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

Abstract

Providing 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, as well as their application in the discovery of advanced diagnostic tools and therapeutic agents, are described herein. In some embodiments, the 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 (wherein the plurality of partitions comprising a plurality of particles); (ii) a loading unit comprising the complex biological sample; and (iii) a loading unit movable over at least the entire surface of the substrate.

Description

Technical Field

[0001] Cross - Reference to Related Applications This application claims priority to and the benefit of U.S. Provisional Application No. 62 / 883,107, filed Aug. 5, 2019, the entire contents of which are incorporated herein by reference.

Background Art

[0002] Background The large - scale construction in the science and medicine of proteomics information has lagged behind genomics mainly due to the complexity inherent in the protein molecules themselves (thus requiring complex workflows that limit the scaling up of such analyses). Systems, methods, and kits for rapid and automated sample preparation, processing of proteomics data, and identification of key biomarkers related to disease states are disclosed herein.

Summary of the Invention

Means for Solving the Problems

[0003] Abstract This disclosure provides automated systems, methods, and kits for the preparation and analysis of protein coronas. In some embodiments, this disclosure provides an automated instrument for generating a subset of biomolecules from a complex biological sample, the automated instrument comprising: (i) a substrate comprising a plurality of partitions (wherein the plurality of partitions comprising a plurality of particles); (ii) a sample storage unit comprising the complex biological sample; and (iii) a loading unit movable over at least the entire 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 bringing the plurality of particles in the plurality of partitions into contact with the biomolecules of the complex biological sample to form a biomolecular corona, thereby generating a subset of the biomolecules of the complex biological sample, wherein the dynamic range of the biomolecular subset 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 types of biomolecule from the complex biological sample within a six-order-of-magnitude concentration range. In some embodiments, the biomolecule subset comprises at least 20% to at least 60% of the types of protein from the complex biological sample within a six-order-of-magnitude concentration range. In some embodiments, the automated equipment generates the biomolecule subset from the complex biological sample in less than seven hours.

[0004] In some embodiments, the automated apparatus includes an incubation element for agitating or heating the volume of the multiple particles within the volume of the composite biological sample in the multiple partitions. In some embodiments, the incubation element is configured to shake, mix, stir, spin, vibrate, be 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 of the plurality of partitions is covered or sealed. In some embodiments, the automated equipment has the ability to add a lid to the substrate or remove a lid from the substrate, wherein the lid covers at least one of the plurality of partitions.

[0006] In some embodiments, the automated equipment includes a unit containing a resuspension solution. In some embodiments, the resuspension solution is Tris-EDTA 150 mM KCl It contains 0.05% CHAPS buffer. In some embodiments, the resuspended solution contains 10 mM Tris-HCl pH 7.4 and 1 mM EDTA.

[0007] In some embodiments, the apparatus includes a unit containing a denaturation solution. In some embodiments, the denaturation solution contains a protease. In some embodiments, the denaturation solution contains a reducing agent, a methylating agent, guanidine, urea, sodium deoxycholate, acetonitrile, or any combination thereof. In some embodiments, the denaturation solution produces an average peptide fragment with a 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 partitions 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 partitions 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 partitions of the plurality of partitions. In some embodiments, the solution is selected from the group consisting of washing solution, resuspension solution, denaturation solution, buffer, and reagents. In some embodiments, the loading unit is configured to dispense 10 μL to 400 μL of the composite biological sample into one or more partitions of the plurality of partitions. In some embodiments, the loading unit is configured to dispense 5 μL to 150 μL of the composite biological sample into one or more partitions of the plurality of partitions. In some embodiments, the loading unit is configured to dispense 35 μL to 80 μL of the complex biological sample into one or more of the partitions.

[0009] In some embodiments, the composite biological sample includes bodily fluids from the subject. In some embodiments, the composite biological sample includes plasma, serum, urine, cerebrospinal fluid, synovial fluid, tears, saliva, whole blood, milk, nipple aspirate, mammary duct lavage, vaginal fluid, nasal secretions, inner ear fluid, gastric juice, pancreatic juice, trabecular meshwork, lung lavage, sweat, gingival crevicular exudate, semen, prostatic fluid, sputum, feces, bronchial lavage, fluid from swabs, bronchial aspirates, fluidized solids, fine needle aspiration samples, tissue homogenates, lymphatic fluid, cell culture samples, or any combination thereof.

[0010] In some embodiments, the automated device further includes a magnet. In some embodiments, one or more 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 fixed onto the substrate.

[0011] In some embodiments, the automated equipment further includes a housing, the substrate and the loading unit are located within the housing, and the housing is at least partially enclosed.

[0012] In some embodiments, the compressed dynamic range includes an increase in the number of biomolecular species whose concentrations are within six orders of magnitude for the most abundant biomolecular in the sample. In some embodiments, the compressed dynamic range includes an increase in the number of biomolecular species whose concentrations are within five orders of magnitude for the most abundant biomolecular in the sample. In some embodiments, the compressed dynamic range includes an increase in the number of biomolecular species whose concentrations are within four orders of magnitude for the most abundant biomolecular in the sample. In some embodiments, the compressed dynamic range includes an increase in the number of protein species whose concentrations are within six orders of magnitude for the most abundant protein in the sample. In some embodiments, the increase in the number of biomolecular species whose concentrations are within six orders of magnitude for the most concentrated biomolecular 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 six orders of magnitude for the most abundant protein in the sample. In some embodiments, the increase in the number of protein types whose concentration is within six orders of magnitude relative to 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 upper decile biomolecules to lower decile biomolecules in the plurality of biomolecular coronas. In some embodiments, the dynamic range of the biomolecules in the biomolecular corona is a first ratio that includes the interquartile range of biomolecules in the plurality of biomolecular coronas.

[0014] In some embodiments, the generation enriches low-abundance biomolecules from the complex biological sample. In some embodiments, the low-abundance biomolecules are biomolecules in the complex biological sample at concentrations of 10 ng / mL or lower. In some embodiments, the subset of biomolecules from the complex biological sample includes proteins.

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

[0016] In some embodiments, at least two of 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 includes 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, and BPEI functionalization. In some embodiments, the particles among the plurality of particles include 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 with a metal core, polymer particles with 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 (polyethlene Phosphonate particles, polyalkylene particles, carboxyvinyl polymer particles, sodium alginate particles, carrageenan particles, xanthan gum particles, acacia gum 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, elcinan particles, gluten particles, collagen particles, whey protein isolate particles, casein particles, milk protein particles, soy protein particles, keratin particles, polyethylene particles, polycarbonate particles, polyacid anhydride particles, polyhydroxy acid particles, polypropyl fumarate particles, polycaprolactone particles, polyamine particles, polyacetal particles, polyether particles, polyester particles, poly(orthoester) particles, polycyanoacrylate particles, polyurethane particles The particles are 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 acid / glycolic acid particles, polyacrylamide anhydride particles, bioreducible polymer particles, and 2-(3-aminopropylamino)ethanol particles, as well as any combination thereof. In some embodiments, one or more of the particles adsorb at least 100 different proteins when in contact with the complex biological sample. In some embodiments, the plurality of particles include 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 unique particle species, or at least thirty unique particle species.

[0017] In some embodiments, the biomolecule corona comprises numerous protein groups. In some embodiments, the numerous protein groups comprise 1 to 20,000 protein groups. In some embodiments, the numerous protein groups comprise 100 to 10,000 protein groups. In some embodiments, the numerous protein groups comprise 100 to 5,000 protein groups. In some embodiments, the numerous protein groups comprise 300 to 2,200 protein groups. In some embodiments, the numerous protein groups comprise 1,200 to 2,200 protein groups.

[0018] In some embodiments, at least two of the multiple partitions contain 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 of the multiple partitions contain the composite biological sample in different ratios of buffer. In some embodiments, one or more of the multiple partitions contain nanoparticles ranging from 1 pM to 100 nM. In some embodiments, at least two of the multiple partitions contain nanoparticles at different concentrations.

[0019] In some embodiments, the automated equipment further includes a purification unit. In some embodiments, the purification unit includes 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 a subset of biomolecules from a biological sample; (ii) 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 (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, includes generating a biomolecular fingerprint and assigning a biological state based on the biomolecular fingerprint.

[0021] In some embodiments, the biomolecular fingerprint includes a plurality of unique biomolecular corona signatures. In some embodiments, the biomolecular fingerprint includes 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 including the intensity of mass spectrometry signals or tandem mass spectrometry signals between the plurality of unique biomolecular corona signatures, APEX, spectral counts or numbers of peptides, or ion mobility behavior. In some embodiments, the computer is configured to process data from 100 to 2000 mass spectrometry signals or tandem mass spectrometry signals between the plurality of unique biomolecular corona signatures. In some embodiments, the computer is configured to process data including the intensity of mass spectrometry signals or tandem mass spectrometry signals between 10,000 to 5,000,000 mass spectrometry signals between 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 operation. In some embodiments, the single mass spectrometry or tandem mass spectrometry operation is performed in less than one hour. In some embodiments, the computer is configured to identify biomolecules or characterize unidentified molecular features based on the mass spectrometry signal or tandem mass spectrometry signal and / or ion mobility and chromatographic behavior, wherein the computer provides at least a 95% certainty threshold for identifying features or characterizing unidentified features. In some embodiments, the automated system is configured to generate the biomolecular fingerprint from the composite biological sample in less than approximately 10 hours. In some embodiments, the determination includes comparing the abundances of two biomolecules in the composite biological sample, with concentrations ranging 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 a biomolecular fingerprint where the difference is 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-phase 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 cancers include 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, head and neck cancer, Hodgkin's disease, non-Hodgkin lymphoma, rectal cancer, urinary tract cancer, uterine cancer, and oral cancer. Cancer, skin cancer, stomach cancer, brain tumor, liver cancer, laryngeal cancer, esophageal cancer, breast tumor, fibrosarcoma, myxosarcoma, liposarcoma, chondrosarcoma, osteosarcoma, chordoma, angiosarcoma, endosarcoma, Ewing's sarcoma, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinoma, cystadenocarcinoma, medullary carcinoma, bronchogenic lung cancer, renal cell carcinoma, hepatocellular carcinoma, cholangiocarcinoma, choriocarcinoma, seminoma, embryonic carcinoma, Wilms' tumor, cervical cancer, testicular cancer, 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 lymphoblastic leukemia and acute myeloid polycythemia vera, multiple myeloma, Waldenström macroglobulinemia, and heavy chain disease, acute nonlymphoblastic leukemia, chronic The biological condition is selected from the group consisting of uterine lymphocytic leukemia, chronic myeloid leukemia, childhood null cell acute lymphoblastic leukemia (ALL), thymic ALL (acute lymphoblastic leukemia), B-cell ALL (acute lymphoblastic leukemia), acute megakaryocytic leukemia, Burkitt lymphoma, and T-cell leukemia, small cell and large cell (non-small cell) lung cancer, acute granulocytic leukemia, germ cell tumors, endometrial cancer, gastric cancer, pilocytic cell leukemia, thyroid cancer, and other cancers known in the art. In some embodiments, the biological condition is a pre-disease condition.

[0023] Various aspects of the present disclosure provide a method for identifying the biological state of a complex biological sample, the method comprising: feeding the complex biological sample into 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 by the biomolecular fingerprint.

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

[0025] In some embodiments, the assay includes desorbing a plurality of biomolecules from the biomolecular coronas of the plurality of biomolecular coronas. In some embodiments, the assay includes chemically modifying the biomolecules among the plurality of adsorbed biomolecules. In some embodiments, the assay includes fragmenting the biomolecules among the plurality of adsorbed biomolecules. In some embodiments, the fragmentation includes protease digestion. In some embodiments, the fragmentation includes 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 depletes 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 the biomolecular corona within 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 includes analyzing the biomolecular coronas within the plurality of biomolecular coronas by mass spectrometry, tandem mass spectrometry, mass cytometry, mass cytometry, potentiometric measurement, fluorescence quantification, absorption spectroscopy, Raman spectroscopy, chromatography, electrophoresis, immunohistochemistry, PCR, next-generation sequencing (NGS), or any combination thereof. In some embodiments, the assay includes mass spectrometry or tandem mass spectrometry. In some embodiments, the assay includes identifying the three-dimensional structures of proteins within the subset of biomolecules. In some embodiments, the assay includes identifying post-translational modifications to proteins within the subset of biomolecules. In some embodiments, the identification includes comparing the relative abundances of at least 200 to at least 1000 biomolecules from the subset of biomolecules. In some embodiments, the assay identifies biomolecules in the composite biological sample at concentrations less than 10 ng / mL.

[0028] Various embodiments of this disclosure provide an automated apparatus for generating a subset of biomolecules from a complex biological sample, the automated apparatus comprising a plurality of particles and the complex biological sample, wherein the automated apparatus is configured to generate the subset of biomolecules by contacting the plurality of particles with the complex biological sample, thereby forming a plurality of biomolecular coronas comprising the subset of biomolecules, 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 apparatus 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 apparatus generates the subset of biomolecules from a complex biological sample in less than 7 hours.

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

[0030] In some embodiments, the automated apparatus includes at least one solution selected from the group consisting of a washing solution, a resuspension solution, a denaturation solution, a buffer, and a reagent. In some embodiments, the resuspension solution includes Tris-EDTA buffer, phosphate buffer, and / or water. In some embodiments, the denaturation solution includes a protease. In some embodiments, the denaturation solution includes small molecules capable of peptide cleavage.

[0031] In some embodiments, the automated device includes a loading unit comprising a plurality of pipettes. In some embodiments, each of the plurality of pipettes is configured to dispense approximately 5 μL to 150 μL of the solution, the composite biological sample, and / or the plurality of particles. In some embodiments, the composite biological sample includes plasma, serum, urine, cerebrospinal fluid, synovial fluid, tears, saliva, whole blood, milk, nipple aspirate, mammary duct lavage, vaginal fluid, nasal secretions, inner ear fluid, gastric juice, pancreatic juice, trabecular meshwork, lung lavage, sweat, gingival crevicular exudate, semen, prostatic fluid, sputum, feces, bronchial lavage, fluid from swabs, bronchial aspirates, fluidized solids, fine needle aspiration samples, tissue homogenates, lymph fluid, cell culture samples, 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 biomolecular types whose concentrations are within a range of 4 to 6 orders of magnitude relative to the most abundant biomolecule in the sample. In some embodiments, the biomolecular types include proteins. In some embodiments, the dynamic range of the biomolecular corona is a first ratio of upper-decile biomolecules to lower-decile biomolecules in the plurality of biomolecular coronas. In some embodiments, the generation enriches low-abundance biomolecules from the complex biological sample. In some embodiments, the low-abundance biomolecules are biomolecules in the complex biological sample at concentrations of 10 ng / mL or lower.

[0033] In some embodiments, at least two of 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 particles among the plurality of particles include 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 with a metal core, polymer particles with 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 (polyethlene Phosphonate particles, polyalkylene particles, carboxyvinyl polymer particles, sodium alginate particles, carrageenan particles, xanthan gum particles, acacia gum 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, elcinan particles, gluten particles, collagen particles, whey protein isolate particles, casein particles, milk protein particles, soy protein particles, keratin particles, polyethylene particles, polycarbonate particles, polyacid anhydride particles, polyhydroxy acid particles, polypropyl fumarate particles, polycaprolactone particles, polyamine particles, polyacetal particles, poly- The particles are selected from the group consisting of tel 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, polyacrylamide anhydride particles, bioreducible polymer particles, and 2-(3-aminopropylamino)ethanol particles, as well as any combination thereof.

[0034] In some embodiments, the biomolecule corona comprises numerous protein groups. In some embodiments, the numerous protein groups comprise 1 to 20,000 protein groups. In some embodiments, the numerous protein groups comprise 100 to 10,000 protein groups. In some embodiments, the numerous protein groups comprise 100 to 5,000 protein groups. In some embodiments, the numerous protein groups comprise 300 to 2,200 protein groups. In some embodiments, the numerous protein groups comprise 1,200 to 2,200 protein groups.

[0035] In some embodiments, the automated equipment includes a purification unit. In some embodiments, the purification unit includes a solid-phase extraction (SPE) plate.

[0036] Various aspects of the present disclosure provide a method for generating a subset of biomolecules from a complex biological sample, the method comprising supplying 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 processing the biomolecular corona to generate a subset of biomolecules, wherein the dynamic range of the biomolecular subset 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 in the composite biological sample at concentrations less than 10 ng / mL. In some embodiments, the assay includes analyzing the biomolecular corona by mass spectrometry, tandem mass spectrometry, mass cytometry, mass cytometry, potentiometrics, fluorescence quantification, 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 the biological state of the composite biological sample by the biomolecular fingerprint. In some embodiments, the biomolecular fingerprint includes a plurality of unique biomolecular corona signatures. In some embodiments, the biomolecular fingerprint includes 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-phase 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 cancers include 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, head and neck cancer, Hodgkin's disease, non-Hodgkin lymphoma, rectal cancer, urinary tract cancer, uterine cancer, and oral cancer. Cancer, skin cancer, stomach cancer, brain tumor, liver cancer, laryngeal cancer, esophageal cancer, breast tumor, fibrosarcoma, myxosarcoma, liposarcoma, chondrosarcoma, osteosarcoma, chordoma, angiosarcoma, endosarcoma, Ewing's sarcoma, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinoma, cystadenocarcinoma, medullary carcinoma, bronchogenic lung cancer, renal cell carcinoma, hepatocellular carcinoma, cholangiocarcinoma, choriocarcinoma, seminoma, embryonic carcinoma, Wilms' tumor, cervical cancer, testicular cancer, 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 lymphoblastic leukemia and acute myeloid polycythemia vera, multiple myeloma, Waldenström macroglobulinemia, and heavy chain disease, acute The group consists of non-lymphocytic leukemia, chronic lymphocytic leukemia, chronic myeloid leukemia, childhood null cell acute lymphoblastic leukemia (ALL), thymic ALL (acute lymphocytic leukemia), B-cell ALL (acute lymphocytic leukemia), acute megakaryocytic leukemia, Burkitt lymphoma, and T-cell leukemia, small cell and large cell (non-small cell) lung cancer, acute granulocytic leukemia, germ cell tumors, endometrial cancer, gastric cancer, pilocytic cell leukemia, or thyroid cancer. In some embodiments, the biological condition is a pre-disease condition.

[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 having multiple channels; multiple pipettes; multiple solutions; multiple nanoparticles, wherein the automated instrument is configured to cause protein corona formation 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 having multiple channels; multiple pipettes; multiple solutions; multiple nanoparticles, wherein the automated instrument is configured to cause protein corona formation 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 plurality of nanoparticles to the substrate using the plurality of pipettes. In some embodiments, the sample preparation unit is configured to add the biological sample to the substrate using the plurality of pipettes. In some embodiments, the sample preparation unit is configured to incubate the plurality of nanoparticles and 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 apparatus includes a magnetic source. In some embodiments, the automated apparatus is configured for trypsin digestion of BCA, gel, or protein corona. In some embodiments, the automated apparatus is enclosed. In some embodiments, the automated apparatus is sterilized before use. In some embodiments, the automated apparatus is adapted for mass spectrometry. In some embodiments, the automated apparatus is temperature-controlled.

[0043] Various aspects of this disclosure provide a method for identifying proteins in a biological sample, the method comprising: adding the biological sample to an automated instrument; causing the automated instrument to generate proteomics data; and quantifying the proteomics 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 the 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, the quantification of the proteomics data comprises providing the proteomics data to mass spectrometry. In some embodiments, the biological sample is a body fluid. In some embodiments, the body fluid is serum or plasma.

[0044] In some embodiments, the Disclosure provides an automated system comprising a network of units having differentiated functions in identifying the state of a complex biological sample using a plurality of particles having surfaces having different physicochemical properties, wherein a first unit comprises a multi-channel fluid transfer device for transferring fluid from unit to unit within 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 containing the plurality of particles having surfaces having different physicochemical properties for combining a population of analytes in the complex biological sample; and a fourth unit comprises a support for storing a plurality of reagents. The system includes a body; a fifth unit includes a support for storing reagents that will be discarded; a sixth unit includes a support for storing consumables used by the multi-channel fluid transfer device; and the system is programmed to perform a series of steps including: bringing the complex biological sample into contact 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 the population of analytes interacting with the particles; and preparing a sample for mass spectrometry.

[0045] In some embodiments, the first unit has a degree of mobility that allows access to all other units in the system. In some embodiments, the first unit has the capability to perform a pipetting function.

[0046] In some embodiments, the support of the second and / or third unit includes 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 unit includes a thermal unit capable of controlling the temperature of the support and the sample. In some embodiments, the second and / or third unit includes a rotating 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 together 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 include a plurality of magnetic nanoparticles having different physicochemical properties for binding together a population of analytes in the complex biological sample. In some embodiments, the system includes the step of transferring the sensor array plate to an additional seventh unit which includes a magnetization support and a thermal unit capable of controlling the temperature of the support and the sample, and incubating it for an additional time.

[0048] In some embodiments, the fourth unit includes a set of reagents for generating the sensor array plate; washing unbound samples; and / or preparing samples for mass spectrometry. In some embodiments, contacting the biological sample with a particular partition of the sensor array includes pipetting a particular volume of the biological sample into the particular partition of the sensor array. In some embodiments, contacting the biological sample with a particular partition of the sensor array includes pipetting volumes corresponding to a ratio of multiple particles in a 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, bringing the biological sample into contact with a specific partition of the sensor array involves 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.

[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, Includes incubation time of at least approximately 25 minutes, at least approximately 30 minutes, at least approximately 45 minutes, at least approximately 50 minutes, at least approximately 60 minutes, at least approximately 90 minutes, at least approximately 2 hours, at least approximately 3 hours, at least approximately 4 hours, at least approximately 5 hours, at least approximately 6 hours, at least approximately 7 hours, at least approximately 8 hours, at least approximately 9 hours, at least approximately 10 hours, at least approximately 12 hours, at least approximately 14 hours, at least approximately 15 hours, at least approximately 16 hours, at least approximately 17 hours, at least approximately 18 hours, at least approximately 19 hours, at least approximately 20 hours, or at least approximately 24 hours.

[0051] In some embodiments, incubating the biological sample with the plurality of particles contained within the partition of the substrate involves 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 involve 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 involve 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 analytes interacting with the particles, includes a series of washing steps.

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

[0054] In some embodiments, the Disclosure provides an automated instrument for identifying proteins in a biological sample, the automated instrument comprising a sample preparation unit; a substrate having multiple channels; multiple pipettes; multiple solutions; multiple nanoparticles, wherein the automated instrument is configured to cause protein corona formation 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 having multiple channels; multiple pipettes; multiple solutions; multiple nanoparticles, wherein the automated instrument is configured to cause protein corona formation 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 plurality of nanoparticles to the substrate using the plurality of pipettes. In some embodiments, the sample preparation unit is configured to add the biological sample to the substrate using the plurality of pipettes. In some embodiments, the sample preparation unit is configured to incubate the plurality of nanoparticles and 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. In 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 apparatus further includes a magnetic source. In some embodiments, the automated apparatus is configured for trypsin digestion of BCA, gel, or protein corona.

[0059] In some embodiments, the automated equipment is enclosed. In some embodiments, the automated equipment is sterilized before use. In some embodiments, the automated equipment is adapted for mass spectrometry. In some embodiments, the automated equipment is temperature-controlled.

[0060] In some embodiments, the Disclosure provides a method for identifying proteins in a biological sample, the method comprising: adding the biological sample to an automated instrument disclosed herein; causing the automated instrument to generate proteomics data; and quantifying the proteomics data.

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

[0062] In some embodiments, the method further includes washing the digested sample within the automated apparatus. In some embodiments, the quantification of the proteomics data includes providing the proteomics data to mass spectrometry.

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

[0064] Another aspect of this disclosure provides a non-temporary 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 in this specification.

[0065] Another aspect of this disclosure provides a system comprising one or more computer processors and computer memory connected thereto. The computer memory contains machine executable code that, when executed by the one or more computer processors, performs any of the methods described above or elsewhere in this specification.

[0066] Additional aspects and advantages of the present disclosure will be readily apparent to those skilled in the art from the following detailed description (wherein only exemplary embodiments of the present disclosure are shown and described). As will be recognized, other different embodiments of the present disclosure are possible, and some of its details can be modified in various obvious ways, all of which do not depart from the present disclosure. Accordingly, the drawings and description should be considered illustrative and not limiting in nature. Reference

[0067] All publications, patents, and patent applications referenced herein are incorporated herein by reference to the same extent as any individual publication, patent, or patent application is indicated to be incorporated by reference specifically and individually. [Brief explanation of the drawing]

[0068] Novel features of the present invention are described in detail in the appended claims. A better understanding of the features and advantages of the present invention will be obtained by referring to the following detailed description of illustrative embodiments (in which the principles of the present invention are utilized) and the appended drawings (also referred to herein as "Figure" and "FIG.").

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

[0070] [Figure 2] Figure 2 shows an illustrative schematic diagram of the steps for generating data using nanoparticles or protein corona spectroscopy, and the units of an automated system in which they may be performed.

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

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

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

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

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

[0076] [Figure 8] Figure 8 shows an example of a protein-corona-based method for detecting disease biomarkers in cancer patients (referencing US20180172694A1, which is 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, which can be performed in parallel across a diverse range of samples in a few hours. The process includes 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 the sample at a time.

[0078] [Figure 10]Figure 10 shows the protein count (number of proteins identified by corona analysis) collected on multiple particles containing 1 to 12 particle types. Each particle in the multiple particles may have unique material, surface functionalization, and / or physical properties (e.g., size or shape). Pooled plasma from a group of healthy subjects was used. The count is the number of unique proteins collected from the multiple particles and observed during a mass spectrometry (MS) procedure lasting approximately 2 hours. 1318 proteins were identified from samples in contact with multiple particles containing 12 particle types.

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

[0080] [Figure 12]Figure 12 shows altered properties in a non-small cell lung cancer (NSCLC) pilot study using poly(N-(3-(dimethylamino)propyl)methacrylamide)(PDMAPMA) coated SPION particles. Seven MS properties were identified as statistically significant differences between 28 subjects with stage IV NSCLC (with associated comorbidities and treatment response) and 28 clearly healthy subjects, age- and sex-matched. The table below lists the seven significantly different proteins. This includes five known proteins and two unknown proteins. Where a peptide spectrum matched the MS2 data associated with the property, the peptide sequence (and charge) and potential parent protein are shown; where the MS2 match was not associated with the property, both the peptide and protein are described as "unknown".

[0081] [Figure 13] Figure 13 shows the correlation between the maximum intensities of particle corona protein and plasma protein and the published concentrations of the same proteins. The lines plotted in blue are linear regression models for the data, and the shaded areas show the standard errors of the model fit. The dynamic range of samples assayed with particles ("S-003", "S-007", and "S-011", listed in Table 1) showed a compressed dynamic range compared to plasma samples not assayed with particles ("Plasma"), as indicated by the decrease in the slope of the linear fit. The slopes for 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 protein corona analysis assay by mass spectrometry compared to mass spectrometry without particle corona formation. The protein intensity of common proteins identified in particle corona in plasma samples assayed in Figure 13 ("nanoparticle MS(intensity)") is plotted against the protein intensity identified by mass spectrometry of plasma without particles ("plasma MS(intensity)"). The largest dotted line has a slope of 1 and represents the dynamic range of mass spectrometry without particles. The slopes of the linear fit for protein intensity are 0.12, 0.36, and 0.093 for S-003, S-007, and S-011 particles, respectively. The gray area represents the standard error region of the regression fit. [Modes for carrying out the invention]

[0083] While various embodiments of the present invention are shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided for illustrative purposes only. Numerous variations, modifications, and substitutions can be found by those skilled in the art without departing from the present invention. It should be understood that various alternatives to embodiments of the present invention may be employed.

[0084] The term "at least (at)" is placed 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" are used, those terms always apply to each individual number in that set of numbers. 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] When the terms "no more than," "less than," or "less than or equal to" precede the first number in a set of two or more numbers, those terms always apply to each number in that set. 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, “feature” identified by mass spectrometry includes signals at specific combinations of retention time and m / z (mass-to-charge ratio), where each feature has a corresponding 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 capable of binding to multiple biomolecules upon contact with a sample, and encompasses the term “nanoscale sensor element.” A sensor element may be a particle (e.g., nanoparticles or microparticles). A sensor element may be a surface or a portion of a surface. A sensor element may comprise one or more particles. A sensor element may comprise multiple surfaces capable of adsorbing or binding biomolecules. A sensor element may comprise a porous material (e.g., a material into which biomolecules can enter).

[0088] As used herein, “sensor array” may include a plurality of sensor elements, wherein the plurality of sensor elements (e.g., particles) include a variety of types of sensor elements. The sensor elements may be of different types, each having at least one different physicochemical property from the others. A sensor array may be a substrate having a plurality of partitions, each containing a plurality of sensor elements (e.g., particles). For example, a sensor array may include a multiwell plate having a plurality of particles distributed between a plurality of wells. A sensor array may be a substrate having a plurality of partitions, wherein the plurality of partitions contain a plurality of particles. In some embodiments, each sensor element or particle may bind to a plurality of biomolecules in the sample, generating 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 multiple different biomolecules that bind to the sensor element. The term “biomolecular corona” may also refer to proteins, lipids, and other plasma components that bind to particles (e.g., nanoparticles) when in contact with a biological sample or biological system. For use herein, the term “biomolecular corona” also refers to Milani et al.'s “Reversible versus Irreversible Binding of Transferring to Polystyrene Nanoparticles: Soft and Hard This includes both soft protein coronas and hard protein coronas, as referred to in "Corona" (ACS NANO, 2012, 6(3), pp.2532-2541; 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 these are incorporated herein by reference). As described in those texts, the adsorption curve shows an accumulation of a single, strongly bound layer, extending to a saturation point of the single layer (at a geometrically defined protein-to-NP ratio), beyond which a second, weakly bound layer is formed. The first layer is irreversibly bound (hard corona), while the second layer (soft corona) may exhibit dynamic exchange. Proteins adsorbing with high affinity may form a "hard" corona containing strongly bound proteins that do not readily desorb, while proteins adsorbing with low affinity may form a "soft" corona containing loosely bound proteins. Soft and hard coronas can also be characterized based on their exchange time. Hard coronas can exhibit much longer exchange times, on the level of several hours. See, for example, M. Rahman et al., Protein-Nanoparticle Interactions, Spring Series in BiopHysics 15, 2013 (the entire work is referenced by reference).

[0090] The term “biomolecules” refers to biological components that can form a corona (including, but not limited to, proteins, polypeptides, polysaccharides, sugars, lipids, lipoproteins, metabolites, oligonucleotides, metabolomes, or combinations thereof). A biomolecular corona of a specific particle may contain several of the same biomolecules, may contain biomolecules specific to other sensor elements, and / or the level, quantity, type, or conformation of the biomolecules that bind to each sensor element may differ. In one embodiment, the biomolecules are selected from the group consisting of proteins, nucleic acids, lipids, and metabolomes.

[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 quantity, level, or amount of the biomolecules bound to the sensor element, or to differences in the three-dimensional structure of the biomolecules bound to the particle or sensor element. It is intended that the biomolecular corona signature of each unique type of sensor element may contain some of the same biomolecules, some unique to other sensor elements, and / or may differ in the quantity, type, or three-dimensional structure of various biomolecules. The biomolecular corona signature may depend not only on the physicochemical properties of the sensor element (e.g., particles), but also on the nature of the sample and the duration of exposure to the biological sample.

[0092] This specification discloses compositions and methods for multi-omics analysis. “Multi-omics” or “multi-omics” may refer to analytical approaches for the analysis of biomolecules on a large scale, where the dataset is a diverse ome (e.g., proteome, genome, transcriptome, lipidome, and metabolome). Non-exclusive examples of multi-omics data include proteomics data, genomic data, lipidomics data, glycomics data, transcriptomics data, or metabolomics data.

[0093] The term "biomolecule" in "biomolecule corona" may 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 biomolecules are 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 at plasma proteomic analysis to increase the number of markers, relatively few new candidates have been recognized as clinically useful indicators. The plasma proteome contains >10,000 proteins and potentially more than 10 orders of magnitude more protein isoforms, spanning a concentration range (mg / mL to pg / mL). These attributes, coupled with the lack of convenient molecular tools for proteomic analysis, make comprehensive studies of the plasma proteome extremely difficult. Approaches to overcome the broad dynamic range of proteins in biological samples must be able to identify and quantify against a background of thousands of unique proteins and many more protein variants. However, there are no existing technologies that can simultaneously measure proteins across all plasma concentration ranges in a format with sufficient throughput and a realistic cost profile that allows for studies of an appropriate scale with expected validation and reproducibility robustness. These challenges have not only limited the discovery of novel disease biomarkers but have also hindered the adoption of proteogenomics and protein annotation of genomic variants. Advances in mass spectrometry (MS) and the development of improved data analysis have provided tools for deep and broad proteomic analysis. Several attempts have been made to significantly improve the detection of low-abundance proteins (e.g., depletion of very abundant proteins, plasma fractionation, and peptide fractionation). Currently, it is possible to identify more than 4,500 proteins in plasma. However, current approaches are quite complex and time-consuming (days to weeks), thus requiring a trade-off between the range of protein coverage and sample throughput. Therefore, a simple and robust strategy for comprehensive and rapid analysis of the available information in the proteome remains an unmet need.

[0095] Furthermore, the earlier a disease is diagnosed, the greater the likelihood that it will be cured or well managed, thereby leading to a better prognosis for the patient. Early treatment of a disease can prevent or delay problems caused by the disease, potentially improving 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 well treated in their early stages. For example, the five-year survival rates for breast, ovarian, and lung cancers are 15%, 5%, and 10%, respectively, for patients diagnosed at the most advanced stages of the disease, compared to 90%, 90%, and 70% after early diagnosis and treatment. Once cancer cells leave the primary tissue, the chances of successful treatment with available established therapies become very slim. Early diagnosis can be achieved by recognizing warning signs of cancer and taking prompt action, but the vast majority of cancers (e.g., lung cancer) only show signs after cancer cells have invaded surrounding tissues and metastasized throughout the body. For example, more than 60% of patients with breast, lung, colon, and ovarian cancer may have latent or even metastatic colonies by the time the cancer is discovered. Therefore, there is an urgent need to develop effective approaches for the early detection of cancer. Such an approach should possess 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. 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, but a broadly valid platform for the early detection of a wide range of cancers remains difficult to achieve.

[0097] Because various types of cancer can alter plasma composition even in their 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 the early detection of the vast majority of cancers still do not 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 (and other diseases) at a very early stage. Automated sample preparation

[0098] This disclosure provides systems and methods for automated sample preparation, data generation, and protein corona analysis. As illustrated in Figure 1, the systems and methods may 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 a sample for analysis (e.g., using mass spectrometry ("MS"). For example, in (1), the method of this disclosure may include contacting a biological sample with a plurality of particles. In (2), the sample may be incubated with the plurality of particles to promote the adsorption of biomolecules to the particles. In (3), the unbound sample may be removed while retaining the particles and the biomolecules adsorbed to those particles. In (4), the adsorbed biomolecules may be desorbed from the particles and prepared for mass spectrometry (which may generate illustrative data).

[0099] This disclosure provides an automated system, method, and kit for the preparation and analysis of biomolecular corona. The automated instrument can perform the data generation steps schematicly described in Figure 1, using at least the various units illustrated in Figure 2. The automated instrument may include a substrate having a plurality of partitions containing a sensor element 205 and a biological sample 210. A loading unit 215 on the instrument may transfer portions of the biological sample 210 into partitions on the substrate 205, causing adsorption of biomolecules from the biological sample onto the sensor element in the partitions on the substrate. The automated instrument may then remove unbound biomolecules from the partitions and optionally transfer the unbound sample into a waste storage unit 220. The remaining biomolecules (e.g., biomolecules adsorbed onto the sensor element) can be desorbed, collected, and prepared for mass spectrometry. The reagent 225 may include a buffer (e.g., a resuspension buffer capable of desorbing biomolecules from biomolecular corona, or a denaturing buffer capable of denaturing or fragmenting biomolecules). Reagent (e.g., buffer protease) 225 may also be loaded using the loading unit 215 to facilitate any of the aforementioned actions.

[0100] In some embodiments, the Disclosure provides an automated system comprising a network of units having differentiated functions in identifying the state of a complex biological sample using a plurality of particles having surfaces having different physicochemical properties, wherein a first unit comprises a multi-channel fluid transfer device for transferring fluid from unit to unit within the system; a second unit comprises a support for storing the plurality of biological samples; and a third unit comprises a sensor array plate having partitions containing the plurality of particles having surfaces having different physicochemical properties (e.g., a substrate containing a plurality of partitions containing sensor elements (e.g., a 96-well plate containing nanoparticles) for detecting the binding interaction between a population of analytes in the complex biological sample and the plurality of particles; The system includes a support for (etc.) a fourth unit, which includes a support for storing a plurality of reagents; a fifth unit, which includes a support for storing reagents to be discarded; a sixth unit, which includes a support for storing consumables used by the multi-channel fluid transfer equipment; and further, the system is programmed to perform a series of steps including: bringing the biological sample into contact with a specific 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 the population of analytes interacting with the particles; and preparing a sample for mass spectrometry.

[0101] An example of such equipment is shown in Figure 3. The equipment 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 equipment can perform a biomolecular corona assay, which includes transferring a portion of the biological sample into a partition within the substrate containing the sensor elements, incubating the portion of the sample with the sensor elements to bind biomolecules from the biological sample to the sensor elements, removing contents from the partition containing biomolecules not bound to the sensor elements, and then preparing the remaining biomolecules in the partition for mass spectrometry (MS) analysis (e.g., LC-MS).

[0102] The loading mechanism may have some degree of mobility to allow access to all other units within the system. The loading mechanism may also have the capability to perform a pipetting function.

[0103] The systems or apparatus of the present disclosure 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 systems or apparatus of the present disclosure may include a thermal unit capable of controlling the temperature of the supports and the sample. In some embodiments, the systems or apparatus of the present disclosure may include a rotating unit capable of physically stirring and / or mixing the sample.

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

[0105] In some embodiments, the fourth unit includes a set of reagents for generating the sensor array plate; washing unbound samples; and / or preparing samples for mass spectrometry. In some embodiments, contacting the biological sample with a particular partition of the sensor array includes pipetting a particular volume of the biological sample into the particular partition of the sensor array. In some embodiments, contacting the biological sample with a particular partition of the sensor array includes pipetting volumes corresponding to a ratio of multiple particles in a 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, bringing the biological sample into contact with a specific partition of the sensor array involves 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. automation equipment

[0107] In some embodiments, the Disclosure provides an automated apparatus for generating a subset of biomolecules from a biological sample, comprising a substrate having multiple partitions, a first unit containing the biological sample, and a loading unit that is movable across the entire substrate and capable of transferring a volume (e.g., a volume of buffer) between different units of the apparatus. In some cases, the substrate is a multiwell 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 fine particles).

[0109] The partitions within the aforementioned multiple partitions may contain 1 to 100 types of sensor elements (e.g., unique particle species). The partitions within the aforementioned multiple partitions may contain 2 to 50 types of sensor elements. The partitions within the aforementioned multiple partitions may contain 2 to 5 types of sensor elements. The partitions within the aforementioned multiple partitions may contain 3 to 8 types of sensor elements. The partitions within the aforementioned multiple partitions may contain 4 to 10 types of sensor elements. The partitions within the aforementioned multiple partitions may contain 5 to 12 types of sensor elements. The partitions within the aforementioned multiple partitions may contain 6 to 15 types of sensor elements. The partitions within the aforementioned multiple partitions may contain 8 to 20 types of sensor elements.

[0110] Two or more partitions among the aforementioned multiple partitions may contain different numbers of sensor elements. Two or more partitions among the aforementioned multiple partitions may contain different types of sensor elements. Partitions within the multiple partitions may contain a combination of types and / or numbers of sensor elements (one or more) that differ from other partitions in the multiple. A subset of partitions within the multiple partitions may each contain a unique combination of sensor elements that is unique to the other partitions in the multiple.

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

[0112] The partitions within the plurality of partitions may contain sensor elements of different concentrations or amounts (e.g., in mass / moles per unit volume of sample). The partitions within the plurality of partitions may contain sensor elements of 1 pM to 100 nM. The partitions within the plurality of partitions may contain sensor elements of 1 pM to 500 pM. The partitions within the plurality of partitions may contain sensor elements of 10 pM to 1 nM. The partitions within the plurality of partitions may contain sensor elements of 100 pM to 10 nM. The partitions within the plurality of partitions may contain sensor elements of 500 pM to 100 nM. The partitions within the plurality of partitions may contain sensor elements of 50 μg / ml to 300 μg / ml. The partitions within the plurality of partitions may contain sensor elements of 100 μg / ml to 500 μg / ml. The partitions within the plurality of partitions may contain sensor elements of 250 μg / ml to 750 μg / ml. The partitions within the aforementioned multiple partitions may include sensor elements with concentrations of 400 μg / ml to 1 mg / ml. The partitions within the aforementioned multiple partitions may include sensor elements with concentrations of 600 μg / ml to 1.5 mg / ml. The partitions within the aforementioned multiple partitions may include sensor elements with concentrations of 800 μg / ml to 2 mg / ml. The partitions within the aforementioned multiple partitions may include sensor elements with concentrations of 1 mg / ml to 3 mg / ml. The partitions within the aforementioned multiple partitions may include sensor elements with concentrations of 2 mg / ml to 5 mg / ml. The partitions within the aforementioned multiple partitions may include sensor elements with concentrations exceeding 5 mg / ml.

[0113] The loading unit may be configured to move between any unit, compartment, or partition within the instrument and to transfer a volume (e.g., a volume of solution or powder) between them. The loading unit may be configured to move a precise volume (e.g., within 0.1%, 0.01%, or 0.001% of a particular volume). The loading unit may 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 a portion of the volume into different units, compartments, or partitions. The loading unit may be configured to move a variety of volumes (e.g., 2 to 400 separate volumes) simultaneously. The loading unit may include multiple pipette tips.

[0114] The loading unit may be configured to move a volume of lipids. The volume may be approximately 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 lipids may be biological samples or solutions.

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

[0116] Partly due to these functionalities, the loading unit may be able to partition the sample. In some embodiments, this includes distributing the sample into a number of partitions. The 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. The sample may be distributed into 96, 192, or 384 partitions. The automated equipment may include a variety of substrates including partitions. The automated equipment may include 1, 2, 3, 4, 5, or more substrates including 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 the biological sample loaded into the partition may be approximately 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 the biological sample loaded into the partition may be approximately 10 μl to 400 μl. The volume of the biological sample loaded into the partition may be approximately 5 μl to 150 μl. The volume of the biological sample loaded into the partition may be approximately 35 μl to 80 μl. In some cases, the loading unit may dispense two or more biological samples. For example, a sample storage unit may contain two biological samples that the system dispenses into a one-well plate.In some embodiments, the loading unit may facilitate the transfer of the sample to the mass spectrometry unit for mass spectrometry.

[0117] The system may be configured to dilute a sample or sample partition. The sample or sample partition may be diluted with a buffer, water (e.g., purified water, a non-aqueous solvent, or any combination thereof). The diluent may be stored in the automated equipment before being distributed into the substrate partition. The automated equipment may store multiple diluents with different pH, salinity, osmolality, viscosity, dielectric constant, or any combination thereof. The diluent may be used to adjust the chemical properties of the sample or sample partition. The automated equipment may 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. Superdilution is possible. The automated equipment can perform different dilutions on two samples or sample partitions. The system can perform different dilutions on each partition in a plurality of partitions. For example, the system can perform different dilutions on each of the 96 sample partitions in a 96-well plate. In some cases, the different dilutions include dilutions of different degrees (e.g., 2x versus 4x). In some cases, the different dilutions include dilutions with different solutions (e.g., different buffers). In some cases, the two sample partitions can be prepared to have one or more different chemical properties (e.g., pH, salinity, or viscosity).

[0118] In some cases, the system may modify the chemical composition of a 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 involve mixing the reagent from the fourth unit with the sample or sample partition. The system may modify two samples or sample partitions to have different chemical compositions.

[0119] The systems or automated equipment of this disclosure may also include incubation elements. The incubation elements may contact, support, or hold another component of the automated equipment (e.g., the substrate or unit). The incubation unit may contact, support, or hold various components of the automated equipment. The incubation elements may contact the substrate and facilitate heat transfer between the incubation element and the substrate. The incubation unit may be configured to regulate the temperature of one or more components of the automated equipment (e.g., by heating or cooling). The incubation elements may be capable of cooling the components of the equipment to 20°C to 1°C. The incubation elements may be capable of heating the components of the equipment to 25°C to 100°C. The incubation elements may be capable of adjusting the temperature of the components of the equipment to 4°C to 37°C. The incubation element may be configured to heat or cool different parts of the automated equipment 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 the sample or partition. The incubation element may include a temperature sensor (e.g., a thermocouple) for detecting the temperature inside the partition or container. The incubation element may calibrate its heating or cooling based on readings from the temperature sensor.

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

[0121] The automated apparatus 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 within the plurality of partitions of the substrate. In some cases, this may result in dilution of the sample present in the partition and further desorption of multiple biomolecules from the biomolecular corona placed 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 in the partition, and the type of sensor element and biomolecular composition in the biomolecular corona. Transferring a volume of the resuspension solution into the partition may result in the desorption of less than 5% of the biomolecules from the biomolecular corona. Transferring a volume of the resuspension solution into the partition may result in the desorption of 10% to 20% of the biomolecules from the biomolecular corona. Transferring a volume of the resuspended solution into the partition may result in the desorption of 20% to 30% of the biomolecules from the biomolecule corona. Transferring a volume of the resuspended solution into the partition may result in the desorption of 30% to 40% of the biomolecules from the biomolecule corona. Transferring a volume of the resuspended solution into the partition may result in the desorption of 40% to 50% of the biomolecules from the biomolecule corona. Transferring a volume of the resuspended solution into the partition may result in the desorption of 50% to 60% of the biomolecules from the biomolecule corona. Transferring a volume of the resuspended solution into the partition may result in the desorption of 60% to 70% of the biomolecules from the biomolecule corona. Transferring a volume of the resuspended solution into the partition may result in the desorption of 70% to 80% of the biomolecules from the biomolecule corona. Transferring a volume of the resuspended solution into the partition may result in the desorption of 80% to 90% of the biomolecules from the biomolecule corona.Transferring the volume of the resuspended solution into the partition can result in the desorption of more 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 may be collected, analyzed, or discarded. The types and amounts of biomolecules in the supernatant may differ between desorption rounds. The automated instrument may perform one or more desorption and discard cycles (i.e., washing) (followed by one or more desorption cycles including sample collection and / or analysis).

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

[0124] The automated apparatus may include a unit containing a denaturation solution. The denaturation solution may contain a protease. The denaturation solution may contain a chemical capable of peptide cleavage (e.g., cyanogen bromide, formic acid, or hydroxylamine, 2-nitro-5-thiocyanatobenzoic acid). The denaturation solution may contain 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 denaturation solution may contain a reducing agent (e.g., 2-mercaptoethanol, dithiothreitol, or tris(2-carboxyethyl)phosphine). The protease may be trypsin. The denaturation solution may be added to a partition following desorption. The denaturation solution may be added to a partition containing a biomolecule corona.

[0125] The automated device may include magnets or arrays of magnets. The automated device may be capable of moving the substrate onto and away from the magnets or arrays of magnets. The array of magnets may be constructed so that multiple magnets of the array of magnets can rest directly beneath multiple partitions of the substrate. The magnets may be capable of fixing 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 cause pellets to be generated from a collection of magnetic particles. The magnets may cause particle pellets to be generated in less than 10 minutes. The magnets may cause particle pellets to be generated in less than 5 minutes. The particle pellets may include particles having a biomolecular corona.

[0126] The automated apparatus may include a purification unit. The purification unit may include a plurality of partitions containing an adsorbent or resin. 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 apparatus may include a unit having a conditioning solution for the purification unit (e.g., a conditioning solution for the solid-phase extraction material). The automated apparatus may include a unit having an eluent 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 the group of analytes interacting with the plurality of sensor elements (i.e., a washing step). In some examples, the automated equipment 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 that are 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 most of the hard corona analytes bound to the sensor elements.

[0128] In some embodiments, the Disclosure provides an automated instrument for identifying proteins in a biological sample, the automated instrument comprising a sample preparation unit; a substrate having multiple channels; multiple pipettes; multiple solutions; multiple particles, wherein the automated instrument is configured to form and digest a 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 having multiple channels; multiple pipettes; multiple solutions; multiple nanoparticles, wherein the automated instrument is configured to cause protein corona formation 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 plurality of nanoparticles to the substrate using the plurality of pipettes. In some embodiments, the sample preparation unit is configured to add the biological sample to the substrate using the plurality of pipettes. In some embodiments, the sample preparation unit is configured to incubate the plurality of nanoparticles and 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. In 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 apparatus further includes a magnetic source. In some embodiments, the automated apparatus is configured for trypsin digestion of BCA, gel, or protein corona.

[0133] In some embodiments, the automated equipment is enclosed. In some embodiments, the automated equipment is sterilized before use. In some embodiments, the automated equipment is adapted for mass spectrometry. In some embodiments, the automated equipment is temperature-controlled. Assay method

[0134] In some embodiments, the 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; having the automated instrument generate proteomics data; and quantifying the proteomics data.

[0135] In some embodiments, the method includes incubating a plurality of biomolecules with the biological sample in the automated apparatus 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 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 2 The incubation time includes 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 includes 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 includes an incubation temperature of about 4°C to about 100°C.

[0136] The methods, systems, and apparatus of the present disclosure may include covering or sealing a partition on the substrate. This may include covering the surface of the apparatus with a lid or seal. The lid or seal may prevent a solution or seed from leaving the partition (e.g., evaporating from the partition). The automated apparatus may be configured to add and / or remove the lid or seal. The lid or seal may be punctureable (e.g., including a septum) so that a syringe or needle can enter the substrate partition without removing the lid or seal.

[0137] In some cases, the systems, instruments, and methods of the present disclosure 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 instrument. The biomolecular corona may be separated from the supernatant by removing the supernatant and then desorbing a plurality of proteins from the biomolecular corona into a desorbing solution (e.g., a resuspension solution). In some cases, a first portion of the biomolecules from the biomolecular corona is desorbed from the biomolecular corona and discarded, and a second portion of the biomolecules from the biomolecular corona is desorbed from the biomolecular corona and collected (e.g., for analysis). Multiple portions of the biomolecules from the biomolecular corona may be desorbed, collected, and analyzed separately.

[0138] In some cases, the biomolecules within the biomolecular corona undergo denaturation, fragmentation, chemical modification, or any combination thereof. These treatments may be performed on the desorbed biomolecules or on the biomolecular corona. The plurality of biomolecules desorbed from the biomolecular corona may contain 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 may be carried out over different durations 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 involves physical stirring (e.g., shaking or sonication). The percentage of protein desorbed from the particle corona may depend on the desorption time, the chemical composition of the desorbing agent solution into which the protein is desorbed (e.g., pH or buffer species), the desorption temperature, the form and intensity of the physical stirring applied, or any combination thereof. Furthermore, the type of protein desorbed from the protein corona may be responsive to the desorption conditions and method. The types of proteins detached from the protein corona may 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 detachment conditions or methods.

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

[0140] The desorbed biomolecules can be collected for further analysis (e.g., mass spectrometry). The automated instrument may perform the collection by, for example, collecting a sample volume from a substrate partition containing biomolecules desorbed from the biomolecular corona. The method involves setting up a partition or a group of partitions (e.g., a well plate) (these may be installed directly within the instrument for performing the analysis).

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

[0142] Different rounds of analyte preparation may be performed in separate partitions. Partitions may also be exposed to different analyte preparation conditions. Performing more rounds of analyte preparation may increase the number or types of proteins collected for analysis (e.g., producing more proteins within the concentration range available for simultaneous mass spectrometry detection). The number or types of proteins detected when multiple rounds of analyte preparation are performed may 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 a partition. Such immobilization can prevent the sensor element from being removed from the partition when volume is removed from the partition (for example, the loading unit removes 95% of the solution from the partition). Immobilization may be performed chemically, for example (e.g., covalent or non-covalent bonding to a substrate). Chemical immobilization may include reacting the sensor element with the surface of the partition. Chemical immobilization may also include non-covalent bonding of the sensor element to the surface of the partition. For example, the sensor element may include a biotin moiety that binds to streptavidin bound to the surface of the partition. Immobilization may be achieved by applying a magnetic field to hold a magnetic sensor element within a partition. For example, a plurality of sensor elements may include a plurality of magnetic particles, and the substrate and the magnet may be in close proximity such that one or more of the magnetic particles are immobilized within the partition in the substrate. Immobilization may be achieved by supplying the substrate with the sensor element formed or embedded within the partition on the substrate. For example, the sensor element may be a semiparticle formed on the surface of a partition within the substrate.

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

[0145] Figure 4 shows an example of a method including the immobilization of a sensor element, which can be performed by the automated equipment of this disclosure. These methods utilize particles 402 and 411 to capture subsets 403 and 404 of biomolecules in the 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 cannot be adsorbed by the particles and will instead be suspended within the partition. Panel 410 shows an alternative method, which includes particles 411 formed on the surface of the partition.

[0147] Panels 420 and 430 illustrate 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 crosslinked 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 the washing step on the partition from panels 410, 420, and 430. In all three cases, the washing removes unbound biomolecules from the partition while leaving the immobilized particles and the biomolecules adsorbed thereon. Panel 450 then shows the desorption of the biomolecules from the corona, where a first plurality of biomolecules 451 are eluted from the particles and a second plurality of biomolecules 403 remain 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). The eluted biomolecules can be collected for further processing (e.g., fragmentation) or direct analysis (e.g., by a loading unit).

[0149] Figure 5 shows an example of a sample preparation method that may be performed by the automated equipment of this disclosure. The method utilizes a sensor element 512 to generate a subset of biomolecules from a biological sample 502. The biological sample (shown in panel 500) (stored in a sample container 501) contains a number of biomolecules. The volume of the sample may optionally be processed 504 (for example, cells in 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 the sensor element 512. As illustrated in panel 520, portions of the biomolecules 521 may bind to the sensor element and thereby be separated from portions of biomolecules 522 that do not bind to the sensor element. As shown in panel 530, the sensor element may then be immobilized within the partition by bringing the partition into contact with a magnet 531. The partition may then be subjected to a washing cycle (e.g., adding a buffer to the partition and then removing the sample from the partition) to remove any biomolecules 522 that are not bound to the sensor element (as shown in panel 540). The bound biomolecules 521 are eluted from the sensor element and can be collected for further processing or analysis.

[0150] Figure 6 shows a sample preparation method that may be performed by the automated equipment 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 holding 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 the unbound biomolecules, leaving the sensor element 512 and the sensor element-bound biomolecules 521 within the partition. These biomolecules can 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 may include filtering a sensor element from a solution. For example, the method may include desorbing a plurality of biomolecules from a biomolecular corona bound to the sensor element, and filtering the solution such that the sensor element is collected on a filter and the plurality of biomolecules remain in the solution. The filtration may be performed after denaturation (e.g., digestion). The filtration may also remove a plurality of 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 the 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 the purification unit. The purification may include transferring multiple sample partitions from the substrate into separate partitions within 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 denatured solution. Following the purification, the biological sample may be recollected in the substrate or purification unit for further enrichment or chemical treatment, or collected for direct analysis (e.g., mass spectrometry).

[0153] In summary, the methods of the present disclosure enable advanced profiling depth for biological samples. The subset of biomolecules collected in the methods of the present disclosure may enable 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 a given type of biomolecule in the biological sample from which the subset of biomolecules was collected, without further manipulation or modification of the subset of biomolecules. The subset of biomolecules may enable the 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 a particular type of protein 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 enable the simultaneous mass spectrometric detection of two biomolecules (e.g., proteins) in a sample at concentrations of 6, 7, 8, 9, 10, 11, 12, or more orders of magnitude, without further manipulation or modification of the subset of biomolecules. For example, the two biomolecules may be desorbed and collected at concentrations within a 6-order-of-magnitude range in a single sample, fragmented, and then subjected to mass spectrometry.

[0154] In some cases, a certain type of sensor element (for example, all sensor elements of a given type in contact with a single sample) adsorbs at least 100–300 types of proteins when in contact with a biological sample. A certain type of sensor element adsorbs at least 200–500 types of proteins when in contact with a biological sample. A certain type of sensor element adsorbs at least 300–800 types of proteins when in contact with a biological sample. A certain type of sensor element adsorbs at least 400–1000 types of proteins when in contact with a biological sample. A certain type of sensor element adsorbs at least 500–1200 types of proteins when in contact with a biological sample.

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

[0156] The method described herein may result in enrichment of low-abundance biomolecules (e.g., proteins) from a biological sample. Low-abundance biomolecules may be biomolecules with a concentration of 10 ng / mL or less in the biological sample.

[0157] The method of this disclosure may 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 may 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 for characterizing plasma proteomes (Keshishian et al., Mol. Cell Proteomics 14, 2375-2393 (2015), Plasma Proteome Database (plasmaproteomedatabase.org))) may provide a basis for comparison to determine whether the detected protein or biomolecule is enriched by other biomolecules (one or more) present in the plasma sample. Similar databases may be used for other types of biological samples.

[0158] In certain cases, the biological sample includes blood, plasma, or serum, and the biomolecular corona has a lower albumin-to-non-albumin proteins ratio than the biological sample. In the biomolecular corona, the albumin-to-non-albumin proteins ratio may be 20%, 30%, 40%, 50%, 60%, or 70% lower than in the sample from which the proteins have been 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 biomolecular species whose concentrations are within six orders of magnitude of the most concentrated biomolecules in the sample by at least 25%, 50%, 100%, 200%, 300%, 500%, or 1000%. Similarly, the automated instrument may increase the number of protein species whose concentrations are within six orders of magnitude of the most abundant biomolecules in the sample within the compressed dynamic range. The automated instrument can increase the number of protein species whose concentrations are within six orders of magnitude of the most concentrated proteins 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 this subset of biomolecules may include at least 10% of the biomolecular species from the biological sample within a six-order-of-magnitude concentration range. The automated device can enrich a subset of biomolecules from a biological sample, and the subset of biomolecules may contain at least 20% of the types of biomolecules from the biological sample in a concentration range of six orders of magnitude. The automated device can enrich a subset of biomolecules from a biological sample, and the subset of biomolecules may contain at least 30% of the types of biomolecules from the biological sample in a concentration range of six orders of magnitude. The automated device can enrich a subset of biomolecules from a biological sample, and the subset of biomolecules may contain at least 40% of the types of biomolecules from the biological sample in a concentration range of six orders of magnitude. The automated device can enrich a subset of biomolecules from a biological sample, and the subset of biomolecules may contain at least 50% of the types of biomolecules from the biological sample in a concentration range of six orders of magnitude. The automated apparatus can enrich a subset of biomolecules from a biological sample, and the subset of biomolecules may contain at least 60% of the types of biomolecules from the biological sample in a concentration range of six orders of magnitude.The automated device can enrich a subset of biomolecules from a biological sample, and the subset of biomolecules may contain at least 70% of the types of biomolecules from the biological sample in a concentration range of six orders of magnitude. The automated device can enrich a subset of biomolecules from a biological sample, and the subset of biomolecules may contain at least 10% of the types of proteins from the biological sample in a concentration range of six orders of magnitude. The automated device can enrich a subset of biomolecules from a biological sample, and the subset of biomolecules may contain at least 20% of the types of proteins from the biological sample in a concentration range of six orders of magnitude. The automated device can enrich a subset of biomolecules from a biological sample, and the subset of biomolecules may contain at least 30% of the types of proteins from the biological sample in a concentration range of six orders of magnitude. The automated device can enrich a subset of biomolecules from a biological sample, and the subset of biomolecules may contain at least 40% of the types of proteins from the biological sample in a concentration range of six orders of magnitude. The automated device can enrich a subset of biomolecules from a biological sample, and the subset of biomolecules may contain at least 50% of the types of proteins from the biological sample in a concentration range of six orders of magnitude. The automated device can enrich a subset of biomolecules from a biological sample, and the subset of biomolecules may contain at least 60% of the types of proteins from the biological sample in a concentration range of six orders of magnitude. The automated device can enrich a subset of biomolecules from a biological sample, and the subset of biomolecules may contain at least 70% of the types of proteins from the biological sample in a concentration range of six orders of magnitude.

[0160] The methods and sensor elements of this disclosure can be adapted such that the composition of the biomolecular corona remains invariant to the sample lipid concentration. A change of up to 10% in the lipid concentration in the biological sample may result in a change of less than 5%, 2%, 1%, or 0.1% in the protein composition of the biomolecular corona. A change of up to 10% in the lipid concentration in the biological sample may result in a change of less than 5%, 2%, 1%, or 0.1% in the number of protein types in the biomolecular corona. A change of up to 10% in the lipid concentration in the biological sample may result in a change of less than 5%, 2%, 1%, or 0.1% in the total number of proteins in the biomolecular corona.

[0161] In some embodiments, the method further includes washing the digested sample in the automated apparatus. In some embodiments, quantifying the proteomics data includes providing the proteomics data to a mass spectrometer. In some embodiments, the biological sample is a body fluid. In some embodiments, the body fluid is serum or plasma.

[0162] In some cases, the total assay time (including sample preparation and LC-MS) required for a single sample (e.g., a pooled plasma sample) can be approximately 8 hours. The total assay time (including sample preparation and LC-MS) required for a single sample (e.g., a pooled plasma sample) can be approximately: 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. It could be minutes, at least 30-40 minutes, at least 40-50 minutes, at least 50-60 minutes, at least 1-1.5 hours, at least 1.5-2 hours, at least 2-2.5 hours, at least 2.5-3 hours, at least 3-3.5 hours, at least 3.5-4 hours, at least 4-4.5 hours, at least 4.5-5 hours, at least 5-5.5 hours, at least 5.5-6 hours, at least 6-6.5 hours, at least 6.5-7 hours, at least 7-7.5 hours, at least 7.5-8 hours, at least 8-8.5 hours, at least 8.5-9 hours, at least 9-9.5 hours, or at least 9.5-10 hours. Dynamic range

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

[0164] The biomolecular corona analysis methods described herein may compress the dynamic range of the assay. The dynamic range of an assay may be compressed compared to another assay if the slope of the assay signal intensity as a function of biomolecular 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 may have a compressed dynamic range compared to a plasma sample assayed using mass spectrometry alone (the assay is performed directly on the sample or by comparing it to the abundance values ​​of plasma proteins 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 may allow for the detection of lower abundances of biomolecules in a biological sample when biomolecular corona analysis is used in conjunction with mass spectrometry than when mass spectrometry is used alone.

[0165] In some embodiments, the dynamic range of a proteomics analysis assay may be the ratio of the signal generated by the most abundant protein (e.g., the top 10% of proteins) to the signal generated by the least abundant protein (e.g., the bottom 10% of proteins). Compressing the dynamic range of a proteomics analysis may involve reducing the ratio of the signal generated by the most abundant protein to the signal generated by the least abundant protein in a first proteomics analysis assay compared to the ratio in a second proteomics analysis assay. 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] This specification provides several methods for compressing the dynamic range of a biomolecular analysis assay to facilitate the detection of low-abundance biomolecules compared to high-abundance biomolecules. For example, the particle species of this disclosure may be used to sequentially examine a sample. When the particle species is incubated in the sample, a biomolecular corona is formed on the surface of the particle species. If the biomolecules are detected directly in the sample without using the particle species (e.g., by direct mass spectrometry of the sample), the dynamic range may extend to a wider concentration range or by a greater number of orders of magnitude than when the biomolecules are detected on the surface of the particle species. Therefore, using the particle species disclosed herein may be used to compress the dynamic range of biomolecules in a sample. Although not bound by theory, this effect may be observed because the biomolecular corona of the particle species captures more biomolecules with higher affinity and lower abundance, and fewer biomolecules with lower affinity and higher abundance.

[0167] The dynamic range of a proteomics analysis assay may be the slope of the plot of the protein signal measured by the proteomics analysis assay as a function of the total abundance of protein in the sample. Compressing the dynamic range may involve reducing the slope of the plot of the protein signal measured by the proteomics analysis assay as a function of the total abundance of protein in the sample compared to the slope of the plot of the protein signal measured by a second proteomics analysis assay as a function of the total abundance of protein in the sample. 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 system

[0168] Various aspects of the present disclosure provide an automated system comprising: 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; a computer including one or more computer processors; and a computer-readable medium including machine-executable code that generates a biological fingerprint (when its code is executed) and assigns a biological state based on the biological fingerprint.

[0169] In many cases, the automated equipment includes a sensor element or a set of sensor elements that adsorb biomolecules from a biological solution, thereby forming a biomolecular corona. The type, quantity, and category of biomolecules constituting 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, not only is the composition of the biomolecular corona affected by which biomolecules interact with the sensor element, but which other different biomolecules may similarly interact with that particular sensor element.

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

[0171] The plurality of sensor elements, upon contact 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 each of the plurality of sensor elements. The biomolecular fingerprint may contain at least 5, 10, 20, 40, 80, 150, or 200 unique biomolecular corona signatures.

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

[0173] Similarly, the computer may 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 attachment and detachment). This can achieve profiling sensitivity that is not possible with conventional methods. Many biological conditions (such as many pre-disease conditions) cause minute changes in patient biological samples (e.g., blood, urine, etc.) that are often unidentifiable by biomarker analysis alone. The capabilities of this instrument, system, method, and sensor element derive in part from the interdependence of the sensor element's properties and the composition of the biological sample with respect to the biomolecular corona composition. Therefore, small collective changes in biomolecules with low abundance, small changes in chemical state (e.g., post-translational modification state), or even 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 apparent from a single set of data can be clearly elucidated by the correlation between different biomolecular abundances across diverse biomolecular corona signature or sample partition measurements. Therefore, healthy subjects and cancer-affected subjects can be distinguished with high accuracy by using combinations of nearly identical biomolecular coronavirus signatures.

[0174] In some cases, the computer is configured to process data including the intensity of mass spectrometry signals or tandem mass spectrometry signals between a plurality of the unique biomolecular corona signatures, APEX, spectral counts or numbers of peptides, and ion mobility behavior. 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 may 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 may 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] Aspects of this disclosure provide methods for generating a biomolecular fingerprint 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 may be used to determine the concentration of biomolecules from a biological sample. Multiple sample partitions may be subjected to a single mass spectrometry or tandem mass spectrometry run. Multiple sample partitions may also be pooled and analyzed together in a single mass spectrometry or tandem mass spectrometry run. Multiple mass spectrometry runs may be coupled with a variety of different chromatographic methods (e.g., different columns, buffers, or gradients). Single mass spectrometry or tandem mass spectrometry runs are performed for less than 2 hours, less than 1 hour, or less than 1 / 2 hour.

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

[0177] As used herein, the term “sensor element” refers to an element capable of binding to multiple biomolecules upon contact with a sample, and also encompasses the term “particle.” The sensor element may be an element measuring approximately 5 nanometers (nm) to approximately 50,000 nm in at least one direction.Suitable sensor elements are not limited to these, but include, for example, sensor elements from approximately 5 nm to approximately 50,000 nm in at least one direction, and also include approximately 5 nm to approximately 40,000 nm, or approximately 5 nm to approximately 30,000 nm, or approximately 5 nm to approximately 20,000 nm, or approximately 5 nm to approximately 10,000 nm, or approximately 5 nm to approximately 5,000 nm, or approximately 5 nm to approximately 1,000 nm, or approximately 5 nm to approximately 500 nm, or approximately 5 nm to approximately 50 nm, or approximately 10 nm to 100 nm, or approximately 20 nm to 200 nm, or approximately 30nm~300nm, or approximately 40nm~400nm, or approximately 50nm~500nm, or approximately 60nm~600nm, or approximately 70nm~700nm, or approximately 80nm~800nm, or approximately 90nm~900nm, or approximately 100nm~1000nm, or approximately 1000nm~10000nm, or approximately 10000nm~50000nm, and combinations thereof, or intermediates thereof (e.g., 5nm, 10nm, 15nm, 20nm, 25nm, 30nm, 35nm, 40nm, 45nm, 0nm, 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, This includes 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 the range of nanoscale sensor elements, though not limited to them, include, for example, elements from approximately 5 nm to approximately 1000 nm in one direction, such as approximately 5 nm to approximately 500 nm, or approximately 5 nm to approximately 400 nm, or approximately 5 nm to approximately 300 nm, or approximately 5 nm to approximately 200 nm, or approximately 5 nm to approximately 100 nm, or approximately 5 nm to approximately 50 nm, or approximately 10 nm to approximately 1000 nm, or approximately 10 nm to approximately 750 nm, or approximately 10 nm to approximately 500 nm, or approximately 10 nm to approximately 250 nm, or approximately 10 nm to approximately 200 nm, or approximately 10 nm to approximately 100 nm, or approximately 0 nm to approximately 1000 nm, or approximately 50 nm to approximately 500 nm. Alternatively, this includes approximately 50nm to 250nm, or approximately 50nm to 200nm, or approximately 50nm to 100nm, and any combination thereof, as well as ranges or values ​​between them (e.g., 5nm, 10nm, 15nm, 20nm, 25nm, 30nm, 35nm, 40nm, 45nm, 0nm, 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, etc.). With respect to the sensor arrays described herein, the term "use of sensor element" includes the use of nanoscale sensor elements for said sensor element and related methods.

[0178] The term "multiple sensor elements" refers to more than one, for example, at least two sensor elements. In some embodiments, the multiple sensor elements are at least two to at least ten sensor elements. 15 It includes a sensor element. In some embodiments, the plurality of sensor elements are 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 and includes different sensor elements of 10.

[0179] In some embodiments, the multiple sensor elements include multiple types of sensor elements. The multiple sensor elements include 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 30 types of sensor elements, or at least 2 to 20 types of sensor elements, or at least 2 to 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 types of sensor elements, or at least 4 to at least 10 types of sensor elements, and any number of types of sensor elements intended to be in between (for example, 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 This includes 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 increasing the panel size can increase the number of proteins identified. This figure shows the number of proteins identified by corona analysis in assays using panels containing 1 to 12 particle species. In these assays, unique proteins were identified by mass spectrometry, as opposed to protein groups. The number of unique protein types identified increased with increasing number of particle species, ranging from 419 unique identified proteins (when proteins were collected using one particle species) to 1318 unique identified proteins (when proteins were collected using 12 particle species).

[0181] The sensor element may be functionalized to provide 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.). Functionalization is not limited to these but includes, for example, aminopropyl functionalization, amine functionalization, boronic acid functionalization, carboxylic acid functionalization, methyl functionalization, succinimidyl ester functionalization, PEG functionalization, streptavidin functionalization, methyl ether functionalization, triethoxylpropylaminosilane functionalization, thiol functionalization, PCP functionalization, citrate functionalization, lipoic acid functionalization, BPEI functionalization, carboxyl functionalization, hydroxyl functionalization, and the like. In one embodiment, the sensor element may 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 carboxyl groups, hydroxyl groups, thiol groups, cyano groups, nitro groups, ammonium groups, imidazolium groups, sulfonium groups, pyridinium groups, pyrrolidinium groups, phosphonium groups, or any combination thereof. In some embodiments, the functional group may be 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.). Examples include polar functional groups (such as guanidine groups), carbamoyl groups, hydroxyl groups, and aldehyde groups. In some embodiments, the polar functional group is an ionic functional group. Non-limiting examples of the ionic functional group include ammonium groups, imidazolium groups, sulfonium groups, pyridinium groups, pyrrolidinium groups, and phosphonium groups. In some embodiments, the sensor element is functionalized with polymerizable functional groups. Non-limiting examples of the polymerizable functional group 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, and the like.

[0182] The physicochemical properties of the sensor element can be modified by altering its surface charge. For example, the surface can be modified to produce a neutral effective charge, a positive effective surface charge, a negative effective surface charge, or an amphoteric charge. The surface charge can be controlled during the synthesis of the element or by post-synthesis modification of the charge through surface functionalization. For polymer sensor elements (e.g., polymer particles), differences in charge 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) a plurality of sensor elements made of the same material but with different physiological and chemical properties; (b) a plurality of sensor elements in which one or more sensor elements are made of different substances having the same or different physiological and chemical properties; (c) a plurality of sensor elements made of the same material but with different sizes; (d) a plurality of sensor elements made of different materials but with approximately the same size; (e) a plurality of sensor elements made of different materials but with different sizes; (f) a plurality of sensor elements in which each element is made of a different material; and (g) a plurality of sensor elements having different charges. The plurality of sensor elements may be any suitable combination of two or more sensor elements in which each sensor element gives rise to a unique biomolecular corona signature. For example, the plurality of sensor elements may include one or more liposomes and one or more particles as described herein. In one embodiment, the plurality of sensor elements may be a plurality of liposomes having various lipid content and / or various charges (cationic / anionic / neutral). In another embodiment, the plurality of sensors may comprise one or more nanoparticles made of the same material but differing in size and physiological and chemical properties. In another embodiment, the plurality of sensors may comprise one or more particles made of different materials (e.g., silica and polystyrene) having similar or different sizes and / or physiological and chemical properties (e.g., modifications, e.g., -NH2, -COOH functionalization). These combinations are provided purely as examples and do not limit the scope of the present disclosure.

[0184] The sensor element may include particles (e.g., nanoparticles or microparticles). The sensor element may be a particle (e.g., nanoparticles or microparticles). The sensor element may include the surface or a portion of the surface of a material. The sensor element may include a porous material (e.g., a polymer matrix) into which biomolecules can enter. The sensor element may include a material having protrusions (e.g., polymers, oligomers, or metal dendrites). The sensor element may include aggregates of particles (e.g., nanoworms). particle material

[0185] The particles disclosed herein may be made of various different materials. The particles may include certain types of nanoparticles for identifying a wide range of proteins in the sample, or for selectively assaying specific proteins or sets of proteins of interest.

[0186] Multiple particles include at least one singular particle species, at least two singular particle species, at least three singular particle species, at least four singular particle species, at least five singular particle species, at least six singular particle species, at least seven singular particle species, at least eight singular particle species, at least nine singular particle species, at least ten singular particle species, at least eleven singular particle species, at least twelve singular particle species, at least thirteen singular particle species, at least fourteen singular particle species, and at least fifteen Singular particle species, at least 16 singular particle species, at least 17 singular particle species, at least 18 singular particle species, at least 19 singular particle species, at least 20 singular particle species, at least 25 singular particle species, at least 30 singular particle species, at least 35 singular particle species, at least 40 singular particle species, at least 45 singular particle species, at least 50 singular particle species, at least 55 singular particle species, at least 60 singular particle species, at least 65 singular 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 to 5 unique particle species, 5 to 10 unique particle species, 10 to 15 unique particle species, 15 to 20 unique particle species, 20 to 25 unique particle species, 25 to 30 unique particle species, 30 to 35 unique particle species, 35 to 40 unique particle species, 40 to 45 unique particle species, 45 to 50 unique particle species, It 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. Multiple particles may include 3-10 unique particle species. Multiple particles may include 4-11 unique particle species. Multiple particles may contain 5 to 15 unique particle species. Multiple particles may contain 5 to 15 unique particle species.Multiple particles may include 8 to 12 unique particle species. Multiple particles may include 9 to 13 unique particle species. Multiple particles may include 10 unique particle species. The particle species may include nanoparticles.

[0187] For example, this disclosure provides for at least two unique particle species, at least three different surface chemistrys, at least four different surface chemistrys, at least five different surface chemistrys, at least six different surface chemistrys, at least seven different surface chemistrys, at least eight different surface chemistrys, at least nine different surface chemistrys, at least ten different surface chemistrys, at least eleven different surface chemistrys, at least twelve different surface chemistrys, at least thirteen different surface chemistrys, at least fourteen different surface chemistrys, at least fifteen different surface chemistrys, at least twenty different surface chemistrys, at least twenty-five different surface chemistrys, at least thirty different surface chemistrys, at least thirty-five different surface chemistrys, at least forty different surface chemistrys, at least forty-five different surface chemistrys, and at least fifty different Multiple particles having a surface chemistry such as: at least 100 different surface chemistrys, at least 150 different surface chemistrys, at least 200 different surface chemistrys, at least 250 different surface chemistrys, at least 300 different surface chemistrys, at least 350 different surface chemistrys, at least 400 different surface chemistrys, at least 450 different surface chemistrys, at least 500 different surface chemistrys, 2 to 500 different surface chemistrys, 2 to 5 different surface chemistrys, 5 to 10 different surface chemistrys, 10 to 15 different surface chemistrys, 15 to 20 different surface chemistrys, 20 to 40 different surface chemistrys, 40 to 60 different surface chemistrys, 60 to 80 different surface chemistrys, 80 to 100 different surface chemistrys, 100 to 500 different surface chemistrys, 4 to 15 different surface chemistrys, or 2 to 20 different surface chemistrys.

[0188] This disclosure relates to at least two different physical properties, at least three different physical properties, at least four different physical properties, at least five different physical properties, at least six different physical properties, at least seven different physical properties, at least eight different physical properties, at least nine different physical properties, at least ten different physical properties, at least eleven different physical properties, at least twelve different physical properties, at least thirteen different physical properties, at least fourteen different physical properties, at least fifteen different physical properties, at least twenty different physical properties, at least twenty-five different physical properties, at least thirty different physical properties, at least thirty-five different physical properties, at least forty different physical properties, at least forty-five different physical properties, at least fifty different physical properties, and a small number of different physical properties. The present invention provides 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 a variety of materials. For example, nanoparticle materials conforming to this disclosure include metals, polymers, magnetic materials, and lipids. Magnetic nanoparticles may be iron oxide nanoparticles. Examples of metallic materials include any one or any combination thereof of gold, silver, copper, nickel, cobalt, palladium, platinum, iridium, osmium, rhodium, ruthenium, rhenium, vanadium, chromium, manganese, niobium, molybdenum, tungsten, tantalum, iron, and cadmium, or any other material described in US7749299.

[0190] Examples of polymers include polyethylene, polycarbonate, polyacid anhydride, polyhydroxy acid, polypropylfumerate, 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., a copolymer of polyalkylene glycol (e.g., PEG) and polyester (e.g., PLGA)), or any combination thereof. In some embodiments, the polymer is lipid-terminated polyalkylene glycol and polyester, or any other material disclosed in US9549901. The polymer can also be a liposome.

[0191] Lipids that can be used to form the nanoparticles of this disclosure include, for example, cationic, anionic, and neutrally charged lipids. For example, the nanoparticles may be dioleoylphosphatidylglycerol (DOPG), diacylphosphatidylcholine, diacylphosphatidylethanolamine, ceramide, sphingomyelin, cephalin, cholesterol, cerebroside, and diacylglycerol, dioleoylphosphatidylcholine (DOPC), dimyristoylphosphatidylcholine (DMPC), and dioleoylphosphatidylserine (DOPS), phosphatidylglycerol, cardiolipin, and diacylphospha Tidylserine, diacylphosphatidic acid, N-dodecanoylphosphatidylethanolamine, N-succinylphosphatidylethanolamine, N-glutarylphosphatidylethanolamine, lysylphosphatidylglycerol, palmitoyloleyolphosphatidylglycerol (POPG), lecithin, lysolecithin, phosphatidylethanolamine, lysophosphatidylethanolamine, geoleo Ilphosphatidylethanolamine (DOPE), dipalmitoylphosphatidylethanolamine (DPPE), dimyristoylphosphoethanolamine (DMPE), distearoyl-phosphatidyl-ethanolamine (DSPE), palmitoyloleoyl-phosphatidylethanolamine (POPE), palmitoyloleoylphosphatidylcholine (POPC), egg phosphatidylcholine (EPC), distearoylphosphatidylcholine (DSPC), dioleoylphosphatidyl Phosphorus (DOPC), dipalmitoylphosphatidylcholine (DPPC), dioleoylphosphatidylglycerol (DOPG), dipalmitoylphosphatidylglycerol (DPPG), palmitoyloleoylphosphatidylglycerol (POPG), 16-O-monomethylPE, 16-O-dimethylPE, 18-1-transPE, palmitoyloleoylphosphatidylethanolamine (POPE),It may consist of 1-stearoyl-2-oleoyl-phosphatidyethanolamine (SOPE), phosphatidylserine, phosphatidylinositol, sphingomyelin, cephalin, cardiolipin, phosphatidic acid, cerebroside, dicetyl phosphate, and cholesterol, or any one or any combination thereof of any other material disclosed in US9445994.

[0192] In various cases, the core of the nanoparticles may include organic particles, inorganic particles, or particles containing both organic and inorganic materials. For example, the particles may have a core structure that is a metal particle, quantum dot particle, metal oxide particle, or core-shell particle, or a core structure containing these. For example, the core structure may be or contain polymer particles or lipid-based particles, and the linker may contain lipids, surfactants, polymers, hydrocarbon chains, or amphiphilic polymers. For example, the linker may contain polyethylene glycol or polyalkylene glycol, for example, the first end of the linker may contain a lipid bonded to polyethylene glycol (PEG), and the second end may contain a functional group bonded to the PEG. The particles may have a core-shell structure. In some cases, the particles have a core containing a first material or composite material, and multiple shells containing different materials or composite materials. In some cases, the particles have a magnetic core surrounded by one non-magnetic shell or multiple non-magnetic shells. For example, the particles may contain 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 species that conform to this disclosure are shown in Table 1 below. Examples of additional particles (e.g., magnetic core nanoparticles (MNPs)) and their corresponding surface chemistry are shown in Figure 7. [Table 1] Particle properties

[0194] Nanoparticles conforming to this disclosure can be produced and used in a wide range of sizes in methods for forming protein coronas after incubation in bodily fluids. For example, the nanoparticles disclosed herein include 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, and 500 nm to 550 nm. m, 550nm~600nm, 600nm~650nm, 650nm~700nm, 700nm~750nm, 750nm~800nm, 800nm~850nm, 850nm~900nm, 100nm~300nm, 150nm~350nm, 200nm~400nm, 250nm~450nm, 300nm~500nm, 350nm~550nm, 400nm~600nm, 450nm~650nm, 500nm~700nm, 550nm~750nm, 600nm~800nm, 650nm~850nm, 700nm~900nm, or 10nm~900nm.

[0195] Furthermore, particles can have a uniform or heterogeneous 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 low PDI means that the particle size distribution is more uniform, while a high PDI means that the particle size distribution is more heterogeneous. In some cases, the PDI of multiple 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 may have different surface charges within a range. The particles may be negatively charged, positively charged, or neutral in charge. In some embodiments, the surface charges of the particles range from -500mV to -450mV, -450mV to -400mV, -400mV to -350mV, -350mV to -300mV, -300mV to -250mV, -250mV to -200mV, -200mV to -150mV, -150mV to -100mV, -100mV to -90mV, and -90mV to -80mV. 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 It could be 400mV~450mV, 450mV~500mV, -500mV~-400mV, -400mV~-300mV, -300mV~-200mV, -200mV~-100mV, -100mV~0mV, 0mV~100mV, 100mV~200mV, 200mV~300mV, 300mV~400mV, or 400mV~500mV.

[0197] Various particle morphologies correspond to the particle types in the panel of this disclosure. For example, particles may be spherical, colloidal, cubic, rod-shaped, wire-shaped, conical, pyramidal, or elliptical. Biomolecular coronavirus

[0198] This specification provides automated apparatus, systems, methods, and sensor elements capable of generating a biomolecular corona comprising, essentially comprising, or comprising a plurality of sensor elements, wherein the plurality of sensor elements differ from each other 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 to generate 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 may be a protein. The biomolecular corona may be a protein corona. The level of enrichment may be an increase in percentage or multiplier of the relative abundance of the biomolecule of interest in the biomolecular corona compared to the biological sample from which the biomolecular corona was collected (e.g., the number of copies of the biomolecule of interest relative to the total number of biomolecules). The biomolecule of interest can be enriched in the biomolecular corona by increasing its abundance compared to a sample not in contact with the sensor element. The biomolecule of interest can be enriched by decreasing the abundance of a biomolecule that is present in a high-abundance biological sample.

[0200] Biomolecular corona analysis assays can be used to rapidly identify low-abundance biomolecules in biological samples (e.g., body fluids). Using biomolecular corona analysis, at least about 500 low-abundance biomolecules in a biological sample can be identified within about 8 hours of initial contact with a sensor element (e.g., a particle). Biomolecular corona analysis can identify at least about 1000 low-abundance biomolecules in a biological sample within about 8 hours of initial contact 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 initial contact with a sensor element (e.g., a particle). Biomolecular corona analysis can identify at least about 1000 low-abundance biomolecules in a biological sample within about 4 hours of initial contact 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 lipidomycete corona signature. The biomolecular corona signature may include the biomolecules found in soft coronas 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 of the separate sensor elements or each of the nanoparticles. 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 another case, the biomolecular corona signature is a metabolite corona signature. In some cases, the biomolecular corona signature is a lipidomic corona signature. The signature may refer to the different biomolecules. The signature may also refer to differences in the quantity, level, or amount of the biomolecules bound to the sensor elements or nanoparticles, or differences in the three-dimensional structure of the biomolecules bound to the sensor elements or particles. The biomolecular corona signature of each sensor element is intended to contain some of the same biomolecules, to contain biomolecules unique to other sensor elements or nanoparticles, and / or to differ in the quantity, type, or conformation of the biomolecules. The biomolecular corona signature may depend not only on the physicochemical properties of the sensor element or particles, but also on the properties of the sample and the duration of exposure. In some embodiments, the biomolecular corona signature includes biomolecules found in soft corona and hard corona.

[0203] In some embodiments, the plurality of sensor elements include a first sensor element that causes the sensor array to generate a first biomolecular corona signature when it comes into contact with a complex biological sample, and at least one second sensor element (e.g., at least one nanoparticle) that causes at least one second biomolecular corona signature. In some cases, each type of sensor element among the plurality of sensor elements causes a unique biomolecular corona signature.

[0204] The plurality of sensor elements generate a plurality of biomolecular corona signatures that, when in contact with a sample, can together form a biomolecular fingerprint. “Biomolecular fingerprint” refers to the combined composition or pattern of biomolecules of at least two biomolecular corona signatures relating to the plurality of sensor elements. The biomolecular fingerprint is intended to be formed from at least two biomolecular corona signatures—for example, at least 1000 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 can also be 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, where disclosed herein, refers to the intensity of the signal from an analytical measurement (e.g., the intensity of the mass-to-charge ratio from a mass spectrometry run of the sample). Using the data analysis methods described herein, the characterization 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 may refer to one protein identified using a unique identification sequence. For example, if a common peptide sequence is assayed between two proteins (protein 1: XYZZX and protein 2: XYZYZ) in a given sample, the protein group may be an "XYZ protein group" having two members (protein 1 and protein 2). Alternatively, if the peptide sequence is unique to only one protein (protein 1), the protein group may be a "ZZX" protein group having one member (protein 1). Each protein group may be supported by one or more peptide sequences. The proteins detected or identified in accordance with this disclosure may refer to specific proteins detected in the sample (e.g., specific to other proteins detected by mass spectrometry). Thus, analysis of proteins present in the specific corona corresponding to the specific sensor element species results in a number of characteristic intensities. This number decreases when the characteristic intensities are treated with specific peptides, further decreases when the specific peptides are treated with specific proteins, and further decreases when the peptides are grouped into protein groups (two or more proteins sharing a specific peptide sequence).

[0206] The automated devices, systems, methods, and sensor elements (e.g., particles) disclosed herein include 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, and at least 230 Protein groups of 0, at least 2400, at least 2500, at least 2600, at least 2700, at least 2800, at least 2900, at least 3000, at least 3100, at least 3200, at least 3300, at least 3400, at least 3500, at least 3600, at least 3700, at least 3800, at least 3900, at least 4000, at least 4100, at least 4200, at least 4300, at least 4400, at least 4500, at least 4600, at least 4700,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 Protein groups of ~3500, 700~3200, 800~2900, 900~2600, 1000~2300, 1000~3000, 3000~4000, 4000~5000, 5000~6000, 6000~7000, 7000~8000, 8000~9000 Protein group of 9000-10000, protein group of 10000-11000, protein group of 11000-12000, protein group of 12000-13000, protein group of 13000-14000, protein group of 14000-15000, protein group of 15000-16000, protein group of 16000-17000, protein group of 17000-18000, protein group of 18000-19000 This can be used to identify protein groups such as the 19,000-20,000 protein group, the 20,000-25,000 protein group, the 25,000-30,000 protein group, the 10,000-20,000 protein group, the 10,000-50,000 protein group, the 20,000-100,000 protein group, the 2,000-20,000 protein group, the 1,800-20,000 protein group, or the 10,000-100,000 protein group.

[0207] The automated devices, systems, methods, and sensor elements (e.g., particles) disclosed herein contain 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, and 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 protein, at least 4600 protein, at least 4700 protein, at least 4800 protein, at least 4900 protein, at least 5000 protein, 100-5000 protein, 200-4700 protein, 300-4400 protein, 400-4100 protein, 500-3800 protein, 600-3500 protein, 700-3200 protein, 800-2900 protein, 900-2600 protein, 1000-2300 protein,1000-3000 protein, 3000-4000 protein, 4000-5000 protein, 5000-6000 protein, 6000-7000 protein, 7000-8000 protein, 8000-9000 protein, 9000-10000 protein, 10000-11000 protein, 11000-12000 protein, 12000-13000 protein, 13000-1 It can be used to identify 4000 proteins, 14000-15000 proteins, 15000-16000 proteins, 16000-17000 proteins, 17000-18000 proteins, 18000-19000 proteins, 19000-20000 proteins, 20000-25000 proteins, 25000-30000 proteins, or 10000-20000 proteins.

[0208] The sensor elements disclosed herein may be used to identify any of the numerous specific proteins disclosed herein, and / or any of the particular proteins disclosed herein, over a wide dynamic range. For example, a group of particles containing specific particle species disclosed herein may enrich a protein in a sample (which can be identified using the methods of this disclosure) over the entire dynamic range in which the protein may be present in the sample (e.g., a plasma sample). A panel of particles may contain any number of specific particle species disclosed herein and may enrich and identify biomolecules in the sample over a concentration range of at least two orders of magnitude to at least twelve orders of magnitude. Disease detection

[0209] The systems and methods disclosed herein may be used for the detection of markers in a sample from a subject (which correspond to a specific biological (e.g., disease) condition). The biological condition may be a disease, disorder, or tissue abnormality. The disease condition may be an early-phase or intermediate-phase disease condition.

[0210] The systems and methods of this disclosure may be used to detect a wide range of disease conditions in a given sample. For example, the systems and methods of this disclosure may be used to detect cancer. The cancer may 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 disease state in a subject, diagnose or predict disease in a subject, or identify unique patterns 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 disease or disorder (e.g., disease state) in a subject. As disclosed herein, the ability to detect disease (e.g., cancer) at an early stage (even before the disease has fully manifested or metastasized) enables a significant improvement in the patient's positive outcomes, as well as the ability to extend life expectancy and reduce mortality associated with the disease.

[0212] The automated devices, systems, methods, and sensor elements (e.g., particles) disclosed herein may offer a unique opportunity to generate biomolecular fingerprints associated with the pre- or precursor stages of disease in a high-throughput manner. This disclosure provides large-scale, rapid sample processing for generating biomolecular fingerprints in a high-throughput manner, thereby enabling large-scale determination of disease status in a subject, diagnosis or prediction of disease in a subject, or identification of unique patterns of biomarkers associated with disease status, disease, or disorder across a wide range of subjects.

[0213] In some embodiments, methods are provided for detecting disease or disorder in an object. The method comprises the steps of (a) obtaining a sample from the object; (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 the health status of an object in a diseased state from, for example, a health status without disease or disorder, a health status with a precursor state of disease or disorder, and a health status with disease or disorder.

[0214] Determining whether a biomolecular fingerprint is associated with the sample may involve detecting the biomolecular corona signature for at least two sensor elements, where the 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 devices, systems, sensor arrays, and methods described herein may be used to determine disease conditions and / or to predict or diagnose diseases or disorders. The diseases or disorders intended include, but are not limited to, cancer, cardiovascular disease, endocrine disease, inflammatory disease, neurological disease, and others.

[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 proliferation, including tumors and benign growths. Cancer may be, for example, lung cancer, pancreatic cancer, or skin cancer. In suitable embodiments, the automated devices, systems, sensor arrays, and methods described herein can not only diagnose cancer (for example, determine whether a subject is (a) cancer-free, (b) in a precancerous stage, (c) in an early stage of cancer, or (d) in a late stage of cancer), but in some embodiments, they can also determine the type of cancer. As demonstrated in the following examples, a sensor array comprising six sensor elements was able to accurately determine a disease state in which cancer is present or absent. Furthermore, in the examples, a sensor array comprising six sensor elements was able to distinguish between different types of cancer (for example, lung cancer, glioblastoma, meningioma, myeloma, and pancreatic cancer).

[0217] The automated devices, systems, sensor arrays, and methods of this disclosure can also be used to treat other cancers, such as acute lymphoblastic leukemia (ALL); acute myeloid leukemia (AML); cancers in young people; adrenocortical carcinoma; pediatric adrenocortical carcinoma; rare cancers in children; AIDS-related cancers; Kaposi's sarcoma (soft tissue sarcoma); AIDS-related lymphoma (lympHoma); primary lymphoma of the central nervous system (lympHoma); anal cancer; appendiceal cancer (see gastrointestinal carcinoid tumor); astrocytoma, pediatric (brain cancer); dysplastic teratoid / rhabdoid tumor, pediatric, central nervous system (brain cancer); basal cell carcinoma of the skin (skin cancer). See also); cholangiocarcinoma; bladder cancer; pediatric bladder cancer; bone cancer (Ewing's sarcoma, osteosarcoma, and malignant fibrous histiocytoma); brain tumor; breast cancer; pediatric breast cancer; bronchial tumor, pediatric; Burkitt lymphoma (see non-Hodgkin lymphoma); carcinoid tumor (gastrointestinal); pediatric carcinoid tumor; carcinoma of unknown primary origin; carcinoma of unknown primary origin in pediatrics; cardiac (heart) tumor, pediatric; central nervous system; dysplastic teratoid / rhabdoid tumor, pediatric (brain cancer); germ cell tumor, pediatric (brain cancer); germ cell tumor, pediatric (brain cancer); primary CNS lymphoma; cervical cancer; pediatric cervical cancer; pediatric cancer; small Childhood cancer, rare; cholangiocarcinoma (see cholangiocarcinoma); chordoma, pediatric; chronic lymphocytic leukemia (CLL); chronic myeloid leukemia (CML); chronic myeloproliferative neoplasm; colorectal cancer; pediatric colorectal cancer; craniopharyngioma, pediatric (brain cancer); cutaneous T-cell lymphoma (see lymphoma) (mycosis fungoides and Sézary syndrome); ductal carcinoma in situ (DCIS) (see breast cancer); germ cell tumor, central nervous system, pediatric (brain cancer); endometrial cancer (uterine cancer); ependymoma, pediatric (brain cancer); esophageal cancer; pediatric esophageal cancer; nasal neuroblastoma (head and neck cancer); Ewing's sarcoma (bone cancer); extracranial germ cell tumor, pediatric Extragonadal germ cell tumors; eye cancer; pediatric intraocular melanoma; intraocular melanoma; retinoblastoma; fallopian tube cancer; fibrous histiocytoma, malignant, and osteosarcoma of bone; gallbladder cancer; gastric (stomach) cancer; pediatric gastric (stomach) cancer; gastrointestinal carcinoid tumors; gastrointestinal stromal tumors (GIST) (soft tissue sarcoma); pediatric gastrointestinal stromal tumors; germ cell tumors; pediatric central nervous system germ cell tumors (brain cancer); pediatric extracranial germ cell tumors; extragonadal germ cell tumors; ovarian germ cell tumors; testicular cancer; gestational trophoblastic disease; pilocytic cell leukemia; head and neck cancer; cardiac tumors, pediatric; hepatocellular carcinoma (liver)Histiocytosis, Langerhans cell tumors; Hodgkin lymphoma; Hypopharyngeal cancer (head and neck cancer); Intraocular melanoma; Pediatric intraocular melanoma; 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; Lip and 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; Intraocular melanoma (eye); Pediatric intraocular melanoma; Merkel cell carcinoma (skin cancer); Malignant mesothelioma; Pediatric mesothelioma; Metastatic cancer; Metastatic squamous cell carcinoma of the neck of unknown primary origin (head and neck cancer); Midline carcinoma with nut gene mutation; Oral cancer (mouth Cancer (head and neck cancer); Multiple endocrine neoplasia syndrome; Multiple myeloma / plasmacytic neoplasm; Mycosis fungoides (lymphoma); Myelodysplastic syndrome, myelodysplastic / myeloproliferative neoplasm; Chronic myeloid leukemia (CML); Acute myeloid leukemia (AML); Chronic myeloproliferative neoplasm; Nasal cavity and paranasal sinus cancer (head and neck cancer); Nasopharyngeal cancer (head and neck cancer); Neuroblastoma; Non-Hodgkin 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 (islet tumors); papilloma (larynx in children); paraganglioma; pediatric paraganglioma; sinus and nasal cavity cancer (head and neck cancer); parathyroid cancer; penile cancer; pharyngeal cancer (head and neck cancer); chromaffin cell tumor; pediatric chromaffin cell tumor; pituitary tumor; plasma cell neoplasm / multiple myeloma; pleuropulmonary blastoma; pregnancy-related breast cancer; primary central nervous system (CNS) lymphoma; primary peritoneal cancer; prostate cancer; rectal cancer Cancer; recurrent cancer; renal cell carcinoma; retinoblastoma; rhabdomyosarcoma, pediatric (soft tissue sarcoma); salivary gland cancer (head and neck cancer); sarcoma; pediatric rhabdomyosarcoma (soft tissue sarcoma); pediatric angiomoma (soft tissue sarcoma); Ewing's sarcoma (bone cancer); Kaposi's sarcoma (soft tissue sarcoma); osteosarcoma (bone cancer); soft tissue sarcoma; uterine sarcoma; Sézary 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); cervical squamous cell carcinoma of unknown primary origin, metastatic (head and neck cancer);Stomach cancer; pediatric stomach cancer; T-cell lymphoma, cutaneous lymphoma (see Lymphoma) (mycosis fungoides and Sézary syndrome); testicular cancer; pediatric 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 (renal cell carcinoma); carcinoma of unknown primary origin; pediatric cancer of unknown primary origin; rare cancers in children; transitional cell carcinoma of the ureter and renal pelvis (renal cell carcinoma); urethral cancer; uterine cancer, endometrium; uterine sarcoma; vaginal cancer; pediatric vaginal cancer; hemangiomas (soft tissue sarcomas); vulvar cancer; Wilms' tumor and other pediatric renal tumors; or it may be used to detect cancer in young adults.

[0218] In some cases, the disease or disorder is a cardiovascular disease. As used herein, the term “cardiovascular disease” (CVD) or “cardiovascular disease” is used to classify a vast number of conditions affecting the heart, heart valves, and the vascular structures of the body (e.g., veins and arteries), and encompasses, but is not limited to, diseases and conditions including, but not limited to, atherosclerosis, myocardial infarction, acute coronary syndrome, angina pectoris, congestive heart failure, aortic aneurysm, aortic dissection, iliac or femoral artery 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, but is not limited to, causes that ultimately result in cardiovascular events or cardiovascular complications (the manifestation of an adverse condition in a subject caused by cardiovascular disease), including myocardial infarction, unstable angina, aneurysm, stroke, heart failure, non-fatal myocardial infarction, stroke, and angina. Risks include pectoris, transient ischemic attack, aortic aneurysm, aortic dissection, cardiomyopathy, abnormal cardiac catheterization, abnormal cardiac imaging, stent or graft revascularization, abnormal stress testing, abnormal myocardial perfusion, and death.

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

[0220] Atherosclerosis (also known as arteriosclerotic vascular disease or ASVD) is a cardiovascular disease in which the arterial wall thickens as a result of the infiltration, accumulation, and deposition of arterial plaque containing leukocytes into the innermost layer of the arterial wall, thereby narrowing and hardening the artery. Arterial plaque is an accumulation of macrophage cells or debris and contains 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 artery disease, chronic kidney disease, acute coronary syndrome, vascular stenosis, myocardial infarction, aneurysm, or stroke. In one embodiment, the automated apparatus, composition, and method of this disclosure can identify different stages of atherosclerosis (including, but not limited to, different degrees of stenosis in the subject).

[0221] In some cases, the disease or disorder is an endocrine disorder. The term “endocrine disorder” refers to a disorder related to dysregulation of the endocrine system in question. Endocrine disorders may result from glands that produce too much or too little endocrine hormone, causing hormonal imbalance, or from the development of lesions in the endocrine system (e.g., nodules or tumors) (which may or may not affect hormone levels). Appropriate endocrine disorders that can be treated include, but are not limited to, acromegaly, Addison's disease, adrenal carcinoma, adrenal dysfunction, non-histological thyroid carcinoma, Cushing's syndrome, De Quervain's thyroiditis, diabetes mellitus, follicular thyroid carcinoma, gestational diabetes mellitus, goiter, Graves' disease, growth disorders, growth hormone deficiency, Hashimoto's thyroiditis, Haasle 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, chromaffin cell tumor, pituitary disorder, pituitary tumor, polycystic ovary syndrome, prediabetes, silent thyroiditis, thyroiditis, thyroid cancer, thyroid disease, thyroid nodules, thyroiditis, Turner syndrome, type 1 diabetes mellitus, type 2 diabetes mellitus.

[0222] In some cases, the disease or disorder is an inflammatory disease. Where used herein, an inflammatory disease refers to a disease caused by uncontrolled inflammation in the body of the subject. Inflammation is a biological response of the subject to harmful stimuli that may be external or internal (e.g., pathogens, necrotic cells and tissues, irritants, etc.). However, when the inflammatory response becomes abnormal, it can lead to damage to the tissue itself and cause a variety of diseases and disorders. Inflammatory diseases include, but are not limited to, asthma, glomerulonephritis, inflammatory bowel disease, rheumatoid arthritis, hypersensitivity, pelvic inflammatory disease, autoimmune diseases, arthritis; necrotizing enterocolitis (NEC), gastroenteritis, pelvic inflammatory disease (PID), emphysema, pleurisy, pyelonephritis, pharyngitis, angina, acne vulgaris, urinary tract infections, appendicitis, bursitis, colitis, cystitis, and skin diseases. This may include inflammation, phlebitis, rhinitis, tendinitis, 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, Behçet'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 aforementioned disease or disorder may be a neurological disorder. Neurological disorder or neurological disorder is used interchangeably and refers to the brain, spinal cord, and the nerves connecting them. Neurological disorders include, but are not limited to, brain tumors, epilepsy, Parkinson's disease, Alzheimer's disease, ALS, arteriovenous malformations, cerebrovascular diseases, cerebral aneurysms, epilepsy, multiple sclerosis, peripheral neuropathy, postherpetic neuralgia, stroke, frontotemporal dementia, demyelinating disorders (including, but not limited to, multiple sclerosis, Devic's disease (i.e., neuromyelitis optica), central pontine myelin collapse, 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 are diseases in which at least one component of the immune system responds to host proteins present in the central or peripheral nervous system and contributes to the pathology of the disease. IMNDs may include, but are not limited to, demyelinating diseases, paraneoplastic neurological syndromes, immune-mediated encephalomyelitis, immune-mediated autonomic neuropathy, myasthenia gravis, autoimmune encephalopathy, and acute disseminated encephalomyelitis.

[0224] The methods, systems, and / or devices of this disclosure may be capable of accurately distinguishing between patients with and without Alzheimer's disease. These may be capable of detecting patients who may develop Alzheimer's disease several years after screening, before symptoms appear. This has the advantage of being able to treat patients at a very early stage (even before the onset of the disease).

[0225] The methods, systems, and devices of this disclosure can detect predisease stages of disease or disorder. A predisease stage is a stage in which a patient has not yet presented any signs or symptoms of the disease. A precancerous stage would be a stage in which no cancer, tumor, or cancerous cells have been identified in the patient's body. A preneurological stage would be a stage in which a person has not yet presented any symptoms of one or more of the aforementioned neurological disorders. The ability to diagnose a disease before signs or symptoms of one or more of the disease appear enables the ability to monitor subjects in detail and treat the disease at a very early stage, improving the prospects of being able to halt the progression of the disease or reduce its severity.

[0226] The automated devices, systems, sensor arrays, and methods of this disclosure are capable of detecting the early stages of a disease or disorder in some embodiments. The early stages of a disease may refer to the time when the first signs or symptoms of the disease may become apparent within the subject. The early stages of a disease may be a stage in which no outward signs or symptoms are present. For example, in Alzheimer's disease, the early stages may be the pre-Alzheimer's stage, in which no symptoms are yet detected, but the patient will develop Alzheimer's disease in months or years.

[0227] Identifying a disease either before its onset or in its early stages can often increase the likelihood of a positive outcome for the patient. For example, diagnosing cancer in its early stages (stage 0 or stage 1) can improve survival chances by more than 80%. Stage 0 cancer may refer to cancer before it begins to metastasize to surrounding tissues. Cancer at this stage is often highly curable (usually through surgical removal of the entire tumor). Stage 1 cancer may be a small cancer or tumor that has not grown deeply into surrounding 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 may be performed using the automated equipment of this disclosure. Whole blood samples may be collected from a range of patients, including healthy individuals and patients with different types and stages of cancer. The whole blood may be fractionated into plasma samples and then contacted with several types of particles, including positively charged particles, negatively charged particles, and neutral particles. Each particle type collects different types of proteins from the plasma sample, resulting in a unique biomolecular fingerprint for each patient. The biomolecular fingerprint includes not only the relative abundance of proteins for each particle type, but also the relative abundance of proteins across all particle types. For example, an increase in the abundance of fibronectin for a first particle type may only be a relevant indicator if the abundance of complement component 4 is low for a second particle type. The biomolecular fingerprint may 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 means 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, in the case of cancer, stage II or III cancer is considered an intermediate stage and refers to a larger cancer or tumor that has grown deeply into the surrounding tissue. In some examples, stage II or III cancer may also have metastases to the 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. These late or advanced stages are also referred to as “severe” or “advanced,” and typically refer to a subject experiencing a variety of symptoms and effects of the disease. For example, severe-stage cancer includes stage IV, in which case the cancer has metastasized to other organs or parts of the body and may be called advanced or metastatic cancer.

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

[0232] In some cases, the devices, systems, compositions, and methods of the present disclosure may distinguish not only between different types of diseases, but also between different stages of such diseases (e.g., early stages of cancer). This may include distinguishing between healthy subjects and subjects with a pre-disease condition. Such pre-disease conditions may include stage 0 or stage 1 cancer, neurodegenerative diseases, dementia, coronary diseases, kidney diseases, cardiovascular diseases (e.g., coronary artery disease), diabetes, or liver diseases. Distinguishing between different stages of such diseases 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 this disclosure may be used to generate proteomics data from protein coronas, which may then be associated with any of the biological states described herein. Samples conforming to this disclosure include biological samples from subjects, which may be human or non-human animals. Biological samples may be bodily fluids, such as plasma, serum, CSF, urine, tears, or saliva. The biological samples may contain multiple proteins or proteomics data, which may be analyzed after adsorption of various sensor element (e.g., particle) species on the surface of the protein panel and subsequent digestion of the protein corona. Proteomics data may include nucleic acids, peptides, or proteins.

[0234] A wide range of biological samples are suitable for use in the automated instruments of this disclosure. The biological samples may include plasma, serum, urine, cerebrospinal fluid, synovial fluid, tears, saliva, whole blood, milk, nipple aspirate, mammary duct lavage, vaginal fluid, nasal secretions, inner ear fluid, gastric juice, pancreatic juice, trabecular meshwork, lung lavage, sweat, gingival crevicular exudate, semen, prostatic fluid, sputum, feces, bronchial lavage, fluids from swabs, bronchial aspirates, fluidized solids, fine-needle aspiration samples, tissue homogenates, lymph, cell culture samples, or any combination thereof. The biological samples may include diverse biological samples (e.g., pooled plasma from diverse subjects, or diverse tissue samples from a single source). The biological samples may include a single type of body fluid or biomaterial from a single source.

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

[0236] In some embodiments, the panel of this disclosure provides identification and measurement of specific proteins in the biological sample by processing the proteomics data by digesting a corona formed on a sensor element. Proteins that can be identified and measured include, for example, highly abundant proteins, moderately abundant proteins, and low-abundance proteins. Examples of highly abundant proteins include albumin and IgG.

[0237] In some embodiments, proteins that can 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), glycan 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, type B natriuretic peptide, and 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 (FOSHU) hormone (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 α (ERα), antigen K (Ki-67), oncoprotein (p53), cathepsin D, β-catenin, von Willebrand factor (VWF), CD15, k-ras, caspase 3, ENTH domain-containing protein (EPN), CD10, FAS, BRCA2 (breast cancer type 2) Susceptibility proteins, 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), differentiation antigen cluster 340, trypsin, cyclin D1, MG Examples include B, XBP-1, HG-1, YKL-40, S-gamma, NESP-55, Netrin-1, Geminin, GADD45A, CDK-6, CCL21, BrMS1 (BrMS1), 17βHDI, platelet-derived growth factor receptor A (PDGRFA), P300 / CBP-related factor (Pcaf), chemokine ligand 5 (CCL5), matrix metalloproteinase-3 (MMP3), claudin-4, and claudin-3. Analysis method

[0238] The proteomics data of the sample can be identified, measured, and quantified using a number of different analytical techniques. For example, the proteomics 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 assay (ELISA)). Alternatively, the proteomics 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 signatures of the at least two sensor elements. This step may be carried out by separating the plurality of biomolecules bound to each sensor element (for example, 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 the 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 (for example, 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 (for example, by desorbing the biomolecules and analyzing them by mass spectrometry and / or chromatography, or by detecting the multiple 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 single solution and assaying the solution to determine the biomolecular signature.

[0240] Methods for assaying the plurality of biomolecules constituting the biomolecular corona signature or biomolecular fingerprint may 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 includes analyte-specific identification techniques, such as nucleic acid capture by ELISA, immunostaining, or hybridization. In preferred embodiments, the assay includes liquid chromatography, mass spectrometry, or combinations thereof.

[0241] Where used herein, nucleic acids may be processed by standard molecular biological techniques for downstream applications. Embodiments of methods and compositions disclosed herein relate to nucleic acid (polynucleotide) sequencing. In some methods and compositions described herein, the nucleotide sequence of a target nucleic acid or a portion of its fragment may be determined by using various methods and devices. Examples of sequencing methods include electrophoretic methods, synthetic sequencing methods, ligation sequencing methods, hybridization sequencing methods, monomolecular sequencing methods, and real-time sequencing methods. In some embodiments, the method for determining the nucleotide sequence of a target nucleic acid or its fragment may be an automated process. In some embodiments, a capture probe may function as a primer that enables the initiation of a nucleotide synthesis reaction using a polynucleotide from a nucleic acid sample as a template. Thus, information about the sequence of the polynucleotide supplied to the array can be obtained. In some embodiments, if a primer that hybridizes to the polynucleotide bound to the capture probe and a sequencing reagent are further supplied to the array, the polynucleotide hybridized to the capture probe on the array may act as a sequencing template. Sequencer methods using arrays have been previously described in the art.

[0242] In some embodiments, paired-end reads can be obtained on nucleic acid clusters with respect to sequencing on a substrate such as an array. Methods for obtaining paired-end reads are described in WO / 07010252 and WO / 07091077 (which are respectively incorporated herein by reference in their entirety). Paired-end sequencing facilitates the reading of 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 by paired primers. During the amplification of the template strands, a cross-linked double-stranded structure can be generated. This can be processed to release a portion of one strand of each double-stranded structure from the surface. The single-stranded nucleic acid can be used for a sequencing, primer hybridization, and primer extension cycle. After a first sequencing run, the ends of the first single-stranded template can be hybridized to the immobilized primers remaining from the first cluster amplification procedure. The immobilized primer may be extended using a hybridized first single strand as a template to resynthesize the original double-stranded structure. The double-stranded structure may be processed to remove at least a portion of the first template strand in order to release the immobilized resynthesis strand in a single-stranded form. The resynthesis strand may be sequenced to determine a second lead (whose position begins at the reverse end of the original template fragment obtained from the fragmentation process).

[0243] Nucleic acid sequencing can be single-molecule sequencing or synthetic sequencing. 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 may include first-generation sequencing methods (e.g., Maxam-Gilbert sequencing or Sanger sequencing) or high-throughput sequencing methods (e.g., next-generation sequencing or NGS). High-throughput sequencing methods can sequence at least about 10,000, 100,000, 1 million, 100 million, 1 billion, or more polynucleotide molecules simultaneously (or substantially simultaneously). Sequencing methods are not limited to these, but may include pyrosequencing, synthesis sequencing, single-molecule sequencing, nanopore sequencing, semiconductor sequencing, ligation sequencing, hybridization sequencing, Digital Gene Expression (Helicos), and 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] The sensor element may comprise a first component and a composite of the first component and a polymer fluorophore or other quencher component that is chemically complementary to the first component (where such a composite has initial background fluorescence or reference fluorescence). When the first component comes into contact with a biomolecule (e.g., during the formation of a biomolecular corona), the quenching of the fluorophore may be affected, and this change in fluorescence may be measured. After the sensor is irradiated and / or excited by a laser, the effect and / or change in fluorescence for each sensor element may be measured, and may be compared to or treated against the background fluorescence to produce the biomolecular fingerprint. computer system

[0245] This disclosure provides a computer-controlled system programmed to perform the methods of this disclosure. These decisions, analyses, or statistical classifications are performed by methods known in the art (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 forest), logistic regression, decision trees, support vector machines (SVM), k-nearest neighbors, Naive Bayes, linear regression, polynomial regression, SVM for regression, K-means, and hidden Markov models). The computer system can perform various aspects of analyzing protein sets or protein coronas of this disclosure (e.g., comparing / analyzing biomolecular coronas of several samples to determine, with statistical significance, what patterns are common among individual biomolecular coronas, and determining the protein sets associated with the biological state). The computer system can also be used to generate classification indices (e.g., characteristic features of protein corona composition) for detecting and identifying different protein sets or protein coronas. Data collected from the sensor array disclosed herein may be used to train machine learning algorithms (in particular, algorithms that receive array measurements from patients and output specific biomolecular corona compositions from each patient). Before training the algorithms, the raw data from the arrays may be denoised to reduce the variability of each variable.

[0246] Machine learning can be generalized as the ability of a learning machine to accurately perform on new, unseen examples / tasks after experiencing a learning dataset. Machine learning can include the following concepts and methods. The supervised concept can include 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; the group method of data handling (GMDH); inductive logic programming; example-based learning; lazy learning;

[0247] This may include: learning automata; learning vector quantization; logistic model trees; minimum message length (decision trees, decision graphs, etc.), e.g., nearest neighbor algorithm and analogy modeling; probably approximately correct (PAC) learning; ripple-down rules, knowledge acquisition methods; symbolic machine learning algorithms; support vector machines; random forests; ensembles of classifiers, e.g., bootstrap aggregation (bagging) and boosting (meta-algorithms); ordered classification; information fuzzy networks (IFNs); conditional random fields; ANOVA; linear classifiers, e.g., Fisher's linear discriminant, linear regression, logistic regression, multinomial logistic regression, naive Bayesian classifier, perceptron, support vector machine, etc.; quadratic classifiers; k-nearest neighbors; boosting; decision trees, e.g., C4.5, random forest, ID3, CART, SLIQ SPRINT, etc.; Bayesian networks, naive Bayes, etc.; and hidden Markov models. Unsupervised learning concepts may include expectation maximization algorithms; vector quantization; GTM (generative topographic map); information bottleneck methods; artificial neural networks, e.g., self-organizing maps; association rule learning, e.g., a priori algorithm, Eclat algorithm, and FP-growth algorithm; hierarchical clustering, e.g., single-link clustering and conceptual clustering; cluster analysis, e.g., K-means algorithm, fuzzy clustering, DBSCAN, and OPTICS algorithm; and outlier detection, e.g., local outlier factorization. Semi-supervised learning concepts may include generative models; low-density separation; graph-based methods; and cotraining.

[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 HTM (Hierarchical Temporal Memory). Computer systems 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 called 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); electronic storage units (e.g., hard disks); communication interfaces for communicating with one or more other systems (e.g., network adapters); and peripherals (which may include caches, other memory, data storage devices, and / or electronic display adapters). The memory, storage units, interfaces, and peripherals communicate with the processor through a communication bus (solid wire) (e.g., a motherboard). The storage unit may be a data storage unit for storing data. The server is operably coupled to a computer network ("Network") with the help of the communication interface. The Network may be the Internet, an intranet and / or extranet, an intranet and / or extranet communicating with the Internet, a telecommunications network, or a data network. In some cases, the Network may, with the help of the server, constitute a peer-to-peer network (which may allow devices coupled to the server to behave as clients or servers).

[0249] The memory unit may store files, such as a target report, and / or communications with data about an individual, any aspect of the data related to the present disclosure.

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

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

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

[0253] The methods described herein may be executed by machine (or computer processor) executable code (or software) stored in an electronic storage area of the server (such as in a memory or an electronic memory unit, etc.). During use, the code may be executed by the processor. In some cases, the code may be retrieved from the memory unit and stored in the memory for immediate access by the processor. In some situations, the electronic memory unit may be excluded and the machine executable instructions may be stored in the memory.

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

[0255] Embodiments of the systems and methods provided herein (e.g., the server) may be incorporated into programming. Various embodiments of this technology may typically be considered “products” or “manufactured articles” in the form of machine (or processor) executable code and / or related data, held in or incorporated into some kind of machine-readable medium. Machine executable code may be stored in electronic storage units (e.g., memory (e.g., read-only memory, random-access memory, flash memory)) or hard disks. “Storage” media may include any or all tangible memories (e.g., various semiconductor memories, tape drives, disk drives, etc.) of the computer, processor, etc., or its associated modules, which may provide non-temporary storage at any point in the software programming. The whole or part of the software may be communicated from time to time via the internet or various other telecommunication networks. Such communication may enable, for example, loading software from one computer or processor to another, for example, from a management server to a host computer to an application server computer platform. Thus, other types of media that can carry software elements include light waves, radio waves, or electromagnetic waves (e.g., used across physical interfaces between local devices through wired and optical terrestrial networks, and by various wireless connections). The physical elements that carry such waves (e.g., wired or wireless connections) may also be used. Likes, optical connections, etc., can also be considered as a medium for carrying the software. As used herein, unless limited to non-temporary tangible “storage” media, terms such as computer or machine “readable medium” can refer to any medium involved in providing instructions to a processor for execution.

[0256] The computer systems described herein may include computer executable code for performing 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] The data relating to this disclosure may be transmitted by a network or connection for reception and / or review by a recipient. The recipient is, but is not limited to, the subject to which the report relates; or its caregivers, e.g., healthcare providers, administrators, other healthcare professionals, or other caregivers; 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 includes a medium suitable for transmitting the results of the analysis of a biological sample using the method described herein.

[0258] Embodiments of the systems and methods provided herein may be incorporated into programming. Various embodiments of this technology may typically be considered “products” or “manufactured articles” in the form of machine (or processor) executable code and / or related data, held or incorporated on some kind of machine-readable medium. Machine executable code may be stored in electronic storage units (e.g., memory (e.g., read-only memory, random-access memory, flash memory)) or hard disks. The “storage” medium may include any or all of the tangible memory (e.g., various semiconductor memories, tape drives, disk drives, etc.) of the computer, processor, etc., or its accessory modules, which may provide non-temporary storage at any point in the software programming. The whole or part of the software may be communicated from time to time via the Internet or various other telecommunication networks. Such communication may, for example, be from one computer or processor to another computer or processor, for example, for management This may enable the loading of software from a server to a host computer to an application server computer platform. Therefore, other types of media that can carry software elements include light waves, radio waves, or electromagnetic waves (for example, those used across physical interfaces between local devices through wired and optical terrestrial 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 for carrying the software. As used herein, unless limited to non-temporary tangible “storage” media, terms such as computer or machine-readable media refer to any medium involved in providing instructions to a processor for execution.

[0259] Therefore, machine-readable media (e.g., computer executable code) can take many forms, including, but not limited to, tangible storage media, carrier media, or physical transmission media. Non-volatile storage media include optical or magnetic disks, such as any storage device in any computer, as shown in the drawings, which can be used to implement databases. Volatile storage media include dynamic memory (such as the main memory of such a computer platform). Tangible transmission media include coaxial cables; copper wires and optical fibers (including wires including buses in computer systems). Carrier media can take the form of electrical or electromagnetic signals, or sound or light waves, such as those generated between radio frequency (RF) data communications and infrared (IR) data communications. Common forms of computer-readable media include, for example, floppy disks, flexible disks, hard disks, magnetic tapes, any other magnetic media, CD-ROMs, DVDs or DVD-ROMs, any other optical media, punch cards, paper tapes, any other physical storage media having a pattern of holes, RAM, ROMs, PROMs and EPROMs, flash EPROMs, any other memory chips or cartridges, carrier waves carrying data or instructions, cables or links that transmit such carrier waves, or any other media from which a computer can read code and / or data. Many of these forms of computer-readable media may be involved in the transport of one or more instructions or sequences of one or more instructions to a processor for execution. [Examples]

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

[0261] This exemplary procedure is applicable to the manual generation of protein coronas in a body fluid sample using a panel of magnetic nanoparticles by complete resuspension of the nanoparticles. The systems and methods of the present disclosure may be applied to the procedures described herein.

[0262] Materials:

[0263] The materials used in the generation of the protein corona are shown in Table 2. [Table 2]

[0264] Storage and Handling:

[0265] As shown in Table 3, the following reagents were stored at room temperature. [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 sample was taken out of the freezer and completely thawed. The nanoparticles were sonicated and vortexed approximately 10 minutes before use. Before starting the assay, a TE 150 mM KCl 0.05% CHAPS buffer was prepared.

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

[0270] Nanoparticle preparation. The aqueous nanoparticles were diluted in reagent-grade water to the appropriate specified concentration. For dry powder nanoparticles, the dry powder nanoparticles were weighed out using a balance to the required concentration before adding the appropriate volume of water.

[0271] Sample preparation. The sample was removed from the freezer. The sample was completely thawed and then centrifuged at 16,000 G for approximately 2 minutes. This sample was either diluted (1:5) with TE 150mM KCl 0.05% CHAPS buffer or left undiluted.

[0272] Figure 9 shows a sample preparation method consistent with the present disclosure. This method comprises four steps for generating a subset of biomolecules from a biological sample and then using that 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 at 37°C for 1 hour with shaking, thereby generating a biomolecular corona on the sensor elements. The partitions are then exposed to a magnetic field strong enough to immobilize the sensor elements within them. 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 subset of biomolecules from the biomolecular corona. The subset of biomolecules is then subjected to a set of denaturation and chemical processing steps (including heating to 95°C, reduction and alkylation, protease digestion, and further washing). A subset of biomolecules is then subjected to mass spectrometry, which yields a biomolecular fingerprint of the sample.

[0273] procedure:

[0274] The reagents and equipment were prepared as described in the previous section (see "Preparation"). 100 μL of diluted nanoparticles were loaded into each well using a multichannel pipette. 100 μL of diluted sample was added to each nanoparticle well using a pipette. The wells were mixed 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 to 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 nanoparticle corona pellet to form at the bottom of the wells. 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 to thoroughly resuspend the nanoparticles. The solution was again placed on a magnet for approximately 5 minutes. The washing step was repeated three times. The nanoparticle pellets were resuspended in appropriate reagents for BCA, gel, or trypsin digestion. Example 2: Digestion of Trypsin Gold

[0275] material:

[0276] Table 5 shows the ingredients used in Trypsin Gold digestion. [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 obtain 10 mL of 50 mM ABC. This solution was vortexed and stored at 4°C for up to one week.

[0279] 8M urea. Weigh 4.8g of urea and add 50mM ABC to near the 10mL mark. Vortex this solution and, if necessary, swirl it in a 37°C incubator to promote dissolution. Add 50mM ABC to the 10mL mark and vortex.

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

[0281] 200 mM IAA. 400 μL of 50 mM ABC was added to 0.015 g of IAA that had been weighed beforehand. This solution was vortexed and stored at 4°C. This solution should be measured accurately before use.

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

[0283] Sample / Trypsin Preparation

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

[0285] This example demonstrates proteomics analysis of NSCLC samples and healthy controls. To demonstrate the usefulness of the corona analysis platform, its capabilities were evaluated using a single particle species (poly(N-(3-(dimethylamino)propyl)methacrylamide)(PDMAPMA)-coated SPION) and serum samples from 56 subjects (28 stage IV NSCLCs and 28 age- and sex-matched controls), and differences between these groups were observed. The selected subject samples provided a reasonably balanced study for identifying potential MS characteristics that differed between these groups. All data on subject annotation, including disease status and comorbidities, are summarized in Table 5. [Table 5-2]

[0286] MS1 characteristics were collected and filtered, followed by log2 transformation of their intensities, and then the dataset was median-scaled independently of classification. Figure 11 shows the normalized intensity distribution for all 56 target datasets. All 56 sample MS raw data files from the NSCLC versus control study were processed by an OpenMS pipeline script to extract MS1 characteristics and their intensities, and they were clustered into characteristic groups based on overlapping mz and RT values ​​within identified tolerances. Only characteristic groups that 1) contained at least 50% of the characteristics in the group from at least one arm of the comparison, and 2) had characteristic group cluster quality above the 25th percentile were retained. The retained characteristics were median-normalized independently of classification and used for subsequent univariate analysis comparisons. No outliers were found by inspection of the distribution, and all datasets were retained for univariate analysis.

[0287] Inspection revealed no outliers. Univariate comparisons of trait strengths between classifications were performed using a nonparametric Wilcoxon test (two-sided). The p-values ​​obtained for this comparison were adjusted for the number of trials using the Benjamini-Hochberg method. Using an adjusted p-value cutoff of 0.05, a total of seven trait groups showed statistical significance, as summarized in Figure 12.

[0288] All five proteins identified as differing in abundance between the NSCLC disease group and the control group have previously been shown to be involved in cancer, if not NSCLC itself. PON1 (also known as paraoxanase-1) has a complex pattern in lung cancer, including the involvement of a relatively common minor allele variant (Q192R) as a risk factor. At the protein level, PON1 is moderately reduced in lung adenocarcinoma. SAA1 is an acute-phase protein that has been shown to be overexpressed in NSCLC in MS-related studies, and the identified peptide was found to be 5.4-fold increased in the diseased group. Matrisomal factor tenascin C (TENA) has been shown to be increased in primary lung tumors and associated lymph node metastases compared to normal tissue, and in this study, associated MS characteristics were found to be 2-fold increased. Neuronal 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 progression of lung cancer. Of particular note are two previously unknown characteristics (group 2 and group 7) that indicate differences between the control and disease groups. Group 2 was found in 54 out of 56 subjects and moderately (33%) decreased in the disease group. In contrast, group 7 was found only in the disease group (14 out of 28 members in this classification). These results demonstrate the potential utility of particle corona in aiding 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 to collect proteins from a plasma sample.

[0290] To evaluate the particle's ability to compress the measured dynamic range, the measured and identified protein characteristic intensities were compared to published values ​​for the same protein concentrations. First, for each protein, the obtained peptide characteristics were selected by the maximum intensity determined by MS for all possible characteristics of the protein (extracting single isotope peak values ​​using the OpenMS mass data processing tool), and then these intensities were modeled against published values ​​for the same protein abundance levels. Figure 13 shows the correlation between the maximum intensities of particle corona protein and plasma protein and the published concentrations of the same protein. The lines plotted in blue are linear regression models for the data, and the shaded areas show 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) showed a compressed dynamic range compared to plasma samples not assayed with particles ("Plasma"), as indicated by the decrease in the 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. Figure 14 shows the dynamic range compression of the protein corona analysis assay by mass spectrometry compared to mass spectrometry without particle corona formation. The protein intensity of common proteins identified in particle corona in plasma samples assayed in Figure 13 ("nanoparticle MS (intensity)") is plotted against the protein intensity identified by mass spectrometry of plasma without particles ("plasma MS (intensity)"). The largest dotted line indicates a slope of 1, representing the dynamic range of mass spectrometry without particles. The slopes of the linear fit against protein intensity are 0.12, 0.36, and 0.093 for S-003, S-007, and S-011 particles, respectively. The gray area indicates the standard error region of the regression fit.

[0291] A comparison of the slope of the regression model and the intensity range of the measured data revealed that the biomolecular corona contains more protein than plasma, even at lower abundances (measured or reported values). The dynamic range of these measurements was compressed for particle measurements compared to plasma measurements (the slope of the regression model was reduced). This is consistent with previous observations that particles can effectively compress the measured dynamic range for abundance in the obtained corona compared to the original dynamic range in plasma, which may be due to a combination of the absolute concentration of the protein, binding affinity to the particle, and interactions with adjacent proteins. This result indicates that the biomolecular corona strategy facilitates the identification of plasma proteins across a broad spectrum, particularly at low abundances where rapid detection by conventional proteomics is difficult.

[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 merely illustrative. Numerous modifications, alterations, and substitutions will be readily apparent to those skilled in the art without departing from the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed. The following claims define the scope of the present invention, and the methods and structures within the claims, as well as their equivalents, are intended to be encompassed within the claims. The present invention provides, for example, the following items: (Item 1) An automated device for generating a subset of biomolecules from a complex biological sample, (a) A substrate comprising a plurality of partitions, wherein the plurality of partitions comprises a plurality of particles; (b) A sample storage unit containing the composite biological sample; and (c) A loading unit that is movable over at least the entire surface of the substrate, Includes, 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 bringing the plurality of particles in the plurality of partitions into contact with the biomolecules of the complex biological sample to form a biomolecular corona, thereby generating a subset of the biomolecules of the complex biological sample. An automated instrument in which 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) The automated apparatus of item 1 further includes an incubation element for stirring or heating the volume of the plurality of particles within the volume of the composite biological sample in the plurality of partitions. (Item 3) The automated apparatus of item 2, wherein the incubation element is configured to shake, mix, stir, spin, vibrate, be static, or any combination thereof. (Item 4) The automated apparatus 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) An automated device according to any one of items 1 to 4, wherein the aforementioned multiple partitions are at least partially covered or sealed. (Item 6) The automated device of item 5 has the ability to add a lid to the substrate or remove a lid from the substrate, wherein the lid covers at least one partition among the plurality of partitions. (Item 7) An automated apparatus according to any one of items 1 to 6, further comprising a unit containing a resuspension solution. (Item 8) The resuspended solution contains Tris-EDTA 150 mM KCl 0.05% CHAPS buffer, as described in item 7 of the automated instrumentation. (Item 9) The resuspended solution contains 10 mM Tris HCl pH 7.4 and 1 mM EDTA, as described in item 7 of the automated equipment. (Item 10) An automated apparatus according to any one of items 1 to 9, further comprising a unit containing a denaturing solution. (Item 11) The denatured solution contains a protease, as described in item 10 of the automated equipment. (Item 12) The automated equipment of item 10 or 11 wherein the denatured solution comprises a reducing agent, a methylating agent, guanidine, urea, sodium deoxycholate, acetonitrile, or any combination thereof. (Item 13) An automated apparatus according to any one of items 10 to 12, wherein the denatured solution produces an average peptide fragment with a mass of less than 4600 Daltons. (Item 14) An automated apparatus according to any one of items 1 to 13, wherein the substrate is a multiwell plate. (Item 15) The loading unit is an automated device according to any one of items 1 to 14, including multiple pipettes. (Item 16) An automated apparatus according to any one of items 1 to 15, wherein the loading unit is configured to dispense 10 μL to 400 μL of solution into one or more of the partitions. (Item 17) The automated apparatus of item 16, wherein the loading unit is configured to dispense 5 μL to 150 μL of solution into one or more of the partitions. (Item 18) The automated apparatus of item 16, wherein the loading unit is configured to dispense 35 μL to 80 μL of solution into one or more of the partitions. (Item 19) An automated apparatus according to any one of items 16 to 18, wherein the solution is selected from the group consisting of a washing solution, the resuspension solution, the denaturation solution, a buffer, and a reagent. (Item 20) An automated apparatus according to any one of items 1 to 19, wherein the loading unit is configured to dispense 10 μL to 400 μL of the complex biological sample into one or more of the partitions. (Item 21) The automated apparatus of item 20, wherein the loading unit is configured to dispense 5 μL to 150 μL of the composite biological sample into one or more of the partitions. (Item 22) 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 partitions. (Item 23) The aforementioned complex biological sample includes bodily fluids from the subject, and is handled by an automated apparatus according to any one of items 1 to 22. (Item 24) Automated equipment for item 23, in which the aforementioned complex biological samples include plasma, serum, urine, cerebrospinal fluid, synovial fluid, tears, saliva, whole blood, milk, nipple aspirate, mammary duct lavage, vaginal fluid, nasal secretions, inner ear fluid, gastric juice, pancreatic juice, trabecular meshwork, lung lavage, sweat, gingival crevicular exudate, semen, prostatic fluid, sputum, feces, bronchial lavage, fluids from swabs, bronchial aspirates, fluidized solids, fine-needle aspiration samples, tissue homogenates, lymphatic fluid, cell culture samples, or any combination thereof. (Item 25) Automated equipment according to any one of items 1 through 18, further including magnets. (Item 26) The automated apparatus of item 25, wherein one or more 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 fixed onto the substrate. (Item 27) An automated device according to any one of items 1 to 26, further comprising a housing, wherein the substrate and the loading unit are disposed within the housing, and the housing is at least partially enclosed. (Item 28) An automated instrument according to any one of items 1 to 27, wherein the compressed dynamic range includes an increase in the number of types of biomolecules whose concentration is within six orders of magnitude for the most abundant biomolecule in the sample. (Item 29) Automated instrument of item 28, wherein the compressed dynamic range includes an increase in the number of biomolecular species whose concentration is within a five-order-of-magnitude range for the most abundant biomolecule in the sample. (Item 30) The automated instrument of item 29, wherein the compressed dynamic range includes an increase in the number of biomolecular species whose concentration is within four orders of magnitude for the most abundant biomolecule in the sample. (Item 31) Automated instrument of item 30, wherein the compressed dynamic range includes an increase in the number of protein types whose concentration is within six orders of magnitude for the most abundant protein in the sample. (Item 32) Automated apparatus of item 31, wherein the increase in the number of types of biomolecules whose concentration is within six orders of magnitude relative to the most concentrated biomolecule in the sample is at least 25%, 50%, 100%, 200%, 300%, 500%, or 1000%. (Item 33) An automated instrument according to any one of items 1 to 32, wherein the compressed dynamic range includes an increase in the number of protein types whose concentration is within six orders of magnitude for the most abundant protein in the sample. (Item 34) Automated apparatus of item 33, wherein the increase in the number of protein types whose concentration is within six orders of magnitude relative to the most abundant protein in the sample is at least 25%, 50%, 100%, 200%, 300%, 500%, or 1000%. (Item 35) An automated instrument according to any one of items 1 to 34, wherein the subset of biomolecules comprises at least 20% to at least 60% of the types of biomolecules from the composite biological sample, within a six-order-of-magnitude concentration range. (Item 36) Automated instrument of item 35, wherein the subset of biomolecules comprises at least 20% to at least 60% of the protein types from the composite biological sample, within a six-order-of-magnitude concentration range. (Item 37) An automated device according to any one of items 1 to 36, wherein the dynamic range of the biomolecule corona is a first ratio of the upper decile biomolecule to the lower decile biomolecule in the plurality of biomolecular coronas. (Item 38) An automated device according to any one of items 1 to 36, wherein the dynamic range of the biomolecules in the biomolecular corona is a first ratio that includes the interquartile range of the biomolecules in the plurality of biomolecular coronas. (Item 39) An automated apparatus according to any one of items 1 to 38, wherein the generation enriches low-abundance biomolecules from the complex biological sample. (Item 40) The automated instrument of item 33 wherein the low-abundance biomolecule is a biomolecule at a concentration of 10 ng / mL or lower in the complex biological sample. (Item 41) An automated instrument according to any one of items 1 to 34, wherein the subset of biomolecules from the aforementioned complex biological sample includes proteins. (Item 42) An automated instrument of item 41, wherein a change of up to 10 mg / mL in the lipid concentration of the composite biological sample results in a change of less than 10%, 5%, 2%, or 1% in the protein composition of the subset of biomolecules generated from the composite biological sample. (Item 43) An automated device according to any one of items 1 to 42, wherein at least two of the plurality of particles have at least one different physicochemical property. (Item 44) Automated equipment 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) Automated equipment for item 44, wherein the surface functionalization includes 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, and BPEI functionalization. (Item 46) Among the plurality of particles, the particles include 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 with a metal core, polymer particles with 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, acacia gum 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, elcinan particles, gluten particles, collagen particles, whey protein isolate particles, casein particles, milk protein particles, soy protein particles, keratin particles, polyethylene particles, polycarbonate particles, polyacid anhydride particles, polyhydroxy acid particles, polypropyl fumarate particles, polycaprolactone particles, polyamine particles, polyacetal particles, polyether particles, poly Automated equipment according to any one of items 1 to 45, selected from the group consisting of tel 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, polyacrylamide anhydride particles, bioreducible polymer particles, and 2-(3-aminopropylamino)ethanol particles, and any combination thereof. (Item 47) An automated apparatus according to any one of items 1 to 46, wherein when one or more of the aforementioned plurality of particles come into contact with the complex biological sample, at least 100 types of proteins are adsorbed. (Item 48) An automated device according to any one of items 1 to 47, wherein the plurality of particles include 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 unique particle species, or at least thirty unique particle species. (Item 49) An automated device according to any one of items 1 to 48, wherein the biomolecule of the aforementioned biomolecule coronavirus includes a large number of protein groups. (Item 50) The aforementioned numerous protein groups include 49 automated devices, each containing 1 to 20,000 protein groups. (Item 51) The aforementioned numerous protein groups comprise 50 automated devices, each containing 100 to 10,000 protein groups. (Item 52) The aforementioned numerous protein groups include 100 to 5000 protein groups, as per item 51 of the automated equipment. (Item 53) The aforementioned numerous protein groups comprise 52 automated devices, each containing 300 to 2,200 protein groups. (Item 54) The aforementioned numerous protein groups comprise 53 automated devices, each containing 1,200 to 2,200 protein groups. (Item 55) An automated device according to any one of items 1 to 54, wherein at least two of the multiple partitions contain different buffers. (Item 56) The aforementioned different buffers differ in pH, salinity, osmolality, viscosity, dielectric constant, or any combination thereof, as described in Item 55 of the automated equipment. (Item 57) An automated apparatus according to any one of items 1 to 56, wherein at least two of the multiple partitions contain buffers in different ratios and the composite biological sample. (Item 58) An automated apparatus according to any one of items 1 to 57, wherein one or more of the aforementioned partitions contain nanoparticles ranging from 1 pM to 100 nM. (Item 59) An automated apparatus according to any one of items 1 to 58, wherein at least two of the plurality of partitions contain nanoparticles of different concentrations. (Item 60) An automated apparatus according to any one of items 1 through 59, further including a purification unit. (Item 61) The aforementioned purification unit is an automated apparatus of item 60, including a solid-phase extraction (SPE) plate. (Item 62) The automated equipment according to any one of items 1 to 61, which generates a subset of biomolecules from a complex biological sample in less than 7 hours. (Item 63) (a) an automated apparatus according to any one of items 1 to 56 configured to isolate a subset of biomolecules from the biological sample; (b) A mass spectrometer configured to receive a subset of biomolecules and generate data including a mass spectrometry signal or a tandem mass spectrometry signal; and (c) A computer having one or more computer processors, and when being executed by the one or more computer processors, i. To generate a biomolecular fingerprint, and ii. Assigning a biological state based on the biomolecular fingerprint, Computer-readable media containing machine-executable code that performs a method including, An automated system, including (Item 64) The aforementioned biomolecular fingerprint is an automated system of item 63, which includes multiple unique biomolecular corona signatures. (Item 65) An automated system of item 64, wherein the biomolecular fingerprint includes at least 5, 10, 20, 40, or 80, 150, or 200 unique biomolecular corona signatures. (Item 66) An automated system according to any one of items 63 to 65, wherein the computer is configured to process data including the intensity of mass spectrometry signals or tandem mass spectrometry signals between a plurality of the unique biomolecular corona signatures, APEX, spectral counts or numbers of peptides, or ion mobility behavior. (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) The automated system of item 67, wherein the computer is configured to process data including the intensities of 10,000 to 5,000,000 mass spectrometry signals or tandem mass spectrometry signals between a plurality of the unique biomolecular corona signatures. (Item 69) An automated system according to any one of items 63-68, wherein the biomolecular fingerprint is generated from data obtained from a single mass spectrometry or tandem mass spectrometry operation. (Item 70) An automated system for item 69 in which the single or tandem mass spectrometry analysis described above is performed in less than one hour. (Item 71) The computer is configured to identify biomolecules or characterize unidentified molecular properties based on a mass spectrometry signal or a tandem mass spectrometry signal and / or ion mobility and chromatographic behavior, wherein the computer provides an automated system according to any one of items 63 to 70, which results in at least a 95% certainty threshold for identifying properties or characterizing unidentified properties. (Item 72) An automated system according to 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 approximately 10 hours. (Item 73) An automated system according to any one of items 63-72, wherein the computer can identify between two or more biological states associated with a biomolecular fingerprint where the difference is less than 10%, 5%, 2%, or 1%. (Item 74) An automated system of any one of items 63-73, wherein the aforementioned biological condition is a disease, disorder, or tissue abnormality. (Item 75) An automated system for item 75, wherein the disease is in its initial and intermediate phases. (Item 76) The automated system of item 74 or 75, wherein the disease is cancer. (Item 77) The automated system of item 76, wherein the cancer is stage 0 or stage 1. (Item 78) The aforementioned cancers include 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, head and neck cancer, Hodgkin's disease, non-Hodgkin lymphoma, rectal cancer, urinary tract cancer, uterine cancer, and oral cancer. Cancer, skin cancer, stomach cancer, brain tumor, liver cancer, laryngeal cancer, esophageal cancer, breast tumor, fibrosarcoma, myxosarcoma, liposarcoma, chondrosarcoma, osteosarcoma, chordoma, angiosarcoma, endosarcoma, Ewing's sarcoma, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinoma, cystadenocarcinoma, medullary carcinoma, bronchogenic lung cancer, renal cell carcinoma, hepatocellular carcinoma, cholangiocarcinoma, choriocarcinoma, seminoma, embryonic carcinoma, Wilms' tumor, cervical cancer, testicular cancer, 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 lymphoblastic leukemia and acute myeloid polycythemia vera, multiple myeloma, Waldenström macroglobulinemia, and heavy chain disease, acute nonlymphoblastic leukemia, An automated system for item 76 or 77, selected from the group consisting of chronic lymphocytic leukemia, chronic myeloid leukemia, childhood null cell acute lymphoblastic leukemia (ALL), thymic ALL (acute lymphoblastic leukemia), B-cell ALL (acute lymphoblastic leukemia), acute megakaryocytic leukemia, Burkitt lymphoma, and T-cell leukemia, small cell and large cell (non-small cell) lung cancer, acute granulocytic leukemia, germ cell tumors, endometrial cancer, gastric cancer, pilocytic cell leukemia, or thyroid cancer. (Item 79) An automated system of any one of items 74-78, wherein the aforementioned biological condition is a pre-disease state. (Item 80) An automated system according to any one of items 63 to 79, wherein the determination involves comparing the abundance of two biomolecules in the composite biological sample, with concentrations ranging from at least 7 to at least 12 orders of magnitude. (Item 81) A method for identifying the biological state of a complex biological sample, (a) Supplying a complex biological sample to an automated instrument specified in any one of items 1 through 56 to generate a subset of biomolecules; (b) assaying a subset of the biomolecules to generate a biomolecular fingerprint; and (c) Identifying the biological state of the complex biological sample by the biomolecular fingerprint, Methods that include... (Item 82) The method of item 81, wherein the biomolecular fingerprint includes a protein. (Item 83) The method of item 81 or 82, wherein the subset of biomolecules from the composite biological sample has an albumin-to-nonalbumin peptide ratio that is smaller than that of the composite biological sample. (Item 84) The method according to any one of items 81 to 83, wherein the subset of biomolecules includes biomolecules whose concentration range in the composite biological sample is at least 6 to at least 12 orders of magnitude. (Item 85) The method according to any one of items 81 to 84, wherein the subset of biomolecules comprises proteins whose concentration range in the composite biological sample is at least 6 to at least 12 orders of magnitude. (Item 86) The method according to any one of items 81 to 85, wherein the biomolecular fingerprint comprises 1 to 74,000 protein groups. (Item 87) The method according to any one of items 81 to 86, wherein the assay comprises desorbing multiple biomolecules from the biomolecular coronas among the multiple biomolecular coronas. (Item 88) The method of item 87, wherein the assay includes chemically modifying the biomolecules among the plurality of adsorbed biomolecules. (Item 89) The method of item 87, wherein the assay comprises fragmenting the biomolecules among the plurality of adsorbed biomolecules. (Item 90) The aforementioned fragmentation is a method of item 89, which includes protease digestion. (Item 91) The fragmentation method of item 89, wherein the fragmentation includes chemical peptide cleavage. (Item 92) The method according to any one of items 87 to 91, wherein the assay comprises collecting the plurality of adsorbed biomolecules. (Item 93) The method of item 92, wherein the assay comprises purifying the plurality of adsorbed biomolecules collected. (Item 94) The purification method described in item 93 includes solid-phase extraction. (Item 95) The method of item 93 or 94, wherein the purification depletes non-protein biomolecules from the collected adsorbed biomolecules. (Item 96) The method of item 87, wherein the assay includes discarding the plurality of adsorbed biomolecules. (Item 97) The method according to any one of items 87 to 96, wherein the assay comprises desorbing a first subset of biomolecules and a second set of biomolecules from the biomolecular coronas of 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) The method of any one of items 81 to 97, wherein the assay comprises analyzing the biomolecular coronas in the plurality of biomolecular coronas by mass spectrometry, tandem mass spectrometry, mass cytometry, mass cytometry, potentiometric measurement, fluorescence quantification, absorption spectroscopy, Raman spectroscopy, chromatography, electrophoresis, immunohistochemistry, PCR, next-generation sequencing (NGS), or any combination thereof. (Item 99) The method of item 98, wherein the assay includes mass spectrometry or tandem mass spectrometry. (Item 100) The method according to any one of items 81 to 99, wherein the assay includes identifying the three-dimensional structure of a protein in a subset of biomolecules. (Item 101) The method according to any one of items 81 to 100, wherein the assay comprises identifying post-translational modifications to proteins in a subset of biomolecules. (Item 102) The method of any one of items 81 to 101, wherein the identification includes comparing the relative abundances of at least 200 to at least 1000 biomolecules from a subset of biomolecules. (Item 103) The method according to any one of items 81 to 102, wherein the assay identifies biomolecules in the composite biological sample at concentrations of less than 10 ng / mL. (Item 104) An automated device for generating a subset of biomolecules from a complex biological sample, Multiple particles; and The aforementioned complex biological sample includes, The system is configured to generate a subset of the biomolecules by bringing the plurality of particles into contact with the complex biological sample, and to form a plurality of biomolecular coronas containing the subset of the biomolecules. An automated instrument in which 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) The automated equipment of item 104 further comprises a substrate, wherein the substrate is a multiwell plate. (Item 106) Automated equipment of item 104 or 105, further including incubation elements. (Item 107) The automated apparatus of item 106, wherein the incubation element is configured to heat and / or incubate the plurality of particles and the complex biological sample at a temperature of 4°C to 40°C. (Item 108) An automated apparatus according to any one of items 104-107, further comprising at least one solution selected from the group consisting of washing solutions, resuspension solutions, denaturation solutions, buffers, and reagents. (Item 109) The resuspension solution comprises Tris-EDTA buffer, phosphate buffer, and / or water, as described in item 108 of the automated equipment. (Item 110) The aforementioned denatured solution contains a protease, as described in item 108 of the automated equipment. (Item 111) The automated apparatus of item 108, wherein the denatured solution contains small molecules capable of peptide cleavage. (Item 112) The automated equipment according to any one of items 104 to 111, further comprising a loading unit, wherein the loading unit comprises a plurality of pipettes. (Item 113) The automated apparatus of item 112, wherein each of the plurality of pipettes is configured to dispense approximately 5 μL to 150 μL of the solution, the complex biological sample, and / or the plurality of particles. (Item 114) Automated equipment of any one of items 104-113, in which the aforementioned complex biological samples include plasma, serum, urine, cerebrospinal fluid, synovial fluid, tears, saliva, whole blood, milk, nipple aspirate, mammary duct lavage, vaginal fluid, nasal secretions, inner ear fluid, gastric juice, pancreatic juice, trabecular meshwork, lung lavage, sweat, gingival crevicular exudate, semen, prostatic fluid, sputum, feces, bronchial lavage, fluid from swabs, bronchial aspirates, fluidized solids, fine needle aspiration samples, tissue homogenates, lymphatic fluid, cell culture samples, or any combination thereof. (Item 115) Automated equipment of any one of items 104-114, further including magnets. (Item 116) An automated device, including a filter, according to any one of items 104-115. (Item 117) An automated instrument according to any one of items 104 to 116, wherein the compressed dynamic range includes an increase in the number of types of biomolecules whose concentration is within a range of 4 to 6 orders of magnitude for the most abundant biomolecule in the sample. (Item 118) Automated equipment for item 117, in which the aforementioned types of biomolecules include proteins. (Item 119) An automated device according to any one of items 104 to 118, wherein the dynamic range of the biomolecules in the biomolecular corona is a first ratio of upper decile biomolecules to lower decile biomolecules in the plurality of biomolecular coronas. (Item 120) An automated apparatus according to any one of items 104-119, wherein the generation enriches low-abundance biomolecules from the complex biological sample. (Item 121) The automated instrument of item 120 wherein the low-abundance biomolecule is a biomolecule at a concentration of 10 ng / mL or lower in the complex biological sample. (Item 122) An automated apparatus according to any one of items 104 to 121, wherein at least two of the plurality of particles have at least one different physicochemical property. (Item 123) Automated equipment 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) Among the plurality of particles, the particles include 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 with a metal core, polymer particles with 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, acacia gum 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, elcinan particles, gluten particles, collagen particles, whey protein isolate particles, casein particles, milk protein particles, soy protein particles, keratin particles, polyethylene particles, polycarbonate particles, polyacid anhydride particles, polyhydroxy acid particles, polypropyl fumarate particles, polycaprolactone particles, polyamine particles, polyacetal particles, polyether particles, polyester Automated equipment for any one of items 104 to 123, 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 acid / glycolic acid particles, polyacrylamide anhydride particles, bioreducible polymer particles, and 2-(3-aminopropylamino)ethanol particles, and any combination thereof. (Item 125) An automated instrument according to any one of items 104-124, wherein the biomolecule of the aforementioned biomolecule coronavirus includes a large number of protein groups. (Item 126) The aforementioned numerous protein groups include 1 to 20,000 protein groups, and there are 125 automated devices. (Item 127) The aforementioned numerous protein groups include 100 to 10,000 protein groups, and there are 125 automated devices. (Item 128) The aforementioned numerous protein groups include 100 to 5000 protein groups, and there are 125 automated devices. (Item 129) The aforementioned numerous protein groups comprise 125 automated devices, including 300 to 2,200 protein groups. (Item 130) The aforementioned numerous protein groups comprise 1,200 to 2,200 protein groups, and there are 125 automated devices. (Item 131) An automated apparatus of any one of items 104-130, further including a purification unit. (Item 132) The aforementioned purification unit is an automated apparatus of item 131, including a solid-phase extraction (SPE) plate. (Item 133) The automated equipment according to any one of items 104 to 132, which generates a subset of biomolecules from a complex biological sample in less than 7 hours. (Item 134) A method for generating a subset of biomolecules from a complex biological sample, This includes supplying the aforementioned complex biological sample to an automated device. The automated device brings the complex biological sample into contact with multiple particles to generate a biomolecular corona. The automated device processes the biomolecular corona to generate a subset of the biomolecules, A method in which 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) The method of item 134, wherein the automated equipment is an automated equipment of any one of items 104 to 133. (Item 136) The method of item 134 or 135, further comprising assaying a subset of the biomolecules to generate a biomolecular fingerprint. (Item 137) The method of item 136, wherein the assay identifies biomolecules in the composite biological sample at concentrations of less than 10 ng / mL. (Item 138) The method of any one of items 136 or 137, wherein the assay comprises analyzing the biomolecular coronavirus by mass spectrometry, tandem mass spectrometry, mass cytometry, mass cytometry, potentiometric measurement, fluorescence quantification, absorption spectroscopy, Raman spectroscopy, chromatography, electrophoresis, immunohistochemistry, or a combination thereof. (Item 139) The method of item 138, wherein the assay includes mass spectrometry or tandem mass spectrometry. (Item 140) The method according to any one of items 134 to 139, further comprising identifying the biological state of the complex biological sample by the biomolecular fingerprint. (Item 141) The method of item 140 wherein the biomolecular fingerprint includes multiple unique biomolecular corona signatures. (Item 142) The method of item 140 or 141, wherein the biomolecular fingerprint includes at least 5, 10, 20, 40, or 80, 150, or 200 unique biomolecular corona signatures. (Item 143) The method according to any one of items 140-142, wherein the aforementioned biological condition is a disease, disorder, or tissue abnormality. (Item 144) The method of item 143, wherein the disease is in the initial and intermediate phases. (Item 145) The disease is cancer, according to item 143 or 144. (Item 146) The method of item 145, wherein the cancer is stage 0 or stage 1. (Item 147) The aforementioned cancers include 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, head and neck cancer, Hodgkin's disease, non-Hodgkin lymphoma, rectal cancer, urinary tract cancer, uterine cancer, and oral cancer. Cancer, skin cancer, stomach cancer, brain tumor, liver cancer, laryngeal cancer, esophageal cancer, breast tumor, fibrosarcoma, myxosarcoma, liposarcoma, chondrosarcoma, osteosarcoma, chordoma, angiosarcoma, endosarcoma, Ewing's sarcoma, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinoma, cystadenocarcinoma, medullary carcinoma, bronchogenic lung cancer, renal cell carcinoma, hepatocellular carcinoma, cholangiocarcinoma, choriocarcinoma, seminoma, embryonic carcinoma, Wilms' tumor, cervical cancer, testicular cancer, 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 lymphoblastic leukemia and acute myeloid polycythemia vera, multiple myeloma, Waldenström macroglobulinemia, and heavy chain disease, acute nonlymphoblastic leukemia A method of item 145 or 146 selected from the group consisting of disease, chronic lymphocytic leukemia, chronic myeloid leukemia, childhood null cell acute lymphoblastic leukemia (ALL), thymic ALL (acute lymphocytic leukemia), B-cell ALL (acute lymphocytic leukemia), acute megakaryocytic leukemia, Burkitt 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, pilocytic cell leukemia, or thyroid cancer. (Item 148) The method of item 143, wherein the aforementioned biological state is a pre-disease state. (Item 149) An automated system comprising a network of units having differentiated functions in identifying the state of a complex biological sample using multiple particles having surfaces with different physiological and chemical properties, (a) The first unit includes a multi-channel fluid transfer device for transferring fluid from unit to unit within the system; (b) The second unit includes a support for storing multiple biological samples; (c) A third unit comprises a support for a sensor array plate having partitions containing the plurality of particles having surfaces having different physiological and chemical properties for detecting binding interactions between the population of analytes in the complex biological sample and the plurality of particles; (d) The fourth unit includes a support for storing multiple reagents; (e) The fifth unit includes a support for storing reagents that will be discarded; (f) The sixth unit includes a support for storing consumables used by the multi-channel fluid transfer device; i. Bringing the biological sample into contact with a specific partition of the sensor array; ii. Incubating the biological sample with the plurality of particles contained within the partition of the sensor array plate; iii. Removing all components from the partition except for the plurality of particles and the group of analytes that interact with the particles; and iv. An automated system programmed to perform a series of steps, including preparing a sample for mass spectrometry. (Item 150) The automated system of item 149, wherein the first unit has mobility to the extent that it allows access to all other units in the system. (Item 151) An automated system according to item 149 or 150, wherein the first unit has the capability to perform a pipetting function. (Item 152) An automated system according to any one of items 149 to 151, wherein the support of the second and / or third unit includes 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) An automated system according to any one of items 149 to 152, wherein the second and / or unit includes a thermal unit capable of controlling the temperature of the support and the sample. (Item 154) The automated system of any one of items 149 to 153, wherein the second and / or third unit includes a rotary unit capable of physically stirring and / or mixing the sample. (Item 155) An automated system according to any one of items 149 to 154, wherein the plurality of particles having surfaces with different physiological and chemical properties are immobilized on the surface within a partition of the sensor array for detecting binding interactions between a population of analytes in the complex biological sample and the plurality of particles. (Item 156) An automated system according to any one of items 149 to 155, comprising a plurality of magnetic nanoparticles in a solution having different physiological and chemical properties for detecting binding interactions between a population of analytes in the complex biological sample and the plurality of particles having the aforementioned surface. (Item 157) The automated system of item 156, comprising the step of transferring the sensor array plate to an additional seventh unit which includes a magnetized support and a thermal unit capable of controlling the temperature of the support and the sample, and incubating it for an additional time. (Item 158) The aforementioned fourth unit, (a) Generate the sensor array plate; (b) Wash the unbound sample; and / or (c) Prepare the sample for mass spectrometry. An automated system according to any one of items 149-157, including a set of reagents for use. (Item 159) (i) An automated system of any one of items 149 to 158, wherein bringing the biological sample into contact 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. (Item 160) (i) An automated system of any one of items 149 to 159, which includes bringing the biological sample into contact with a specific partition of the sensor array, and pipetting volumes corresponding to a ratio of multiple particles in a 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) An automated system according to any one of items 149 to 160, wherein bringing the biological sample into contact with a specific partition of the sensor array includes 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 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 seconds, at least about 25 seconds, at least about 30 An automated system according to any one of items 149-161, including an incubation period of minutes, at least approximately 45 minutes, at least approximately 50 minutes, at least approximately 60 minutes, at least approximately 90 minutes, at least approximately 2 hours, at least approximately 3 hours, at least approximately 4 hours, at least approximately 5 hours, at least approximately 6 hours, at least approximately 7 hours, at least approximately 8 hours, at least approximately 9 hours, at least approximately 10 hours, at least approximately 12 hours, at least approximately 14 hours, at least approximately 15 hours, at least approximately 16 hours, at least approximately 17 hours, at least approximately 18 hours, at least approximately 19 hours, at least approximately 20 hours, or at least approximately 24 hours. (Item 163) (ii) An automated system according to any one of items 149 to 162, wherein the biological sample is incubated with the plurality of particles contained within the partition of the sensor array plate, the incubation temperature being approximately 4°C to approximately 40°C. (Item 164) An automated system according to any one of items 149-163, comprising a series of washing steps, to remove all components from the partition except for the aforementioned plurality of particles and the analytes interacting with the particles. (Item 165) An automated system according to any one of items 149 to 164, wherein the second unit can facilitate the transfer of the sample to the mass spectrometry unit for mass spectrometry. (Item 166) An automated instrument for identifying proteins in biological samples, Sample preparation unit; Substrate containing multiple channels; Multiple pipettes; It contains multiple solutions and multiple nanoparticles, An automated device configured to form a protein corona and to digest the protein corona. (Item 167) An automated instrument for identifying proteins in biological samples, Sample preparation unit; Substrate containing multiple channels; Multiple pipettes; It contains multiple solutions and multiple nanoparticles, The automated device is configured to form a protein corona and to digest the protein corona. an automated instrument wherein at least one of the solutions is TE 150 mM KCl 0.05% CHAPS buffer. (Item 168) The automated apparatus of item 166 or 167, wherein the sample preparation unit is configured to add the plurality of nanoparticles to the substrate using the plurality of pipettes. (Item 169) An automated apparatus according to any one of items 166 to 168, wherein the sample preparation unit is configured to add the biological sample to the substrate using the plurality of pipettes. (Item 170) An automated apparatus according to any one of items 166 to 169, wherein the sample preparation unit is configured to incubate the plurality of nanoparticles and the biological sample to form the protein corona. (Item 171) An automated apparatus according to 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) An automated instrument according to 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) An automated device relating to any one of items 166 to 172, further including a magnetic source. (Item 174) The automated equipment described herein is configured for the trypsin digestion of BCA, gel, or protein corona, as specified in any one of items 166 to 173. (Item 175) The aforementioned automated equipment is enclosed by any one of the automated equipment items 166-174. (Item 176) The automated equipment is an automated equipment according to any one of items 166 to 175, which is sterilized before use. (Item 177) The automated equipment described above is an automated equipment specified in any one of items 166 to 176, which is adapted for mass spectrometry. (Item 178) The automated equipment is an automated equipment according to any one of items 166 to 177, which is temperature-controlled. (Item 179) A method for identifying proteins in a biological sample, Adding the biological sample to an automated apparatus specified in any one of items 166-178; To generate proteomics data using the aforementioned automated equipment; and To quantify the aforementioned proteomics data. Methods that include... (Item 180) The method of item 179, further comprising incubating multiple nanoparticles with the biological sample in the automated apparatus to form a protein corona. (Item 181) The method of item 179 or 180, further comprising separating the protein corona from the supernatant within the automated apparatus. (Item 182) The method according to any one of items 179 to 181, further comprising digesting the protein corona in the automated apparatus to form a digested sample. (Item 183) The method according to any one of items 179 to 182, further comprising washing the digested sample within the automated apparatus. (Item 184) A method according to any one of items 179 to 183, wherein the quantification of the proteomics data includes providing the proteomics data to mass spectrometry. (Item 185) The method according to any one of items 179-184, wherein the biological sample is a body fluid. (Item 186) The method according to any one of items 179 to 185, wherein the body fluid is serum or plasma.

Claims

[Claim 1] The invention described in the present specification.

Images