said generating enriches low abundance biomolecules from said complex biological sample. (Item 40) 34. The automated instrument of item 33, wherein the low abundance biomolecule is a biomolecule at a concentration of 10 ng / mL or less in the complex biological sample. (Item 41) 35. The automated instrument of any one of items 1 to 34, wherein the subset of biomolecules from the complex biological sample comprises proteins. (Item 42) 42. The automated instrument of claim 41, wherein a change of up to 10 mg / mL in lipid concentration of the complex biological sample results in less than a 10%, 5%, 2%, or 1% change in the composition of the proteins in the subset of biomolecules generated from the complex biological sample. (Item 43) 43. The automated instrument of any one of items 1 to 42, wherein at least two particles in the plurality of particles differ in at least one physicochemical property. (Item 44) Item 44. The automated instrument of item 43, wherein the at least one physicochemical property is selected from the group consisting of composition, size, surface charge, hydrophobicity, hydrophilicity, surface functionality, surface topography, surface curvature, porosity, core material, shell material, shape, and any combination thereof. (Item 45) Item 45. The automated instrument of item 44, wherein the surface functionality comprises aminopropyl functionalization, amine functionalization, boronic acid functionalization, carboxylic acid functionalization, methyl functionalization, N-succinimidyl ester functionalization, PEG functionalization, streptavidin functionalization, methyl ether functionalization, triethoxylpropylaminosilane functionalization, thiol functionalization, PCP functionalization, citrate functionalization, lipoic acid functionalization, BPEI functionalization. (Item 46) The particles among the plurality of particles may be selected from the group consisting of micelles, liposomes, iron oxide particles, silver particles, gold particles, palladium particles, quantum dots, platinum particles, titanium particles, silica particles, metal or inorganic oxide particles, synthetic polymer particles, copolymer particles, terpolymer particles, polymer particles having a metal core, polymer particles having a metal oxide core, polystyrene sulfonate particles, polyethylene oxide particles, polyoxyethylene glycol particles, polyethyleneimine particles, polylactic acid particles, polycaprolactone particles, polyglycolic acid particles, poly(lactide-co-glycolide) polymer particles, cellulose ether polymer particles, polyvinylpyrrolidone particles, polyvinyl acetate particles, polyvinylpyrrolidone-vinyl acetate copolymer particles, polyvinyl alcohol particles, acrylate particles, polyacrylic acid particles, crotonic acid copolymer particles, polyethylene phosphonate (polyethylene phosphonate particles, polyalkylene particles, carboxyvinyl polymer particles, sodium alginate particles, carrageenan particles, xanthan gum particles, gum arabic particles, gum arabic particles, guar gum particles, pullulan particles, agar particles, chitin particles, chitosan particles, pectin particles, karaya gumtumour particles, locust bean gum particles, maltodextrin particles, amylose particles, corn starch particles, potato starch particles, rice starch particles, tapioca starch particles, pea starch particles, sweet potato starch particles, barley starch particles, wheat starch particles, hydroxypropylated high amylose starch particles, dextrin particles, levan particles, elsinan particles, gluten particles, collagen particles, whey protein isolate particles, casein particles, milk protein particles, soy protein particles, keratin particles, polyethylene particles, polycarbonate particles, polyanhydride particles, polyhydroxy acid particles, polypropyl fumarate particles, polycaprolactone particles, polyamine particles, polyacetal particles, polyether particles, polyester 46. ​​The automated instrument of any one of items 1 to 45, wherein the particle is selected from the group consisting of ether particles, poly(orthoester) particles, polycyanoacrylate particles, polyurethane particles, polyphosphazene particles, polyacrylate particles, polymethacrylate particles, polycyanoacrylate particles, polyurea particles, polyamine particles, polystyrene particles, poly(lysine) particles, chitosan particles, dextran particles, poly(acrylamide) particles, derivatized poly(acrylamide) particles, gelatin particles, starch particles, chitosan particles, dextran particles, gelatin particles, starch particles, poly-β-amino-ester particles, poly(amidoamine) particles, polylactic-glycolic acid particles, polyanhydride particles, bioreducible polymer particles, and 2-(3-aminopropylamino)ethanol particles, and any combination thereof. (Item 47) 47. The automated instrument of any one of items 1-46, wherein one or more particles of the plurality of particles adsorb at least 100 proteins upon contact with the complex biological sample. (Item 48) 48. The automated instrument of any one of items 1-47, wherein the plurality of particles comprises at least two unique particle species, at least three unique particle species, at least four unique particle species, at least five unique particle species, at least six unique particle species, at least seven unique particle species, at least eight unique particle species, at least nine unique particle species, at least ten unique particle species, at least eleven unique particle species, at least twelve unique particle species, at least thirteen unique particle species, at least fourteen unique particle species, at least fifteen unique particle species, at least twenty unique particle species, at least twenty-five particle species, or at least thirty unique particle species. (Item 49) 49. The automated instrument of any one of items 1 to 48, wherein the biomolecules of the biomolecular corona comprise a plurality of protein groups. (Item 50) 50. The automated instrument of item 49, wherein the large number of protein groups comprises 1 to 20,000 protein groups. (Item 51) 51. The automated instrument of item 50, wherein the large number of protein groups comprises 100 to 10,000 protein groups. (Item 52) Item 52. The automated instrument of item 51, wherein the large number of protein groups comprises 100 to 5,000 protein groups. (Item 53) Item 53. The automated instrument of item 52, wherein the large number of protein groups comprises 300 to 2,200 protein groups. (Item 54) Item 54. The automated instrument of item 53, wherein the large number of protein groups comprises 1,200 to 2,200 protein groups. (Item 55) 55. The automated instrument of any one of items 1 to 54, wherein at least two partitions of the plurality of partitions contain different buffers. (Item 56) 56. The automated instrument of item 55, wherein the different buffers differ in pH, salinity, osmolality, viscosity, dielectric constant, or any combination thereof. (Item 57) Item 58. The automated instrument of any one of items 1 to 56, wherein at least two partitions of the plurality of partitions contain different ratios of buffer to the complex biological sample. 58. The automated instrument of any one of items 1 to 57, wherein one or more partitions of the plurality of partitions contain between 1 pM and 100 nM of nanoparticles. (Item 59) 59. The automated instrument of any one of items 1 to 58, wherein at least two partitions of the plurality of partitions contain different concentrations of nanoparticles. (Item 60) 59. The automated apparatus of any one of items 1 to 59, further comprising a purification unit. (Item 61) 61. The automated instrument of item 60, wherein the purification unit comprises a solid phase extraction (SPE) plate. (Item 62) 62. The automated instrument of any one of items 1 to 61, wherein the automated instrument generates the subset of biomolecules from a complex biological sample in less than 7 hours. (Item 63) (a) the automated instrument of any one of items 1 to 56 configured to isolate the subset of biomolecules from the biological sample; (b) a mass spectrometer configured to receive the subset of biomolecules and generate data comprising a mass spectrometry signal or a tandem mass spectrometry signal; and (c) a computer including one or more computer processors, and when executed by said one or more computer processors, i. generating a biomolecular fingerprint; and ii. assigning a biological state based on said biomolecular fingerprint; a computer-readable medium containing machine-executable code for performing a method comprising: An automation system, including: (Item 64) Item 64. The automated system of item 63, wherein the biomolecular fingerprint comprises a plurality of unique biomolecular corona signatures. (Item 65) 65. The automated system of item 64, wherein the biomolecular fingerprint comprises at least 5, 10, 20, 40, or 80, 150, or 200 unique biomolecular corona signatures. (Item 66) 66. The automated system of any one of items 63 to 65, wherein the computer is configured to process data comprising mass spectrometry signal or tandem mass spectrometry signal intensity, APEX, peptide spectral count or number, or ion mobility behavior among a plurality of the unique biomolecular corona signatures. (Item 67) Item 67. The automated system of item 66, wherein the computer is configured to process data from 100 to 2000 mass spectrometry signals or tandem mass spectrometry signals among a plurality of the unique biomolecular corona signatures. (Item 68) Item 68. The automated system of item 67, wherein the computer is configured to process data comprising intensities of 10,000 to 5,000,000 mass spectrometry signals or tandem mass spectrometry signals among a plurality of the unique biomolecular corona signatures. (Item 69) 69. The automated system of any one of items 63 to 68, wherein the biomolecular fingerprint is generated from data from a single mass spectrometry or tandem mass spectrometry run. (Item 70) 70. The automated system of item 69, wherein performing the single mass analysis or tandem mass analysis takes less than one hour. (Item 71) 71. The automated system of any one of items 63 to 70, wherein the computer is configured to identify biomolecules or characterize unidentified molecular properties based on mass spectrometry signals or tandem mass spectrometry signals and or ion mobility and chromatographic behavior, and wherein the computer provides a confidence threshold of at least 95% for identifying properties or characterizing unidentified properties. (Item 72) 72. The automated system of any one of items 63 to 71, wherein the automated system is configured to generate the biomolecular fingerprint from the complex biological sample in less than about 10 hours. (Item 73) 73. The automated system of any one of items 63-72, wherein the computer is capable of distinguishing between two or more biological states associated with biomolecular fingerprints that differ by less than 10%, 5%, 2%, or 1%. (Item 74) 74. The automated system of any one of items 63 to 73, wherein the biological condition is a disease, a disorder, or a tissue abnormality. (Item 75) 76. The automated system of item 75, wherein the disease is an early-phase or intermediate-phase disease state. (Item 76) 76. The automated system of item 74 or 75, wherein the disease is cancer. (Item 77) Item 78. The automated system of Item 76, wherein the cancer is stage 0 cancer or stage 1 cancer. The cancer may be lung cancer, pancreatic cancer, myeloma, myeloid leukemia, meningioma, glioblastoma, breast cancer, esophageal squamous cell carcinoma, gastric adenocarcinoma, prostate cancer, bladder cancer, ovarian cancer, thyroid cancer, neuroendocrine cancer, colon cancer, ovarian cancer, head and neck cancer, Hodgkin's disease, non-Hodgkin's lymphoma, rectum cancer, urinary tract cancer, uterine cancer, oral cancer, or the like. cancer), skin cancer, stomach cancer, brain tumor, liver cancer, laryngeal cancer, esophageal cancer, breast tumor, fibrosarcoma, myxosarcoma, liposarcoma, chondrosarcoma, osteosarcoma, chordoma, angiosarcoma, endothelial sarcoma, Ewing's sarcoma, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinoma, cystadenocarcinoma, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatocellular carcinoma, bile duct carcinoma, choriocarcinoma, seminoma, embryonal carcinoma, Wilms' tumor, cervical cancer, testicular tumor, endometrial cancer, lung cancer carcinoma), small cell lung cancer, bladder cancer, epithelial carcinoma, glioblastoma, neuronomas, craniopharingioma, schwannoma, glioma, astrocytoma, meningioma, melanoma, neuroblastoma, retinoblastoma, leukemia and lymphoma, acute lymphocytic leukemia and acute myelocytic polycythemia vera, multiple myeloma, Waldenstrom's macroglobulinemia and heavy chain disease, acute nonlymphocytic leukemia, 78. The automated system of item 76 or 77, wherein the cancer is selected from the group consisting of chronic lymphocytic leukemia, chronic myeloid leukemia, childhood null cell acute lymphocytic leukemia (ALL), thymic ALL (acute lymphocytic leukemia), B-cell ALL (acute lymphocytic leukemia), acute megakaryocytic leukemia, Burkitt's lymphoma, and T-cell leukemia, small cell and large cell (non-small cell) lung cancer, acute granulocytic leukemia, germ cell tumor, endometrial cancer, gastric cancer, hairy cell leukemia, or thyroid cancer. (Item 79) 79. The automated system of any one of items 74 to 78, wherein the biological state is a pre-disease state. (Item 80) 80. The automated system of any one of items 63-79, wherein said determining comprises comparing the abundance of two biomolecules whose concentrations in said complex biological sample span at least 7 to at least 12 orders of magnitude. (Item 81) 1. A method for distinguishing a biological state of a complex biological sample, comprising: (a) providing a complex biological sample to the automated device of any one of items 1 to 56 to generate a subset of biomolecules; (b) assaying a subset of said biomolecules to generate a biomolecular fingerprint; and (c) identifying the biological state of the complex biological sample by the biomolecular fingerprint; A method comprising: (Item 82) 82. The method of claim 81, wherein the biomolecular fingerprint comprises a protein. (Item 83) 83. The method of item 81 or 82, wherein the subset of biomolecules from the complex biological sample has a smaller ratio of albumin to non-albumin peptides than the complex biological sample. (Item 84) 85. The method of any one of claims 81 to 83, wherein the subset of biomolecules includes biomolecules whose concentrations in the complex biological sample span a range of at least 6 to at least 12 orders of magnitude. 85. The method of any one of items 81 to 84, wherein the subset of biomolecules comprises proteins whose concentrations in the complex biological sample span a range of at least 6 to at least 12 orders of magnitude. (Item 86) 86. The method of any one of items 81 to 85, wherein the biomolecular fingerprint comprises 1 to 74,000 protein groups. (Item 87) 87. The method of any one of items 81-86, wherein the assay comprises desorbing a plurality of biomolecules from a biomolecular corona in the plurality of biomolecular coronas. (Item 88) 88. The method of claim 87, wherein the assay comprises chemically modifying a biomolecule in the plurality of adsorbed biomolecules. (Item 89) 88. The method of claim 87, wherein the assay comprises fragmenting biomolecules within the plurality of adsorbed biomolecules. (Item 90) 90. The method of item 89, wherein the fragmentation comprises protease digestion. (Item 91) 90. The method of claim 89, wherein the fragmentation comprises chemical peptide cleavage. (Item 92) 92. The method of any one of items 87-91, wherein the assay comprises collecting the plurality of adsorbed biomolecules. (Item 93) 93. The method of claim 92, wherein the assay comprises purifying the collected plurality of adsorbed biomolecules. (Item 94) Item 94. The method of item 93, wherein the purification comprises solid phase extraction. (Item 95) 95. The method of claim 93 or 94, wherein the purification depletes non-protein biomolecules from the collected plurality of adsorbed biomolecules. (Item 96) Item 88. The method of item 87, wherein the assay includes discarding the plurality of adsorbed biomolecules. (Item 97) 97. The method of any one of items 87-96, wherein the assay comprises desorbing a first subset of biomolecules and a second set of biomolecules from a biomolecular corona in the plurality of biomolecular coronas; analyzing the biomolecules in the first subset of biomolecules; and analyzing the biomolecules in the second subset of biomolecules. (Item 98) 98. The method of any one of paragraphs 81-97, wherein the assay comprises analyzing a biomolecular corona among the plurality of biomolecular coronas by mass spectrometry, tandem mass spectrometry, mass cytometry, potentiometry, fluorimetry, absorption spectroscopy, Raman spectroscopy, chromatography, electrophoresis, immunohistochemistry, PCR, next generation sequencing (NGS), or any combination thereof. (Item 99) 99. The method of claim 98, wherein the assay comprises mass spectrometry or tandem mass spectrometry. (Item 100) 99. The method of any one of items 81 to 99, wherein the assay comprises identifying a three-dimensional structure of a protein in the subset of biomolecules. (Item 101) 101. The method of any one of items 81 to 100, wherein the assay comprises identifying a post-translational modification on a protein in the subset of biomolecules. (Item 102) 102. The method of any one of items 81-101, wherein said distinguishing comprises comparing the relative abundance of at least 200 to at least 1000 biomolecules from said subsets of biomolecules. (Item 103) 103. The method of any one of items 81 to 102, wherein the assay identifies biomolecules at concentrations of less than 10 ng / mL in the complex biological sample. (Item 104) 1. An automated instrument for generating a subset of biomolecules from a complex biological sample, comprising: multiple particles; and said complex biological sample, generating the subset of biomolecules by contacting the plurality of particles with the complex biological sample to form a plurality of biomolecular coronas comprising the subset of biomolecules; The dynamic range of the subset of biomolecules is compressed compared to the dynamic range of biomolecules present in the complex biological sample. (Item 105) 105. The automated instrument of item 104, further comprising a substrate, wherein said substrate is a multiwell plate. (Item 106) The automated instrument of item 104 or 105, further comprising an incubation element. (Item 107) Item 107. The automated instrument of item 106, wherein the incubation element is configured to heat and / or incubate the plurality of particles and the complex biological sample to a temperature between 4°C and 40°C. (Item 108) The automated instrument of any one of Items 104 to 107, further comprising at least one solution selected from the group consisting of a washing solution, a resuspension solution, a denaturing solution, a buffer, and a reagent. (Item 109) Item 109. The automated instrument of item 108, wherein the resuspension solution comprises Tris-EDTA buffer, phosphate buffer, and / or water. (Item 110) Item 109. The automated instrument of item 108, wherein the denaturing solution comprises a protease. (Item 111) 109. The automated instrument of item 108, wherein the denaturing solution comprises a small molecule capable of effecting peptide cleavage. (Item 112) 112. The automated instrument of any one of items 104 to 111, further comprising a loading unit, wherein said loading unit comprises a plurality of pipettes. (Item 113) Item 113. The automated instrument of item 112, wherein each pipette of the plurality of pipettes is configured to dispense between about 5 μL and 150 μL of the solution, the complex biological sample, and / or the plurality of particles. (Item 114) 114. The automated instrument of any one of items 104 to 113, wherein the complex biological sample comprises plasma, serum, urine, cerebrospinal fluid, synovial fluid, tears, saliva, whole blood, milk, nipple aspirate, ductal lavage, vaginal fluid, nasal secretion, inner ear fluid, gastric juice, pancreatic juice, trabecular fluid, pulmonary lavage, sweat, gingival crevicular fluid, semen, prostatic fluid, sputum, feces, bronchial lavage, fluid from a swab, bronchial aspirate, mobilized solids, fine needle aspiration sample, tissue homogenate, lymph, cell culture sample, or any combination thereof. (Item 115) 115. The automated instrument of any one of items 104 to 114, further comprising a magnet. (Item 116) 116. The automated instrument of any one of items 104 to 115, further comprising a filter. (Item 117) 117. The automated instrument of any one of items 104 to 116, wherein the compressed dynamic range comprises an increased number of biomolecule species whose concentrations are within 4 to 6 orders of magnitude relative to the most abundant biomolecule in the sample. (Item 118) Item 118. The automated instrument of item 117, wherein the type of biomolecule comprises a protein. (Item 119) 119. The automated instrument of any one of items 104 to 118, wherein the dynamic range of the biomolecules in the biomolecular corona is a first ratio of top decile biomolecules to bottom decile biomolecules in the plurality of biomolecular coronas. (Item 120) 120. The automated instrument of any one of items 104 to 119, wherein said generating enriches low abundance biomolecules from said complex biological sample. (Item 121) Item 121. The automated instrument of item 120, wherein the low abundance biomolecule is a biomolecule at a concentration of 10 ng / mL or less in the complex biological sample. (Item 122) 122. The automated instrument of any one of items 104 to 121, wherein at least two particles in the plurality of particles differ in at least one physicochemical property. (Item 123) Item 115. The automated instrument of item 114, wherein the at least one physicochemical property is selected from the group consisting of composition, size, surface charge, hydrophobicity, hydrophilicity, surface functionality, surface topography, surface curvature, porosity, core material, shell material, shape, and any combination thereof. (Item 124) The particles among the plurality of particles may be selected from the group consisting of micelles, liposomes, iron oxide particles, silver particles, gold particles, palladium particles, quantum dots, platinum particles, titanium particles, silica particles, metal or inorganic oxide particles, synthetic polymer particles, copolymer particles, terpolymer particles, polymer particles having a metal core, polymer particles having a metal oxide core, polystyrene sulfonate particles, polyethylene oxide particles, polyoxyethylene glycol particles, polyethyleneimine particles, polylactic acid particles, polycaprolactone particles, polyglycolic acid particles, poly(lactide-co-glycolide) polymer particles, cellulose ether polymer particles, polyvinylpyrrolidone particles, polyvinyl acetate particles, polyvinylpyrrolidone-vinyl acetate copolymer particles, polyvinyl alcohol particles, acrylate particles, polyacrylic acid particles, crotonic acid copolymer particles, polyethylene phosphonate (polyethylene phosphonate particles, polyalkylene particles, carboxyvinyl polymer particles, sodium alginate particles, carrageenan particles, xanthan gum particles, gum arabic particles, gum arabic particles, guar gum particles, pullulan particles, agar particles, chitin particles, chitosan particles, pectin particles, karaya gumtum particles, locust bean gum particles, maltodextrin particles, amylose particles, corn starch particles, potato starch particles, rice starch particles, tapioca starch particles, pea starch particles, sweet potato starch particles, barley starch particles, wheat starch particles, hydroxypropylated high amylose starch particles, dextrin particles, levan particles, elsinan particles, gluten particles, collagen particles, whey protein isolate particles, casein particles, milk protein particles, soy protein particles, keratin particles, polyethylene particles, polycarbonate particles, polyanhydride particles, polyhydroxy acid particles, polypropyl fumarate particles, polycaprolactone particles, polyamine particles, polyacetal particles, polyether particles, polyester 124. The automated instrument of any one of items 104 to 123, wherein the polymer particles are selected from the group consisting of: particles, poly(orthoester) particles, polycyanoacrylate particles, polyurethane particles, polyphosphazene particles, polyacrylate particles, polymethacrylate particles, polycyanoacrylate particles, polyurea particles, polyamine particles, polystyrene particles, poly(lysine) particles, chitosan particles, dextran particles, poly(acrylamide) particles, derivatized poly(acrylamide) particles, gelatin particles, starch particles, chitosan particles, dextran particles, gelatin particles, starch particles, poly-β-amino-ester particles, poly(amidoamine) particles, polylactic-glycolic acid particles, polyanhydride particles, bioreducible polymer particles, and 2-(3-aminopropylamino)ethanol particles, and any combination thereof. (Item 125) 125. The automated instrument of any one of items 104 to 124, wherein the biomolecules of the biomolecular corona comprise a number of protein groups. (Item 126) Item 126. The automated instrument of item 125, wherein the large number of protein groups comprises 1 to 20,000 protein groups. (Item 127) Item 126. The automated instrument of item 125, wherein the large number of protein groups comprises 100 to 10,000 protein groups. (Item 128) Item 126. The automated instrument of item 125, wherein the large number of protein groups comprises 100 to 5000 protein groups. (Item 129) Item 126. The automated instrument of item 125, wherein the large number of protein groups comprises 300 to 2,200 protein groups. (Item 130) Item 126. The automated instrument of item 125, wherein the large number of protein groups comprises 1,200 to 2,200 protein groups. (Item 131) 131. The automated apparatus of any one of items 104 to 130, further comprising a purification unit. (Item 132) Item 133. The automated instrument of Item 131, wherein the purification unit includes a solid phase extraction (SPE) plate. 133. The automated instrument of any one of items 104 to 132, wherein the automated instrument generates the subset of biomolecules from a complex biological sample in less than 7 hours. (Item 134) 1. A method for generating a subset of biomolecules from a complex biological sample, comprising: providing the complex biological sample to an automated instrument; the automated instrument contacting the complex biological sample with a plurality of particles to generate a biomolecular corona; the automated instrument processes the biomolecular corona to generate a subset of the biomolecules; The method, wherein the dynamic range of the subset of biomolecules is compressed compared to the dynamic range of biomolecules present in the complex biological sample. (Item 135) Item 135. The method of item 134, wherein the automated device is the automated device of any one of items 104 to 133. (Item 136) 136. The method of item 134 or 135, further comprising assaying the subset of biomolecules to generate a biomolecular fingerprint. (Item 137) 137. The method of claim 136, wherein the assay identifies biomolecules at concentrations of less than 10 ng / mL in the complex biological sample. (Item 138) 138. The method of any one of paragraphs 136 or 137, wherein the assay comprises analyzing the biomolecular corona by mass spectrometry, tandem mass spectrometry, mass cytometry, potentiometry, fluorimetry, absorption spectroscopy, Raman spectroscopy, chromatography, electrophoresis, immunohistochemistry, or a combination thereof. (Item 139) 139. The method of claim 138, wherein the assay comprises mass spectrometry or tandem mass spectrometry. (Item 140) 140. The method of any one of items 134 to 139, further comprising identifying a biological state of the complex biological sample by the biomolecular fingerprint. (Item 141) Item 141. The method of item 140, wherein the biomolecular fingerprint comprises a plurality of unique biomolecular corona signatures. (Item 142) 142. The method of item 140 or 141, wherein the biomolecular fingerprint comprises at least 5, 10, 20, 40, or 80, 150, or 200 unique biomolecular corona signatures. (Item 143) 143. The method of any one of items 140 to 142, wherein the biological condition is a disease, disorder, or tissue abnormality. (Item 144) Item 144. The method of item 143, wherein the disease is an early-phase or intermediate-phase disease state. (Item 145) 145. The method of item 143 or 144, wherein the disease is cancer. (Item 146) Item 146. The method of item 145, wherein the cancer is stage 0 cancer or stage 1 cancer. (Item 147) The cancer may be lung cancer, pancreatic cancer, myeloma, myeloid leukemia, meningioma, glioblastoma, breast cancer, esophageal squamous cell carcinoma, gastric adenocarcinoma, prostate cancer, bladder cancer, ovarian cancer, thyroid cancer, neuroendocrine cancer, colon cancer, ovarian cancer, head and neck cancer, Hodgkin's disease, non-Hodgkin's lymphoma, rectum cancer, urinary tract cancer, uterine cancer, oral cancer, or the like. cancer), skin cancer, stomach cancer, brain tumor, liver cancer, laryngeal cancer, esophageal cancer, breast tumor, fibrosarcoma, myxosarcoma, liposarcoma, chondrosarcoma, osteosarcoma, chordoma, angiosarcoma, endothelial sarcoma, Ewing's sarcoma, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinoma, cystadenocarcinoma, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatocellular carcinoma, bile duct carcinoma, choriocarcinoma, seminoma, embryonal carcinoma, Wilms' tumor, cervical cancer, testicular tumor, endometrial cancer, lung cancer carcinoma), small cell lung cancer, bladder cancer, epithelial carcinoma, glioblastoma, neuronomas, craniopharingioma, schwannoma, glioma, astrocytoma, meningioma, melanoma, neuroblastoma, retinoblastoma, leukemia and lymphoma, acute lymphocytic leukemia and acute myelocytic polycythemia vera, multiple myeloma, Waldenstrom's macroglobulinemia and heavy chain disease, acute nonlymphocytic leukemia (Item 148) The method of item 145 or 146, wherein the cancer is selected from the group consisting of: acute lymphocytic leukemia, chronic myeloid leukemia, childhood null-cell acute lymphocytic leukemia (ALL), thymic ALL (acute lymphocytic leukemia), B-cell ALL (acute lymphocytic leukemia), acute megakaryocytic leukemia, Burkitt's lymphoma, and T-cell leukemia, small cell and large cell (non-small cell) lung cancer, acute granulocytic leukemia, germ cell tumor, endometrial cancer, gastric cancer, hairy cell leukemia, or thyroid cancer. Item 144. The method of item 143, wherein the biological condition is a pre-disease state. (Item 149) 1. An automated system comprising a network of units with differentiated functions in distinguishing between states of a complex biological sample using a plurality of particles having surfaces with different physiochemical properties, (a) a first unit includes a multi-channel fluid transfer device for transferring fluid from unit to unit in the system; (b) the second unit comprises a support for storing a plurality of biological samples; (c) a third unit comprising a support for a sensor array plate having partitions containing a plurality of particles having surfaces with different physiochemical properties for detecting binding interactions between a population of analytes in the complex biological sample and the plurality of particles; (d) a fourth unit including a support for storing a plurality of reagents; (e) a fifth unit including a support for storing reagents to be discarded; (f) a sixth unit including a support for storing consumables used by said multi-channel fluid transfer device; i. contacting the biological sample with a particular partition of the sensor array; ii. incubating the biological sample with the plurality of particles contained within the partitions of the sensor array plate; iii. removing all components from the partition except for the plurality of particles and the population of analytes that interact with the particles; and iv. Preparing the sample for mass spectrometry. (Item 150) Item 149. The automated system of item 149, wherein the first unit has a degree of mobility that allows it access to all other units in the system. (Item 151) 151. The automated system of either item 149 or 150, wherein the first unit is capable of performing a pipetting function. (Item 152) 152. The automated system of any one of items 149 to 151, wherein the support of the second and / or third unit comprises a support for a single plate, a 6-well plate, a 12-well plate, a 96-well plate, or a rack of microtubes. (Item 153) 153. The automated system of any one of items 149 to 152, wherein the second and / or unit comprises a thermal unit capable of regulating the temperature of the support and the sample. (Item 154) 154. The automated system of any one of items 149 to 153, wherein the second and / or third unit comprises a rotation unit capable of physically stirring and / or mixing the sample. (Item 155) 155. The automated system of any one of items 149 to 154, wherein the plurality of particles having surfaces with different physiochemical properties are immobilized on a surface within a partition of the sensor array for detecting binding interactions between a population of analytes within the complex biological sample and the plurality of particles. (Item 156) 156. The automated system of any one of items 149 to 155, wherein the plurality of particles having a surface comprises a plurality of magnetic nanoparticles in solution having different physiochemical properties for detecting binding interactions between a population of analytes within the complex biological sample and the plurality of particles. (Item 157) Item 157. The automated system of item 156, further comprising a step of transferring the sensor array plate to an additional seventh unit comprising a magnetized support and a thermal unit capable of adjusting the temperature of the support and the samples, and incubating for an additional period of time. (Item 158) The fourth unit: (a) generating the sensor array plate; (b) washing away unbound sample; and / or (c) Preparing samples for mass spectrometry 158. The automated system of any one of items 149 to 157, comprising a set of reagents for: (Item 159) 159. The automated system of any one of items 149 to 158, wherein (i) contacting the biological sample with a specific partition of the sensor array comprises pipetting a specific volume of the biological sample into the specific partition of the sensor array. (Item 160) 159. The automated system of any one of items 149 to 159, wherein (i) contacting the biological sample with a particular partition of the sensor array comprises pipetting a volume corresponding to a ratio of a plurality of particles in solution to the biological sample of 1:1, 1:2:1:3, 1:4, 1:5, 1:6, 1:7, 1:8, 1:9, 1:10, 1:15, or 1:20. (Item 161) 161. The automated system of any one of items 149-160, wherein contacting the biological sample with a specific partition of the sensor array comprises pipetting a volume of at least 10 microliters, at least 50 microliters, at least 100 microliters, at least 250 microliters, at least 500 microliters, or at least 1000 microliters of the biological sample into the specific partition of the sensor array. (Item 162) (ii) incubating the biological sample with the plurality of particles contained within the partition of the sensor array plate is at least about 10 seconds, at least about 15 seconds, at least about 20 seconds, at least about 25 seconds, at least about 30 seconds, at least about 40 seconds, at least about 50 seconds, at least about 60 seconds, at least about 90 seconds, at least about 2 minutes, at least about 3 minutes, at least about 4 minutes, at least about 5 minutes, at least about 6 minutes, at least about 7 minutes, at least about 8 minutes, at least about 9 minutes, at least about 10 minutes, at least about 15 minutes, at least about 20 minutes, at least about 25 minutes, at least about 30 minutes, at least about 40 minutes, at least about 50 seconds, at least about 60 seconds, at least about 90 seconds, at least about 2 minutes, at least about 3 minutes, at least about 4 minutes, at least about 5 minutes, at least about 6 minutes, at least about 7 minutes, at least about 8 minutes, at least about 9 minutes, at least about 10 minutes, at least about 15 minutes, at least about 20 minutes, at least about 25 minutes, at least about 30 minutes, at least about 40 minutes, at least about 50 minutes, at least about 60 minutes ...70 minutes, at least about 80 minutes, at least about 90 minutes, at least about 100 minutes, at least about 15 162. The automated system of any one of paragraphs 149-161, comprising an incubation time of at least about 45 minutes, at least about 50 minutes, at least about 60 minutes, at least about 90 minutes, at least about 2 hours, at least about 3 hours, at least about 4 hours, at least about 5 hours, at least about 6 hours, at least about 7 hours, at least about 8 hours, at least about 9 hours, at least about 10 hours, at least about 12 hours, at least about 14 hours, at least about 15 hours, at least about 16 hours, at least about 17 hours, at least about 18 hours, at least about 19 hours, at least about 20 hours, or at least about 24 hours. (Item 163) (ii) The automated system of any one of items 149 to 162, wherein incubating the biological sample with the plurality of particles contained within the partition of the sensor array plate comprises an incubation temperature of about 4°C to about 40°C. (Item 164) 164. The automated system of any one of items 149 to 163, wherein removing all components from the partition except for the plurality of particles and the population of analytes that interact with the particles comprises a series of washing steps. (Item 165) 165. The automated system of any one of items 149 to 164, wherein the second unit is capable of facilitating the transfer of the sample to a mass spectrometry unit for mass analysis. (Item 166) 1. An automated instrument for identifying proteins in a biological sample, comprising: sample preparation units; a substrate containing multiple channels; Multiple pipettes; a plurality of solutions, a plurality of nanoparticles, An automated instrument configured to form a protein corona and digest said protein corona. (Item 167) 1. An automated instrument for identifying proteins in a biological sample, comprising: sample preparation units; a substrate containing multiple channels; Multiple pipettes; a plurality of solutions, a plurality of nanoparticles, the automated instrument is configured to form a protein corona and digest the protein corona; , wherein at least one of the solutions is TE 150 mM KCl 0.05% CHAPS buffer. (Item 168) Item 168. The automated instrument of item 166 or 167, wherein the sample preparation unit is configured to add the plurality of nanoparticles to the substrate with the plurality of pipettes. (Item 169) 169. The automated instrument of any one of items 166 to 168, wherein the sample preparation unit is configured to add the biological sample to the substrate with the plurality of pipettes. (Item 170) 169. The automated instrument of any one of items 166 to 169, wherein the sample preparation unit is configured to incubate the plurality of nanoparticles with the biological sample to form the protein corona. (Item 171) 171. The automated instrument of any one of items 166 to 170, wherein the sample preparation unit is configured to separate the protein corona from the supernatant to form a protein corona pellet. (Item 172) 172. The automated instrument of any one of items 166 to 171, wherein the sample preparation unit is configured to reconstitute the protein corona pellet with TE 150 mM KCl 0.05% CHAPS buffer. (Item 173) 173. The automated instrument of any one of items 166 to 172, further comprising a magnetic source. (Item 174) 174. The automated instrument of any one of items 166 to 173, wherein the automated instrument is configured for trypsin digestion of BCA, gel, or protein corona. (Item 175) The automated device of any one of Items 166 to 174, wherein the automated device is enclosed. (Item 176) 176. The automated instrument of any one of items 166 to 175, wherein the automated instrument is sterilized before use. (Item 177) 177. The automated instrument of any one of items 166 to 176, wherein the automated instrument is adapted for mass spectrometry. (Item 178) 178. The automated instrument of any one of items 166 to 177, wherein the automated instrument is temperature controlled. (Item 179) 1. A method for identifying a protein in a biological sample, comprising: adding the biological sample to the automated device of any one of items 166 to 178; generating proteomic data with said automated instrument; and Quantifying the proteomic data. A method comprising: (Item 180) 180. The method of claim 179, further comprising incubating a plurality of nanoparticles with the biological sample in the automated instrument to form a protein corona. (Item 181) 181. The method of claim 179 or 180, further comprising separating the protein corona from the supernatant in the automated device. (Item 182) 182. The method of any one of items 179 to 181, further comprising digesting the protein corona in the automated instrument to form a digested sample. (Item 183) 183. The method of any one of items 179 to 182, further comprising washing the digested sample in the automated instrument. (Item 184) 184. The method of any one of items 179 to 183, wherein quantifying the proteomic data comprises subjecting the proteomic data to mass spectrometry. (Item 185) 185. The method of any one of items 179 to 184, wherein the biological sample is a body fluid. (Item 186) 186. The method of any one of items 179 to 185, wherein the body fluid is serum or plasma.

Claims

1. 1. An automated system comprising a network of units with differentiated functions in identifying the state of a biological sample, (a) a first unit including a multi-channel fluid transfer device for transferring fluids between units in the system; (b) a second unit comprising a support for storing a plurality of biological samples; (c) a third unit including a support for a magnetic sensor array, the magnetic sensor array including a plurality of partitions including a plurality of magnetic particles for binding the plurality of magnetic particles to a population of proteins in the biological sample; (d) a fourth unit comprising a support for storing a plurality of reagents; (e) a fifth unit containing a support for storing reagents to be discarded; (f) a sixth unit including a support for storing consumables used by said multi-channel fluid transfer device; (g) a seventh unit including a magnetized support including a magnetic source; (h) i. contacting the biological sample with a partition of the magnetic sensor array; ii. incubating the biological sample with the plurality of magnetic particles of the magnetic sensor array under conditions sufficient to bind a group of at least 300 proteins; iii. Immobilizing the plurality of magnetic particles on the partition by contacting the partition with the magnetic support; iv. washing the plurality of magnetic particles to remove unbound biomolecules from the partitions; v. desorbing the population of proteins from the plurality of magnetic particles; and vi. Preparing the sample for mass spectrometry and a computer including one or more computer processors programmed to execute a series of steps including:

2. The automated system described in claim 1, wherein the multiple magnetic particles have surfaces with different physicochemical properties, and at least one of the different physicochemical properties is selected from the group consisting of surface charge, hydrophobicity, hydrophilicity, surface functionality, surface topography, surface curvature, porosity, core material, shell material, shape, size, composition, and combinations thereof.

3. The automated system of claim 1 , wherein the multi-channel fluid transfer device is capable of performing pipetting functions.

4. 10. The automated system of claim 1, wherein the support of the second or third unit comprises a support for a multiwell plate.

5. The automated system of claim 1 , wherein the magnetic sensor array is a multi-well plate.

6. The automated system of claim 1 , wherein the second or third unit comprises a thermal unit capable of regulating the temperature during incubation of the biological sample with the magnetic particles of the magnetic sensor array.

7. The automated system of claim 1 , wherein the second or third unit comprises a rotation unit capable of physically stirring or mixing the sample.

8. The automated system of claim 1 , wherein the plurality of magnetic particles are fixed to the surfaces of the at least two partitions.

9. The automated system of claim 1 , wherein the seventh unit transfers the magnetic sensor array to the magnetized support.

10. The automated system of claim 1 , wherein the fourth unit is configured to support a set of reagents for washing a population of proteins prepared for mass spectrometry.

11. 2. The automated system of claim 1, wherein (i) the step of contacting the biological sample within the partition of the magnetic sensor array comprises pipetting a specific volume of the biological sample into the partition of the magnetic sensor array.

12. The automated system of claim 11 , wherein the specified volume is at least 10 microliters.

13. The automated system of claim 1 , wherein the biological sample comprises plasma or serum.

14. 2. The automated system of claim 1, wherein (ii) incubating the biological sample with the plurality of magnetic particles contained within the partitions of the magnetic sensor array comprises an incubation time of at least 25 minutes.

15. 2. The automated system of claim 1, wherein (ii) the step of incubating the biological sample with the plurality of magnetic particles contained within the partitions of the magnetic sensor array comprises incubation at 25°C to 100°C.

16. 10. The automated system of claim 1, further comprising: (iv) a series of washing steps prior to the step of desorbing the population of proteins from the plurality of magnetic particles.

17. The automated system of claim 1 further comprising digesting the population of proteins.

18. The automated system of claim 1 further comprising a consumable storage unit including a support for storing consumables used by the multi-channel fluid transfer device.

19. 2. The automated system of claim 1, wherein the plurality of reagents includes a diluent comprising a buffer, and the biological sample is diluted 2-fold, 3-fold, 4-fold, 5-fold, 6-fold, or 8-fold with the diluent prior to (ii) incubating.

20. (h) a mass spectrometer configured to receive the desorbed population of proteins and generate data comprising a mass spectrometry signal or a tandem mass spectrometry signal; (i) a computer including one or more computer processors, and when executed by said one or more computer processors: i. generating a biomolecular fingerprint, and ii. Assigning a biological state based on said biomolecular fingerprint a computer-readable medium containing machine-executable code for performing a method comprising: The automated system of claim 1 further comprising:

21. 10. The automated system of claim 1, wherein the automated system produces a desorbed population of proteins in less than 7 hours.

22. The automated system of claim 1 , wherein the plurality of magnetic particles comprises a silica surface.

23. The automated system of claim 1 , wherein the plurality of magnetic particles comprises superparamagnetic iron oxide nanoparticles.

24. The automated system of claim 1 , wherein the plurality of magnetic particles comprises a negative surface charge.

25. The automated system of claim 1 , wherein the plurality of magnetic particles comprises a poly(N-(3-(dimethylamino)propyl)methacrylamide) coating.

26. 2. The automated system of claim 1, wherein the incubation in step (ii) is carried out at 25°C to 100°C.

27. The automated system of claim 1 , wherein the plurality of magnetic particles comprises polyethyleneimine.

28. 1. An automated system comprising a network of units with differentiated functions in identifying the state of a biological sample using a plurality of magnetic particles having a surface with at least one physicochemical property, (a) a first unit includes a multi-channel fluid transfer device for transferring fluids between units in the system, the multi-channel fluid transfer device including a plurality of pipettes; (b) a second unit comprising a magnetized support for a magnetic sensor array, the magnetic sensor array comprising (i) the biological sample, (ii) a diluent comprising a buffer, and (iii) a plurality of partitions comprising a plurality of magnetic particles having surfaces, the plurality of magnetic particles comprising carboxyl-functionalized silica surfaces, the plurality of magnetic particles configured to bind to the biological sample and a population of proteins within the plurality of magnetic particles, the population of proteins comprising a group of at least 300 proteins; (c) a third unit includes a support for storing a plurality of reagents, the plurality of reagents including a buffer, a wash solution, a resuspension solution, and a denaturing solution including a protease, the protease including trypsin; (d) a fourth unit includes a support for storing reagents to be discarded; (e) a fifth unit includes a support for storing consumables used by the multi-channel fluid transfer device, the consumables including a plurality of pipette tips; and (f) i. contacting the biological sample with a partition of the magnetic sensor array; ii. incubating the biological sample with the plurality of magnetic particles of the magnetic sensor array; and iii. Preparing a population of proteins for mass spectrometry by adding a resuspension solution and a denaturing solution to the partition to desorb and digest proteins in the sample. and a computer including one or more computer processors programmed to execute a series of steps including:

29. 29. The automated system of claim 28, wherein the plurality of biological samples are diluted 2-fold to 8-fold with the diluent.

30. 30. The automated system of claim 28, wherein the plurality of reagents comprises TE 150 mM KCL 0.05% CHAPS buffer.

31. 29. The automated system of claim 28, wherein the plurality of magnetic particles comprises a polydispersity index of 0.5 to 1.

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