Compositions, methods, and systems for protein corona analysis, and their uses

Abstract

Providing compositions, methods, and systems for protein corona analysis, as well as their uses. [Solution] Compositions, methods, and systems for analyzing protein coronas are described herein, along with their applications to the discovery of therapeutic targets as well as advanced diagnostic tools. The present invention provides a panel of nanoparticles for the detection of a wide range of diseases and disorders in a subject and for the determination of disease conditions. While the creation and characterization of protein coronas have been carried out in the art, the majority of experiments have been performed using non-magnetic particles, such as liposomes and polymer nanoparticles, or other particle types that can be used for targeted drug delivery.

Description

[Technical Field]

[0001] Cross-reference of related applications This application claims priority to and benefits from U.S. Provisional Patent Application No. 62 / 756,960, filed 7 November 2018, U.S. Provisional Patent Application No. 62 / 824,278, filed 26 March 2019, and U.S. Provisional Patent Application No. 62 / 874,862, filed 16 July 2019, the entire contents of each of these applications incorporated herein by reference. [Background technology]

[0002] The widespread implementation of proteomic information into science and medicine largely lags behind genomics due to the inherent complexity of protein molecules themselves, which requires complex workflows that limit the scalability of such analyses. Compositions and methods for the rapid processing of proteomic data and the identification of critical biomarkers related to disease are disclosed herein. [Overview of the project] [Means for solving the problem]

[0003] This invention provides a panel of nanoparticles for the detection of a wide range of diseases and disorders and for determining the condition of a subject.

[0004] While the creation and characterization of protein coronas are also being carried out in this field, the majority of experiments are conducted using non-magnetic particles, such as liposomes and polymer nanoparticles, or other particle types that can be used for targeted drug delivery.

[0005] A problem with current (generally academic) assays for protein corona formation and characterization is that these assays are not suitable for high-throughput and / or automated formats, as the particles used in the assay need to be isolated for corona recovery. The advantage of SPMNPs used for protein corona is their rapid magnetic response, which allows for easy separation from the suspension mixture by applying an external magnetic field. This type of magnetic particle is a good platform for further chemical modification with different functional groups, which could help fine-tune the interaction between the particle and plasma proteins.

[0006] In some embodiments, the Disclosure provides a method for identifying proteins in a sample, comprising the steps of: incubating a particle panel with a sample to form a plurality of distinct biomolecular coronas corresponding to distinct particle types of the particle panel; magnetically isolating the particle panel from unbound proteins in the sample to enrich proteins in the plurality of distinct biomolecular coronas; and assaying the plurality of distinct biomolecular coronas to identify the enriched proteins.

[0007] In some embodiments, the assay step can identify 1 to 20,000 protein groups. In further embodiments, the assay step can identify 1,000 to 10,000 protein groups. In even further embodiments, the assay step can identify 1,000 to 5,000 protein groups. In even further embodiments, the assay step can identify 1,200 to 2,200 protein groups. In some embodiments, the protein groups include peptide sequences having a minimum length of 7 amino acid residues. In some further embodiments, the assay step can identify 1,000 to 10,000 proteins. In even further embodiments, the assay step can identify 1,800 to 5,000 proteins.

[0008] In some embodiments, the sample comprises multiple samples. In some embodiments, the multiple samples comprises at least two or more spatially isolated samples. In further embodiments, the incubation step comprises simultaneously contacting at least two or more spatially isolated samples with the particle panel. In further embodiments, the magnetic isolation step comprises simultaneously magnetically isolating the particle panel from unbound proteins in at least two or more spatially isolated samples of the multiple samples. In further embodiments, the assay step comprises assaying multiple distinct biomolecular coronas to simultaneously identify proteins in at least two or more spatially isolated samples.

[0009] In some embodiments, the method further includes a step of repeating the method described herein, wherein, if repeated, the incubation, isolation, and assay steps are determined by comparing peptide mass spectrometry characteristics from at least three complete assay repeats for each particle type in the particle panel to produce a quantile-normalized coefficient of variation (QNCV) percentage of 20% or less. In some embodiments, if repeated, the incubation, isolation, and assay steps are determined by comparing peptide mass spectrometry characteristics from at least three complete assay repeats for each particle type in the particle panel to produce a quantile-normalized coefficient of variation (QNCV) percentage of 10% or less. In some embodiments, the assay step can identify proteins over a dynamic range of at least 7, at least 8, at least 9, or at least 10.

[0010] In some embodiments, the method further includes the step of washing the particle panel at least once or at least twice after the step of magnetically isolating the particle panel from unbound proteins. In some embodiments, the method further includes the step of lysing the proteins in a plurality of distinct biomolecular coronas after the assay step.

[0011] In some embodiments, the method further includes the step of digesting proteins in multiple distinct biomolecules within the corona to produce digested peptides.

[0012] In some embodiments, the method further includes the step of purifying the digested peptide.

[0013] In some embodiments, the assay step includes identifying proteins in the sample using mass spectrometry. In some embodiments, the assay is performed within about 2 to 4 hours. In some embodiments, the method is performed within about 1 to 20 hours. In some embodiments, the method is performed within about 2 to 10 hours. In some embodiments, the method is performed within about 4 to 6 hours. In some embodiments, the isolation step takes about 30 minutes or less, about 15 minutes or less, about 10 minutes or less, about 5 minutes or less, or about 2 minutes or less. In some embodiments, the sample group includes at least 10 spatially isolated samples, at least 50 spatially isolated samples, at least 100 spatially isolated samples, at least 150 spatially isolated samples, at least 200 spatially isolated samples, at least 250 spatially isolated samples, or at least 300 spatially isolated samples. In further embodiments, the sample group includes at least 96 samples.

[0014] In some embodiments, the particle panel includes at least two distinct particle types, at least three distinct particle types, at least four distinct particle types, at least five distinct particle types, at least six distinct particle types, at least seven distinct particle types, at least eight distinct particle types, at least nine distinct particle types, at least ten distinct particle types, at least eleven distinct particle types, at least twelve distinct particle types, at least thirteen distinct particle types, at least fourteen distinct particle types, at least fifteen distinct particle types, at least twenty distinct particle types, at least twenty-five distinct particle types, or at least thirty distinct particle types. In further embodiments, the particle panel includes at least ten distinct particle types. In further embodiments, at least two spatially isolated samples differ in at least one physicochemical property.

[0015] In some embodiments, the particle panel comprises a first distinct particle type and a second distinct particle type, wherein the first distinct particle type and the second distinct particle type share at least one physicochemical property but differ in at least one physicochemical property, and therefore the first distinct particle type and the second distinct particle type are different. In some embodiments, the particle panel comprises a first distinct particle type and a second distinct particle type, wherein the first distinct particle type and the second distinct particle type share at least two physicochemical properties but differ in at least two physicochemical properties, and therefore the first distinct particle type and the second distinct particle type are different. In some embodiments, the particle panel comprises a first distinct particle type and a second distinct particle type, wherein the first distinct particle type and the second distinct particle type share at least one physicochemical property but differ in at least two physicochemical properties, and therefore the first distinct particle type and the second distinct particle type are different.

[0016] In some embodiments, the particle panel comprises a first distinct particle type and a second distinct particle type, wherein the first distinct particle type and the second distinct particle type share at least two physicochemical properties but differ in at least one physicochemical property, and thus the first distinct particle type and the second distinct particle type are different. In further embodiments, the physicochemical properties include size, charge, core material, shell material, porosity, or surface hydrophobicity. In further embodiments, the size is diameter or radius measured by dynamic light scattering, SEM, TEM, or any combination thereof.

[0017] In some embodiments, the particle panel comprises a first distinct particle type and a second distinct particle type, wherein the first distinct particle type and the second distinct particle type comprise a carboxylate material, the first distinct particle is a microparticle, and the second distinct particle type is a nanoparticle. In some embodiments, the particle panel comprises a first distinct particle type and a second distinct particle type, wherein the first distinct particle type and the second distinct particle type have surface charges of 0 mV and -50 mV, the first distinct particle type has a diameter of less than 200 nm, and the second distinct particle type has a diameter of greater than 200 nm.

[0018] In some embodiments, the particle panel comprises a first distinct particle type and a second distinct particle type, the first distinct particle type and the second distinct particle type having a diameter of 100 to 400 nm, the first distinct particle type having a positive surface charge, and the second distinct particle type having a neutral surface charge. In some embodiments, the particle panel comprises a first distinct particle type and a second distinct particle type, the first distinct particle type and the second distinct particle type being nanoparticles, the first distinct particle type having a surface charge of less than -20 mV, and the second distinct particle type having a surface charge of greater than -20 mV.

[0019] In some embodiments, the particle panel comprises a first distinct particle type and a second distinct particle type, where the first distinct particle type and the second distinct particle type are microparticles, the first distinct particle type having a negative surface charge, and the second distinct particle type having a positive surface charge. In some embodiments, the particle panel comprises a subset of negatively charged nanoparticles, where each particle in the subset differs in respect to at least one surface chemical group. In some embodiments, the particle panel comprises a first distinct particle type, a second particle, and a third distinct particle type, where the first distinct particle type, the second distinct particle type, and the third distinct particle type comprises an iron oxide core, a polymer shell, and has a diameter of less than approximately 500 nm, the first distinct particle type having a negative charge, the second distinct particle type having a positive charge, and the third distinct particle type having a neutral charge, and the diameter is the average diameter measured by dynamic light scattering. In further embodiments, a first distinct particle type comprises a silica coating, a second distinct particle type comprises poly(N-(3-(dimethylamino)propyl)methacrylamide) (PDMAPMA), and a third distinct particle type comprises a poly(oligo(ethylene glycol)methyl ether methacrylate) (POEGMA) coating.

[0020] In some embodiments, at least one distinct particle type of the particle panel is a nanoparticle. In some embodiments, at least one distinct particle type of the particle panel is a microparticle. In some embodiments, at least one distinct particle type of the particle panel is a superparamagnetic iron oxide particle. In some embodiments, each particle of the particle panel comprises an iron oxide material. In some embodiments, at least one distinct particle type of the particle panel has an iron oxide core. In some embodiments, at least one distinct particle type of the particle panel has an iron oxide crystal embedded in a polystyrene core. In some embodiments, each distinct particle type of the particle panel is a superparamagnetic iron oxide particle. In some embodiments, each distinct particle type of the particle panel comprises an iron oxide core. In some embodiments, each distinct particle type of the particle panel has an iron oxide crystal embedded in a polystyrene core. In some embodiments, at least one distinct particle type of the particle panel comprises a carboxylated polymer, an amination polymer, a zwitterionic polymer, or any combination thereof. In some embodiments, at least one particle type of the particle panel comprises an iron oxide core having a silica shell coating. In some embodiments, at least one particle type of the particle panel comprises an iron oxide core having a poly(N-(3-(dimethylamino)propyl)methacrylamide) (PDMAPMA) coating. In some embodiments, at least one particle type of the particle panel comprises an iron oxide core having a poly(oligo(ethylene glycol)methyl ether methacrylate) (POEGMA) coating.

[0021] In some embodiments, at least one distinct particle type of the particle panel has a negative surface charge. In some embodiments, at least one distinct particle type of the particle panel has a positive surface charge. In some embodiments, at least one distinct particle type of the particle panel has a neutral surface charge. In some embodiments, the particle panel includes one or more distinct particle types selected from Table 10. In some embodiments, the particle panel includes two or more distinct particle types, three or more distinct particle types, four or more distinct particle types, five or more distinct particle types, six or more distinct particle types, seven or more distinct particle types, eight or more distinct particle types, nine or more distinct particle types, or all 10 distinct particle types selected from Table 10. In some embodiments, the particle panel includes one or more distinct particle types selected from Table 12. In some embodiments, the particle panel includes two or more distinct particle types, three or more distinct particle types, four or more distinct particle types, five or more distinct particle types, six or more distinct particle types, seven or more distinct particle types, eight or more distinct particle types, nine or more distinct particle types, or all 10 distinct particle types selected from Table 12.

[0022] In various embodiments, the present disclosure provides compositions comprising three or more distinct magnetic particle types that differ in two or more physicochemical properties, wherein a subset of the three or more distinct magnetic particle types shares two or more physicochemical properties, and such particle types of the subset bind to different proteins.

[0023] In some embodiments, three or more distinct magnetic particle types adsorb proteins from a biological sample over at least 7, at least 8, at least 9 or at least 10 dynamic ranges. In some embodiments, three or more distinct magnetic particle types can adsorb a group of 1 to 20,000 proteins from a biological sample. In some aspects, three or more distinct magnetic particle types can adsorb a group of 1,000 to 10,000 proteins from a biological sample. In a further aspect, three or more distinct magnetic particle types can adsorb a group of 1,000 to 5,000 proteins from a biological sample. In a further aspect, three or more distinct magnetic particle types can adsorb a group of 1,200 to 2,200 proteins from a biological sample. In yet a further aspect, the protein group comprises a peptide sequence having a minimum length of 7 amino acid residues. In some aspects, three or more distinct magnetic particle types can adsorb 1 to 20,000 proteins from a biological sample. In a further aspect, three or more distinct magnetic particle types can adsorb 1,000 to 10,000 proteins from a biological sample. In yet a further embodiment, three or more distinct magnetic particle types can adsorb 1,800 to 5,000 proteins from a biological sample.

[0024] In some embodiments, the composition comprises at least four distinct magnetic particle types, at least five distinct magnetic particle types, at least six distinct magnetic particle types, at least seven distinct magnetic particle types, at least eight distinct magnetic particle types, at least nine distinct magnetic particle types, at least ten distinct magnetic particle types, at least eleven distinct magnetic particle types, at least twelve distinct magnetic particle types, at least thirteen distinct magnetic particle types, at least fourteen distinct magnetic particle types, at least fifteen distinct magnetic particle types, at least twenty distinct magnetic particle types, or at least thirty distinct magnetic particle types. In some embodiments, the composition comprises at least ten distinct magnetic particle types. In some embodiments, the composition comprises a first distinct particle type and a second distinct particle type, the first distinct particle type and the second distinct particle type sharing at least two physicochemical properties but differing in at least two physicochemical properties, and thus the first distinct particle type and the second distinct particle type are different.

[0025] In some embodiments, the composition comprises a first distinct particle type and a second distinct particle type, the first distinct particle type and the second distinct particle type sharing at least two physicochemical properties but differing in at least one physicochemical property, and thus the first distinct particle type and the second distinct particle type are different. In some embodiments, the physicochemical properties include size, charge, core material, shell material, porosity, or surface hydrophobicity. In further embodiments, the size is diameter or radius measured by dynamic light scattering, SEM, TEM, or any combination thereof.

[0026] In some embodiments, the composition includes a first distinct particle type and a second distinct particle type, the first distinct particle type and the second distinct particle type include a carboxylate material, the first distinct particles are microparticles, and the second distinct particle type is nanoparticles. In some embodiments, the particle panel includes a first distinct particle type and a second distinct particle type, the first distinct particle type and the second distinct particle type have surface charges of 0 mV and -50 mV, the first distinct particle type has a diameter of less than 200 nm, and the second distinct particle type has a diameter greater than 200 nm. In some embodiments, the particle panel includes a first distinct particle type and a second distinct particle type, the first distinct particle type and the second distinct particle type have a diameter of 100 to 400 nm, the first distinct particle type has a positive surface charge, and the second distinct particle type has a neutral surface charge.

[0027] In some embodiments, the particle panel comprises a first distinct particle type and a second distinct particle type, where the first distinct particle type and the second distinct particle type are nanoparticles, the first distinct particle type having a surface charge less than -20 mV, and the second distinct particle type having a surface charge greater than -20 mV. In some embodiments, the particle panel comprises a first distinct particle type and a second distinct particle type, where the first distinct particle type and the second distinct particle type are microparticles, the first distinct particle type having a negative surface charge, and the second distinct particle type having a positive surface charge. In some embodiments, the composition comprises a subset of negatively charged nanoparticles, where each particle type in the subset differs in respect to at least one surface chemical group. In some embodiments, the composition comprises a first distinct particle type, a second distinct particle, and a third distinct particle type, the first, second, and third distinct particle types comprising an iron oxide core, a polymer shell, having a diameter of less than approximately 500 nm, the first distinct particle type having a negative charge, the second distinct particle type having a positive charge, and the third distinct particle type having a neutral charge, with the diameter being the average diameter measured by dynamic light scattering. In further embodiments, the first distinct particle type comprises a silica coating, the second distinct particle type comprises poly(N-(3-(dimethylamino)propyl)methacrylamide) (PDMAPMA), and the third distinct particle type comprises a poly(oligo(ethylene glycol)methyl ether methacrylate) (POEGMA) coating.

[0028] In some embodiments, three or more distinct magnetic particle types include nanoparticles. In some embodiments, three or more distinct magnetic particle types include microparticles. In some embodiments, at least one of the three or more distinct magnetic particle types is a superparamagnetic iron oxide particle. In some embodiments, at least one of the three or more distinct magnetic particle types includes an iron oxide material. In some embodiments, at least one of the three or more distinct magnetic particle types has an iron oxide core. In some embodiments, at least one of the three or more distinct magnetic particle types has an iron oxide crystal embedded in a polystyrene core.

[0029] In some embodiments, each of the three or more distinct magnetic particle types is a superparamagnetic iron oxide particle. In some embodiments, each of the three or more distinct magnetic particle types includes an iron oxide core. In some embodiments, each of the three or more distinct magnetic particle types has an iron oxide crystal embedded in a polystyrene core. In some embodiments, at least one of the three or more distinct magnetic particle types includes a polymer coating.

[0030] In some embodiments, three or more distinct magnetic particle types include carboxylated polymers, amination polymers, zwitterionic polymers, or any combination thereof. In some embodiments, at least one of the three or more distinct magnetic particle types includes an iron oxide core having a silica shell coating. In some embodiments, at least one of the three or more distinct magnetic particle types includes an iron oxide core having a poly(N-(3-(dimethylamino)propyl)methacrylamide) (PDMAPMA) coating. In some embodiments, at least one of the three or more distinct magnetic particle types includes an iron oxide core having a poly(oligo(ethylene glycol)methyl ether methacrylate) (POEGMA) coating.

[0031] In some embodiments, at least one of three or more distinct magnetic particle types has a negative surface charge. In some embodiments, at least one of three or more distinct magnetic particle types has a positive surface charge. In some embodiments, at least one of three or more distinct magnetic particle types has a neutral surface charge.

[0032] In some embodiments, three or more distinct magnetic particle types include one or more particle types from Table 10. In some embodiments, the particle panel includes two or more distinct particle types, three or more distinct particle types, four or more distinct particle types, five or more distinct particle types, six or more distinct particle types, seven or more distinct particle types, eight or more distinct particle types, nine or more distinct particle types, or all 10 distinct particle types selected from Table 10. In some embodiments, three or more distinct magnetic particle types include one or more particle types from Table 12. In some embodiments, the particle panel includes two or more distinct particle types, three or more distinct particle types, four or more distinct particle types, five or more distinct particle types, six or more distinct particle types, seven or more distinct particle types, eight or more distinct particle types, nine or more distinct particle types, or all 10 distinct particle types selected from Table 12.

[0033] In some embodiments, the Disclosure provides a method for determining the biological state of a sample from a subject, comprising the steps of: generating multiple protein coronas by exposing the biological sample to a panel comprising multiple nanoparticles; generating proteome data from the multiple protein coronas; determining the protein profiles of the multiple protein coronas; and relating the protein profiles to the biological state, wherein the panel comprises at least two different nanoparticles.

[0034] In some embodiments, the panel comprises at least three different nanoparticles. In some embodiments, the method correlates the protein profile with a biological state with at least 90% accuracy. In some embodiments, the nanoparticles comprise at least one iron oxide nanoparticle.

[0035] In some embodiments, the disclosure provides a method for selecting a panel for protein corona analysis, comprising the step of selecting a plurality of nanoparticles having at least three different physicochemical properties.

[0036] In some embodiments, the different physicochemical properties are selected from the group consisting of surface charge, surface chemical structure, size, and morphology. In some embodiments, the different physicochemical properties include surface charge.

[0037] In various embodiments, the Disclosure provides a method for identifying proteins in a sample, comprising the steps of: incubating a panel containing multiple particle types with the sample to form multiple protein coronas; digesting the multiple protein coronas to generate proteomic data; and identifying proteins in the sample by quantifying the proteomic data. In some embodiments, the sample is from a subject.

[0038] In some embodiments, the method further includes the steps of determining the protein profile of a sample from an identification step and associating the protein profile with the biological state of the subject. In some embodiments, the method further includes the steps of determining the biological state of a sample from a subject by generating proteome data by digesting multiple protein coronas, determining the protein profiles of multiple protein coronas, and associating the protein profiles with the biological state, wherein the panel comprises at least two different nanoparticles. In some embodiments, the association is performed by a trained classifier.

[0039] In some embodiments, the panel includes at least three different particle types, at least four different particle types, at least five different particle types, at least six different particle types, at least seven different particle types, at least eight different particle types, at least nine different particle types, at least ten different particle types, at least eleven different particle types, at least twelve different particle types, at least thirteen different particle types, at least fourteen different particle types, at least fifteen different particle types, or at least twenty different particle types. In some embodiments, the panel includes at least four different particle types. In some embodiments, at least one particle type of the panel has different physical characteristics from a second particle type of the panel. In some embodiments, the physical characteristics are size, polydispersity index, surface charge, or morphology. In some embodiments, the size of at least one of the multiple particle types in the panel is between 10 nm and 500 nm.

[0040] In some embodiments, the polydispersity index of at least one of the multiple particle types in the panel is 0.01 to 0.25. In some embodiments, the morphology of at least one of the multiple particle types includes spherical, colloidal, square, rod, wire, conical, pyramidal, and elliptical. In some embodiments, the surface charge of at least one of the multiple particle types includes a positive surface charge. In some embodiments, the surface charge of at least one of the multiple particle types includes a negative surface charge.

[0041] In some embodiments, the surface charge of at least one of the multiple particle types includes a neutral surface charge. In some embodiments, at least one of the multiple particle types has different chemical characteristics from a second particle type of the panel. In some embodiments, the chemical characteristics are surface functional chemical groups. In some embodiments, the functional chemical groups are amines or carboxylates. In some embodiments, at least one of the multiple particle types is made from a material containing a polymer, lipid, or metal.

[0042] In further embodiments, the polymer includes polyethylene, polycarbonate, polyanhydride, 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.

[0043] In further embodiments, lipids include dioleoylphosphatidylglycerol (DOPG), diacylphosphatidylcholine, diacylphosphatidylethanolamine, ceramide, sphingomyelin, cephalin, cholesterol, cerebroside and diacylglycerol, dioleoylphosphatidylcholine (DOPC), dimyristoylphosphatidylcholine (DMPC), and dioleoylphosphatidylserine (DOPS), phosphatidylglycerol, cardiolipin, diacylphosphatidylserine, diacylphosphatidic acid, N-dodecanoylphosphatidylethanolamine, N-succinylphosphatidylethanolamine, N-glutarylphosphatidylethanolamine, lysylphosphatidylglycerol, palmitoyloleoylphosphatidylglycerol (POPG), lecithin, lysolecithin, phosphatidylethanolamine, lysofo Sphatidylethanolamine, dioleoylphosphatidylethanolamine (DOPE), dipalmitoylphosphatidylethanolamine (DPPE), dimyristoylphosphoethanolamine (DMPE), distearoylphosphatidylethanolamine (DSPE), palmitoyloleoylphosphatidylethanolamine (POPE), palmitoyloleoylphosphatidylcholine (POPC), egg phosphatidylcholine (EPC), distearoylphosphatidylcholine (DSPC), dioleoylphosphatidylcholine (DOPC), dipalmitoylphosphatidylcholine (DPPC), dioleoylphosphatidylglycerol (DOPG), dipalmitoylphosphatidylglycerol (DPPG), palmitoyloleoylphosphatidylglycerol (POPG), 16-O-monomethylPE, 16-O-dimethylPE, 18-1-trans PE, palmitoyl oleoyl-phosphatidylethanolamine (POPE), 1-stearoyl-2-oleoyl-phosphatidyethanolamine (SOPE), phosphatidylserine, phosphatidylinositol It contains sphingomyelin, cephalin, cardiolipin, phosphatidic acid, cerebroside, dicetyl phosphate, or cholesterol.

[0044] In some embodiments, the metals include gold, silver, copper, nickel, cobalt, palladium, platinum, iridium, osmium, rhodium, ruthenium, rhenium, vanadium, chromium, manganese, niobium, molybdenum, tungsten, tantalum, iron, or cadmium. In some embodiments, at least one of a plurality of particulate types is a surface functionalized with polyethylene glycol. In some embodiments, the method relates a protein profile to a biological state with an accuracy 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%. In some embodiments, the method associates a protein profile 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%.

[0045] In some embodiments, the method associates a protein profile with a specificity 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%. In some embodiments, the method identifies at least 100 unique proteins, at least 200 unique proteins, at least 300 unique proteins, at least 400 unique proteins, at least 500 unique proteins, at least 600 unique proteins, at least 700 unique proteins, at least 800 unique proteins, at least 900 unique proteins, at least 1000 unique proteins, at least 1100 unique proteins, at least 1200 unique proteins, at least 1300 unique proteins, at least 1400 unique proteins, at least 1500 unique proteins, at least 1600 unique proteins, at least 1700 unique proteins, at least 1800 unique proteins, at least 1900 unique proteins, or at least 2000 unique proteins. In some embodiments, at least one of the multiple particle types contains iron oxide nanoparticles. In further embodiments, the sample is a body fluid. In further embodiments, the body fluid includes plasma, serum, CSF, urine, tears, or saliva.

[0046] In various embodiments, the disclosure provides a method for selecting a panel for protein corona analysis, comprising the step of selecting a plurality of particle types having at least three different physicochemical properties. In some embodiments, the different physicochemical properties are selected from the group consisting of surface charge, surface chemical structure, size, and morphology. In further embodiments, the different physicochemical properties include surface charge.

[0047] In various embodiments, the disclosure provides compositions comprising a panel of particles, wherein the panel comprises a plurality of particle types, and the plurality of particle types have at least three different physicochemical properties. In some embodiments, the different physicochemical properties are selected from the group consisting of surface charge, surface chemical structure, size, and morphology. In further embodiments, the different physicochemical properties include surface charge.

[0048] In various embodiments, the present disclosure provides a system comprising any one of the above panels.

[0049] In various embodiments, the disclosure provides a system comprising a panel, wherein the panel comprises a plurality of particle types. In some embodiments, the plurality of particle types have at least three different physicochemical properties. In some embodiments, the panel comprises 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 11, or at least 12 different particle types. In some embodiments, the plurality of particle types can adsorb a plurality of proteins from a sample to form a plurality of protein coronas. In some embodiments, the plurality of protein coronas are digested to determine a protein profile. In some embodiments, the protein profile is associated with a biological state using a trained classifier.

[0050] In some embodiments, the sample is a biological sample. In further embodiments, the biological sample is plasma, serum, CSF, urine, tears, cell lysates, tissue lysates, cell homogenates, tissue homogenates, papillary aspirate, fecal samples, synovial fluid and whole blood, or saliva. In some embodiments, the sample is a non-biological sample. In further embodiments, the non-biological sample is water, milk, solvent or homogenate sample. Built-in by reference

[0051] All published documents, patents, and patent applications referenced herein are incorporated herein by reference to the same extent as each individual published document, patent, or patent application is specifically and individually indicated as being incorporated by reference.

[0052] Novel features of the present invention are specifically described in the appended claims. This patent or application file includes at least one drawing drawn in color. Copies of this patent or application document, accompanied by the color drawing, will be provided by the United States Patent and Trademark Office upon request and payment of the required fees. A better understanding of the features and advantages of the present invention will be obtained by referring to the subsequent detailed description illustrating exemplary embodiments in which the principles of the present invention are utilized, and to the appended drawings. [Brief explanation of the drawing]

[0053] [Figure 1] Figure 1 shows an example of the surface chemical structure of magnetic particles (MPs). In some cases, magnetic particles may be magnetic core nanoparticles (MNPs).

[0054] [Figure 2] Figure 2A shows the formation of protein coronas on particles. The protein corona profile depends on protein-particle, protein-protein, and protein concentration factors. Figure 2B shows the formation of protein coronas on three different particles. In some cases, the particles may be nanoparticles. The properties of these particles result in different protein corona profiles.

[0055] [Figure 3] Figure 3 shows some examples of particle types and some examples of methods that can functionalize the particle surface. In some cases, the particles may be nanoparticles.

[0056] [Figure 4]Figure 4 shows the preparation of superparamagnetic iron oxide nanoparticles (SPION) from the residual solution. As shown in the left photograph, SPION disperses in the solution before or immediately upon application of a magnet to the side of the vial, appearing as a dark, opaque solution in the glass vial. Within 30 seconds of applying the magnet to the side of the vial, SPION separates from the solution, as illustrated by the accumulation of dark particles next to the magnet and the increased transparency of the solution in the right photograph. Shaking the separated solution, as shown in the right image, returns the particles to the dispersed state shown in the left image within 5 seconds. SPION exhibits a rapid response.

[0057] [Figure 5] Figure 5 provides an example of the process for generating proteome data and the process for panel selection.

[0058] [Figure 6] Figure 6 shows some examples of different properties of particles and methods for characterizing them.

[0059] [Figure 7] Figure 7 shows an example of the size distribution of nanoparticles characterized by dynamic light scattering. Figure 7 shows the dynamic light scattering overlay of two particle types: SP-002 (phenol-formaldehyde coated particles) and SP-010 (carboxylate, PAA coated particles), both having an iron oxide core. Dynamic light scattering can also be used to measure the size distribution of larger particles, including microparticles.

[0060] [Figure 8]Figures 8A and 8B show the characterization of nanoparticles with different functionalizations before corona formation. Figure 8A shows a transmission electron microscope (TEM) observation of SP-002 (phenol-formaldehyde coated particles). Figure 8B shows a TEM of SP-339 (polystyrene carboxyl particles). TEM can also be used to characterize larger particles, including microparticles.

[0061] [Figure 9] Figure 9 shows Fe 2p / 3 spectra from various nanoparticles, including SP-333 (carboxylate), SP-339 (polystyrene carboxylate), SP-356 (silica amino), SP-374 (silica silanol), HX-20 (SP-003) (silica coated), HX-42 (SP-006) (silica coated, amine), and HX-74 (SP-007) (PDMPAPMA coated (dimethylamine)). The spectra were obtained from XPS (X-ray photoelectron spectroscopy), which provides a chemical fingerprint of the particle surface (measuring the percentage of various elements on the surface). XPS can also be used to measure the spectra of larger particles, including microparticles.

[0062] [Figure 10] Figure 10 shows an example of the process of this disclosure for proteomic analysis. The process shown is optimized for high throughput and automation, and can be performed simultaneously across multiple samples within a few hours. The process includes particle-matrix association, particle washing (x3), protein corona formation, in-plate digestion, and mass spectrometry. Using this process, it may take only 4–6 hours per batch of 96 samples. Typically, one particle type is incubated with the sample at a time.

[0063] [Figure 11]Figure 11 shows protein counts (number of proteins identified from corona analysis) for panel sizes ranging from 1-particle to 12-particle size. Each particle within the panel may have unique underlying material, surface functionalization, and / or physical properties (e.g., size or shape). A single pool of plasma representative of a pool of healthy subjects was used. Counts are the number of unique proteins observed across a 12-particle panel in approximately 2 hours of mass spectrometry (MS) run. 1318 proteins were identified using the 12-particle panel size. As used herein, “features” identified by mass spectrometry include signals at specific combinations of residence time and m / z (mass-to-charge ratio), and each feature has an associated intensity. Some features are further fragmented by a second mass spectrometry analysis (MS2) for identification.

[0064] [Figure 12] Figure 12 shows gel electrophoresis analysis of multiple particle types after incubation in plasma over several days. Each individual assay was typically run for approximately one hour, but triple replicate measurements were performed by different operators on three different days to demonstrate assay accuracy. From left to right, the gels show a ladder, three consecutive rows of SP-339 (polystyrene carboxyl), three consecutive rows of SP-374 (silica silanol), a DNA ladder, and three consecutive rows of HX56 (SP-007) (silica amino).

[0065] [Figure 13] Figure 13 shows mass spectrometry analysis of protein identification across three separate assays for a single particle type (SP-339, polystyrene carboxyl). A total of 180 proteins were generally identified across the three separate assays.

[0066] [Figure 14A]Figures 14A and 14B show the concentration response to the spike protein compared to the control. The spike changed with concentration. The endogenous protein control did not change with concentration. Figure 14A shows data from the CRP spike recovery experiment. The protein was spiked at the following four levels: 2x, 5x, 10x, and 100x. HX-42 (SP-006) (left) and HX-97 (right, same as SP-007) were used. Figure 14B shows the slope of the response for the spike and control. The slope from the regression model was fitted with MS-enzyme-linked immunosorbent assay (ELISA) data. [Figure 14B] Figures 14A and 14B show the concentration response to the spike protein compared to the control. The spike changed with concentration. The endogenous protein control did not change with concentration. Figure 14A shows data from the CRP spike recovery experiment. The protein was spiked at the following four levels: 2x, 5x, 10x, and 100x. HX-42 (SP-006) (left) and HX-97 (right, same as SP-007) were used. Figure 14B shows the slope of the response for the spike and control. The slope from the regression model was fitted with MS-enzyme-linked immunosorbent assay (ELISA) data.

[0067] [Figure 15] Figure 15 shows a comparative study to evaluate particle types for panel selection. 56 plasma samples were obtained from 28 affected subjects and 28 control subjects. Affected subjects had confirmed stage IV non-small cell lung cancer (NSCLC), comorbidities, and treatments, such as diabetes, cardiovascular disease, and hypertension. Control subjects were age and sex matched to the affected subjects to reduce bias. This study was conducted to evaluate particle types between groups with significant differences and demonstrates age and sex matching. Seven particles, as shown in Figure 16A, were used in this study.

[0068] [Figure 16A]Figure 16A shows a comparison of samples (28 affected individuals vs. 28 controls) using a panel of seven particle types including SP-339, HX74 (SP-007), SP-356, SP-333, HX20 (SP-003), SP-364, and HX42 (SP-006). Of the 14,481 filtered MS features compared, 120 (0.8%) were different. [Figure 16B] Figure 16B shows the top hits from SP-339 in Figure 16A. The detected differences confirm that the coronavirus can differentiate between sample types, ultimately allowing for the construction of a classifier to define disease versus healthy individuals.

[0069] [Figure 17] Figure 17A shows a schematic diagram of the process of the present invention, including sample collection from healthy and cancer patients, isolation of plasma from the samples, incubation with uncoated liposomes to form protein coronas, and enrichment of selected plasma proteins. Proteins in the samples were assayed using a particle type panel having distinct particle types to enrich proteins in distinct biomolecular coronas formed on distinct particle types. Protein corona formation was specific to the physicochemical properties of the particles. Figure 17B shows a proteograph of corona analysis in an embodiment of the present disclosure for a three-particle type panel. Plasma was collected from 45 subjects (8 from each of five cancers, including glioblastoma, lung cancer, meningioma, myeloma, and pancreatic cancer, and 5 healthy controls). The output of the corona analysis, a proteograph, was created for each particle in the three-particle type panel. A random forest model was constructed in each of 1000 rounds of cross-validation. This provided strong evidence of a robust corona analysis signal. The initial preliminary analysis was performed using principal component analysis (PCA) on the proteins detected from the combination of three particles.

[0070] [Figure 18]Figures 18A and 18B demonstrate that early-stage cancer can be isolated up to 8 years before symptom onset. The Golestan cohort enrolled 50,000 healthy subjects between 2004 and 2008. Stored plasma from enrollment was tested, as shown in Figure 18A. Eight years after enrollment, approximately 1,000 patients had developed cancer. Figure 18B shows the classification of stored plasma from Figure 18A. Corona analysis of stored plasma from the enrollment date accurately classified cancer in 15 of the 15 subjects tested (5 patients for each of the three cancer types).

[0071] [Figure 19] Figure 19 shows robust classification of five cancers with 95% overall accuracy using three-particle corona analysis on a proteograph. The data demonstrate that adding particle type diversity improves performance. Three different liposomes with negative, neutral, and positive net charges on the surface (pH 7.4) were used.

[0072] [Figure 20] Figure 20 shows an example of scaling particle biosensor production. This platform can be used across multiple assays and samples.

[0073] [Figure 21] Figure 21 shows examples of particle types of the present disclosure. Particle types may include nanoparticles (NPs) and microparticles.

[0074] [Figure 22A]Figures 22A and 22B show schematic diagrams of particulate protein corona formation (Figure 22A) and a proteograph platform workflow (Figure 22B) of embodiments of the present disclosure based on a multi-particle protein corona method and mass spectrometry for plasma proteome analysis. Figure 22A shows three distinct particle types (drawn in the center of the figure, with the top, middle, and bottom spheres representing the three distinct particle types) having at least one physicochemical property that leads to the formation of different protein corona compositions on the particle surface. Figure 22B shows a proteograph-based corona analysis workflow including (1) particle-plasma incubation and protein corona formation, (2) magnetic purification of the particulate protein corona, (3) digestion of the corona protein, and (4) analysis by mass spectrometry. [Figure 22B] Figures 22A and 22B show schematic diagrams of particulate protein corona formation (Figure 22A) and a proteograph platform workflow (Figure 22B) of embodiments of the present disclosure based on a multi-particle protein corona method and mass spectrometry for plasma proteome analysis. Figure 22A shows three distinct particle types (drawn in the center of the figure, with the top, middle, and bottom spheres representing the three distinct particle types) having at least one physicochemical property that leads to the formation of different protein corona compositions on the particle surface. Figure 22B shows a proteograph-based corona analysis workflow including (1) particle-plasma incubation and protein corona formation, (2) magnetic purification of the particulate protein corona, (3) digestion of the corona protein, and (4) analysis by mass spectrometry.

[0075] [Figure 23]Figure 23 shows the characterization of three superparamagnetic iron oxide nanoparticles (SPION) shown in the leftmost column (first column) from top to bottom: SPION (SP-003) coated with silica, SPION (SP-007) coated with poly(N-(3-(dimethylamino)propyl)methacrylamide) (PDMAPMA), and SPION (SP-011) coated with poly(oligo(ethylene glycol)methyl ether methacrylate) (POEGMA). The characterizations are performed by the following methods: scanning electron microscopy (SEM, second column of the image), dynamic light scattering (DLS, third column of the image), transmission electron microscopy (TEM, fourth column of the image), high-resolution transmission electron microscopy (HRTEM, fifth column of the image), and X-ray photoelectron spectroscopy (XPS, sixth column). DLS shows three replicate experiments for each particle type. The HRTEM images were recorded on the surface of individual SP-003, SP-007, and SP-011 particle types, respectively, with arrows pointing to regions of amorphous SiO2 coating (top HRTEM image) and amorphous SiO2 / polymer coating (middle and bottom HRTEM images) on the particle surface.

[0076] [Figure 24] Figure 24 shows the dynamic range of proteins observed in live plasma for SP-003, SP-007, and SP-011 particles, compared with a compiled database (top panel) from Keshishian et al. (Mol Cell Proteomics. 2015 Sep;14(9):2375-93. doi: 10.1074 / mcp.M114.046813. Epub 2015 Feb 27.).

[0077] [Figure 25] Figure 25 shows the correlation between the maximum intensity of particle corona protein relative to plasma proteins and previously reported concentrations of the same protein.

[0078] [Figure 26]Figure 26 shows the reproducibility of particle corona intensity for each particle type (SP-003, SP-007, and SP-011), demonstrated by three replicate experiments using the same plasma sample.

[0079] [Figure 27] Figure 27 shows altered features in a pilot study of non-small cell lung cancer (NSCLC) using SP-007 particles. Seven MS features were identified as statistically significant differences between 28 subjects with stage IV NSCLC (with associated comorbidities and treatment response) and 28 age- and sex-matched apparent healthy subjects. The table below lists the seven proteins that showed significant differences, including five known proteins and two unknown proteins. Where the peptide spectrum matched MS2 data related to the feature, the peptide sequence (and alteration) and possible parent protein are shown; where the MS2 match was not related to the feature, both the peptide and protein are marked "Unknown."

[0080] [Figure 28] Figure 28A shows a schematic diagram of the synthesis of the SPION core. Figure 28B shows a schematic diagram of the synthesis of SPION (SP-003) with silica coating. Figure 28C shows a schematic diagram of the synthesis of vinyl-functionalized SPION. Figure 28D shows a schematic diagram of the synthesis of SPION (SP-007) with poly(N-(3-(dimethylamino)propyl)methacrylamide) (PDMAPMA) coating. Figure 28E shows a schematic diagram of the synthesis of SPION (SP-011) with poly(oligo(ethylene glycol)methyl ether methacrylate) (POEGMA) coating.

[0081] [Figure 29]Figure 29 shows the distribution of median-normalized MS feature intensities, filtered for presence and filtered for cluster quality, for a comparative study of 56 NSCLC samples. Each line represents the density of log2 feature intensities for either affected or control samples. Densities from 0.00 to 0.15 are plotted on the y-axis, and log2 feature intensities from 15 to 35 are plotted on the x-axis. At the highest peak located around log2 feature intensity 28, where the density is in the range of approximately 0.13 to 0.17, the two highest traces correspond to control samples, while the lowest trace corresponds to affected samples. The remaining control and affected traces are distributed between the highest and lowest traces. At the two shoulder peaks located at log2 feature intensities 20 and 23, at log2 feature intensity 20, the two highest traces are control traces, and the two lowest traces are control traces, while at log2 feature intensity 23, the highest trace is affected trace.

[0082] [Figure 30] Figure 30 shows the accuracy of measurements for C-reactive protein (CRP) related to SP-007 nanoparticles in spike recovery experiments with four different peptides.

[0083] [Figure 31] Figure 31 shows the accuracy of measuring the peptide characteristics of angiogenin in spike recovery experiments.

[0084] [Figure 32-1] Figure 32 shows the accuracy of measuring the peptide characteristics of S10A8 in the spike recovery experiment. [Figure 32-2] Figure 32 shows the accuracy of measuring the peptide characteristics of S10A8 in the spike recovery experiment.

[0085] [Figure 33-1] Figure 33 shows the accuracy of measuring the peptide characteristics of S10A9 in the spike recovery experiment.

[0086] [Figure 33-2] Figure 33 shows the accuracy of measuring the peptide characteristics of S10A9 in the spike recovery experiment.

[0087] [Figure 33-3] Figure 33 shows the accuracy of measuring the peptide characteristics of S10A9 in the spike recovery experiment.

[0088] [Figure 33-4] Figure 33 shows the accuracy of measuring the peptide characteristics of S10A9 in the spike recovery experiment.

[0089] [Figure 33-5] Figure 33 shows the accuracy of measuring the peptide characteristics of S10A9 in the spike recovery experiment.

[0090] [Figure 33-6] Figure 33 shows the accuracy of measuring the peptide characteristics of S10A9 in the spike recovery experiment.

[0091] [Figure 33-7] Figure 33 shows the accuracy of measuring the peptide characteristics of S10A9 in the spike recovery experiment.

[0092] [Figure 33-8] Figure 33 shows the accuracy of measuring the peptide characteristics of S10A9 in the spike recovery experiment.

[0093] [Figure 34] Figure 34 shows the accuracy of measuring the peptide characteristics of C-reactive protein (CRP) in spike recovery experiments.

[0094] [Figure 35-1] Figure 35 shows the accuracy of protein feature measurement in spike recovery experiments. [Figure 35-2] Figure 35 shows the accuracy of protein feature measurement in spike recovery experiments.

[0095] [Figure 36] Figure 36 shows the matching and coverage of 10 distinct particle type particle panels against a 5,304 plasma protein database of MS intensities. The ranked intensities of database proteins are shown in the top panel ("Database"), the protein intensities from plain plasma MS assessments are shown in the second panel ("Plasma"), and the intensities of the optimal 10 particle type panels are shown in the remaining panels. The plasma protein intensity database is from Keshishian et al. (2015). Multiplexed, Quantitative Workflow for Sensitive Biomarker Discovery in Plasma Yields Novel Candidates for Early Myocardial Injury. Molecular & Cellular Proteomics, 14(9), 2375-2393.

[0096] [Figure 37] Figure 37 shows various particle types that may be included in the panel disclosed herein. Figure 37A shows a schematic diagram of hollow magnetic particles. Figure 37B shows a schematic diagram of particles having a hydrophobic pocket. Figure 37C shows a schematic diagram of eggshell-yolk type SPION microgel hybrid particles.

[0097] [Figure 38] Figure 38 shows a schematic diagram of a particle surface manipulated to capture proteins and / or peptides, source Mol. Cells 2019, 42(5), 386-396. [Modes for carrying out the invention]

[0098] Compositions and methods for using them to assay peptides and proteins in a sample in a simple and high-throughput manner are disclosed herein. This disclosure provides a particle panel of multiple distinct particle types for concentrating proteins from a sample onto distinct biomolecular coronas formed on the surface of distinct particle types. The particle panels disclosed herein can be used in corona analysis methods to detect thousands of proteins over a wide dynamic range in a few hours.

[0099] Currently, only a small number of protein-based biomarkers are used for clinical diagnosis, and despite significant efforts to analyze the plasma proteome to expand the marker pool, only a relatively small number of new candidates have been accepted as clinically useful diagnostic agents. The plasma proteome contains >10,000 proteins and can contain an extraordinarily large number of protein isoforms with concentration ranges exceeding 10 orders of magnitude (mg / mL to pg / mL). These characteristics, coupled with the lack of conventional molecular tools for protein analysis (e.g., copy or amplification mechanisms), make comprehensive studies of the plasma proteome extremely difficult. Techniques to overcome the wide dynamic range of proteins in biological samples are still limited to robust identification and quantification against a background of thousands of unique proteins and even more protein variants. However, no technology exists that can simultaneously measure proteins across the entire plasma concentration range in a format with sufficient throughput and a realistic cost profile to enable studies of appropriate size with a solid prospect of evaluation and reproducibility. These challenges not only limit the discovery of protein-based biomarkers for disease but also hinder the rapid adoption of proteogenomics and protein annotation of genomic variants. Advances in mass spectrometry (MS) techniques, along with the development of improved data analysis, provide tools for deep and broad proteomic analysis. Several efforts have been made to substantially improve the detection of low-abundance proteins, such as depletion of high-abundance proteins, plasma fractionation, peptide fractionation, and labeled quantification. However, current methods are extremely complex and time-consuming (days to weeks), and therefore require a trade-off between the depth of protein coverage and sample throughput. As a result, a simple and robust strategy for comprehensive and rapid analysis of the much available information about the proteome remains an unmet need.

[0100] In addition, the earlier a disease is diagnosed, the greater the chance of successfully curing or managing it, leading to a better prognosis for the patient. Early treatment of a disease can potentially prevent or delay problems from developing, potentially improving patient outcomes, including extending lifespan and / or quality of life. Early diagnosis of cancer is crucial because many types of cancer can be successfully treated in their early stages. For example, the five-year survival rates after early diagnosis and treatment of breast, ovarian, and lung cancers are 90%, 90%, and 70%, respectively, compared to 15%, 5%, and 10% for patients diagnosed at the most advanced stages of the disease. Once cancer cells have left their primary tissue, the success of treatment using available established therapies becomes extremely unlikely. Recognizing warning signs of cancer and taking prompt action can lead to early diagnosis, but most cancers (e.g., lung cancer) only show symptoms after cancer cells have already invaded surrounding tissues and metastasized throughout the body. For example, more than 60% of patients with breast, lung, colon, and ovarian cancer have subclinical or even metastatic colonies when their cancer is detected. Therefore, there is an urgent need to develop effective methods for the early detection of cancer. Such methods should have sensitivity to identify cancer at various stages and specificity to give a negative result if the person being tested does not have cancer. There has been tremendous effort to develop methods for the early detection of cancer, and a vast number of risk factors and biomarkers have been introduced, but a broadly relevant platform for the early detection of a wide range of cancers remains elusive. Since various types of cancer can alter the composition of plasma—even in their early stages—one promising method for early detection is molecular blood analysis of biomarkers. This strategy has already been studied for a few cancers (e.g., PSA for prostate cancer), but specific biomarkers for the early detection of the vast majority of cancers still do not exist. For such cancers (e.g., lung), none of the defined circulating biomarker candidates have been clinically validated, and very few have reached late clinical development.Therefore, there is an urgent need for new methods to improve our ability to detect cancer and other diseases at a very early stage.

[0101] To meet the need for a high-throughput, simple assay for detecting proteins in a sample, which can be used to detect proteins associated with specific diseases at a very early stage, this disclosure provides particle panels and methods for using said particle panels to assay peptides and proteins in a sample (e.g., a complex biological sample, e.g., plasma) in a simple, rapid, and high-throughput manner. More specifically, this disclosure provides particle panels of multiple distinct particle types for concentrating proteins from a sample onto distinct biomolecular coronas formed on the surface of distinct particle types. The particle types included in the particle panels disclosed herein are particularly well suited for concentrating a large number of proteins over a wide dynamic range in an unbiased manner. The combinations of particle types selected for inclusion in the particle panels of this disclosure vary in their physicochemical properties (e.g., size, surface charge, core material, shell material, surface chemical structure, porosity, morphology, and other properties). However, the particle types may also share some of the aforementioned physicochemical properties. For example, the particle panels disclosed herein may include a first particle type and a second particle type, the first and second particle types sharing at least two physicochemical properties but differing in at least two physicochemical properties, and thus the first and second particle types are distinct. Importantly, a change in at least one physicochemical property between the first and second particle types of the particle panel may lead to the formation of distinct coronas on the corresponding distinct particle types. Therefore, the selection of particle types for inclusion in a particle panel for use in the methods disclosed herein allows for a large number of proteins that can be concentrated over a wide dynamic range in a sample (e.g., plasma).

[0102] The particle panels of this disclosure may include particle types having magnetic properties, which enable easy separation of complex biological samples after incubation. For example, this disclosure provides superparamagnetic iron oxide nanoparticles (SPIONs) that have unique magnetic properties and can rapidly separate biomolecules, drug delivery agents, and contrast agents in magnetic resonance imaging (MRI). The superparamagnetic particles may have a core of iron oxide only (e.g., an iron oxide core) or may have small iron oxide crystals embedded in a polystyrene core.

[0103] This disclosure provides methods developed for the synthesis of SPION. For example, small-sized (typically <30 nm) monodisperse magnetic nanoparticles with high crystalline properties can be synthesized using the thermal decomposition of iron oleate in a nonpolar solvent. These nanoparticles are hydrophobic and require transfer to the aqueous phase by ligand exchange or chemical modification. SPION produced using this method can meet the requirements for biomedical use. The solvothermal method is another method for synthesizing SPION by the reduction of iron(III) chloride with ethylene glycol. Highly water-dispersible SPMNPs can be synthesized by an improved solvothermal method using hydrophilic ligands such as citrate, polyacrylic acid (PAA), and polyvinylpyrrolidone (PVP). The particle surface can be further modified with different silanes having functional groups by the Stöber method. Surface functional groups can also be acquired by a simple method of seed emulsion polymerization in the absence of surfactants to form SPMNP@polymer composite particles in polymer composite particles.

[0104] This disclosure provides compositions, systems, and methods of use thereof for large-scale, high-throughput, efficient, and cost-effective proteomic profiling and machine learning. A scalable parallel protein identification and quantification technique for assaying proteins in a sample by concentrating proteins in a separate biomolecular corona formed on a separate particle type using a particle panel having separate particle types is disclosed herein. As used herein, “biomolecular corona” may be used / referred to synonymously with the term “protein corona” and refers to the formation of a layer of protein on the surface of a particle after contact of the particle with a sample (e.g., plasma). This method may also be synonymously called corona analysis and, in some examples, “proteograph” analysis (shown in Figure 22) which combines a multi-particle protein corona strategy with mass spectrometry (MS). The particle types included in the particle panels disclosed herein are superparamagnetic and are therefore rapidly separated or isolated from unbound proteins in the sample (proteins not adsorbed on the surface of the particles to form a corona) after incubation of the particles in the sample.

[0105] Currently, particles are poorly characterized for high-throughput translational proteome analysis due to steps related to the handling of proteins in the protein corona (e.g., centrifugation or membrane filtration to separate corona proteins from free plasma proteins, and washing to remove loosely bound proteins from the particles), which confound data and lack reproducibility and accuracy. The corona analysis (e.g., “proteograph”) method disclosed herein can be integrated with, for example, liquid chromatography-mass spectrometry (LC-MS) for overlapping but distinct particle-type protein coronas for potential use in large-scale, efficient proteome profiling and machine learning. This particle-type platform can be unbiased (e.g., not limited to a given analyte), the MS data acquisition platform can be unbiased in terms of analyte measurement, and both of these may be suitable for automation. The formation of a layer of protein on the surface of a particle upon contact with plasma, called the protein corona (Figure 22A), is disclosed herein as a method for identifying proteins. The composition and quantity of corona proteins may depend on the physicochemical properties of the particle form, and by altering these manipulated properties, proteins that differ in identity and / or quantity within the corona can be reproducibly obtained.

[0106] In some embodiments, the particles disclosed herein may be superparamagnetic iron oxide nanoparticles (SPIONS). SPIONS may differ distinctly from one another in that they are synthesized to have distinct surface chemical structures. In some embodiments, SPIONS with different surface chemical structures can be used in combination in the analysis of proteins formed on their protein coronas. SPIONS, other particle types, or combinations thereof can be used in combination to form a panel of particle types that can be used for proteomic analysis of a sample.

[0107] In some embodiments, a three-particle panel having distinct surface chemistry, synthesized and used for protein corona formation, is disclosed herein, which can be rapidly separated from unbound proteins by magnets. Each particle type can reproducibly generate unique protein corona patterns by capturing both high-abundance and low-abundance proteins. For example, by integrating distinct proteome profiles generated from at least three particle types, more than 1,500 proteins can be identified in a single pooled colorectal cancer (CRC) plasma sample, many of which may be FDA-approved biomarkers. For example, in some embodiments, screening of three particle types can detect more than 1,500 proteins, 65 of which are FDA-approved biomarkers.

[0108] In some embodiments, the proteograph-based corona analysis workflow (Figure 22B) can take approximately 4–6 hours to prepare a batch of 96 corona samples for MS analysis. Protein identification in the CRC pool can be performed using only three full MS fractions, each run approximately 1 hour, and therefore totaling approximately 3 hours of MS time.

[0109] In some embodiments, using three distinct particle types, it is possible to identify more than 1,500 proteins from a single pool of plasma within 8 hours, including sample preparation and LC-MS, in contrast to fewer than 500 proteins without using the compositions, systems, and methods disclosed herein for using particle-type corona strategies.

[0110] This corona analysis technology can be used to identify diseases. For example, the particles and methods of use disclosed herein can be used to analyze serum samples from patients with non-small cell lung cancer (NSCLC) as well as from age- and sex-matched healthy subjects. MS features, including known and novel features that can distinguish NSCLC samples from control samples, can be discovered using this platform. Thus, the corona analysis platform technology enables larger, more robust validation and iterative studies. Furthermore, the unique properties of corona analysis for high-throughput, unbiased proteomic sample extraction can enable the application of genomic data annotation and machine learning classification methods. The multi-particle protein corona-based platform technology described herein can facilitate efficient and comprehensive proteomic profiling, enabling larger studies for biomarker discovery and validation.

[0111] In some embodiments, the compositions and methods of use disclosed herein exhibit high assay accuracy, as demonstrated by the reference of increasing concentrations to a plasma sample, the addition of C-reactive protein (CRP), and the subsequent detection of CRP levels in the protein corona, showing a slope of 0.9 (95% CI 0.81–0.98) for CRP levels in the particulate corona against spiked plasma. In some embodiments, the median accuracy of the platform in assay replicates may be approximately 24 CV% across 8,738 experimental MS features obtained from three distinct particulate protein coronas.

[0112] SPION with distinct surface chemical structures can be applied to protein corona analysis of a single pooled plasma sample. For example, as described herein, the resulting proteome data demonstrates that increasing the number of particle types in the particle panel can result in the identification of more proteins (particularly low-abundance proteins). By adding more distinct particle types, broader and deeper proteome profiling can be obtained. The particle compositions disclosed herein, and the particle type panels containing the different particle types, can be adjusted to profile the proteome at different levels of depth and width—similar to different levels of coverage in gene sequencing—by varying the number and types of particles in the panel.

[0113] The multi-particle protein corona-based assays disclosed herein exhibit several equally important features for plasma proteomic analysis. Compared to conventional proteomic techniques, which typically involve time-consuming depletion and fractionation workflows, the compositions and methods of use disclosed herein can avoid those complex workflows and be significantly faster. Notably, the corona analysis assays can be robustly automated and therefore further improve the accuracy and reduce the amount of time required for sample analysis, for example, in a 96-well plate format. The corona analysis platform can measure differences between samples with high sensitivity, and moreover, it reduces the dynamic range of their comparisons, thus enabling the observation of more comparisons. Corona analysis techniques can identify novel biomarkers without targeting a given set of proteins. The scalability and efficiency of the corona analysis platform can be used for large-scale proteomic studies, which may lead to a deeper understanding of disease and biological mechanisms. For example, by adding proteome data to multi-ometadata sets and performing machine learning analyses, novel classifications can be generated and incorporated into contextual genomic disease information that is not currently well understood, such as single nucleotide polymorphism (SNP) variants, changes in DNA methylation patterns, and splice variants. In addition, this technique can be extended to other bodily fluids, such as cerebrospinal fluid, cell lysates, and even tissue homogenates, for rapid, error-free, and accurate proteome profiling, which can facilitate the discovery of new biomarkers for different diseases.

[0114] Methods for producing superparamagnetic nanoparticles (SPMNPs) or superparamagnetic iron oxide particles (SPIONs) are disclosed herein. These particles and their embodiments can be used in protein corona assays.

[0115] The method and system described herein improve proteomic analysis by simplifying sample preparation and MS data acquisition. The method and system prepare samples in approximately 5 steps within approximately 0.25 days and acquire MS data for approximately 12 fractions within approximately 0.5 days per sample.

[0116] This disclosure provides compositions for assaying samples for proteins and methods of using them. The compositions described herein include particle panels comprising one or more distinct particle types. The particle panels described herein may differ in the number of particle types and the diversity of particle types within a single panel. For example, particles in a panel may differ in size, polydispersity, shape and morphology, surface charge, surface chemical structure and functionalization, and underlying material. The panel can be incubated with a sample for analysis of proteins and protein concentrations. Proteins in the sample adsorb to the surfaces of different particle types within the particle panel to form protein coronas. The exact protein and protein concentration adsorbed to a particular particle type within the particle panel may depend on the composition, size, and surface charge of the particle type. Thus, each particle type within a panel may have different protein coronas due to adsorption to different sets of proteins, different concentrations of specific proteins, or combinations thereof. Each particle type within a panel may have mutually exclusive protein coronas or overlapping protein coronas. Overlapping protein coronas may have overlapping protein identifications, overlapping protein concentrations, or both.

[0117] This disclosure also provides a method for selecting particle types for inclusion in a panel depending on the sample type. The particle types included in the panel may be a combination of particles optimized for the removal of high-abundance proteins. Particle types that are also suitable for inclusion in the panel are selected to adsorb specific proteins of interest. The particles may be nanoparticles. The particles may be microparticles. The particles may be a combination of nanoparticles and microparticles.

[0118] This disclosure provides a method for selecting a particle panel that exhibits broad coverage of proteins in a biological sample (e.g., a plasma sample). Particles are selected for inclusion in the particle panel using a combination method. Particles having a wide range of physicochemical properties are selected, for example, particles may differ in size, surface charge, core material, shell material, surface chemical structure, porosity, morphology and other properties. However, particles may also share some of the aforementioned physicochemical properties. For example, the particle panel disclosed herein may include a first particle type and a second particle type, where the first and second particle types share at least two physicochemical properties but differ in at least two physicochemical properties, and therefore the first and second particle types are different. The particle panels disclosed herein may include a first particle type and a second particle type, the first and second particle types sharing at least two physicochemical properties but differing in at least one physicochemical property, and thus the first and second particle types are distinct. Non-limiting examples of physicochemical properties include size, charge, core material, shell material, porosity, or surface hydrophobicity. Importantly, a change in at least one physicochemical property between the first and second particle types of the particle panel may lead to the formation of distinct coronas on the corresponding distinct particle types. For example, the first and second particle types with different charges may each adsorb different proteins, the same protein at different concentrations, or both different proteins and the same protein at different concentrations. Thus, the first and second particle types will have distinct biomolecular coronas. Size is an example of a physicochemical property that may differ to result in this outcome.One or more physicochemical properties (e.g., size, charge, core material, shell material, porosity, or surface hydrophobicity, or any combination thereof) can differ to give rise to distinct biomolecular coronas. Other optimization parameters for particle type selection for the panel may include any specific annotation set, e.g., interactome, secretome, FDA markers, or proteins with clinically significant genetic polymorphisms. As seen in Tables 10 and 12, more than one of these particles are nanoparticles but are fabricated with different polymer coatings. In another example, more than one of the particles in Tables 10 and 12 share similar surface charge and similar size but are fabricated with different materials. In yet another example, more than one of the particles in Tables 10 and 12 exhibit porous surfaces. In yet another example, more than one of the particles in Tables 10 and 12 exhibit non-porous surfaces. In yet another example, more than one of the particles in Tables 10 and 12 exhibit carboxylate-coated surfaces. In another example, more than one of the particles in Tables 10 and 12 exhibit amine-coated surfaces. It is surprising and unexpected that, with respect to all the many combinations of particles and particle types that may be present within the particle panel, the particle panel disclosed herein could identify numerous proteins (e.g., plasma proteins) in a sample (e.g., a plasma sample) in an unbiased manner across a wide dynamic range and could be used in methods for assaying proteins in a sample with a high level of reproducibility (e.g., quantile-normalized coefficient of variation <20%).

[0119] This disclosure provides over 200 distinct particle types, each having more than 100 different surface chemical structures and more than 50 diverse physical properties. In detail, more than 23 particle types have been characterized for use in methods of assaying proteins in a sample. Each of these particle types can be combined with other particle types within a panel designed to optimally assay a specific protein of interest or to optimally identify a biomarker for a disease of interest. The panel may include any number of these particle types or any combination of particle types, and the various particle types disclosed herein may enable the assay and detection of a wide range of proteins with diverse physicochemical properties.

[0120] In some embodiments, the present disclosure provides a method for identifying proteins in a sample, comprising the steps of: incubating a panel containing a plurality of particle types with the sample to form a plurality of protein coronas; digesting the plurality of protein coronas to generate proteome data; and identifying proteins in the sample by quantifying the proteome data. particle material

[0121] The particle panels disclosed herein may include particle types made from a variety of different materials. Panels can be assembled with specific particle types to identify a wide range of proteins in a sample or to selectively assay a specific protein or set of proteins of interest. Particle types may include, for example, nanoparticles (NPs), microparticles, magnetic particles (MPs), magnetic nanoparticles (MNPs), paramagnetic iron oxide nanoparticles (SPIONs), or superparamagnetic nanoparticles (SPMNPs). The particles described herein may be magnetic particles. Magnetic particles herein may be superparamagnetic particles (SPMPs), superparamagnetic nanoparticles (SPMNPs), superparamagnetic iron oxide particles (SPIOPs), or superparamagnetic iron oxide nanoparticles (SPIONs). In some cases, SPMNPs may be SPIONs. Magnetic force may be imparted by an iron oxide core or by iron oxide crystals grafted onto the particles. In some cases, the inventors refer herein to SPMNPs, which are magnetic particles. SPMNPs can also be synthesized to become SPMPs.

[0122] Particles can be made from a variety of materials. For example, particles can be made from polymers, lipids, metals, silica, proteins, nucleic acids, small molecules, or large molecules. For example, particle materials consistent with this disclosure include metals, metal oxides, magnetic materials, polymers, and lipids. Examples of metallic materials include gold, silver, copper, nickel, cobalt, palladium, platinum, iridium, osmium, rhodium, ruthenium, rhenium, vanadium, chromium, manganese, niobium, molybdenum, tungsten, tantalum, iron, and cadmium, or any other material described in U.S. Patent No. 7,749,299, one or any combination thereof. Metal oxide particles may be iron oxide particles or titanium oxide particles. Magnetic particles may be iron oxide nanoparticles.

[0123] Examples of polymers include polyethylene, polycarbonate, polyanhydride, polyhydroxy acid, polypropyl fumarate, polycaprolactone, polyamide, polyacetal, polyether, polyester, poly(orthoester), polycyanoacrylate, polyvinyl alcohol, polyurethane, polyphosphazene, polyacrylate, polymethacrylate, polycyanoacrylate, polyurea, polystyrene, or polyamine, polyalkylene glycol (e.g., polyethylene glycol (PEG)), polyester (e.g., poly(lactide-co-glycolide) (PLGA), polylactic acid, or polycaprolactone), polystyrene, or copolymers of two or more polymers, such as a copolymer of polyalkylene glycol (e.g., PEG) and polyester (e.g., PLGA), one or any combination thereof. In some embodiments, the polymer is polyalkylene glycol having lipids at the ends, and polyester, or any other material disclosed in U.S. Patent No. 9,549,901, which is incorporated herein by reference.

[0124] Examples of lipids that can be used to form the particles of this disclosure include cationic lipids, anionic lipids, and neutrally charged lipids. For example, the particles include dioleoyl phosphatidylglycerol (DOPG), diacylphosphatidylcholine, diacylphosphatidylethanolamine, ceramide, sphingomyelin, cephalin, cholesterol, cerebroside and diacylglycerol, dioleoyl phosphatidylcholine (DOPC), dimyristoyl phosphatidylcholine (DMPC), and dioleoyl phosphatidylserine (DOPS), phosphatidylglycerol, cardiolipin, diacylphosphatidylserine, diacylphosphatidic acid, N-dodecanoylphosphatidylethanolamine, N-succinylphosphatidylethanolamine, N-glutarylphosphatidylethanolamine, lysylphosphatidylglycerol, palmitoyloleoylphosphatidylglycerol (POPG), lecithin, lysolecithin, phosphatidylethanolamine, lysophospha Tidylethanolamine, dioleoylphosphatidylethanolamine (DOPE), dipalmitoylphosphatidylethanolamine (DPPE), dimyristoylphosphoethanolamine (DMPE), distearoylphosphatidylethanolamine (DSPE), palmitoyloleoylphosphatidylethanolamine (POPE), palmitoyloleoylphosphatidylcholine (POPC), egg phosphatidylcholine (EPC), distearoylphosphatidylcholine (DSPC), dioleoylphosphatidylcholine (DOPC), dipalmitoylphosphatidylcholine (DPPC), dioleoylphosphatidylglycerol (DOPG), dipalmitoylphosphatidylglycerol (DPPG), palmitoyloleoylphosphatidylglycerol (POPG), 16-O-monomethylPE, 16-O-dimethylPE, 18-1-transPE, palmitoyl oleoyl-phosphatidylethanolamine (POPE), 1-stearoyl-2-oleoyl-phosphatidylethanolamine (SOPE), phosphatidylserine, phosphatidylinositol, sphingomyelin, cephalin, cardiolipin, phosphatidic acid, cerebroside, dicetyl phosphate, and cholesterol, or any one or any combination of any other material listed in U.S. Patent No. 9,445,994, which is incorporated herein by reference in whole.

[0125] The particle panels disclosed herein may include specific particle types having structures particularly suitable for sampling proteins of specific sizes present in a sample. For example, the particle panels may include hollow magnetic particles as shown in Figure 37A. The hollow magnetic particles include nanoparticles having a hollow core, wherein the nanoparticle shell is made of smaller iron oxide primary crystals. The hollow magnetic particles are incorporated herein by reference in Cheng, Wei, et al. ("One-step synthesis of superparamagnetic monodisperse porous") Fe3O4 hollow and core-shell spheres can be synthesized by autoclave reactions based on hydrothermal treatment of FeCl3, citrate, polyacrylamide or sodium polyacrylate, and urea, as described in "Fe3O4 hollow and core-shell spheres." Journal of Materials Chemistry 20.9 (2010): 1799-1805). Nanoparticle shells made from smaller iron oxide primary crystals are porous, allowing for the formation of protein coronas on the surface of the nanoparticles through size exclusion. The binding surface is primarily located inside the pores, thus preventing larger proteins from diffusing and binding into the particles. In some cases, larger proteins may still bind to the outside and be included in the entire corona, but these hollow particles can concentrate smaller proteins more than larger ones. These hollow magnetic particles may be included in any particle panel described herein and can also be easily separated from unbound proteins using magnets.

[0126] In another example, the particle panel may include nanoparticles having hydrophobic pockets, as shown in Figure 37B. Nanoparticles having hydrophobic pockets are particularly suitable for sampling molecules or proteins with specific solubility (e.g., poorly soluble proteins) present in a sample. Nanoparticles having hydrophobic pockets have a superparamagnetic iron oxide core structure, in addition to a polymer coating. Nanoparticles having hydrophobic pockets can be synthesized using an autoclave reaction of SPION core synthesis, as described elsewhere in this specification, followed by free radical polymerization to synthesize a poly(glycidyl methacrylate) coating. This may be followed by post-synthesis modification of the surface with a hydrophobic amine such as a benzylamine moiety. These nanoparticles having hydrophobic pockets are particularly well suited for trapping small molecules for metabolomics. The pore size of the nanoparticles can be modulated to exclude proteins so that only small molecules interact with the hydrophobic pockets. In some embodiments, the pore size can be adjusted during synthesis. For example, synthesis may involve swelling the particles to create porosity, and the degree of swelling may affect the pore size. As another example, corrosion techniques can be used to create different pores in particle types with harder surfaces. Sample collection of protein targets can also be performed using nanoparticles with hydrophobic pockets.

[0127] In another example, the particle panel may include eggshell-yolk type SPION microgel hybrid nanoparticles, as shown in Figure 37C. These eggshell-yolk type SPION microgel hybrid nanoparticles have an outer SPION structure and an inner microgel structure. Eggshell-yolk type SPION microgel hybrid nanoparticles can be synthesized using autoclave reactions based on hydrothermal treatment of FeCl3, citrate, polyacrylamide or sodium polyacrylate, and urea, as described in Cheng et al. ("One-step synthesis of superparamagnetic monodisperse porous Fe3O4 hollow and core-shell spheres." Journal of Materials Chemistry 20.9 (2010): 1799-1805) and Zou et al. ("Facile synthesis of highly water-dispersible and monodispersed Fe3O4 hollow microspheres and their application in water treatment." RSC Advances 3.45 (2013): 23327-23334). In the second step of synthesis, the hydrogel is formed within the hollow nanoparticles.

[0128] In another example, a particle panel may include particle surfaces that have been manipulated to capture proteins and / or peptides. A primary layer of proteins may form an initial corona on the particle surface, either covalently or noncovalently. Direct noncovalent interactions with the particle surface may include ionic, hydrophobic, and hydrogen bonds. In addition, these particles, which may be initially modified with small chemicals, can bind to other proteins in the plasma after the formation of the primary protein corona. By utilizing the modulation of chemical modifications present on the particle surface, noncovalent interactions between the particle surface and peptides and / or proteins in the sample can be regulated. Alternatively or in addition, the noncovalent interactions between the particles and peptides and / or proteins in the sample can be regulated by modulating the solution phase composition (e.g., pH). Covalent coupling of proteins and / or peptides to the particle surface can be achieved by introducing reactive groups (e.g., NHS esters, maleimides, carboxylates, etc.) to the particle surface. Therefore, particles with NHS esters may be particularly suitable for sampling one or more proteins in solution. Alternatively, a coupling reagent capable of sampling a protein from a complex mixture and binding to that protein may be a photoinitiation group that reacts with the protein only after exposure to a photon of a given wavelength (e.g., UV) and intensity. Advantages of using particles functionalized with the photoinitiation group include, but are not limited to, (1) immobilization within the primary protein corona without protein loss due to protein loss and / or substitution with a more affinity protein during the particle washing step, (2) capturing the protein with moderate binding affinity because covalent linkage can be established at any point during the binding process, (3) generating a broader surface area than would result at equilibrium, and (4) generating a surface with a specific protein stoichiometric ratio by using a defined protein mixture for binding (instead of plasma).A defined protein mixture can be any synthetically derived or purified mixture of proteins obtained or prepared by recombination, or isolated and then mixed in defined stoichiometric ratios. For example, for a specific protein of interest for protein-protein interactions (e.g., ubiquitin), the defined protein mixture could be a simple solution of that protein. As another example, the defined protein mixture could be a purified or concentrated set of proteins from a particular class of interest (e.g., glycosylated proteins). Particles can also be modified and functionalized with half of a click chemistry reaction pair, so that the non-natural point mutant protein in the sample contains amino acids having the other half of the click chemistry reaction pair. Particles and other proteins in the sample can also be functionalized with half of a click chemistry reaction pair, so that the non-natural point mutant protein in the sample contains amino acids having the other half of the click chemistry reaction pair. The reaction is carried out in the presence of a chemical catalyst (e.g., copper) or light (e.g., a photo-initiated click chemistry reagent) to lead to the linkage of particles to non-natural point mutant proteins in the sample, and / or the linkage of non-natural point mutant proteins in the sample to other proteins in the sample. For example, this method can be initiated with a simple solution of protein that may contain a mutant protein concentrated in the sample. The main advantage of these systems is that the particle surface can be manipulated with respect to stoichiometric ratios, protein / surface orientation, and protein / protein orientation. This allows for the manipulation of a durable surface that can withstand assay steps (e.g., large-scale washing) described elsewhere in this specification. For example, proteins with non-natural amino acids specific to a desired position in the protein sequence are described in Lee et al. (Mol Cells. 2019 May 31;42(5):386-396. doi: 10.14348 / molcells.2019.0078.). This can be done by introducing non-natural amino acids into the protein, which may have half of a click chemistry pair, and this half may react with the other half of the click chemistry pair on the particle surface. In this way, rather than random adsorption of proteins to the particle surface to form a corona, these modified proteins can be bound to the particle surface in a specific orientation. As a result, the corona formed on the surface of the particle type can be tuned to a controlled, specific 3D orientation using the manipulated proteins. Using this same general methodology, protein complexes can be linked to the surface of particle type. Here, one or more subunits of the complex can be modified to link to each other by covalent bonds and then linked to the surface of the particle type in a second synthesis step, or they can be linked using different chemical actions that may be performed in a later particle surface modification process. A schematic diagram is shown in Figure 38. Particles having surfaces engineered to capture proteins and / or peptides by non-covalent or covalent bonds can still be SPION or polymer-modified SPION particles, and these can be synthesized in a variety of ways, including standard SPION synthesis by solvothermal methods, ligand exchange processes, silica coating processes, and / or by initiating or installing specific reagent coupling strategies. The advantages of these systems include biological surface generation, utilization of the interactome, and assembly of directed coronas.

[0129] In another example, the particle panel may include functionalized particles for histone capture. For example, these particles may be anionic. In addition or alternatively, assay conditions can be optimized to enrich histones in the sample, and labeling methods can be optimized to improve mass spectrometry detection of histones and post-translational modifications. These functionalized particles for histone capture can be made from polymers, silica, target ligands, and / or any combination thereof. Functionalized particles can be synthesized using a variety of methods, including standard SPION synthesis by solvothermal methods, ligand exchange processes, and surface-initiated polymerization. Anionic particle surfaces can be synthesized by functionalizing the surface with polymers, e.g., polycarboxylates, various ligands, e.g., dendrimers, branched ligands, or carboxylate derivatives, and / or sulfanilamide acids. Optimization of assay conditions may include buffering the binding solution to a pH of about 9. While many proteins will show reduced binding to anionic surfaces at this pH, histones are basic proteins with a primary sequence having a pI of approximately 11 (e.g., H4_human pI approximately 11.3, H3_human pI approximately 11), which will maintain a nearly completely positively charged state. As a result, under basic pH conditions, histones can interact strongly with the particle surface by ionic bonding, and thus histones can be selectively enriched on the particle surface. In addition, histones can be selectively enriched on the particle surface by modifying the particle surface with histone-binding proteins. Therefore, histone protein coronas can be obtained using the particles disclosed herein. Labeling optimization methodologies can help simplify the interpretation of mass spectrometry data and may enable improved identification and designation of post-translational modifications (PTMs). Since the types, locations, and occupancies of post-translational modifications to histones can vary and may contribute to the regulation of gene expression, these particles may be useful for selective enrichment of histones and matching of PTMs in a sample, and thus can provide information about how genes in the sample are regulated. As a result, this could be useful in providing information about various medical conditions in which gene expression regulation is abnormal.

[0130] Examples of particle types consistent with this disclosure are shown in Figure 21 and Table 1 below. [Table 1] Particle properties

[0131] Particles consistent with this disclosure can be created and used in methods for forming protein coronas of a wide range of sizes after incubation in bodily fluids. For example, the particles disclosed herein may have a minimum size of 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, at least 1000 nm, at least 1100 nm, at least 1200 nm, at least 1300 nm, at least 1400 nm, at least 1500 nm, at least 1600 nm, at least 1700 nm, at least 1800 nm nm, at least 1900nm, at least 2000nm, at least 2100nm, at least 2200nm, at least 2300nm, at least 2400nm, at least 2500nm, at least 2600nm, at least 2700nm, at least 2800nm, at least 2900nm, at least 3000nm, at least 3100nm, at least 3200nm, at least 3300nm, at least 3400nm, at least 3500nm, at least 3600nm, at least 3700nm, less At least 3800nm, at least 3900nm, at least 4000nm, at least 4100nm, at least 4200nm, at least 4300nm, at least 4400nm, at least 4500nm, at least 4600nm, at least 4700nm, at least 4800nm, at least 4900nm, at least 5000nm, at least 5100nm, at least 5200nm, at least 5300nm, at least 5400nm, at least 5500nm, at least 5600nm, at least 57 00nm, at least 5800nm, at least 5900nm, at least 6000nm, at least 6100nm, at least 6200nm, at least 6300nm, at least 6400nm, at least 6500nm, at least 6600nm, at least 6700nm, at least 6800nm, at least 6900nm, at least 7000nm, at least 7100nm, at least 7200nm, at least 7300nm, at least 7400nm, at least 7500nm, at least 7600nm,At least 7700nm, at least 7800nm, at least 7900nm, at least 8000nm, at least 8100nm, at least 8200nm, at least 8300nm, at least 8400nm, at least 8500nm, at least 8600nm, at least 8700nm, at least 8800nm, at least 8900nm, at least 9000nm, at least 9100nm, at least 9200nm, at least 9300nm, at least 9400nm, at least 9500nm, at least 9600nm, at least 970 0nm, at least 9800nm, at least 9900nm, at least 10000nm, or 10nm-50nm, 50nm-100nm, 100nm-150nm, 150nm-200nm, 200nm-250nm, 250nm-300nm, 300nm-350nm, 350nm-400nm, 400nm-450nm, 450nm-500nm, 500nm-550nm, 550nm-600nm, 600nm-650nm, 650nm-700nm, 700nm-750nm, 750nm-800nm, 800nm-850nm, 850 nm~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, 10~100nm, 100~200nm, 200~300nm, 300~400nm, 400~500nm, 500~600nm, 600~700nm , 700~800nm, 800~900nm, 900~1000nm, 1000~1100nm, 1100~1200nm, 1200~1 300nm, 1300~1400nm, 1400~1500nm, 1500~1600nm, 1600~1700nm, 1700~180 0nm, 1800~1900nm, 1900~2000nm, 2000~2100nm, 2100~2200nm, 2200~2300n m, 2300~2400nm, 2400~2500nm, 2500~2600nm, 2600~2700nm, 2700~2800nm,2800~2900nm, 2900~3000nm, 3000~3100nm, 3100~3200nm, 3200~3300nm, 3300~3400nm, 3400~3500nm, 3500~3600nm, 3600~3700nm, 3 460 0~4700nm, 4700~4800nm, 4800~4900nm, 4900~5000nm, 5000~5100nm, 5100~5200nm, 5200~5300nm, 5300~5400nm, 5400~5500nm, 5500~ 5600nm, 5600~5700nm, 5700~5800nm, 5800~5900nm, 5900~6000nm, 6000~6100nm, 6100~6200nm, 6200~6300nm, 6300~6400nm, 6400~65 00nm, 6500~6600nm, 6600~6700nm, 6700~6800nm, 6800~6900nm, 6900~7000nm, 7000~7100nm, 7100~7200nm, 7200~7300nm, 7300~740 0nm, 7400~7500nm, 7500~7600nm, 7600~7700nm, 7700~7800nm, 7800~7900nm, 7900~8000nm, 8000~8100nm, 8100~8200nm, 8200~8300n It can have diameters of m, 8300-8400nm, 8400-8500nm, 8500-8600nm, 8600-8700nm, 8700-8800nm, 8800-8900nm, 8900-9000nm, 9000-9100nm, 9100-9200nm, 9200-9300nm, 9300-9400nm, 9400-9500nm, 9500-9600nm, 9600-9700nm, 9700-9800nm, 9800-9900nm, and 9900-10000nm. The diameter can be measured as an indirect measure of size by dynamic light scattering (DLS). DLS measurements can be "intensity-weighted" averages, where "intensity-weighted" means that the size distribution from which this average is calculated can be weighted by the radius to the power of 6.In this specification, this may also be referred to as the "z-mean" or "intensity mean."

[0132] Alternatively, the particles 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, at least 1000 nm, at least 1100 nm, at least 1200 nm, at least 1300 nm, at least 1400 nm, at least 1500 nm, at least 1600 nm, at least 1700 nm, at least 1800 nm, at least 1900 nm, At least 2000nm, at least 2100nm, at least 2200nm, at least 2300nm, at least 2400nm, at least 2500nm, at least 2600nm, at least 2700nm, at least 2800nm, at least 2900nm, at least 3000nm, at least 3100nm, at least 3200nm, at least 3300nm, at least 3400nm, at least 3500nm, at least 3600nm, at least 3700nm, at least 3800nm, at least 3900nm, at least 400 0nm, at least 4100nm, at least 4200nm, at least 4300nm, at least 4400nm, at least 4500nm, at least 4600nm, at least 4700nm, at least 4800nm, at least 4900nm, at least 5000nm, at least 5100nm, at least 5200nm, at least 5300nm, at least 5400nm, at least 5500nm, at least 5600nm, at least 5700nm, at least 5800nm, at least 5900nm, at least 6000nm, at least Also 6100nm, at least 6200nm, at least 6300nm, at least 6400nm, at least 6500nm, at least 6600nm, at least 6700nm, at least 6800nm, at least 6900nm, at least 7000nm, at least 7100nm, at least 7200nm, at least 7300nm, at least 7400nm, at least 7500nm, at least 7600nm, at least 7700nm, at least 7800nm, at least 7900nm, at least 8000nm, at least 8100nm,At least 8200nm, at least 8300nm, at least 8400nm, at least 8500nm, at least 8600nm, at least 8700nm, at least 8800nm, at least 8900nm, at least 9000nm, at least 9100nm, at least 9200nm, at least 9300nm, at least 9400nm, at least 9500nm, at least 9600nm, at least 9700nm, at least 9800nm, at least 9900nm, at least 10000nm, or 10nm~50nm, 50nm~1 00nm, 100nm~150nm, 150nm~200nm, 200nm~250nm, 250nm~300nm, 300nm~350 nm, 350nm~400nm, 400nm~450nm, 450nm~500nm, 500nm~550nm, 550nm~600nm , 600nm~650nm, 650nm~700nm, 700nm~750nm, 750nm~800nm, 800nm~850nm, 8 50nm~900nm, 100nm~300nm, 150nm~350nm, 200nm~400nm, 250nm~450nm, 300 nm~500nm, 350nm~550nm, 400nm~600nm, 450nm~650nm, 500nm~700nm, 550nm~750nm, 600nm~800nm, 650nm~850nm, 700nm~900nm, or 10nm~900nm, 10~100nm, 100~200nm, 200~300nm, 300~400nm, 400~500nm, 500~600nm, 600~700nm, 700~800nm, 800~900nm, 900~1000nm, 1000~1100nm, 1100~1200nm, 1200 ~1300nm, 1300~1400nm, 1400~1500nm, 1500~1600nm, 1600~1700nm, 1700~1 800nm, 1800~1900nm, 1900~2000nm, 2000~2100nm, 2100~2200nm, 2200~230 0nm, 2300~2400nm, 2400~2500nm, 2500~2600nm, 2600~2700nm, 2700~2800n m, 2800~2900nm, 2900~3000nm, 3000~3100nm, 3100~3200nm, 3200~3300nm,3300~3400nm, 3400~3500nm, 3500~3600nm, 3600~3700nm, 3700~3800nm, 3800~3900nm, 3900~4000nm, 4000~4100nm, 4100~4 200nm, 4200~4300nm, 4300~4400nm, 4400~4500nm, 4500~4600nm, 4600~4700nm, 4700~4800nm, 4800~4900nm, 4900~5000nm, 5000~5100nm, 5100~5200nm, 5200~5300nm, 5300~5400nm, 5400~5500nm, 5500~5600nm, 5600~5700nm, 5700~5800nm, 5800~5 900nm, 5900~6000nm, 6000~6100nm, 6100~6200nm, 6200~6300nm, 6300~6400nm, 6400~6500nm, 6500~6600nm, 6600~6700nm, 6 700~6800nm, 6800~6900nm, 6900~7000nm, 7000~7100nm, 7100~7200nm, 7200~7300nm, 7300~7400nm, 7400~7500nm, 7500~76 00nm, 7600~7700nm, 7700~7800nm, 7800~7900nm, 7900~8000nm, 8000~8100nm, 8100~8200nm, 8200~8300nm, 8300~8400nm, 8 It can have radii in the following ranges: 400-8500nm, 8500-8600nm, 8600-8700nm, 8700-8800nm, 8800-8900nm, 8900-9000nm, 9000-9100nm, 9100-9200nm, 9200-9300nm, 9300-9400nm, 9400-9500nm, 9500-9600nm, 9600-9700nm, 9700-9800nm, 9800-9900nm, and 9900-10000nm.

[0133] In certain examples, the particles disclosed herein have a diameter of 100 nm to 400 nm. In other examples, the particles disclosed herein have a radius of 100 nm to 400 nm. Particle size can be determined by several techniques, such as dynamic light scattering or electron microscopy (e.g., SEM, TEM). The particles disclosed herein may be nanoparticles or microparticles.

[0134] In addition, particles may have a uniform or heterogeneous size distribution. The polydispersity index (PDI), which can be measured by techniques such as dynamic light scattering, is a measure of the size distribution. A low PDI indicates a more uniform size distribution, while a higher PDI indicates a more heterogeneous size distribution. For example, the particles disclosed herein may have a PDI of less than 0.5, less than 0.4, less than 0.3, less than 0.2, less than 0.15, or less than 0.1. In certain embodiments, the particles disclosed herein have a PDI of less than 0.1.

[0135] The particles disclosed herein may have a range of different surface charges. The particles may be negatively charged, positively charged, or electrically neutral. In some embodiments, the particles may have charges ranging 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, -90mV to -80mV, and -80mV. ~-70mV, -70mV~-60mV, -60mV~-50mV, -50mV~-40mV, -40mV~-30mV, -30mV~-20mV, -20mV~-10mV , -10mV~0mV, 0mV~10mV, 10mV~20mV, 20mV~30mV, 30mV~40mV, 40mV~50mV, 50mV~60mV, 60mV~70mV , 70mV~80mV, 80mV~90mV, 90mV~100mV, 100mV~110mV, 110mV~120mV, 120mV~130mV, 130mV~140m V, 140mV~150mV, 150mV~200mV, 200mV~250mV, 250mV~300mV, 300mV~350mV, 350mV~400mV, 400m The particles have surface charges of V~450mV, 450mV~500mV, -500mV~-400mV, -400mV~-300mV, -300mV~-200mV, -200mV~-100mV, -100mV~0mV, 0mV~100mV, 100mV~200mV, 200mV~300mV, 300mV~400mV, or 400mV~500mV. In certain examples, the particles disclosed herein have surface charges of -60mV~60mV.

[0136] A variety of particle morphologies are consistent with the particle types in the panel of this disclosure. For example, particles may be spherical, colloidal, cubic, square, rod, wire, conical, pyramidal, and elliptical. The particles of this disclosure may be solid particles, porous particles, or mesoporous particles. Particles may have small or large surface areas. Particles may have various magnetic properties, which can be measured by a SQUID that determines the magnetic force depending on the external field. In some cases, particles have a core-shell structure or a yolk-shell structure. Particle panel

[0137] Using the particle panels disclosed herein, several proteins, peptides, or protein groups can be identified using the proteograph workflow described herein (MS analysis of distinct biomolecular coronas corresponding to distinct particle types within the particle panel). Feature intensity, where disclosed herein, refers to the intensity of distinctly different spikes ("features") observed in a plot of mass-to-charge ratio intensity from a mass spectrometry run of the sample. These features may also correspond to variably ionized fragments of peptides and / or proteins. Feature intensity can be sorted into protein groups using the data analysis methods described herein. A protein group refers to two or more proteins identified by a shared peptide sequence. Alternatively, a protein group may refer to a single protein identified using a unique identification sequence. For example, if a peptide sequence shared between two proteins (protein 1: XYZZX, and protein 2: XYZYZ) in a sample is assayed, the protein group may be the "XYZ protein group" having two members (protein 1 and protein 2). Alternatively, if a peptide sequence is specific to a single protein (protein 1), the protein group could be a "ZZX" protein group having one member (protein 1). Each protein group may be supported by one or more peptide sequences. Proteins detected or identified according to this disclosure may refer to distinct proteins detected in a sample (e.g., distinctly different from other proteins detected using mass spectrometry). Thus, analysis of proteins present in distinct coronas corresponding to distinct particle types in a particle panel yields a number of feature intensities. This number decreases when the feature intensities are processed into distinct peptides, further decreases when the distinct peptides are processed into distinct proteins, and further decreases when the peptides are grouped into protein groups (two or more proteins sharing distinct peptide sequences).

[0138] Using the particle panel disclosed herein, at least 100 proteins, at least 200 proteins, at least 300 proteins, at least 400 proteins, at least 500 proteins, at least 600 proteins, at least 700 proteins, at least 800 proteins, at least 900 proteins, at least 1000 proteins, at least 1100 proteins, at least 1200 proteins, at least 1300 proteins, at least 1400 proteins, at least 1500 proteins, at least 1600 proteins, at least 1700 proteins, at least 1800 proteins, at least 1900 proteins, at least 2000 proteins, at least 2100 proteins, at least 2200 proteins, at least 2300 proteins, at least 2400 proteins, at least 2500 proteins, at least 2600 proteins, at least 2700 proteins, at least 2800 proteins, at least 2900 proteins, and at least 3000 proteins. Quality, 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, at least 4500 proteins, at least 4 600 proteins, at least 4700 proteins, at least 4800 proteins, at least 4900 proteins, at least 5000 proteins, at least 10000 proteins, at least 20000 proteins, at least 50000 proteins, at least 100000 proteins, 100-5000 proteins, 200-4700 proteins, 300-4400 proteins, 400-4100 proteins, 500-3800 proteins, 600-3500 proteins, 700-3200 proteins,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, 13000-14000 protein, 14000-150 Proteins with values ​​of 00, 15000-16000, 16000-17000, 17000-18000, 18000-19000, 19000-20000, 20000-25000, 25000-30000, 10000-20000, 10000-50000, 20000-100000, 2000-20000, 1800-20000, or 10000-100000 can be identified.

[0139] Using the particle panel disclosed in HPLC, 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, and at least 14 Protein groups of 00, at least 1500, at least 1600, at least 1700, at least 1800, at least 1900, at least 2000, at least 2100, at least 2200, at least 2300, at least 2400, at least 2500, at least 2600, at least 2700, at least 2800 , at least 2900 protein groups, at least 3000 protein groups, at least 3100 protein groups, at least 3200 protein groups, at least 3300 protein groups, at least 3400 protein groups, at least 3500 protein groups, at least 3600 protein groups, at least 3700 protein groups, at least 3800 protein groups, at least 3900 protein groups, at least 4000 protein groups, at least 4100 protein groups, at least 4200 protein groups, at least 430 Protein groups of 0, protein groups of at least 4400, protein groups of at least 4500, protein groups of at least 4600, protein groups of at least 4700, protein groups of at least 4800, protein groups of at least 4900, protein groups of at least 5000, protein groups of at least 10000, protein groups of at least 20000, protein groups of at least 100000, protein groups of 100-5000, protein groups of 200-4700, protein groups of 300-4400, protein groups of 400-4100,Protein groups of 500-3800, 600-3500, 700-3200, 800-2900, 900-2600, 1000-2300, 1000-3000, 3000-4000, 4000-5000, 5000-6000, 6000-7000, 7000-8000, 8000-9000, 9000-10000, 10000-11000, 11000-12000, 12000-13000, 1300 Protein groups can be identified in the following ranges: 0-14000, 14000-15000, 15000-16000, 16000-17000, 17000-18000, 18000-19000, 19000-20000, 20000-25000, 25000-30000, 10000-20000, 10000-50000, 20000-100000, 2000-20000, 1800-20000, or 10000-100000.

[0140] Using the particle panels disclosed herein, a number of distinct proteins and / or any of the specific proteins disclosed herein can be identified over a wide dynamic range. For example, a particle panel disclosed herein, including distinct particle types, can concentrate proteins in a sample, and these proteins can be identified using a proteographic workflow over the entire dynamic range in which the proteins are present in the sample (e.g., a plasma sample). In some embodiments, a particle panel including any number of distinct particle types disclosed herein concentrates and identifies proteins over a dynamic range of at least 2. In some embodiments, a particle panel including any number of distinct particle types disclosed herein concentrates and identifies proteins over a dynamic range of at least 3. In some embodiments, a particle panel including any number of distinct particle types disclosed herein concentrates and identifies proteins over a dynamic range of at least 4. In some embodiments, a particle panel including any number of distinct particle types disclosed herein concentrates and identifies proteins over a dynamic range of at least 5. In some embodiments, a particle panel containing any number of distinct particle types disclosed herein enriches and identifies proteins over a dynamic range of at least 6. In some embodiments, a particle panel containing any number of distinct particle types disclosed herein enriches and identifies proteins over a dynamic range of at least 7. In some embodiments, a particle panel containing any number of distinct particle types disclosed herein enriches and identifies proteins over a dynamic range of at least 8. In some embodiments, a particle panel containing any number of distinct particle types disclosed herein enriches and identifies proteins over a dynamic range of at least 9. In some embodiments, a particle panel containing any number of distinct particle types disclosed herein enriches and identifies proteins over a dynamic range of at least 10.In some embodiments, a particle panel containing any number of distinct particle types disclosed herein concentrates and identifies proteins over a dynamic range of at least 11. In some embodiments, a particle panel containing any number of distinct particle types disclosed herein concentrates and identifies proteins over a dynamic range of at least 12. In some embodiments, a particle panel containing any number of distinct particle types disclosed herein concentrates and identifies proteins over a dynamic range of at least 13. In some embodiments, a particle panel containing any number of distinct particle types disclosed herein concentrates and identifies proteins over a dynamic range of at least 14. In some embodiments, a particle panel containing any number of distinct particle types disclosed herein concentrates and identifies proteins over a dynamic range of at least 15. In some embodiments, a particle panel containing any number of distinct particle types disclosed herein concentrates and identifies proteins over a dynamic range of at least 20. In some embodiments, a particle panel containing any number of distinct particle types disclosed herein concentrates and identifies proteins over a dynamic range of 2 to 100. In some embodiments, a particle panel containing any number of distinct particle types disclosed herein enriches and identifies proteins over a dynamic range of 2 to 20. In some embodiments, a particle panel containing any number of distinct particle types disclosed herein enriches and identifies proteins over a dynamic range of 2 to 10. In some embodiments, a particle panel containing any number of distinct particle types disclosed herein enriches and identifies proteins over a dynamic range of 2 to 5. In some embodiments, a particle panel containing any number of distinct particle types disclosed herein enriches and identifies proteins over a dynamic range of 5 to 10.

[0141] A particle panel comprising any number of distinct particle types disclosed herein enriches and identifies a single protein or group of proteins. In some embodiments, a single protein or group of proteins may comprise proteins having different post-translational modifications. For example, a first particle type in the particle panel may enrich a protein or group of proteins having a first post-translational modification, a second particle type in the particle panel may enrich the same protein or group of proteins having a second post-translational modification, and a third particle type in the particle panel may enrich the same protein or group of proteins lacking the post-translational modification. In some embodiments, a particle panel comprising any number of distinct particle types disclosed herein enriches and identifies a single protein or group of proteins by binding to different domains, sequences, or epitopes of the single protein or group of proteins. For example, a first particle type in the particle panel may enrich a protein or group of proteins by binding to a first domain of the protein or group of proteins, and a second particle type in the particle panel may enrich the same protein or group of proteins by binding to a second domain of the protein or group of proteins.

[0142] In some embodiments, a particle panel may have more than one particle type. Increasing the number of particle types in the panel may be a way to increase the number of proteins that can be identified in a given sample. An example of how much increasing the panel size can increase the number of proteins identified is shown in Figure 11. For example, as shown in Figure 11, a 1-particle panel size identified 419 unique proteins, a 2-particle panel size identified 588 proteins, a 3-particle panel size identified 727 proteins, a 4-particle panel size identified 844 proteins, a 5-particle panel size identified 934 proteins, a 6-particle panel size identified 1008 proteins, a 7-particle panel size identified 1075 proteins, an 8-particle panel size identified 1133 proteins, a 9-particle panel size identified 1184 proteins, a 10-particle panel size identified 1230 proteins, an 11-particle panel size identified 1275 proteins, and a 12-particle panel size identified 1318 proteins. The particle size may include nanoparticles.

[0143] In some embodiments, a 1-particle panel size can identify 200–600 unique proteins. In some embodiments, a 2-particle panel size can identify 300–700 unique proteins. In some embodiments, a 3-particle panel size can identify 500–900 unique proteins. In some embodiments, a 4-particle panel size can identify 600–1000 unique proteins. In some embodiments, a 5-particle panel size can identify 700–1100 unique proteins. In some embodiments, a 6-particle panel size can identify 800–1200 unique proteins. In some embodiments, a 7-particle panel size can identify 850–1250 unique proteins. In some embodiments, an 8-particle panel size can identify 900–1300 unique proteins. In some embodiments, a 9-particle panel size can identify 950–1350 unique proteins. In some embodiments, a 10-particle panel size can identify 1000–1400 unique proteins. In some embodiments, an 11-particle panel size can identify 1050–1450 unique proteins. In some embodiments, a 12-particle panel size can identify 1100–1500 unique proteins. The particle type may include nanoparticles.

[0144] This disclosure provides more than 200 distinct particle types having more than 100 different surface chemical structures and more than 50 diverse physical properties. Each distinct particle type has at least one physicochemical property that differs between the first particle type and the second particle type. For example, this disclosure provides more than 200 distinct particle types, more than 200 distinct particle types, more than 25 distinct particle types, more than 30 distinct particle types, more than 35 distinct particle types, more than 40 distinct particle types, more than 45 distinct particle types, and more than 50 distinct particle types. The present invention provides a particle panel having a particle type, at least 100 distinct particle types, at least 150 distinct particle types, at least 200 distinct particle types, at least 250 distinct particle types, at least 300 distinct particle types, at least 350 distinct particle types, at least 400 distinct particle types, at least 450 distinct particle types, at least 500 distinct particle types, 2 to 500 distinct particle types, 2 to 5 distinct particle types, 5 to 10 distinct particle types, 10 to 15 distinct particle types, 15 to 20 distinct particle types, 20 to 40 distinct particle types, 40 to 60 distinct particle types, 60 to 80 distinct particle types, 80 to 100 distinct particle types, 100 to 500 distinct particle types, 4 to 15 distinct particle types, or 2 to 20 distinct particle types. The particle type may include nanoparticle types.

[0145] In some embodiments, the Disclosure provides for at least one distinct particle type, at least two distinct particle types, at least three distinct particle types, at least four distinct particle types, at least five distinct particle types, at least six distinct particle types, at least seven distinct particle types, at least eight distinct particle types, at least nine distinct particle types, at least ten distinct particle types, at least eleven distinct particle types, at least twelve distinct particle types, at least thirteen distinct particle types, at least fourteen distinct particle types, and at least one distinct particle type. 15 distinct particle types, at least 16 distinct particle types, at least 17 distinct particle types, at least 18 distinct particle types, at least 19 distinct particle types, at least 20 distinct particle types, at least 25 distinct particle types, at least 30 distinct particle types, at least 35 distinct particle types, at least 40 distinct particle types, at least 45 distinct particle types, at least 50 distinct particle types, at least 55 distinct particle types, at least 60 distinct particle types, at least 65 distinct particle types, at least 70 100 distinct particle types, at least 75 distinct particle types, at least 80 distinct particle types, at least 85 distinct particle types, at least 90 distinct particle types, at least 95 distinct particle types, at least 100 distinct particle types, 1 to 5 distinct particle types, 5 to 10 distinct particle types, 10 to 15 distinct particle types, 15 to 20 distinct particle types, 20 to 25 distinct particle types, 25 to 30 distinct particle types, 30 to 35 distinct particle types, 35 to 40 distinct particle types, 40 to 45 distinct particle types, 45 to 50 distinct particle types, 50 to The present disclosure provides panel sizes of 55 distinct particle types, 55-60 distinct particle types, 60-65 distinct particle types, 65-70 distinct particle types, 70-75 distinct particle types, 75-80 distinct particle types, 80-85 distinct particle types, 85-90 distinct particle types, 90-95 distinct particle types, 95-100 distinct particle types, 1-100 distinct particle types, 20-40 distinct particle types, 5-10 distinct particle types, 3-7 distinct particle types, 2-10 distinct particle types, 6-15 distinct particle types, or 10-20 distinct particle types. In certain embodiments, the present disclosure provides panel sizes of 3-10 particle types. In certain embodiments, the present disclosure provides panel sizes of 4-11 distinct particle types.In certain embodiments, the disclosure provides panel sizes of 5 to 15 distinct particle types. In certain embodiments, the disclosure provides panel sizes of 5 to 15 distinct particle types. In certain embodiments, the disclosure provides panel sizes of 8 to 12 distinct particle types. In certain embodiments, the disclosure provides panel sizes of 9 to 13 distinct particle types. In certain embodiments, the disclosure provides panel sizes of 10 distinct particle types. The particle type may include nanoparticle types.

[0146] For example, this disclosure provides for at least two distinct particle types, at least three different surface chemical structures, at least four different surface chemical structures, at least five different surface chemical structures, at least six different surface chemical structures, at least seven different surface chemical structures, at least eight different surface chemical structures, at least nine different surface chemical structures, at least ten different surface chemical structures, at least eleven different surface chemical structures, at least twelve different surface chemical structures, at least thirteen different surface chemical structures, at least fourteen different surface chemical structures, at least fifteen different surface chemical structures, at least twenty different surface chemical structures, at least twenty-five different surface chemical structures, at least thirty different surface chemical structures, at least thirty-five different surface chemical structures, at least forty different surface chemical structures, at least forty-five different surface chemical structures, and at least fifty different surface chemical structures. The present invention provides a particle panel having a structure, at least 100 different surface chemical structures, at least 150 different surface chemical structures, at least 200 different surface chemical structures, at least 250 different surface chemical structures, at least 300 different surface chemical structures, at least 350 different surface chemical structures, at least 400 different surface chemical structures, at least 450 different surface chemical structures, at least 500 different surface chemical structures, 2 to 500 different surface chemical structures, 2 to 5 different surface chemical structures, 5 to 10 different surface chemical structures, 10 to 15 different surface chemical structures, 15 to 20 different surface chemical structures, 20 to 40 different surface chemical structures, 40 to 60 different surface chemical structures, 60 to 80 different surface chemical structures, 80 to 100 different surface chemical structures, 100 to 500 different surface chemical structures, 4 to 15 different surface chemical structures, or 2 to 20 different surface chemical structures.

[0147] This disclosure relates to at least two different surface chemical structures, at least three different surface chemical structures, at least four different surface chemical structures, at least five different surface chemical structures, at least six different surface chemical structures, at least seven different surface chemical structures, at least eight different surface chemical structures, at least nine different surface chemical structures, at least ten different surface chemical structures, at least eleven different surface chemical structures, at least twelve different surface chemical structures, at least thirteen different surface chemical structures, at least fourteen different surface chemical structures, at least fifteen different surface chemical structures, at least twenty different surface chemical structures, at least twenty-five different surface chemical structures, at least thirty different surface chemical structures, at least thirty-five different surface chemical structures, at least forty different surface chemical structures, at least forty-five different surface chemical structures, and at least fifty different surface chemical structures. The present invention provides a particle panel having at least 100 different surface chemical structures, at least 150 different surface chemical structures, at least 200 different surface chemical structures, at least 250 different surface chemical structures, at least 300 different surface chemical structures, at least 350 different surface chemical structures, at least 400 different surface chemical structures, at least 450 different surface chemical structures, at least 500 different surface chemical structures, 2 to 500 different surface chemical structures, 2 to 5 different surface chemical structures, 5 to 10 different surface chemical structures, 10 to 15 different surface chemical structures, 15 to 20 different surface chemical structures, 20 to 40 different surface chemical structures, 40 to 60 different surface chemical structures, 60 to 80 different surface chemical structures, 80 to 100 different surface chemical structures, 100 to 500 different surface chemical structures, 4 to 15 different surface chemical structures, or 2 to 20 different surface chemical structures.

[0148] 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, and at least fifty different physical properties. The present invention provides a particle panel 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.

[0149] In some embodiments, panels that optimally identify proteins and best associate biomarkers with disease include panels selected from the particle types listed in Table 1. For example, panels that optimally identify proteins and best associate biomarkers with disease include panels containing SP-339, HX74, SP-356, SP-333, HX20, SP-374, HX42, SP-003, SP-007, and SP-011. Particle panels particularly suitable for identifying a large number of proteins in a sample (e.g., more than 1500 proteins) include 5 to 10 distinct particle types in the assay. The number of distinct particle types included in the particle panel can be adjusted for specific applications (e.g., detection of a particular subset of proteins or detection of a group of markers associated with a particular disease). In some embodiments, panels having physicochemically distinct particle types that optimally identify proteins and best associate biomarkers with disease include silica-coated SPIONs, acrylamide-based SPIONs, and acrylate-based SPIONs. For example, panels of particles disclosed herein that generate information-rich proteome data by their protein coronas that can be associated with biomarkers and diseases with high sensitivity and specificity include SPION (SP-003) coated with silica, SPION (SP-007) coated with poly(N-(3-(dimethylamino)propyl)methacrylamide) (PDMAPMA), and SPION (SP-011) coated with poly(oligo(ethylene glycol)methyl ether methacrylate) (POEGMA).

[0150] In some embodiments, the total assay time from a single pool of plasma, including sample preparation and LC-MS, may be approximately 8 hours. In some embodiments, the total assay time from a single pool of plasma, including sample preparation and LC-MS, may be approximately 1 hour, 2 hours, 3 hours, 4 hours, 5 hours, 6 hours, 7 hours, 8 hours, 9 hours, 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, at least 3 It could be 0 to 40 minutes, at least 40 to 50 minutes, at least 50 to 60 minutes, at least 1 to 1.5 hours, at least 1.5 to 2 hours, at least 2 to 2.5 hours, at least 2.5 to 3 hours, at least 3 to 3.5 hours, at least 3.5 to 4 hours, at least 4 to 4.5 hours, at least 4.5 to 5 hours, at least 5 to 5.5 hours, at least 5.5 to 6 hours, at least 6 to 6.5 hours, at least 6.5 to 7 hours, at least 7 to 7.5 hours, at least 7.5 to 8 hours, at least 8 to 8.5 hours, at least 8.5 to 9 hours, at least 9 to 9.5 hours, or at least 9.5 to 10 hours. Early detection

[0151] The panels and methods of use described herein can be used to detect markers in samples from subjects that are consistent with specific disease conditions. As shown in Figures 18A and 18B, early-stage cancers can be isolated up to 8 years before symptom onset. The Golestan cohort enrolled 50,000 healthy subjects between 2004 and 2008. Stored plasma from the enrollment was tested as shown in Figure 18A. Eight years after enrollment, approximately 1,000 patients had developed cancer. Figure 18B shows the classification of three types of cancer (brain, lung, and pancreatic cancer) from stored plasma by principal component analysis. Here, the corona results are plotted against three principal component axes representing three distinct statistical populations. Corona analysis of stored plasma from the enrollment date (including the measurement of multiple properties to profile multiple proteins) accurately classified cancer in 15 of the 15 subjects tested (5 patients for each of the three types of cancer). In some embodiments, the panel of this disclosure can be used to diagnose a medical condition up to one year, two years, three years, four years, five years, six years, seven years, eight years, nine years, ten years, fifteen years, twenty years, or twenty-five years before the onset of symptoms of that condition.

[0152] The panel of this disclosure can be used to detect a wide range of medical conditions in a given sample. For example, cancer can be detected using the panel of this disclosure. Cancer may be brain cancer, lung cancer, pancreatic cancer, glioblastoma, meningioma, myeloma, or pancreatic cancer. Corona analysis signals for these cancers are shown in Figures 17B and 18.

[0153] In addition, the panel of this disclosure can be used to detect any of the other cancers listed at https: / / www.cancer.gov / types, including: acute lymphoblastic leukemia (ALL); acute myeloid leukemia (AML), adolescent cancer; adrenocortical carcinoma; pediatric adrenocortical carcinoma; rare childhood cancers; AIDS-related cancers; Kaposi's sarcoma (soft tissue sarcoma); AIDS-related lymphoma (lymphoma); primary CNS lymphoma (lymphoma); anal cancer; appendiceal cancer - see gastrointestinal carcinoid tumor; astrocytoma, childhood (brain cancer). Atypical teratoma / rhabdoid tumor, childhood, central nervous system (brain cancer); basal cell carcinoma - see skin cancer; bile duct cancer; bladder cancer; pediatric bladder cancer; bone cancer (including Ewing's sarcoma, osteosarcoma, and malignant fibrous histiocytoma); brain tumor; breast cancer; pediatric breast cancer; bronchial tumor, childhood; Burkitt lymphoma - see non-Hodgkin lymphoma; carcinoid tumor (gastrointestinal tract); pediatric carcinoid tumor; cancer of unknown cause; childhood cancer of unknown cause; cardiac tumor, childhood; central nervous system; atypical teratoma / rhabdoid tumor, childhood ( Brain cancer); germ cell tumor, childhood (brain cancer); germ cell tumor, childhood (brain cancer); primary CNS lymphoma; cervical cancer; pediatric cervical cancer; childhood cancer; rare cancers in childhood; cholangiocarcinoma - see cholangiocarcinoma; chordoma, childhood; chronic lymphocytic leukemia (CLL); chronic myeloid leukemia (CML); myeloproliferative neoplasm; colorectal cancer; childhood colorectal cancer; craniopharyngioma, childhood (brain cancer); cutaneous T-cell lymphoma - see lymphoma (mycosis fungoides and Sézary syndrome); ductal carcinoma in situ (DCIS) - see breast cancer; germ cell tumor, central nervous system, childhood (brain cancer) ); Endometrial cancer (uterine cancer); Ependymoma, childhood (brain cancer); Esophageal cancer; Pediatric esophageal cancer; Nasal neuroblastoma (head and neck cancer); Ewing's sarcoma (bone cancer); Extracranial germ cell tumor, childhood; Extragonadal germ cell tumor; Eye cancer; Pediatric intraocular melanoma; Intraocular melanoma; Retinoblastoma; Fallopian tube cancer; Bone fibrous histiocytoma, malignant, and osteosarcoma; Gallbladder cancer; Gastric cancer; Pediatric gastric cancer; Gastrointestinal carcinoid tumor; Gastrointestinal stromal tumor (GIST) (soft tissue sarcoma); Pediatric gastrointestinal stromal tumor; Germ cell tumor;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; hairy cell leukemia; head and neck cancer; cardiac tumors, childhood; hepatocellular carcinoma (liver cancer); histiocytic proliferative disorder, Langerhans cell tumors; Hodgkin lymphoma; hypopharyngeal cancer (head and neck cancer); intraocular melanoma; pediatric intraocular melanoma; islet cell tumors, pancreatic neuroendocrine tumors; Kaposi's sarcoma (soft tissue sarcoma); renal (renal cell) cancer; Langerhans cell histiocytosis; laryngeal cancer (head and neck cancer); leukemia; lip and oral 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 of bone and Osteosarcoma; melanoma; childhood melanoma; intraocular melanoma (eye); childhood intraocular melanoma; Merkel cell carcinoma (skin cancer); malignant mesothelioma; childhood mesothelioma; metastatic cancer; metastatic cervical squamous cell carcinoma of unknown primary origin (head and neck cancer); median duct cancer with nut gene mutation; oral 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 tumor (islet cell tumor); papillomatosis (pediatric pharyngeal cancer); paraganglioma; pediatric paraganglioma; sinus and nasal cavity cancer (head and neck cancer); parathyroid cancer; penile cancer; pharyngeal cancer (head and neck cancer); pheochromocytoma; pediatric pheochromocytoma; pituitary tumor; plasmacytoma / multiple myeloma; pleuroblastoma; lactation Primary central nervous system (CNS) lymphoma; primary peritoneal cancer; prostate cancer; rectal cancer; recurrent cancer; renal cell carcinoma (kidney cancer); retinoblastoma; rhabdomyosarcoma, childhood (soft tissue sarcoma); salivary gland cancer (head and neck cancer); sarcoma; childhood rhabdomyosarcoma (soft tissue sarcoma); childhood angiotumor (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; cutaneous squamous cell carcinoma - see skin cancer; squamous cell carcinoma of unknown primary origin, metastatic (head and neck cancer); stomach cancer; pediatric stomach cancer; T-cell lymphoma, cutaneous lymphoma (mycosis fungoides and Sézary syndrome) - see T-cell lymphoma; testicular cancer; pediatric testicular cancer; throat 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 (renal cell) carcinoma); cancer of unknown cause; childhood cancer of unknown cause; rare childhood cancer; transitional cell carcinoma of the ureter and renal pelvis (renal (renal cell) carcinoma); urethral cancer; uterine cancer, endometrium; uterine sarcoma; vaginal cancer; childhood vaginal cancer; hemangiomas (soft tissue sarcomas); vulvar cancer; Wilms' tumor and other childhood renal tumors; or cancers of young adults. Furthermore, the particle panel of this disclosure can be used to detect other diseases, such as Alzheimer's disease and multiple sclerosis. sample

[0154] Using the panels of this disclosure, proteome data can be generated from protein coronas and subsequently associated with any of the biological states described herein. Samples consistent with this disclosure include biological samples from subjects, which may be human or non-human animals. Biological samples may be body fluids. For example, body fluids may be plasma, serum, CSF, urine, tears, cell lysates, tissue lysates, cell homogenates, tissue homogenates, papillary aspirates, fecal samples, synovial fluid and whole blood, or saliva. Samples may also be non-biological samples, such as water, milk, solvents, or anything that is homogenized into a fluid state. The biological samples may contain multiple protein or proteome data, which can be analyzed after the adsorption of proteins to the surface of various particle types in the panel and subsequent digestion of protein coronas. Proteome data may include nucleic acids, peptides, or proteins. Any of the samples herein may contain several different analytes, which can be analyzed using the compositions and methods disclosed herein. The analyte may be any molecule that can bind to or interact with the surface of a particulate matter, such as a protein, peptide, small molecule, nucleic acid, metabolite, lipid, or particulate matter.

[0155] Compositions and methods for multi-omics analysis are disclosed herein. “Multi-omics” or “multi-omics” can refer to analytical techniques for large-scale biomolecular analysis where the dataset consists of multiple omes, e.g., proteome, genome, transcriptome, lipidome, and metabolome. Non-limiting examples of multi-omics data include proteomic data, genomics data, lipidomics data, glycomics data, transcriptomics data, or metabolomics data. “Biomolecules” in “biomolecular corona” can refer to any molecule or biocomponent that can be produced by or present within a biological organism. Non-limiting examples of biomolecules include proteins (protein corona), polypeptides, polysaccharides, sugars, lipids, lipoproteins, metabolites, oligonucleotides, nucleic acids (DNA, RNA, microRNA, plasmids, single-stranded nucleic acids, double-stranded nucleic acids), metabolomes, and small molecules, e.g., primary metabolites, secondary metabolites, and other natural products, or any combination thereof. In some embodiments, the biomolecules are selected from the group consisting of proteins, nucleic acids, lipids, and metabolome.

[0156] In some embodiments, the sample of this disclosure may be multiple samples. At least two of the multiple samples may be spatially isolated. Spatially isolated means samples contained in separate volumes. For example, spatially isolated samples may be samples in separate wells in a plate or in separate tubes. Spatially isolated samples may also be samples in separate wells in a plate or in separate tubes that are assayed together with the same instrument. In some embodiments, the Disclosure relates to the analysis of multiple samples, for example, at least two spatially isolated samples, at least five spatially isolated samples, at least ten spatially isolated samples, at least fifteen spatially isolated samples, at least 20 spatially isolated samples, at least 25 spatially isolated samples, at least 30 spatially isolated samples, at least 35 spatially isolated samples, at least 40 spatially isolated samples, at least 45 spatially isolated samples, at least 50 spatially isolated samples, at least 55 spatially isolated samples, at least 60 spatially isolated samples, at least 65 spatially isolated samples, at least 70 spatially isolated samples, at least 75 spatially isolated samples, and at least All 80 spatially isolated samples, at least 85 spatially isolated samples, at least 90 spatially isolated samples, at least 95 spatially isolated samples, at least 96 spatially isolated samples, at least 100 spatially isolated samples, at least 120 spatially isolated samples, at least 140 spatially isolated samples, at least 160 spatially isolated samples, at least 180 spatially isolated samples, at least 200 spatially isolated samples, at least 220 spatially isolated samples, at least 240 spatially isolated samples, at least 260 spatially isolated samples, at least 280 spatially isolated samples, at least 300 spatially isolated samples, at least 320 spatially isolated samples,At least 340 spatially isolated samples, at least 360 spatially isolated samples, at least 380 spatially isolated samples, at least 400 spatially isolated samples, at least 420 spatially isolated samples, at least 440 spatially isolated samples, at least 460 spatially isolated samples, at least 480 spatially isolated samples, at least 500 spatially isolated samples, at least 600 spatially isolated samples, at least 700 spatially isolated samples, at least 800 spatially isolated samples, at least 900 spatially isolated samples, at least 1000 spatially isolated samples, at least 1100 spatially isolated samples, at least 1200 spatially isolated samples, at least 1300 spatially isolated samples, at least 1400 spatially isolated samples, at least 1500 spatially isolated samples, at least 1600 spatially isolated samples, at least 1700 spatially isolated samples , at least 1800 spatially isolated samples, at least 1900 spatially isolated samples, at least 2000 spatially isolated samples, at least 5000 spatially isolated samples, at least 10000 spatially isolated samples, 2 to 10 spatially isolated samples, 2 to 100 spatially isolated samples, 2 to 200 spatially isolated samples, 2 to 300 spatially isolated samples, 50 to 150 spatially isolated samples, 10 to 20 spatially isolated samples, 20 to 30 spatially isolated samples, 30-40 spatially isolated samples, 40-50 spatially isolated samples, 50-60 spatially isolated samples, 60-70 spatially isolated samples, 70-80 spatially isolated samples, 80-90 spatially isolated samples, 90-100 spatially isolated samples, 100-150 spatially isolated samples, 150-200 spatially isolated samples, 200-250 spatially isolated samples, 250-300 spatially isolated samples,300-350 spatially isolated samples, 350-400 spatially isolated samples, 400-450 spatially isolated samples, 450-500 spatially isolated samples, 500-600 spatially isolated samples, 600-700 spatially isolated samples, 700-800 spatially isolated samples, 800-900 spatially isolated samples, 900-1000 spatially isolated samples, 1000-2000 spatially isolated samples, 2 This invention provides particle panels and methods for using them, suitable for the analysis of spatially isolated samples in the following ranges: 000-3000, 3000-4000, 4000-5000, 5000-6000, 6000-7000, 7000-8000, 8000-9000, or 9000-10000.

[0157] The method disclosed herein includes the step of isolating a particle panel (having multiple particle types) from one or more samples. A particle panel having a superparamagnetic particle type can be rapidly isolated or separated from a sample using a magnet. Furthermore, multiple spatially isolated samples can be processed in parallel. Thus, the method disclosed herein provides simultaneous isolation or separation of a particle panel from unbound proteins in multiple spatially isolated panels by using a magnet. For example, a particle panel can be incubated with multiple spatially isolated samples, each of which is located in a well of a well plate (e.g., a 96-well plate). After incubation, the particle panel in each well of the well plate can be separated from the unbound proteins present in the spatially isolated samples by placing the entire plate on a magnet. This simultaneously pulls down the superparamagnetic particles in the particle panel. The supernatant in each well can be removed to remove the unbound proteins. These steps (incubating, pulling down using a magnet) can be repeated to effectively wash the particles and, consequently, remove any residual background unbound proteins that may be present in the sample. This is just one example, but those skilled in the art can imagine a great many other scenarios in which superparamagnetic particles are simultaneously and rapidly isolated from one or more spatially isolated samples.

[0158] In some embodiments, the panels of this disclosure provide identification and measurement of specific proteins in a biological sample by processing proteome data through the digestion of corona formed on the surface of particles. Examples of proteins that can be identified and measured include high-abundance proteins, moderate-abundance proteins, and low-abundance proteins. Low-abundance proteins may be present in the sample at concentrations of about 10 ng / mL or less. High-abundance proteins may be present in the sample at concentrations of about 10 μg / mL or higher. Moderate-abundance proteins may be present in the sample at concentrations between about 10 ng / mL and about 10 μg / mL. Examples of high-abundance proteins include albumin, IgG, and the top 14 proteins in terms of abundance, which contribute 95% of the mass in plasma. In addition, any protein that can be purified using conventional removal columns can be directly detected in a sample using the particle panels disclosed herein. Examples of proteins include Keshishian et al. (Mol Cell Proteomics. 2015 Sep;14(9):2375-93. doi: 10.1074 / mcp.M114.046813.Epub 2015 Feb 27.) and Farr et al. (J Proteome It could be any protein listed in a public database, such as Res. 2014 Jan 3;13(1):60-75. doi: 10.1021 / pr4010037.Epub 2013 Dec 6.), or Pernemalm et al. (Expert Rev Proteomics. 2014 Aug;11(4):431-48. doi: 10.1586 / 14789450.2014.901157. Epub 2014 Mar 24.).

[0159] In some embodiments, examples of proteins that can be measured and identified using the particle panel disclosed herein include albumin, IgG, lysozyme, CEA, HER-2 / neu, bladder tumor antigen, thyroglobulin, alpha-fetoprotein, PSA, CA125, CA19.9, CA15.3, leptin, prolactin, osteopontin, IGF-II, CD98, phascin, sPigR, 14-3-3 eta, troponin I, type B natriuretic peptide, BRCA1, c-Myc, IL-6, fibrinogen, EGFR, gastrin, PH, G-CSF, desmin, NSE, FSH, VEGF, P21, PCNA, calcitonin, PR, CA125, LH, somatostatin, S100, insulin, alpha-prolactin, Examples include ACTH, Bcl-2, ER-alpha, Ki-67, p53, cathepsin D, beta-catenin, VWF, CD15, k-ras, caspase-3, EPN, CD10, FAS, BRCA2, CD30L, CD30, CGA, CRP, prothrombin, CD44, APEX, transferrin, GM-CSF, E-cadherin, IL-2, Bax, IFN-gamma, beta-2-MG, TNF-alpha, c-erbB-2, trypsin, cyclin D1, MG B, XBP-1, HG-1, YKL-40, S-gamma, NESP-55, netrin-1, geminin, GADD45A, CDK-6, CCL21, BrMS1, 17-beta-HDI, PDGFRA, Pcaf, CCL5, MMP3, claudin-4, and claudin-3. In some embodiments, other examples of proteins that can be measured and identified using the particle panel disclosed herein are any protein or group of proteins listed in the open targets database for a specific disease indication of interest (e.g., prostate cancer, lung cancer, or Alzheimer's disease). Analysis method

[0160] Proteomic data of a sample can be identified, measured, and quantified using several different analytical techniques. For example, proteomic data can be analyzed using SDS-PAGE or any gel-based separation technique. Peptides and proteins can also be identified, measured, and quantified using immunoassays such as ELISA. Alternatively, proteomic data can be identified, measured, and quantified using mass spectrometry, high-performance liquid chromatography, LC-MS / MS, Edman degradation, immunoaffinity techniques, methods disclosed in EP3548652, WO2019083856, WO2019133892 (each of which is incorporated herein by reference in whole), and other protein separation techniques. Computer control system

[0161] This disclosure provides a computer-controlled system programmed to perform the methods of this disclosure. This determination, analysis, and statistical classification can be performed by methods known in the art, including, but not limited to, a wide range of supervised and unsupervised data analysis and clustering techniques, 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 clustering, and hidden Markov models. The computer system can perform various aspects of analyzing the protein sets or protein coronas of this disclosure, such as comparing / analyzing the biomolecular coronas of several samples to determine, using statistical significance, what patterns are common among individual biomolecular coronas, and thereby determining protein sets associated with a biological state. Using the computer system, classifiers can be developed to detect and distinguish different proteins or protein coronas (e.g., those specific to the composition of the protein corona). Using data collected from the sensor arrays disclosed herein, machine learning algorithms can be trained, in particular, algorithms that receive array measurements from patients and output specific biomolecular corona compositions from each patient. Before training the algorithms, noise in the raw data from the arrays can be removed to reduce the variability of individual variables.

[0162] Machine learning can be generalized as the ability of a learning machine to accurately perform new, previously unseen examples / tasks after experiencing a training dataset. Machine learning may include the following concepts and methods: Supervised learning concepts such as AODE; artificial neural networks, e.g., backpropagation, autoencoders, Boltzmann machines, restricted Boltzmann machines, and spiking neural networks; Bayesian statistics, e.g., Bayesian networks and Bayesian knowledge bases; case-based inference; Gaussian process regression; gene expression programming; group data processing methods (GMDH); inductive logic programming; case-based learning; lazy learning; learning automata; learning vector quantization; logistic model trees; minimum message length (decision trees, decision graphs, etc.), e.g., nearest neighbor algorithms and analogical modeling; probabilistic and approximately correct learning (PAC) learning; ripple-down rules, knowledge acquisition methodologies. 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 linear discriminant, linear regression, logistic regression, multinomial logistic regression, naive Bayesian classifier, perceptron, support vector machine; quadratic classifiers; k-nearest neighbors; boosting; decision trees, e.g., C4.5, random forest, ID3, CART, SLIQ, SPRINT; Bayesian networks, e.g., naive Bayes; and hidden Markov models may also be included. Unsupervised learning concepts may include expectation maximization algorithms; vector quantization; generative phase maps; information bottleneck methods; artificial neural networks, e.g., self-organizing maps; correlation rule learning, e.g., a priori algorithms, Eclat algorithms, and FPgrowth algorithms; hierarchical clustering, e.g., shortest distance clustering, and conceptual clustering; cluster analysis, e.g., K-means algorithms, fuzzy clustering, DBSCAN, and OPTICS algorithms; and outlier detection, e.g., local outlier factorization.Semi-supervised learning concepts may include generative models; low-density separation; graph-based methods; and co-training. Reinforcement learning concepts may include temporal difference learning; Q-learning; learning automata; and SARSA. Deep learning concepts may include deep belief networks; deep Boltzmann machines; deep convolutional neural networks; deep recurrent neural networks; and hierarchical temporal memory. A computer system can be configured 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, or "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 devices (e.g., hard disks); communication interfaces for communicating with one or more other systems (e.g., network adapters); and peripheral devices which may include caches, other memory, data storage, and / or electronic display adapters. The memory, storage devices, interfaces, and peripherals are connected to the processor via a communication bus (solid line), such as a motherboard. A storage device can be a data storage device for storing data. A server is operationally coupled to a computer network ("Network") by utilizing a communication interface. The Network can be the Internet, an intranet and / or extranet, an intranet and / or extranet connected to the Internet, a telecommunications network, or a data network. In some cases, the Network can utilize a server to implement a peer-to-peer network, which may allow devices coupled to the server to operate as either clients or servers.

[0163] The storage device may store files, such as subject reports, and / or communications relating to data about individuals, or any aspect of data relating to this disclosure.

[0164] A computer server can communicate with one or more remote computer systems via a network. These one or more remote computer systems may be, for example, personal computers, laptops, tablets, telephones, smartphones, or personal digital assistants (PDAs).

[0165] In some applications, a computer system includes a single server. In other situations, the system includes multiple servers connected to each other via an intranet, extranet, and / or the internet.

[0166] The server can be configured to store measurement data or databases, patient information from subjects such as medical history, family history, demographic data, and / or other clinical or personal information about potential relevance to a particular application, as provided herein. Such information can be stored in a storage device or server, and such data can be transmitted over a network.

[0167] The methods described herein can be executed by machine (or computer processor) executable code (or software) stored in an electronic storage location of the server, such as memory or an electronic storage device. During use, the code may be executed by the processor. In some cases, the code may be read from the storage device and stored in memory to facilitate access by the processor. In some situations, an electronic storage device may be omitted, and the machine-executable instructions are stored in memory. Alternatively, the code may be executed on a second computer system.

[0168] Embodiments of systems and methods provided herein, such as servers, can be implemented by programming. Various embodiments of the technology can generally be considered as “products” or “manufactured articles” in the form of machine (or processor) executable code and / or related data contained on or implemented on some kind of machine-readable medium. Machine-executable code can be stored in electronic storage devices, such as memory (e.g., read-only memory, random-access memory, flash memory) or hard disks. “Storage” media can include any or all of the tangible memory of a computer, processor or the like, or related modules thereof, such as various semiconductor memories, tape drives, disk drives and the like, which can provide non-transient storage at any given time for software programming. All or part of the software can sometimes be connected via the Internet or various other telecommunication networks. Such communication can enable, for example, the loading of software from one computer or processor to another computer or processor, for example, from a management server or host computer to an application server computer platform. Therefore, other types of media that can carry software elements include optical waves, radio waves, and electromagnetic waves, such as those used across physical interfaces between local devices, via wired and optical telephone network lines, and through various air links. Physical elements that carry such waves, such as wired or wireless links, optical links, or similar, can also be considered media that carry software. As used herein, unless limited to non-transient, tangible “storage” media, terms such as computer or machine-readable media can refer to any medium involved in giving instructions to a processor for execution.

[0169] The computer systems described herein may include computer executable code for performing either the algorithms described herein or methods based on those algorithms. In some applications, the algorithms described herein will use a storage device consisting of at least one database.

[0170] Data relating to this disclosure may be transmitted over a network or connection for reception and / or re-examination by recipients. Recipients may be, but are not limited to, the subjects to whom the report relates; or their caregivers, e.g., healthcare providers, administrators, other medical professionals, or other caregivers; or individuals or companies that perform and / or order the analysis. Recipients may also be local or remote systems (e.g., servers or other systems in a “cloud computing” architecture) for sorting such reports. In one embodiment, computer-readable media include media suitable for transmitting the results of analysis of biological samples using the methods described herein.

[0171] Embodiments of the systems and methods provided herein can be implemented by programming. Various embodiments of this technique can generally be considered as “products” or “manufactured articles” in the form of machine (or processor) executable code and / or related data contained on or implemented on some kind of machine-readable medium. Machine-executable code can be stored in electronic storage devices, e.g., memory (e.g., read-only memory, random-access memory, flash memory) or hard disks. “Storage” media can include any or all of the tangible memory of a computer, processor or the like, or related modules thereof, e.g., various semiconductor memories, tape drives, disk drives and the like, which can provide non-transient storage at any given time for software programming. All or part of the software can sometimes be connected via the Internet or various other telecommunication networks. Such communication can enable, for example, the loading of software from one computer or processor to another computer or processor, e.g., from a management server or host computer to an application server computer platform. Therefore, other types of media that can carry software elements include optical waves, radio waves, and electromagnetic waves, such as those used across physical interfaces between local devices, via wired and optical telephone network lines, and through various air links. Physical elements that carry such waves, such as wired or wireless links, optical links, or similar, can also be considered media that carry software. As used herein, unless limited to non-transient, tangible “storage” media, terms such as computer or machine-readable media refer to any medium involved in giving instructions to a processor for execution.

[0172] Therefore, machine-readable media such as 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 any or similar storage devices in any computer, such as optical or magnetic disks, for example, those that may be used to implement databases, etc., as shown in the drawings. Volatile storage media include dynamic memory, for example, the main memory of such a computer platform. Tangible transmission media include coaxial cables; copper wires and optical fibers, for example, wires including buses in computer systems. Carrier media can take the form of electrical or electromagnetic signals, or sound waves or light waves, such as those generated during radio frequency (RF) and infrared (IR) data communications. Therefore, 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-transmitted data or instructions, cables or links that transport such carriers, or any other media from which a computer can read programming code and / or data. Many of these forms of computer-readable media can be involved in transporting one or more sequences of one or more instructions to a processor for execution. Classification of protein-coronaviruses using machine learning

[0173] A method for determining a set of proteins associated with a disease or disorder and / or medical condition involves the analysis of coronas in at least two samples. This determination, analysis, or statistical classification is performed by methods known in the art, including, but not limited to, a wide range of supervised and unsupervised data analysis, machine learning, deep learning, and clustering techniques, such as hierarchical cluster analysis (HCA), principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), random forests, logistic regression, decision trees, support vector machines (SVM), k-nearest neighbors, naive Bayes, linear regression, polynomial regression, SVM for regression, K-means clustering, and hidden Markov models. In other words, the proteins in the coronas of each sample are compared / analyzed to determine a set of proteins associated with a disease or disorder or medical condition by determining, using statistical significance, what patterns are common among the individual coronas.

[0174] Generally, machine learning algorithms are used to build models that accurately assign class labels to examples based on input features that describe the examples. In some cases, it may be advantageous to utilize machine learning and / or deep learning techniques in the methods described herein. For example, machine learning can be used to associate protein corona with various medical conditions (e.g., being disease-free, a precursor to disease, being in the early or late stages of disease). For example, in some cases, one or more machine learning algorithms are used in connection with the methods of the present invention to analyze the detected and obtained data for each set of protein corona and proteins derived therefrom. For example, in one embodiment, machine learning can be coupled with the sensor array described herein to distinguish between types of cancer, as well as to determine whether a subject is in the precancerous stage, has cancer, or has cancer but has not yet developed it. Numbered Embodiments

[0175] The following embodiments describe non-limiting permutations of combinations of features disclosed herein. Other permutations of combinations of features are also possible. In particular, each of these numbered embodiments is considered to be dependent on or related to any preceding or succeeding numbered embodiment, regardless of the order in which they are listed. 1. A method for identifying proteins in a sample, comprising the steps of incubating a panel containing a plurality of particle types with the sample to form a plurality of protein coronas, digesting the plurality of protein coronas to generate proteome data, and identifying proteins in the sample by quantifying the proteome data. 2. The method of Embodiment 1, wherein the sample is from a subject. 3. The method of Embodiment 2, further comprising the steps of determining the protein profile of the sample from the step of identification and relating the protein profile to the biological state of the subject. 4. The method of Embodiment 1, further comprising the steps of generating proteome data by digesting a plurality of protein coronas, determining the protein profiles of the plurality of protein coronas, and relating the protein profile to the biological state, wherein the panel contains at least two different particle types. 5. The method of Embodiment 4, wherein association is performed by a trained classifier. 6. Any one of Embodiments 1 to 5, wherein the panel comprises at least 3 different particle types, at least 4 different particle types, at least 5 different particle types, at least 6 different particle types, at least 7 different particle types, at least 8 different particle types, at least 9 different particle types, at least 10 different particle types, at least 11 different particle types, at least 12 different particle types, at least 13 different particle types, at least 14 different particle types, at least 15 different particles, or at least 20 different particle types. 7. Any one of Embodiments 1 to 6, wherein the panel comprises at least 4 different particle types. 8. Any one of Embodiments 1 to 7, wherein at least one particle type of the panel has different physical characteristics from a second particle type of the panel. 9. The method of Embodiment 8, wherein the physical characteristics are size, polydispersity index, surface charge, or morphology.10. Any one of Embodiments 1 to 9, wherein the size of at least one particle type among the multiple particle types in the panel is 10 nm to 500 nm. 11. Any one of Embodiments 1 to 10, wherein the polydispersity index of at least one particle type among the multiple particle types in the panel is 0.01 to 0.25. 12. Any one of Embodiments 1 to 11, wherein the morphology of at least one particle type among the multiple particle types includes spherical, colloidal, square, rod, wire, conical, pyramidal, or elliptical. 13. Any one of Embodiments 1 to 12, wherein the surface charge of at least one particle type among the multiple particle types includes a positive surface charge. 14. Any one of Embodiments 1 to 12, wherein the surface charge of at least one particle type among the multiple particle types includes a negative surface charge. 15. Any one of Embodiments 1 to 12, wherein the surface charge of at least one particle type among the multiple particle types includes a neutral surface charge. 16. Any one of Embodiments 1 to 15, wherein at least one of the multiple particle types has different chemical characteristics from the second particle type of the panel. 17. The method of Embodiment 16, wherein the chemical characteristic is a surface functional chemical group. 18. The method of Embodiment 17, wherein the functional chemical group is an amine or a carboxylate. 19. Any one of Embodiments 1 to 18, wherein at least one of the multiple particle types is made of a material comprising a polymer, lipid, or metal, silica, protein, nucleic acid, small molecule or large molecule. 20. The method of Embodiment 19, wherein the polymer comprises polyethylene, polycarbonate, polyanhydride, polyhydroxy acid, polypropyl fumarate, polycaprolactone, polyamide, polyacetal, polyether, polyester, poly(orthoester), polycyanoacrylate, polyvinyl alcohol, polyurethane, polyphosphazene, polyacrylate, polymethacrylate, polycyanoacrylate, polyurea, polystyrene, or polyamine, polyalkylene glycol (e.g., polyethylene glycol (PEG)), polyester (e.g., poly(lactide-co-glycolide) (PLGA), polylactic acid, or polycaprolactone), polystyrene, or a copolymer of two or more polymers.21. Lipids include dioleoylphosphatidylglycerol (DOPG), diacylphosphatidylcholine, diacylphosphatidylethanolamine, ceramide, sphingomyelin, cephalin, cholesterol, cerebroside and diacylglycerol, dioleoylphosphatidylcholine (DOPC), dimyristoylphosphatidylcholine (DMPC), and dioleoylphosphatidylserine (DOPS), phosphatidylglycerol, cardiolipin, diacylphosphatidylserine, diacylphosphatidic acid, N-dodecanoylphosphatidylethanolamine, N-succinylphosphatidylethanolamine, N-glutarylphosphatidylethanolamine, lysylphosphatidylglycerol, palmitoyloleoylphosphatidylglycerol (POPG), lecithin, lysolecithin, phosphatidylethanolamine, lysophosphatidic acid Dylethanolamine, dioleoylphosphatidylethanolamine (DOPE), dipalmitoylphosphatidylethanolamine (DPPE), dimyristoylphosphoethanolamine (DMPE), distearoylphosphatidylethanolamine (DSPE), palmitoyloleoylphosphatidylethanolamine (POPE), palmitoyloleoylphosphatidylcholine (POPC), egg phosphatidylcholine (EPC), distearoylphosphatidylcholine (DSPC), dioleoylphosphatidylcholine (DOPC), dipalmitoylphosphatidylcholine (DPPC), dioleoylphosphatidylglycerol (DOPG), dipalmitoylphosphatidylglycerol (DPPG), palmitoyloleoylphosphatidylglycerol (POPG), 16-O-monomethylPE, 16-O-dimethylPE, 18-1-trans The method of Embodiment 19, comprising PE, palmitoyloleoyl-phosphatidylethanolamine (POPE), 1-stearoyl-2-oleoyl-phosphatidylethanolamine (SOPE), phosphatidylserine, phosphatidylinositol, sphingomyelin, cephalin, cardiolipin, phosphatidic acid, cerebroside, dicetyl phosphate, or cholesterol.22. The method of Embodiment 19, wherein the metal comprises gold, silver, copper, nickel, cobalt, palladium, platinum, iridium, osmium, rhodium, ruthenium, rhenium, vanadium, chromium, manganese, niobium, molybdenum, tungsten, tantalum, iron, or cadmium, titanium, or gold. 23. Any one of Embodiments 1 to 22, wherein at least one of a plurality of particle types is surface-functionalized with polyethylene, polycarbonate, polyanhydride, polyhydroxy acid, polypropyl fumarate, polycaprolactone, polyamide, polyacetal, polyether, polyester, poly(orthoester), polycyanoacrylate, polyvinyl alcohol, polyurethane, polyphosphazene, polyacrylate, polymethacrylate, polycyanoacrylate, polyurea, polystyrene, or polyamine, polyalkylene glycol (e.g., polyethylene glycol (PEG)), polyester (e.g., poly(lactide-co-glycolide) (PLGA), polylactic acid, or polycaprolactone), polystyrene, or copolymers of two or more polymers, or polymers containing polyethylene glycol. 24. Any one of Embodiments 3 to 23 for relating a protein profile to a biological state with at least 70% accuracy, at least 75% accuracy, at least 80% accuracy, at least 85% accuracy, at least 90% accuracy, at least 92% accuracy, at least 95% accuracy, at least 96% accuracy, at least 97% accuracy, at least 98% accuracy, at least 99% accuracy, or 100% accuracy. 25. Any one of Embodiments 3 to 24 for relating a protein profile to a biological state with at least 70% sensitivity, at least 75% sensitivity, at least 80% sensitivity, at least 85% sensitivity, at least 90% sensitivity, at least 92% sensitivity, at least 95% sensitivity, at least 96% sensitivity, at least 97% sensitivity, at least 98% sensitivity, at least 99% sensitivity, or 100% sensitivity.26. Any one of Embodiments 3 to 25, relating a protein profile to a biological state with a specificity 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%. 27. Any method from Embodiments 1 to 26 for identifying at least 100 unique proteins, at least 200 unique proteins, at least 300 unique proteins, at least 400 unique proteins, at least 500 unique proteins, at least 600 unique proteins, at least 700 unique proteins, at least 800 unique proteins, at least 900 unique proteins, at least 1000 unique proteins, at least 1100 unique proteins, at least 1200 unique proteins, at least 1300 unique proteins, at least 1400 unique proteins, at least 1500 unique proteins, at least 1600 unique proteins, at least 1700 unique proteins, at least 1800 unique proteins, at least 1900 unique proteins, or at least 2000 unique proteins. 28. Any method from Embodiments 1 to 27, wherein at least one of a plurality of particle types comprises superparamagnetic iron oxide nanoparticles. 29. Any method from Embodiments 1 to 28, wherein the sample is a body fluid. 30. The method of Embodiment 29, wherein the body fluid comprises plasma, serum, CSF, urine, tears, or saliva. 31. A method for selecting a panel for protein corona analysis, comprising the step of selecting a plurality of particle types having at least three different physicochemical properties. 32. The method of Embodiment 31, wherein the different physicochemical properties are selected from the group consisting of surface charge, surface chemical structure, size, and morphology. 33. The method of Embodiment 32, wherein the different physicochemical properties include surface charge. 34. A composition comprising a panel of particles, wherein the panel comprises a plurality of particle types, and the plurality of particle types have at least three different physicochemical properties.35. A composition of Embodiment 35, wherein the different physicochemical properties are selected from the group consisting of surface charge, surface chemical structure, size, and morphology. 36. A composition of Embodiment 36, wherein the different physicochemical properties include surface charge. 37. A system comprising one panel from any one of Embodiments 1 to 34. 38. A system comprising a panel, wherein the panel comprises a plurality of particle types. 39. A system of Embodiment 38, wherein the plurality of particle types have at least three different physicochemical properties. 40. Embodiment 3, wherein the panel comprises 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 11, or at least 12 different particle types. 8 systems. 41. The system of Embodiment 38, in which multiple particle types can adsorb multiple proteins from a sample to form multiple protein coronas. 42. The system of Embodiment 41, in which the multiple protein coronas are digested to determine the protein profile. 43. The system of Embodiment 42, in which the protein profile is associated with a biological state using a trained classifier. [Examples]

[0176] The following embodiments are included to further illustrate some aspects of the present disclosure, but should not be used to limit the scope of the invention. (Example 1) Synthesis of SPNMP or superparamagnetic iron oxide nanoparticles (SPION)

[0177] SPMNP or SPION is described in the literature (Angew. Chem. Int. Ed. 2009, 48, 5875). - By following the method in 5879 & Langmuir 2012, 28, 3271 - 3278) It was synthesized by a solvothermal reaction at 200°C involving the reduction of FeCl3 with ethylene glycol (EG) in the presence of sodium acetate (NaOAc) as an alkali source and trisodium citrate (Na3Cit) as an electrostatic stabilizer. Excess EG acted as both a solvent and a reducing agent.

[0178] Starting materials: Iron(III) chloride hexahydrate (FeCl3·6H2O), MW270.30, CAS No. 10025-77-1; sodium acetate (NaOAc), MW82.03, CAS No. 127-09-3; trisodium citrate dihydrate (Na3Cit·2H2O), MW294.10, CAS No. 6132-04-3; ethylene glycol (EG), MW62.07, CAS No. 107-21-1.

[0179] Procedure for Fe3O4 nanoparticles by solvothermal reaction:

[0180] (1) Typically, FeCl3 (0.65 g, 4.0 mmol) and sodium citrate (0.20 g, 0.68 mmol) were first dissolved in EG (20 mL), and then sodium acetate (1.20 g, 14.6 mmol) was added while stirring. The mixture was vigorously stirred for 30 minutes at 160°C in a Teflon®-lined stainless steel autoclave (capacity 50 mL), and then sealed. (2) The autoclave was heated to 200°C and maintained for 12 hours, then allowed to stand and cool to room temperature. (3) The black product was isolated by magnet and washed >5 times with DI water. (4) The final Fe3O4 nanoparticle product was dried under vacuum or freeze-dried at 60°C for 12 hours to obtain a black powder. Fe3O4@SiO2 cores / shells were prepared by a modified Stöber method following the methods described in the literature (J. Am. Chem. Soc. 2008, 130, 28 - 29 & J. Mater. Chem. B, 2013, 1, 4684 - 4691).

[0181] Starting materials: Fe3O4 nanoparticles synthesized by solvothermal reaction; tetraethyl orthosilicate (TEOS), MW208.33, CAS number 78-10-4; 25% ammonia solution; cetrimonium bromide (CTAB), MW364.45, CAS number 57-09-0; (3-aminopropyl)triethoxysilane (APTES), MW221.37, CAS number 919-30-2.

[0182] Procedure for Fe3O4@SiO2 core / shell nanoparticles:

[0183] (1) Superparamagnetic Fe3O4 NPs were synthesized according to a previously reported solvothermal reaction (Angew. Chem. Int. Ed. 2009, 48, 5875 - 5879 & Langmuir 2012, 28, 3271 - 3278). Then, using the obtained Fe3O4 nanoparticles, highly aminated superparamagnetic mesoporous composite nanoparticles were prepared by a two-step coating procedure. (2) Briefly, 0.08 g of Fe3O4 nanoparticles were uniformly dispersed in a mixture of ethanol (50 mL), DI water (1 mL), and concentrated aqueous ammonia (1.7 mL, 25 wt%), and then TEOS (140 μL) was added. After stirring at 40°C for 6 hours, amorphous silica-coated superparamagnetic nanoparticles (denoted as Fe3O4@SiO2) were obtained and washed five times with water. (3) Next, Fe3O4@SiO2 nanoparticles were coated with a highly aminated mesoporous silica shell by a base-catalyzed sol-gel silica reaction using CTAB as a template. Typically, the above-prepared Fe3O4@SiO2 nanoparticles (5 mg) were dispersed in a mixed solution containing CTAB (0.08 g), ethyl acetate (0.7 mL), DI water (113 mL), and concentrated ammonia (2.42 mL, 25 wt%). TEOS (0.18 mL) and APTES (0.22 mL) were added to the dispersion using a stirring speed of 300 rpm. After a reaction at room temperature for 3 hours, the product was collected with a magnet and repeatedly washed with water and ethanol, respectively.

[0184] (4) To remove the pore-forming template (CTAB), the synthesized material was transferred to ethanol (60 mL) at 60°C for 3 hours with continuous stirring. The surfactant extraction step was repeated twice to ensure the removal of the CTAB. The template removal product was washed twice with ethanol to obtain sandwich-structured, highly aminated superparamagnetic mesoporous composite nanoparticles (Fe3O4@SiO2@mSiO2-NH2).

[0185] Fe3O4@polymer composite nanoparticles were synthesized by seed emulsion polymerization in the absence of surfactants (Langmuir 2012, 28, 3271 - 3278).

[0186] Fe3O4 nanoparticles synthesized by solvothermal reaction.

[0187] Starting materials: Fe3O4 nanoparticles synthesized by solvothermal reaction; tetraethyl orthosilicate (TEOS), MW208.33, CAS No. 78-10-4; 3-(trimethoxysilyl)propyl methacrylate (MPS), MW248.35, CAS No. 2530-85-0; 25% ammonia solution; divinylbenzene (DVB), MW130.19, CAS No. 1321-74-0; styrene (St), MW104.15, CAS No. 100-42-5; methacrylic acid (MAA), MW86.09, CAS No. 79-41-4; ammonium persulfate (APS), MW228.20, CAS No. 7727-54-0.

[0188] Procedure for Fe3O4 polymer core / shell nanoparticles:

[0189] (1) SPION was synthesized according to a previously reported solvothermal reaction (Angew. Chem. Int. Ed. 2009, 48, 5875 - 5879 & Langmuir 2012, 28, 3271 - 3278). (2) Fe3O4@MPS was prepared by a modified Stöber method. Typically, 1 g of Fe3O4 nanoparticles were uniformly dispersed in a mixture of ethanol (50 mL), DI water (2 mL), and concentrated aqueous ammonia (2 mL, 25 wt%), and then TEOS (200 μL) and MPS (2 mL) were added. After stirring at 70 °C for 24 hours, superparamagnetic NPs coated with MPS were obtained, washed five times with water, lyophilized to a dark brown powder, and stored at -20 °C. (3) Fe3O4@Polymer nanoparticles were synthesized by seed emulsion polymerization in the absence of surfactants. Typically, 100 mg of Fe3O4@MPS was uniformly dispersed in 125 mL of DI water. After bubbling with N2 for 30 minutes, 2 mL of St, 0.2 mL of DVB, and 0.4 mL of MAA were added to the Fe3O4@MPS suspension. After adding 0.2 g of NaOH and 5 mL of 40 mg of APS aqueous solution, the resulting mixture was heated overnight at 75°C. (4) After cooling, Fe3O4@P(St-co-MAA) was obtained, washed five times with water, and freeze-dried to obtain a dark brown powder. (Example 2) Construction of particle-type panels

[0190] This example describes the construction of a particle type panel. A particle library containing approximately 170 total particle types was constructed. Biomolecular coronas were generated for each particle type by incubating each particle with a biological sample. Proteome data from the protein coronas were analyzed for each particle type, including qualitative analysis based on electrophoresis, as well as quantitative analysis based on mass spectrometry data and heatmaps. Panels were constructed by selecting specific particle types based on particle and corona characteristics. Particle types for the particle panel were selected based on the breadth of coverage they had for proteins in the plasma sample. Figure 5 shows the process for generating proteome data and the process for panel selection. As shown in Figure 6, particle characteristics include size / geometry, charge, surface functional groups, and magnetic force, in addition to other characteristics. Each of these characteristics can be assayed by various tests during the complete characterization of the particle type before adding it to the panel. As shown in Figure 7, the size distribution of two particle types: SP-002 (phenol-formaldehyde coated particles) and SP-010 (carboxylate, PAA coated particles) was characterized using dynamic light scattering. As shown in Figure 8, the size and morphology of two particle types, including SP-002 (phenol-formaldehyde coated particles) (Figure 8A) and SP-339 (polystyrene carboxyl particles) (Figure 8B), were characterized using TEM. As shown in Figure 9, the chemical groups present on the surface of various particle types, including SP-333 (carboxylate), SP-339 (polystyrene carboxylate), SP-356 (silica amino), SP-374 (silica silanol), HX-20 (silica coated, amine), and HX-74 (PDMPAPMA coated (dimethylamine)), were analyzed using XPS. (Example 3) Synthesis and Characterization of Iron Oxide NPs with Distinct Surface Chemical Structures

[0191] This example describes the synthesis and characterization of iron oxide NPs with distinct surface chemical structures. To address the need for robust particles that can withstand repeated centrifugation or membrane filtration to separate particulate protein coronas from free plasma proteins and to wash away loosely bound proteins from the particles, but can be easily prepared without requiring such processes, superparamagnetic iron oxide NPs (SPION) were developed for protein corona formation (Figure 23, top). The iron oxide particle core facilitated rapid separation of particles from plasma solution in <30 seconds using a magnet (Figure 4). This significantly reduced the time required for extraction of particulate protein coronas for analysis by LC-MS / MS. Furthermore, SPION was robustly modified with various surface chemistrys that facilitated the generation of distinctly different patterns of protein coronas for broader proteome matching.

[0192] Three SPION particles with different surface functionalizations (SP-003, SP-007, and SP-011) were synthesized (Figure 28). SP-003 was coated with a thin layer of silica using tetraethyl orthosilicate (TEOS) via a modified Stöber process. For the synthesis of SPION (SP-007) coated with poly(dimethylaminopropyl methacrylamide) (PDMAPMA) and SPION (SP-011) coated with poly(ethylene glycol) (PEG), iron oxide particle cores were first modified with vinyl groups using TEOS and 3-(trimethoxysilyl)propyl methacrylate via a modified Stöber process. Next, the vinyl-functionalized SPION particles were surface-modified with N-[3-(dimethylamino)propyl]methacrylamide and poly(ethylene glycol) methyl ether methacrylate, respectively, to prepare SP-007 and SP-011.

[0193] Three SPIONs were characterized using various techniques, including scanning electron microscopy (SEM), dynamic light scattering (DLS), transmission electron microscopy (TEM), high-resolution TEM (HRTEM), and X-ray photoelectron spectroscopy (XPS), to evaluate their size, morphology, and surface properties (Figure 23). DLS measurements showed that SP-003, SP-007, and SP-011 had average sizes of approximately 233 nm, 283 nm, and 238 nm, respectively. This was consistent with SEM measurements, which showed that all three SPIONs had spherical and hemispherical morphologies with sizes in the 200 nm to 300 nm range. The surface charge of the SPIONs was evaluated by zeta potential (ζ) analysis, which showed ζ potential values ​​of -36.9 mV, +25.8 mV, and -0.4 mV for SP-003, SP-007, and SP-011 at pH 7.4 (Tables 2-4). [Table 2] [Table 3] [Table 4]

[0194] This demonstrated that SP-003, SP-007, and SP-011 have negative, positive, and neutral surfaces, corresponding to the charge of the coating functional group used to modify the surface of each particle, as shown in the schematic diagram in Figure 23. The thickness of the coating was evaluated using HRTEM. For SP-003, a complete amorphous shell was observed around the iron oxide core with a thickness of more than 10 nm (Figure 23, top fifth column). For SP-007 and SP-011, relatively thin (<10 nm) amorphous features were observed on the surface of the particles (arrows in Figure 23, middle and bottom fifth columns). In addition, XPS was performed for surface analysis, and the XPS, along with the HRTEM images, confirmed the successful coating of the particles with each functional group. (Example 4) Protein detection in panels and association of protein profiles with cancer

[0195] This example describes the detection of proteins in a panel and the association of protein profiles with cancer. The particle panel includes three different cross-reactive liposomes with varying surface charges: anionic (DOPG(1,2-dioleoyl-sn-glycero-3-phospho-(1'-rac-glycerol))), cationic (DOTAP(1,2-dioleoyl-3-trimethylammoniumpropane)-DOPE(dioleoylphosphatidylethanolamine)), and neutral (dioleoylphosphatidylcholine with cholesterol (DOPC))).

[0196] Figure 17A shows a schematic diagram of the process of this application, including sample collection from healthy and cancer patients, isolation of plasma from the samples, incubation with uncoated liposomes for protein corona formation, and enrichment of selected plasma proteins. Protein corona formation may vary based on the physicochemical properties of the particles. Figure 17B shows the corona analysis signals for a three-particle panel. Plasma was collected from 45 subjects (8 from each of five cancers, including glioblastoma, lung cancer, meningioma, myeloma, and pancreatic cancer, and 5 healthy controls). Corona analysis was performed for each particle in the three-particle panel. A random forest model was constructed in each of 1000 rounds of cross-validation. There is strong evidence of robust corona analysis signals. The initial preliminary analysis was performed by PCA. Figures 18A and 18B show that early cancers can be isolated up to 8 years before symptom onset. The Golestan cohort enrolled 50,000 healthy subjects between 2004 and 2008. As shown in Figure 18A, stored plasma from registration was tested. Eight years after registration, approximately 1000 patients had developed cancer. Figure 18B shows the classification of stored plasma. Corona analysis of stored plasma from the registration date accurately classified cancer in 15 out of 15 subjects tested (5 patients for each of the three cancers). Figure 19 shows the robustner classification of the five cancers with 95% overall accuracy using three-particle corona analysis. The data demonstrate that adding particle type diversity improves performance. Three different liposomes with negative, neutral, and positive net charges (pH 7.4) on their surfaces were used. (Example 5) Rapid and in-depth proteomic analysis using a corona analysis workflow.

[0197] This example describes rapid and in-depth proteomic analysis using a corona analysis workflow. To evaluate the multi-particle protein corona analysis platform for plasma proteome analysis (Figure 22B), SPION was tested using combined pooled plasma samples from eight colorectal cancer (CRC) patients. Each of these three particle types was first incubated with the plasma sample at approximately 37°C for about 1 hour for protein corona formation, followed by magnetic purification of the particles from unbound proteins (3 cycles of 6 minutes each). The proteins bound to the particles were then lysed, digested, purified, and eluted, a process that took approximately 2–4 hours in total, followed by analysis by MS. Notably, this preparation workflow required only about 4–6 hours in total for one batch of 96 corona samples.

[0198] After MS analysis and data processing, the resulting MS2 peptide-spectral match (PSM) was used to identify proteins present in each particle-type corona. In parallel, direct detection of proteins from live plasma samples, without particle corona formation, was also performed. The depth and extent of coverage by particle corona or plasma were investigated by comparing the identified proteins from the samples with a compiled database of MS-measured or estimated plasma protein concentrations, and plotting the measured proteins against previously reported database values ​​(Figure 24). First, 1,255 proteins from a database covering nearly 11 orders of magnitude were plotted sequentially from the most abundant to the least abundant. For each of the experimentally evaluated samples (live plasma vs. SP-003 / SP-007 / SP-011 particle coronas), proteins matching the database were similarly plotted. As can be seen in Figure 24, when defined by the concentration range for database-matched proteins, the dynamic range of the measured plasma proteome was twice as large for particle corona (e.g., 40 mg / mL to 0.54 ng / mL for SP-007) as for live plasma (40 mg / mL to 1.2 ng / mL), and the number of low-abundance proteins present below 100 ng / mL increased tenfold (842 for particles and 84 for live plasma). Only 12 proteins in the database were annotated at concentrations lower than the least abundant protein detected on particles. In addition, the total number of unique proteins for each particle-type corona (approximately 1,000) was greater (>2 times) than that observed for live plasma (<500), as clearly demonstrated in Table 5. [Table 5]

[0199] In addition, a comparison with literature MS compilation revealed that a larger proportion of previously unobserved proteins were found in particles (61-64%) compared to live plasma (45%). In other words, more proteins not previously annotated in public databases for MS concentrations were identified in particle corona than in live plasma. Plots of particle protein identifications overlapping with databases confirm that different particle types select different subsets of plasma proteins. This may be due to the different surface characteristics of the three SPION particle types, which primarily determine the protein composition of the corona.

[0200] To evaluate the particle's ability to compress the measured dynamic range, the measured and identified protein feature intensities were compared to previously reported values ​​for the same protein concentrations. First, the obtained peptide features for each protein (as presented in Figure 24) were selected using the maximum MS decision intensity of all possible protein features (by extracting monoisotopic peak values ​​using the OpenMS MS data processing tool), and then these intensities were modeled against previously reported abundance levels for these same proteins (Figure 25). Comparing the slope of the regression model with the intensity span of the measured data, particle coronas, as in Figure 24, contain more of the protein at lower abundances (measured or reported amounts) than plasma. 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 the abundance of corona obtained compared to the original dynamic range in plasma, and could be attributed to a combination of the absolute concentration of the protein, its binding affinity to the particle, and its interaction with neighboring proteins. All of the above results indicate that the multi-particle protein corona strategy facilitated the identification of a wide range of plasma proteins, particularly those present in low abundances that are difficult to detect rapidly using conventional proteomic techniques.

[0201] To evaluate the robustness of protein identification using particle corona MS assays, a complete assay triple replicate was performed using a three-particle type panel to generate individual protein corona samples from the same pool of CRC plasma. Table 6 shows the number of unique proteins listed for each combination of particle types, ranging from one to two all-groups and three single-groups. [Table 6]

[0202] Protein counts in the "One Only" column were obtained by using each of the three repeats independently, and then obtaining the mean and standard deviation for all combined counts. As can be seen from the table, more proteins were found as the number of particle types in the particle panel increased, with the 3-particle-type group finding >1,500 unique proteins (65 of which are FDA-approved biomarkers, as listed in Table 7 below). Protein counts in the "Any One" repeat column were obtained using the union of the particle-type repeat protein lists. Protein counts in the "All Three" repeat column were obtained using the intersection of the particle-type repeat protein lists. As a further measure of overlap in particle repeats of identified proteins, the Jackard coefficient, a metric of inter-set similarity, was calculated for each pairwise comparison. The values ​​for SP-003, SP-007, and SP-011 were 0.74±0.018, 0.65±0.078, and 0.76±0.019 (mean ± sd), respectively. Enumerating protein content in a given MS sample depends on the probabilistic nature of MS2 data acquisition and may result in an underestimation of proteins exhibited within a sample or commonly shared across samples. PSM mapping to shared MS1 features is one representative technique that can mitigate this problem and will be developed for future analyses. [Table 7-1] [Table 7-2] Table 7-3

[0203] Dynamic Range. The three-particle panel was evaluated for its ability to assay proteins in a sample across a wide dynamic range of protein concentrations. Feature intensities corresponding to proteins identified by mass spectrometry were compared to values ​​determined by other assays for the same protein at the same concentration. After mass spectrometry analysis and data processing, MS2-peptide spectral matching (PSM) was used to identify peptides and associated proteins present in the corona of distinct particle types within the particle panel. In parallel, direct detection of peptides in plasma samples was also performed using a proteographic workflow without the use of the three-particle panel for corona analysis. Monoisotopic peak values ​​were extracted and determined using the OpenMS MS data processing tool to select the obtained peptide features with the highest MS determination intensity for all measured features for each protein. The MS determination intensity was then modeled against comparable previously reported abundance levels for the same protein. Figure 25 shows the correlation between the maximum protein intensity in distinct coronas from distinct nanoparticle types for each particle type within the three-particle panel for plasma proteins and the concentrations of the same protein determined using other models. As indicated by the slope of the regression model and the intensity span of the measurement data, particle coronas had more low-abundance protein hits than plasma. In addition, the dynamic range of these measurements was compressed for particle measurements compared to plasma measurements, as indicated by the reduction in the slope of the regression model, thus indicating that particles effectively compressed the measurement dynamic range of protein abundance in coronas compared to plasma. This may be due to a combination of absolute protein concentration, protein binding affinity to the particle, and protein interactions with neighboring proteins. These results indicate that the method disclosed herein, which uses a multi-particle panel for protein enrichment in separate coronas corresponding to separate particle types, facilitates the identification of a wide range of plasma proteins, particularly those in low abundances that are difficult to rapidly detect with conventional proteomic techniques. (Example 6) Accuracy of coronavirus analysis assays

[0204] This example describes the accuracy of a coronavirus analysis assay. Accuracy, a measure of assay repeatability and reproducibility, was assessed by comparing multiple measurements under the same conditions and determining the variability between individual measurements. To investigate the reproducibility of the particle protein coronavirus analysis proteograph workflow, peptide MS feature intensities were extracted and compared from three complete assay repeats for each of the three particle types. The raw MS files from each repeat were converted to the standard compatible MS file format, mzML, using the msconvert.exe utility from the openMS suite of programs. MS1 features were also extracted from the raw data by superimposing residence time and mz values ​​using the openMS processing pipeline, and then aligned and grouped. Groups containing features from each of the three repeats were selected and filtered based on the quality score of a clustering algorithm to remove the bottom 10 (90% of the feature groups were retained for subsequent accuracy analysis). For SP-003, SP-007, and SP-011 nanoparticles, a total of 2,744, 2,785, and 3,209 clustered feature groups, respectively, were used for accuracy analysis. The log-transformed raw intensity distributions for each iteration of these feature sets are plotted in Figure 26, and the values ​​for each particle type are presented in a labeled panel. As shown in Figure 26, the feature data for each particle type was highly reproducible, and this reproducibility was consistent across both high-intensity and low-intensity features.

[0205] To quantitatively assess performance rather than relying on visual inspection of raw data, the overall precision of particle corona was estimated after quantile-normalizing the group feature intensities. This normalization method was based on the assumption that all distributions being compared should be identical, and therefore the intensity was adjusted for each distribution being compared. This assumption is reasonable given the physical characteristics of the particle types themselves and the reproducibility of these physical characteristics from other analyses of these particles (e.g., X-ray photoelectron spectroscopy, transmission electron microscopy, and other analytical methods). Using the normalized values, the standard deviation was evaluated, and the coefficient of variation (CV) was determined using appropriate transformations of the logarithmically processed data. For each particle, the median CV (percentage of quantile-normalized CV, i.e., QNCV%) is shown in Table 8. These results demonstrate that the protein MS feature intensities measured by the particles have sufficient precision over thousands of MS feature intensities observed to detect relatively small differences in reasonable small-scale studies. For example, assuming a 25% CV, the power to detect a twofold change using Bonferroni-corrected significance was approximately 100%.

[0206] The overall accuracy of particle corona was estimated by normalizing group feature intensities using quantile normalization, which presupposes that all distributions being compared are identical and appropriately adjusts the intensity for each distribution being compared. The normalized values ​​were used to evaluate the standard deviation, and the coefficient of variation (CV) was determined using appropriate transformations of the logarithmically processed data. For each particle type, the median CV (percentage of quantile-normalized CV, i.e., QNCV%) is shown in Table 8. A low coefficient of variation (CV) indicates high assay accuracy. [Table 8]

[0207] These results demonstrate that protein MS feature intensities measured by particles have sufficient precision over thousands of observed MS feature intensities to detect relatively small differences in reasonable small-scale studies. (Example 7) Accuracy of coronavirus analysis assays

[0208] This example illustrates the accuracy of a corona analysis assay. The accuracy of the method should be robust enough to detect true intergroup differences in samples in biomarker discovery and validation studies. The accuracy of the corona analysis assay was determined by comparing the results of the corona analysis assay with those obtained by other methods. To evaluate the accuracy of the corona analysis assay, a spike recovery study was performed using SP-007 nanoparticles. C-reactive protein (CRP) was selected for analysis based on its measured endogenous levels. Known amounts of purified protein (see "Methods") were spiked to testable multiples of its endogenous level using endogenous plasma levels determined by enzyme-linked immunosorbent assay (ELISA). Post-spiking CRP levels were determined by ELISA experiments, and the values ​​for 1x (unspiked), 2x, 5x, 10x, and 100x samples were 4.11, 7.10, 11.5, 22.0, and 215.0 μg / mL, respectively. For four identified CRP trypsin peptides detected by MS using SP-007 particles, the extracted MS1 feature intensity was plotted against CRP concentration (Figure 14A). Figure 30 also shows the accuracy of CRP protein measurement in SP-007 particles in spike recovery experiments for four different peptides. MS1 feature intensity could not be detected for two of the peptides at non-spike 1x concentrations of CRP. The line fitted using the spike intensity of a given feature was a linear model.

[0209] Fitting regression models to all four CRP trypsin peptides yielded a slope of 0.9 (95% CI 0.81–0.98) close to a slope of 1, considered to represent perfect analytical performance, for the response of corona MS signal intensity to ELISA plasma levels. In contrast, a similar regression model fitted to 1,308 other (non-spiking) MS features identified in at least four of five plasma samples, where no sample-specific differences in signals from the relevant MS features should be observed, had a slope of -0.086 (95% CI -0.1–-0.068). These results demonstrate that the particle shape's ability to accurately represent differences between samples makes it a useful tool for quantifying potential markers in comparative studies. If protein levels in a sample change due to some factor, the method disclosed herein will detect similar level changes in proteins bound to the particle shape of the particle panel, which is a crucial characteristic of this particle shape for its validity in any given assay. Furthermore, the response of the spiked protein's peptide features suggests that, with proper calibration, it was possible to determine not only the relative quantification but also the absolute analyte level using the particle protein corona method. (Example 8) Proteomic analysis of NSCLC samples and healthy controls

[0210] This example describes the proteomic analysis of NSCLC samples and healthy controls. To demonstrate the potential usefulness of the corona analysis platform, the platform's capabilities were evaluated by observing group differences using a single particle type, SP-007, and serum samples from 56 subjects (28 with stage IV NSCLC and 28 age- and sex-matched controls). The selected subject samples represented a fairly balanced study for identifying MS features that may differ between groups. The age and sex characteristics of the subjects are summarized in Table 6, and all data regarding subject annotation, including disease status and comorbidities, are compiled in Table 9. [Table 9]

[0211] After collecting and filtering MS1 features, followed by a log2 transformation of their intensities, the dataset was the median scaled without regard to class.

[0212] Figure 29 shows the normalized intensity distribution for all 56 target datasets. All 56 sample MS raw data files from this NSCLC versus control study were processed using an OpenMS pipeline script to extract MS1 features and their intensities, and these were clustered into feature groups based on overlaps between mz and RT values ​​within a specified tolerance range. Only feature groups that 1) had at least 50% of the features present in groups from at least one of the comparison arms, and 2) had a feature group cluster quality above the first quartile were retained. The retained features were normalized medians without considering class, and these features were used for subsequent univariate analysis comparisons. No outliers were found by checking the distribution, and all datasets were retained for univariate analysis.

[0213] No outliers were found in the dataset during the examination. Univariate comparisons of feature group strengths between classes were performed using a nonparametric Wilcoxon test (two-sided). The p-values ​​obtained for the comparisons were corrected for multiple testing using the Benjamin-Hochberg method. As summarized in Figure 27, a total of seven feature groups demonstrated statistical significance using an adjusted p-value cutoff of 0.05.

[0214] All five proteins identified as having different levels of presence between the NSCLC-affected group and the control group had previously been linked to cancer, if not NSCLC itself. PON1, or paraoxanase-1, is a risk factor in lung cancer. The complex pattern involves a relatively common small allele variant (Q192R). At the protein level, PON1 is slightly decreased 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 times increased in affected subjects. Matrisome factor tenascin C (TENA) has been shown to be increased compared to normal tissue in primary lung tumors and associated lymph node metastases, and in this study, the associated MS feature was found to be 2 times increased. Neuronal cell adhesion molecule 1 (NCAM1) is useful as a marker for diagnosing pulmonary neuroendocrine tumors. The FIBA ​​peptide was identified by MS analysis at levels increased and correlated with the progression of lung cancer worsening. Of particular note are two previously unknown features, Group 2 and Group 7, that differ between the control and affected subjects. Group 2 was observed in 54 out of 56 controls and showed a small 33% decrease in affected subjects. In contrast, group 7 was observed only in affected individuals (14 out of 28 members in this class). These results demonstrate the potential utility of particle corona in identifying known and unknown markers for different disease states. (Example 9) Particle panel for assaying proteins in a sample

[0215] This example illustrates a 10-particle-type particle panel for assaying proteins in a sample. Shown in Table 10, this particle panel includes 10 distinct particle types differing in size, charge, and polymer coating. All particle types in this panel are superparamagnetic. Proteins in the sample were assayed using the panel shown below. [Table 10]

[0216] Protein coverage of the V1 panel. To evaluate the overall protein group coverage observed across multiple samples in a clinical sample set, plasma samples from 16 individuals were evaluated using the sample preparation, MS data acquisition, and MS data analysis methods described herein for 10 distinct particle type panels, shown in Table 10, referred to as the V1 panel. Using combinations of small cell lung cancer (NSCLC) patients and healthy individuals (n=8 for each), a diverse set of proteins and protein groups present in both healthy and cancer cells was obtained for analysis and identification using the methods described herein. A total of 2,009 protein groups were efficiently identified at a 1% FDR (protein and peptide) rate. For comparison, a previously mentioned published study detected 4,500 protein groups across 16 individual plasma samples using a complex workflow involving over 70 steps and over 30 MS fractions per sample, likely taking several weeks to complete. (Example 10) 10-particle panel for protein assays

[0217] This example illustrates the development of a 10-particle particle panel for the method of assaying proteins using biomolecular corona analysis as described herein.

[0218] Particle screen. Biomolecular corona from 43 particle types having distinct physicochemical properties, screened in a manner similar to the 3-particle-type particle panel disclosed herein, to demonstrate that the corona analysis platform can expand its coverage by adding guide particles. [Table 11-1] [Table 11-2]

[0219] Forty-three particle types were evaluated using the six conditions described in the Methods section, and the best combination was selected based on the total number of proteins identified, using the optimal conditions in a secondary analysis. To demonstrate platform validation across biological samples, 43 particle types were screened using plasma pools from healthy individuals and lung cancer patients, different from the CRC pools used for the three particle type particle panels. Protein diversity was increased using pooled samples. Potential proteins for panel selection and optimization were identified using stringent conditions. For maximum probability assessment, proteins had to be shown to count as “identified” by at least one peptide-spectral match (PSM; 1% false positive rate (FDR)) in each of three complete assay repeats. Panels with the maximum number of individual unique Uniprot identifiers were selected for the 10 particle type particle panels.

[0220] Protein coverage of 10 particle-type particle panels. The data disclosed herein confirm that changes in proteome content can be determined across many biological samples using the provided particle panels. The particle panels disclosed herein offer a method that is highly accurate and precise and employs an unbiased approach that does not require specific ligands for known proteins. Therefore, these panels are particularly well suited for biomarker discovery. The breadth and depth of plasma protein coverage using the 10 particle-type panels were investigated. Using a database of MS-derived plasma protein intensities (one of which is closely correlated with concentration) (n=5,304), the coverage of the 10 particle-type panels was compared to the entire range of the database, and also to the coverage obtained by MS evaluation of plain plasma (direct analysis by MS of the same plasma without particle-based sample extraction). Figure 36 shows the matching and coverage of 10 distinct particle-type particle panels against a 5,304 plasma protein database of MS intensities. The ranked intensities for database proteins are shown in the top panel ("Database"), the protein intensities from plain plasma MS assessments are shown in the second panel ("Plasma"), and the intensities of the optimal 10-particle panel are shown in the remaining panels. The plasma protein intensity database is from Keshishian et al. (2015). Multiplexed, Quantitative Workflow for Sensitive Biomarker Discovery in Plasma Yields Novel Candidates for Early Myocardial Injury. Molecular & Cellular Proteomics, 14(9), 2375-2393. The results shown in Figure 36 confirm and expand upon the results shown for the three distinct particle types used in the precision experiment shown in Figure 26. The 10 distinct particle types identified 1,598 proteins compared to 268 proteins for plain plasma. Furthermore, each individual particle type detected substantially more proteins than direct analysis by MS of plain plasma.Unlike MS analysis of plain plasma, the particle panel of 10 distinct particle types matched the entire range of plasma protein concentrations. In other words, while proteins identified from plain plasma samples were biased towards higher-intensity proteins (i.e., more abundant proteins), the proteins identified from the particle panel of 10 distinct particle types expanded the concentration dynamic range in the database by more than eight orders of magnitude. Only 21 proteins in the database had a lower intensity than the lowest-matched protein from the particle panel of 10 distinct particle types. As demonstrated in Figure 36, the particle panel of 10 distinct particle types demonstrates high precision, accuracy, and broad coverage across a wide range of protein concentrations in plasma, enabling large-scale, unbiased proteomic analysis across numerous biomolecules that can match the cost and speed currently possible with genomic data acquisition.

[0221] Accuracy of a particle panel containing 10 distinct particle types. This example describes the reproducibility of particle corona for a particle panel containing 10 distinct nanoparticle types. The particles were analyzed to determine the coefficient of variation (CV) of each feature group between iterative runs for each particle type in the particle panel containing 10 distinct nanoparticle types. A low CV indicated high accuracy and reproducibility between iterative runs. The data were processed using the software program OpenMS, and feature groups with measured precursor features from each of three iterations were retained. Statistical outliers were eliminated by removing the bottom 5% of the data based on the quality score of the clustering algorithm. The group feature intensities were median-normalized to estimate the overall accuracy of corona for each particle type. Normalization was performed so that the overall median intensity of each injection remained the same, and intensities were adjusted to constitute intensity shifts for each of the distributions being compared, e.g., intensity shifts due to overall differences in the machine response. Differences in machine response can occur in various analytical methods, including X-ray photoelectron spectroscopy, high-resolution transmission electron microscopy, and other analytical methods. Next, the normalized coefficient of variation (CV) for each feature was evaluated for each particle type in the particle panel, which included 10 distinct nanoparticle types. Table 12 shows the optimized panel for the 10 distinct particle types.

Table 12

[0222] Table 13 shows the median of the quantile-normalized CV percentage (QNCV%) of the accuracy assessment of the protein corona-based proteograph workflow for the characteristics, peptides, and proteins of plasma and a particle panel containing 10 distinct particle types. A 1% peptide and 1% protein false discovery rate (FDR) was applied. The data was processed using MaxLFQ analysis software, applying conditions to reduce the number of groups used in the accuracy analysis such that each protein group has a count value of at least one peptide ratio and detection in all replicates. Table 13 shows the median CV, including the percentage of the quantile-normalized CV, i.e., QNCV%, for each particle type of the particle panel containing 10 distinct nanoparticle types. Similar analyses were performed at the peptide and protein levels using MaxQuant to align the identifiable feature groups with the features, peptides, and proteins (Table 13). Since peptides can contain multiple features and proteins can contain multiple peptides, the number of identifiable feature groups decreases from features to peptides and then to proteins. This nanoparticle panel detected 1,184 protein groups at a 1% false discovery rate (FDR).

Table 13

[0223] The coefficient of variation (CV) was investigated independently at the feature, peptide, and protein levels. Analysis of feature, peptide, and protein CVs provides a comprehensive view of assay precision. The OpenMS and MaxQuant software engines were used for feature, peptide, and protein matching. MaxQuant was used for protein grouping at FDR. OpenMS was used to perform peptide-spectrum matching (PSM) using the X!Tandem matching tool. MaxQuant was configured to use the Andromeda algorithm. Peptide CV and protein CV were used to assess the precision of the platform for use with biological variables. The average CV decreased with increasing peptide size, and thus, the average CV was lower for proteins than for peptides. Peptides maintained a CV similar to plasma, while particles had a higher incidence of features, peptides, and proteins than plasma. Specifically, the number of proteins on particles of any given particle type was higher than in plasma (on average: 218% higher, range: 133% - 296% higher), while maintaining an equivalent CV (21.1% vs 17.1% for particles and plasma, respectively). Furthermore, the panel of particle types identified 1,184 proteins, while plasma alone identified only 162 proteins.

[0224] Accuracy of particle panels containing 10 distinct particle types. The accuracy of particle panels containing 10 distinct particle types in detecting real-world intergroup differences in samples in biomarker discovery and validation studies was assessed. Accuracy was determined by measuring spike recovery in the presence of nanoparticle type SP-007 and C-reactive protein (CRP). Spike recovery data were further measured in the presence of one or three additional polypeptides (S100A8 / 9 and angiogenin) in combination with each of the three particle types (SP-006, SP-339, SP-374). Known amounts of each polypeptide were spiked in at different concentrations increasing in multiples of 10 (e.g., 1x, 2x, 5x, 10x, and 100x). The levels of each polypeptide were measured by ELISA. Derived peptide and protein intensities were plotted against ELISA protein concentrations. Peptide intensities were derived using the OpenMS MS1 / MS2 pipeline to identify clustered feature sets with target protein MS2 IDs designated for at least one feature within the cluster. Only clusters that showed indications for higher spike levels in at least one iteration were used for analysis. Protein intensities were derived using MaxQuant software. Intensity values ​​for each protein were summarized, and the data were scaled so that the maximum concentration was 2. The MS dataset was run three times for each spike concentration (e.g., 1x, 2x, 5x, 10x, and 100x) to obtain 15 individual protein or peptide measurements. Not all peptides were detected in all particle types or particle repeats. The results for the MS dataset are shown in Figures 31–34. Figure 31 shows the accuracy of peptide characterization of angiogenin in the spike recovery experiment. Figure 32 shows the accuracy of peptide characterization of S10A8 in the spike recovery experiment. Figure 33 shows the accuracy of peptide characterization of S10A9 in the spike recovery experiment. Figure 34 shows the accuracy of peptide characterization of CRP in the spike recovery experiment. The fitted lines are linear fits of each feature to the spike intensity.

[0225] Figures 31-34 show the results of three spike recovery rate experiments to determine the accuracy of the angiogenin, S10A8, S10A9, and CRP peptide characterization, respectively. The data are based on the peptide (average r 2 (where 0.81) and protein (average r 2 High correlations were demonstrated between individual measurements for (where r is 0.97). The mean slope across all proteins is 1.06. Table 14 shows r for each comparison. 2 It shows correlation, and the average r for each protein. 2 Correlation was also observed. Of the 20 peptides, only two did not show correlation between ELISA assays for two different particle types; in this case, one peptide existed in two charge states. These abnormalities decreased with increasing peptide size, and therefore the frequency of abnormalities was lower for peptides than for proteins. The two peptides that showed correlation in ELISA for two different particle types here also showed high correlation in ELISA for other particle types. Problematic peptides may co-elute with other peptides that mask their signal, for example, by taking charge.

[0226] Table 14 provides a summary of the regression fit for protein intensity when measured by corona analysis or ELISA. The average values ​​across four replicates are shown for each individual particle type. Protein concentrations, when measured by corona analysis, agreed across various conditions and different particle types. As shown in Table 14, protein measurements were high r 2 The values ​​(mean 0.97, inter-particle range 0.92–1.0; inter-particle average range 0.94–0.99) showed good correlation. This consistent behavior among the four proteins when measured by ELISA illustrates the accuracy of the coronavirus analysis assay. [Table 14]

[0227] Comparison with other platforms. The method disclosed herein (e.g., corona analysis using a proteograph workflow) which uses a multi-particle panel to enrich proteins in separate coronas corresponding to each protein type in the panel provides broad, unbiased coverage of protein identification in the proteome. Other methods that attempt to achieve broad coverage of the proteome require multiple fractionation steps, complex workflows, and are slower compared to the method presented herein. Other methods lack the breadth and unbiasedness of the method disclosed herein, and are compared here with the method disclosed herein for assaying proteins.

[0228] Geyer et al. (Cell Systems 2016) used a fast shotgun proteomic method. An average of 284 protein groups were obtained per assay, and 321 protein groups were obtained across all replicates. Assessment utilized a slower, multi-day protocol with fractionation resulting in approximately 1,000 protein groups. Repeat experiments were not performed, likely due to exorbitant cost and time requirements, and therefore variance could not be determined.

[0229] Geyer generated 321 protein groups using short-run tests and determined the CV of each protein. The 321 groups assessed by Geyer et al., and the 1,184 protein groups identified by the 10-particle panel, included 88 protein groups common to both models. The identification of these 88 common protein groups is unexpectedly sophisticated, as each protein group may contain multiple related proteins that can be combined differently based on the detected peptides.

[0230] For 88 common proteins, data from Geyer et al. were analyzed to determine a median CV of 12.1%. In contrast, the same 88 common proteins had a lower CV of only 7.2% when analyzed by proteography. Therefore, this method for corona analysis using a multi-particle panel and proteographic workflow provides improved accuracy compared to Geyer et al.'s method. In addition, Geyer et al.'s rating for assay accuracy was 0.99 for four proteins. 2 This showed that the proteographic assay yielded a value of 0.97 r. 2 This was shown.

[0231] Geyer et al. further assessed the number of protein groups with a CV < 20%, which is a cutoff commonly used in in vitro diagnostic assays. This particle panel method detected 761 protein groups with a CV < 20%, which was 3.7 times more than the number identified by Geyer et al. Further assessment by Dr. Mann (Niu et al, 2019) identified 272 protein groups with a CV < 20%, which was 1 / 2.8 of the number identified by multi-particle panels and their methods of use disclosed herein.

[0232] Bruderer et al. evaluated protein group CV using data generated by the Biognosys platform (Bruderer et al, 2019). This evaluation was based on 4 Sixty-five proteins were identified, of which 465 had a median CV of 5.2%, and 404 of these proteins had a CV < 20%. In contrast, the best 465 proteins from 1,184 proteins identified using the methods disclosed herein had a median CV of 4.7%, and 761 of the 1,184 proteins identified by proteography had a CV < 20%.

[0233] Compared to the assessments of Geyer et al., Niu et al., and Bruderer et al., this particle panel yielded improved CV not only in the number of proteins that met the CV threshold but also in an equivalent number of proteins compared to other identification methods. The method disclosed herein further reduces bias compared to other methods, e.g., targeted mass spectrometry and other analyte-specific reagents (e.g., Olink). Such methods introduce bias during the protein panel selection process by measuring a small number of pre-selected proteins. As a result, these methods yield lower CV and higher r for proteins in their panel compared to proteins identified by proteography. 2 It has the capability to detect proteins in a panel. (Example 11) Materials and methods for particle synthesis

[0234] This example illustrates materials and methods for particle synthesis.

[0235] Materials: Iron(III) chloride hexahydrate (ACS), sodium acetate (anhydrous ACS), ethylene glycol, 28-30% ammonium hydroxide, ammonium persulfate (APS) (≧98%, Pro-Pure, proteome grade), ethanol (reagent alcohol ACS), and methanol (≧99.8% ACS) were purchased from VWR. N,N'-methylenebisacrylamide (99%) was purchased from EMD Millipore. Trisodium citrate dihydrate (ACS reagent, ≧99.0%), tetraethyl orthosilicate (TEOS) (reagent grade, 98%), 3-(trimethoxysilyl)propyl methacrylate (MPS) (98%), and poly(ethylene glycol) methyl ether methacrylate (OEGMA, average Mn500, containing 100 ppm MEHQ as an inhibitor and 200 ppm BHT as an inhibitor) were purchased from Sigma-Aldrich. 4,4'-Azobis(4-cyanovaleric acid) (ACVA, 98%, approximately 18% water) and divinylbenzene (DVB, 80%, mixture of isomers) were purchased from Alfa Aesar and purified by passing through a short silica column to remove the inhibitor. N-(3-dimethylaminopropyl)methacrylamide (DMAPMA) was purchased from TCI and purified by passing through a short silica column to remove the inhibitor. An ELISA kit for measuring human C-reactive protein (CRP) was purchased from R&D Systems (Minneapolis, MN). Human CRP protein purified from human serum was from Sigma Aldrich.

[0236] Synthesis of SP-003, SP-007, and SP-011 based on superparamagnetic iron oxide nanoparticles (SPION). Iron oxide cores were synthesized by solvothermal reaction (Figures 28A-E, top (Figure 28A)) (Liu, J., et al. Highly water-dispersible biocompatible). magnetite particles with low cytotoxicity stabilized by citrate groups. Angew Chem Int Ed Engl 48, 5875-5879 (2009);Xu, S., et al. Toward designer magnetite / polystyrene colloidal composite microspheres with controllable nanostructures and desirable surface functionalities. Langmuir 28, 3271-3278 (2012). Typically, about 26.4 g of iron(III) chloride hexahydrate was dissolved in about 220 mL of ethylene glycol at about 160°C for about 10 minutes with stirring. Then, about 8.5 g of trisodium citrate dihydrate and about 29.6 g of anhydrous sodium acetate were added and mixed at 160°C for a further 15 minutes until completely dissolved. The solution was then sealed in a Teflon®-lined stainless steel autoclave (300 mL capacity) and heated at about 200°C for about 12 hours. After cooling to room temperature, the black paramagnetic product was isolated by magnet and washed 3-5 times with DI water. The final product was freeze-dried to a black powder for further use.

[0237] Silica-coated iron oxide nanoparticles (SP-003) were prepared by the previously reported modified Stöber method (Figure 28B) (Deng, Y., Qi, D., Deng, C., Zhang, X. & Zhao, D. Superparamagnetic high-magnetization microspheres with an Fe3O4@SiO2 core and perpendicularly aligned mesoporous SiO2 shell for removal of microcystins. J Am Chem Soc 130, 28-29 (2008);Teng, ZG, et al. Superparamagnetic high-magnetization composite spheres with highly aminated ordered mesoporous silica shell for biomedical applications. J Mater Chem B 1, 4684-4691 (2013). Typically, about 1 g. SPION was uniformly dispersed in a mixture of ethanol (approximately 400 mL), DI water (approximately 10 mL), and concentrated ammonia aqueous solution (approximately 10 mL, 28-30% by weight), and then TEOS (approximately 2 mL) was added. After stirring at approximately 70°C for approximately 6 hours, an amorphous silica-coated SPION (denoted as Fe3O4@SiO2) was obtained, which was washed three times with methanol and three more times with water, and the final product was freeze-dried to obtain a powder.

[0238] To prepare SP-007 (PDMAPMA-modified SPION) and SP-011 (PEG-modified SPION), vinyl-functionalized SPION (indicated as Fe3O4@MPS) was first prepared by a previously reported modified Stover method (Figure 28C) (Crutchfield, CA, Thomas, SN, Sokoll, LJ & Chan, DW Advances in mass spectrometry-based clinical biomarker discovery. Clin Proteomics 13, 1 (2016)). Briefly, with the help of vortexing (or sonication) Next, approximately 1 g of SPION was uniformly dispersed in a mixture of ethanol (approximately 400 mL), DI water (approximately 10 mL), and concentrated ammonia aqueous solution (approximately 10 mL, 28-30% by weight), and then TEOS (approximately 2 mL) was added. After stirring at 70°C for approximately 6 hours, approximately 2 mL of 3-(trimethoxysilyl)propyl methacrylate was added to the reaction mixture and stirred overnight at approximately 70°C. Vinyl-functionalized SPION was obtained, washed three times with methanol and three more times with water, and the final product was freeze-dried to obtain a powder. Next, for the synthesis of SPION coated with poly(dimethylaminopropyl methacrylamide) (PDMAPMA) (denoted as Fe3O4@PDMAPMA; SP-007 in Figure 28D), approximately 100 mg of Fe3O4@MPS was uniformly dispersed in approximately 125 mL of DI water. After bubbling with N2 for approximately 30 minutes, approximately 2 g of N-[3-(dimethylamino)propyl]methacrylamide (DMAPMA) and approximately 0.2 g of divinylbenzene (DVB) were added to the Fe3O4@MPS suspension under N2 protection. The resulting mixture was heated to approximately 75°C, then approximately 40 mg of ammonium persulfate (APS) in approximately 5 mL of DI water was added, and the mixture was stirred overnight at approximately 75°C. After cooling, Fe3O4@PDMAPMA was isolated by magnetism and washed 3-5 times with water. The final product was freeze-dried to obtain a dark brown powder. For the synthesis of SPION (denoted as Fe3O4@PEGOMA; SP-011 in Figure 28E) with a poly(ethylene glycol) (PEG) coating, approximately 100 mg of Fe3O4@MPS was uniformly dispersed in approximately 125 mL of DI water. After bubbling with N2 for approximately 30 minutes, approximately 2 g of poly(ethylene glycol) methyl ether methacrylate (OEGMA, average Mn 500) and approximately 50 mg of N,N'-methylenebisacrylamide (MBA) were added to the Fe3O4@MPS suspension under N2 protection. The resulting mixture was heated to approximately 75°C, and then approximately 50 mg of 4,4'-azobis(4-cyanovaleric acid) (ACVA) in approximately 5 mL of ethanol was added, and the mixture was stirred overnight at approximately 75°C. After cooling, Fe3O4@OEGMA was isolated by magnetism and washed 3-5 times with water. The final product was freeze-dried to obtain a dark brown powder. (Example 12) Patient sample

[0239] This example describes the patient samples used in this disclosure. A set of eight colorectal cancer (CRC) plasma samples and eight age- and gender-matched controls were purchased from BioIVT (Westbury, NY). A set of 28 non-small cell lung cancer (NSCLC) serum samples and 28 age- and gender-matched controls were also purchased from BioIVT. Detailed information regarding the CRC / NSCLC patient samples and controls is shown in Tables 15 and 16.

Table 15-1

Table 15-2

Table 15-3

Table 15-4

Table 15-5

Table 15-6

Table 16

[0240] This example describes the characterization of particle physicochemical properties using various techniques. Dynamic light scattering (DLS) and zeta potential were performed using a Zetasizer Nano ZS (Malvern Instruments, Worcestershire, UK). Before testing, particles were suspended in water at 10 mg / mL using bath sonication for approximately 10 minutes. The samples were then diluted to approximately 0.02 wt% in their respective buffers for both DLS and zeta potential measurements. DLS was performed in water at approximately 25°C in a disposable polystyrene semi-micro cuvette (VWR, Randor, PA, USA) with a temperature equilibrium time of approximately 1 minute, using a 633 nm laser in 173° backscatter mode, and consisted of the average of three runs of approximately 1 minute each. The DLS results were analyzed using the cumulant method. Zeta potentials were measured in disposable, foldable capillary cells (Malvern Instruments, PN DTS1070) at approximately 25°C in 5% pH 7.4 PBS (Gibco, PN 10010-023, USA) with an equilibrium time of approximately 1 minute. Three measurements were performed using an automated measurement time, with a minimum of 10 runs and a maximum of 100 runs, with a 1-minute hold between measurements. Zeta potentials were determined from electrophoretic mobility using the Smoluchowski model.

[0241] Scanning electron microscopy (SEM) was performed using an FEI Helios 600 Dual-Beam FIB-SEM. An aqueous dispersion of particles was redispersed in DI water from the weighed particle powder by sonication for approximately 10 minutes to a concentration of approximately 10 mg / mL. The sample was then diluted fourfold with methanol (from Fisher) to prepare a dispersion in water / methanol, which was used directly for electron microscopy. SEM substrates were prepared by drop-casting approximately 6 μL of particle sample onto a Si wafer from Ted Pella, and the droplets were then completely dried in a vacuum desiccator for approximately 24 hours before measurement.

[0242] The Titan 80-300 transmission electron microscope (TEM) was used at an accelerating voltage of 300 kV for both low-resolution TEM measurement and high-resolution TEM measurement. TEM grids were prepared by drop-casting approximately 2 μL of the particle dispersion in a water-ethanol mixture (25-75 v / v%) to a final concentration of approximately 0.25 mg / mL and were dried in a vacuum desiccator for approximately 24 hours before TEM analysis. All measurements were performed using lacey holey TEM grids from Ted Pella.

[0243] X-ray photoelectron spectroscopy (XPS) was performed by using a PHI VersaProbe and a Thermo Scientific ESCALAB 250e III. Analyses by XPS were carried out using fine powders of the particles, which were kept sealed and stored under drying before measurement. The materials were placed on carbon tape so as to make a uniform surface for analysis. A light Al K-alpha X-ray source (50 W and 15 kV) was used with a pass energy of 140 eV for the scanning region, and all binding energies were referenced to the C-C peak at 284.8 eV. Both survey scans and high-resolution scans were performed to evaluate the target elements in detail. The atomic concentration of each element was determined from the integrated intensity of the photoelectron emission characteristics of the element, corrected by the relative atomic sensitivity factor by averaging the results from two different positions of the sample. In some cases, four or more positions were averaged to evaluate the uniformity. 2 Scanning area, and all binding energies were referenced to the C-C peak at 284.8 eV. Both survey scans and high-resolution scans were performed to evaluate the target elements in detail. The atomic concentration of each element was determined from the integrated intensity of the photoelectron emission characteristics of the element, corrected by the relative atomic sensitivity factor by averaging the results from two different positions of the sample. In some cases, four or more positions were averaged to evaluate the uniformity. (Example 14) Protein corona preparation and proteome analysis

[0244] This example describes protein corona preparation and proteome analysis. Plasma and serum samples were added to TE buffer (10 mM Tris, 1 mM containing 0.05% CHAPS) The sample was diluted 1:5 with a dilution buffer consisting of disodium EDTA (150 mM KCl). The particulate powder was reconstituted by sonication in DI water for approximately 10 minutes, followed by vortexing for approximately 2-3 seconds. To prepare the protein corona, approximately 100 μL of the particulate suspension (SP-003, 5 mg / ml; SP-007, 2.5 mg / ml; SP-011, 10 mg / ml) was mixed with approximately 100 μL of the diluted biological sample in a microtiter plate. The plate was sealed and incubated at 37°C for approximately 1 hour with shaking at 300 rpm. After incubation, the plate was placed on a magnetic assembly for approximately 5 minutes to pelletize and settle the nanoparticles. Unbound proteins in the supernatant were removed by pipette. The protein corona was further washed three times with approximately 200 μL of dilution buffer while magnetically separating. The five additional assay conditions evaluated for the 10-particle type particle panel screen were identical to those described above, with the exception of one of the following: First, low-concentration particles were evaluated at 50% of the original particle concentration (2.5–15 mg / ml per particle, depending on the expected peptide yield). For the second and third assay variations, both low and high particle concentrations were performed using undiluted fresh plasma rather than diluting the particles with a buffer. For the fourth and fifth assay variations, both low and high particle concentrations were performed using pH 5 citrate buffer for both dilution and rinsing.

[0245] To digest the proteins bound to the nanoparticles, a trypsin digestion kit (iST 96X, PreOmics, Germany) was used according to the provided protocol. Briefly, approximately 50 μL of lysis buffer was added to each well, and the plates were heated with agitation at approximately 95°C for approximately 10 minutes. After the plates cooled to room temperature, trypsin digestion buffer was added, and the plates were incubated with shaking at approximately 37°C for approximately 3 hours. The digestion process was stopped with a stop buffer. The supernatant was separated from the nanoparticles using a magnetic collector and further purified with the peptide cleanup cartridge included in the kit. The peptides were eluted twice with approximately 75 μL of elution buffer and combined. Peptide concentrations were measured using a quantitative colorimetric peptide assay kit from Thermo Fisher Scientific (Waltham, MA).

[0246] Next, the peptide eluates were lyophilized and reconstituted with 0.1% TFA. A 2 μg fraction from each sample was analyzed by nano LC-MS / MS using a Waters NanoAcquity HPLC system interfaced to a Thermo Fisher Scientific Orbitrap Fusion Lumos Tribrid Mass Spectrometer. The peptides were loaded onto the capture column and eluted at 350 nL / min using a 75 μm analytical column. Both columns were packed with Luna C18 resin from Phenomenex (Torrance, CA). The mass spectrometer was operated in data-dependent mode, and MS and MS / MS were performed in Orbitrap at 60,000 FWHM resolution and 15,000 FWHM resolution, respectively. The instrument was run in 3-second cycles for MS and MS / MS. (Example 15) Mass Spectrometry Data Analysis

[0247] This example describes a mass spectrometry data analysis method. Acquired MS data files were processed using a suite of OpenMS tools. These tools include modules and pipeline scripts for converting vendor-provided instrument-attached raw files to mzML files, for MS1 feature identification and intensity extraction, for MS dataset execution time alignment and feature clustering, and for MS2 spectral database matching with the X!Tandem search engine. Acceptable tolerances for precursor ion and fragment ion matching during spectral-database searches were set to 10 and 30 ppm, respectively. Default settings were enabled for fixed carbamide methyl (C) modifications and variable acetyl (N-terminal) and oxidative (M) modifications. The UniProtKB / Swiss-Prot protein sequence database (accepted January 27, 2019) was used for searches, and peptide spectral matches (PSMs) were scored using a standard reverse sequence decoy database strategy at 1% FDR. Using PSMs, a protein list for each particle repeat was compiled, with a single PSM used as sufficient evidence to add a protein to the enumerated protein list for a given particle repeat. In addition, a PSM that matches one or more proteins adds all possible proteins to the enumerated protein list for a given particle repeat. This threshold for protein enumeration is tolerant and may include false positives (higher sensitivity, lower specificity), but more stringent tests requiring two or more peptides (including at least one unique peptide) suffer from the opposite problem of false negatives (lower sensitivity, higher specificity). For quantitative analysis of known peptides, a custom R script was used to specify MS2 PSMs to MS1 feature groups based on positional overlap with acceptable mz and residence time tolerances of 1 da and 30 seconds, respectively. In the event that more than one PSM initially located on an MS1 ​​feature within a pre-specified tolerance range, the PSM closest to the MS1 feature (within the MS dataset) or the PSM closest to the center of the MS1 feature cluster (across MS datasets) was used.It should be noted that not all MS2s are designated to MS1 feature clusters, and not all MS1 feature cluster clusters have designated MS2s; therefore, research in this area will continue to improve mapping and subsequent peptide feature identification. (Example 16) Identification of protein groups

[0248] This example describes a method for identifying protein groups by mass spectrometry. For protein group-level analysis, MS data at the protein group level were prepared as follows: Raw MS files were processed with MaxQuant (v.1.6.7(49) and Andromeda(50)), and MS / MS spectra were searched against the UniProtKB Human FASTA database (UP000005640, 74,349 forward entries; version from August 2019) using standard settings. Enzyme digestion specificity was set to trypsin, allowing for N-terminal proline cleavage and up to two miscleavages. Minimum peptide length was set to 7 amino acids, and maximum peptide mass was set to 4,600 Da. Methionine oxidation and protein N-terminal acetylation were set as variable modifications, and cysteine ​​carbamide methylation was set as a fixed modification. MaxQuant improves precursor ion mass accuracy with a time-dependent recalibration algorithm and defines the acceptable range of individual masses for each peptide. The permissible initial maximum precursor mass tolerance was 20 ppm during the initial search and 4.5 ppm during the main search. The MS / MS mass tolerance was set to 20 ppm. For analysis, a 1% false positive rate (FDR) cutoff was applied at the peptide and protein levels (all protein groups are reported with their corresponding q values ​​in the proteinGroups.txt table). "Matching between runs" was disabled. The number of identifications was counted based on protein intensity requiring at least one razor peptide (counting only proteins with q values ​​lower than 1%). MaxLFQ normalized protein intensity (requiring at least one peptide ratio count) was reported in raw output, and these intensities were used only for CV precision analysis. Peptides that could be distinguished were sorted into their own protein groups, and proteins that could not be distinguished based on unique peptides were grouped together. Furthermore, proteins were filtered for a list of common contaminants included in MaxQuant. Proteins identified only by site modification were strictly excluded from the analysis. (Example 17) Spike recovery rate

[0249] This example describes a method for an experiment on the spike recovery rate of C-reactive protein (CRP). The baseline concentration of CRP in pooled healthy plasma samples was measured using the ELISA kit described above ("Materials") according to the manufacturer's suggested protocol. Stock CRP and appropriate dilutions were prepared and spiked into the same pooled plasma samples to final concentrations that were 2, 5, 10, and 100 times the baseline endogenous concentration of CRP. The volume added to the pooled plasma was 10% of the total sample volume. A spike control was prepared by adding the same volume of buffer to the pooled plasma sample. The concentration of the spiked samples was measured again by ELISA to confirm the CRP level at each spike level. These samples were used to evaluate the particle corona measurement accuracy as described above ("Results"). (Example 18) Proteomic analysis of NSCLC samples and healthy controls

[0250] This example describes the proteomic analysis of NSCLC samples and healthy controls. Serum samples were commercially purchased from 56 subjects, consisting of 28 individuals with stage IV NSCLC and 28 age- and sex-matched controls, and evaluated for SP-007 nanoparticle formation (see above for sample collection, corona formation, and processing). MS spectral data for each corona were collected as described, and the raw data were processed as described above (MS data analysis). 19,214 feature groups were identified and extracted across the 56 subject samples with group sizes ranging from 1 (singleton features in only one sample, n=6,249, i.e., 0.29% of the data) to 56 (features present in all samples, n=450, i.e., 12% of the data). The clustering algorithm calculated the "group_quality" metric regarding the spatial uniformity of feature grouping across datasets. The lower quartile of groups was then excluded due to the slope characteristics of the distribution of low-quality scores, resulting in 15,967 groups. As an additional filter before the analysis, only groups with features present in at least 50% of either the class, affected, or control were advanced, resulting in a set of 2,507 feature groups for analysis.

[0251] Peptide and protein identity were specified as feature sets as follows. MS2 PSM and MS1 feature sets were specified together as described above (MS data analysis). Using this method, 25% of the 19,249 original feature sets were associated with peptide sequences. Univariate statistical comparisons were performed between all feature sets, whether they had or did not have the specified peptide sequences. (Example 19) statistical analysis

[0252] This example illustrates the statistical analysis of the data disclosed herein. Statistical analysis and visualization were performed using R (v3.5.2) with appropriate packages (R: Language and environment for statistical computation; R Foundation for Statistical Analysis). Computing, Vienna, Austria.URL https: / / www.R-project.org / ).

[0253] Preferred embodiments of the present invention have been shown and described herein, but it will be apparent to those skilled in the art that such embodiments are provided merely as examples. A great many variations, modifications, and substitutions will now come to mind to those skilled in the art without departing from the present invention. It should be understood that alternative forms of the embodiments of the present invention described herein can be used when carrying out the present invention. The following claims define the scope of the present invention, and methods and structures within the scope of these claims, as well as their equivalents, are intended to be encompassed by these claims. In certain embodiments, for example, the following items are provided: (Item 1) A method for identifying proteins in a sample, A step of incubating the particle panel with the sample to form a plurality of distinct biomolecular coronas corresponding to distinct particle types of the particle panel; A step of magnetically isolating the particle panel from unbound proteins in the sample in order to concentrate proteins in the multiple separate biomolecular coronas; and Steps to assay the multiple distinct biomolecules of corona to identify concentrated proteins. A method that includes this. (Item 2) The method according to item 1, wherein the assay step can identify 1 to 20,000 protein groups. (Item 3) The method according to any one of items 1 to 2, wherein the assay step can identify 1,000 to 10,000 protein groups. (Item 4) The method according to any one of items 1 to 3, wherein the assay step can identify 1,000 to 5,000 protein groups. (Item 5) The method according to any one of items 1 to 4, wherein the assay step can identify 1,200 to 2,200 protein groups. (Item 6) The method according to any one of items 2 to 5, wherein the protein group includes a peptide sequence having a minimum length of 7 amino acid residues. (Item 7) The method according to any one of items 1 to 6, wherein the assay step can identify 1,000 to 10,000 proteins. (Item 8) The method according to any one of items 1 to 7, wherein the assay step can identify 1,800 to 5,000 proteins. (Item 9) The method according to any one of items 1 to 8, wherein the sample comprises multiple samples. (Item 10) The method according to item 9, wherein the plurality of samples include at least two or more spatially isolated samples. (Item 11) The method according to item 10, wherein the incubation step includes bringing the at least two or more spatially isolated samples into contact with the particle panel at the same time. (Item 12) The method according to any one of items 10 to 11, wherein the magnetic isolation step includes simultaneously magnetically isolating the particle panel from unbound proteins in at least two or more spatially isolated samples of the plurality of samples. (Item 13) The method according to any one of items 10 to 12, wherein the assay step includes assaying the plurality of distinct biomolecular coronas to simultaneously identify proteins in at least two or more spatially isolated samples. (Item 14) The method according to any one of items 1 to 13, further comprising the step of repeating the method according to any one of items 1 to 7, wherein, if repeated, the incubation step, isolation step and assay step result in a quantile-normalized coefficient of variation (QNCV) of 20% or less, as determined by comparing peptide mass spectrometry characteristics from at least three complete assay repeats for each particle type in the particle panel. (Item 15) The method according to any one of items 1 to 13, wherein, when repeated, the incubation step, isolation step and assay step result in a quantile-normalized coefficient of variation (QNCV) of 10% or less, as determined by comparing peptide mass spectrometry characteristics from at least three complete assay repeats for each particle type in the particle panel. (Item 16) The method according to any one of items 1 to 15, wherein the assay step can identify proteins over a dynamic range of at least 7, at least 8, at least 9, or at least 10. (Item 17) The method according to any one of items 1 to 16, further comprising the step of magnetically isolating the particle panel from the unbound protein, followed by washing the particle panel at least once or at least twice. (Item 18) The method according to any one of items 1 to 17, further comprising the step of lysing the protein in the plurality of separate biomolecular coronas after the assay step. (Item 19) The method according to item 18, further comprising the step of digesting the protein in the multiple separate biomolecular coronas to produce digested peptides. (Item 20) The method according to item 19, further comprising the step of purifying the digested peptide. (Item 21) The method according to any one of items 1 to 20, wherein the assay step includes identifying the protein in the sample using mass spectrometry. (Item 22) The method according to any one of items 1 to 21, wherein the assay step is performed within approximately 2 to 4 hours. (Item 23) A method described in any one of items 1-22, performed within approximately 1 to 20 hours. (Item 24) A method described in one of items 1-23, performed within approximately 2 to 10 hours. (Item 25) A method described in one of items 1-24, performed within approximately 4 to 6 hours. (Item 26) The isolation step is performed according to any one of items 1 to 25, taking approximately 30 minutes or less, approximately 15 minutes or less, approximately 10 minutes or less, approximately 5 minutes or less, or approximately 2 minutes or less. (Item 27) The method according to any one of items 3 to 26, wherein the plurality of samples include at least 10 spatially isolated samples, at least 50 spatially isolated samples, at least 100 spatially isolated samples, at least 150 spatially isolated samples, at least 200 spatially isolated samples, at least 250 spatially isolated samples, or at least 300 spatially isolated samples. (Item 28) The method according to item 27, wherein the plurality of samples comprises at least 96 samples. (Item 29) The method according to any one of items 1 to 28, wherein the particle panel includes at least two distinct particle types, at least three distinct particle types, at least four distinct particle types, at least five distinct particle types, at least six distinct particle types, at least seven distinct particle types, at least eight distinct particle types, at least nine distinct particle types, at least ten distinct particle types, at least eleven distinct particle types, at least twelve distinct particle types, at least thirteen distinct particle types, at least fourteen distinct particle types, at least fifteen distinct particle types, at least twenty distinct particle types, at least twenty-five distinct particle types, or at least thirty distinct particle types. (Item 30) The method according to item 29, wherein the particle panel comprises at least 10 distinct particle types. (Item 31) The method according to any one of items 10 to 30, wherein the at least two spatially isolated samples differ in at least one physicochemical property. (Item 32) The method according to any one of items 1 to 31, wherein the particle panel comprises a first distinct particle type and a second distinct particle type, the first distinct particle type and the second distinct particle type share at least one physicochemical property but differ in at least one physicochemical property, and therefore the first distinct particle type and the second distinct particle type are different. (Item 33) The method according to any one of items 1 to 32, wherein the particle panel comprises a first distinct particle type and a second distinct particle type, the first distinct particle type and the second distinct particle type share at least two physicochemical properties but differ in at least two physicochemical properties, and therefore the first distinct particle type and the second distinct particle type are different. (Item 34) The method according to any one of items 1 to 33, wherein the particle panel comprises a first distinct particle type and a second distinct particle type, the first distinct particle type and the second distinct particle type share at least one physicochemical property but differ in at least two physicochemical properties, and therefore the first distinct particle type and the second distinct particle type are different. (Item 35) The method according to any one of items 1 to 34, wherein the particle panel comprises a first distinct particle type and a second distinct particle type, the first distinct particle type and the second distinct particle type share at least two physicochemical properties but differ in at least one physicochemical property, and therefore the first distinct particle type and the second distinct particle type are different. (Item 36) The method according to any one of items 31 to 35, wherein the physicochemical properties include size, charge, core material, shell material, porosity, or surface hydrophobicity. (Item 37) The method according to item 36, wherein the size is a diameter or radius measured by dynamic light scattering, SEM, TEM, or any combination thereof. (Item 38) The method according to any one of items 1 to 37, wherein the particle panel comprises a first distinct particle type and a second distinct particle type, the first distinct particle type and the second distinct particle type comprising a carboxylate material, the first distinct particle being a microparticle and the second distinct particle type being a nanoparticle. (Item 39) The method according to any one of items 1 to 37, wherein the particle panel comprises a first distinct particle type and a second distinct particle type, the first distinct particle type and the second distinct particle type having surface charges of 0 mV and -50 mV, the first distinct particle type having a diameter of less than 200 nm, and the second distinct particle type having a diameter of greater than 200 nm. (Item 40) The method according to any one of items 1 to 37, wherein the particle panel comprises a first distinct particle type and a second distinct particle type, the first distinct particle type and the second distinct particle type having a diameter of 100 to 400 nm, the first distinct particle type having a positive surface charge, and the second distinct particle type having a neutral surface charge. (Item 41) The method according to any one of items 1 to 37, wherein the particle panel comprises a first distinct particle type and a second distinct particle type, the first distinct particle type and the second distinct particle type being nanoparticles, the first distinct particle type having a surface charge of less than -20 mV, and the second distinct particle type having a surface charge of greater than -20 mV. (Item 42) The method according to any one of items 1 to 37, wherein the particle panel comprises a first distinct particle type and a second distinct particle type, the first distinct particle type and the second distinct particle type being microparticles, the first distinct particle type having a negative surface charge, and the second distinct particle type having a positive surface charge. (Item 43) The method according to any one of items 1 to 37, wherein the particle panel comprises a subset of negatively charged nanoparticles, and each particle of the subset differs in respect to at least one surface chemical group. (Item 44) The method according to any one of items 1 to 37, wherein the particle panel comprises a first distinct particle type, a second particle, and a third distinct particle type, the first distinct particle type, the second distinct particle type, and the third distinct particle type each comprises an iron oxide core, a polymer shell, has a diameter of less than approximately 500 nm, the first distinct particle type has a negative charge, the second distinct particle type has a positive charge, the third distinct particle type has a neutral charge, and the diameter is the average diameter measured by dynamic light scattering. (Item 45) The method according to item 44, wherein the first distinct particle type comprises a silica coating, the second distinct particle type comprises poly(N-(3-(dimethylamino)propyl)methacrylamide) (PDMAPMA), and the third distinct particle type comprises a poly(oligo(ethylene glycol)methyl ether methacrylate) (POEGMA) coating. (Item 46) The method according to any one of items 1 to 45, wherein at least one distinct particle type of the particle panel is a nanoparticle. (Item 47) The method according to any one of items 1 to 46, wherein at least one distinct particle type of the particle panel is a microparticle. (Item 48) The method according to any one of items 1 to 47, wherein at least one distinct particle type of the particle panel is superparamagnetic iron oxide particles. (Item 49) The method according to any one of items 1 to 48, wherein each particle in the particle panel comprises an iron oxide material. (Item 50) The method according to any one of items 1 to 49, wherein at least one distinct particle type of the particle panel has an iron oxide core. (Item 51) The method according to any one of items 1 to 49, wherein at least one distinct particle type of the particle panel has iron oxide crystals embedded in a polystyrene core. (Item 52) The method according to any one of items 1 to 47, wherein each of the distinct particle types of the particle panel is a superparamagnetic iron oxide particle. (Item 53) The method according to any one of items 1 to 52, wherein each of the distinct particle types of the particle panel comprises an iron oxide core. (Item 54) The method according to any one of items 1 to 52, wherein each distinct particle type of the particle panel has iron oxide crystals embedded in a polystyrene core. (Item 55) The method according to any one of items 1 to 55, wherein at least one distinct particle type of the particle panel comprises a carboxylated polymer, an amination polymer, a zwitterionic polymer, or any combination thereof. (Item 56) The method according to any one of items 1 to 56, wherein at least one particle type of the particle panel comprises an iron oxide core having a silica shell coating. (Item 57) The method according to any one of items 1 to 56, wherein at least one particle type of the particle panel comprises an iron oxide core having a poly(N-(3-(dimethylamino)propyl)methacrylamide) (PDMAPMA) coating. (Item 58) The method according to any one of items 1 to 56, wherein at least one particle type of the particle panel comprises an iron oxide core having a poly(oligo(ethylene glycol) methyl ether methacrylate) (POEGMA) coating. (Item 59) The method according to any one of items 1 to 58, wherein at least one distinct particle type of the particle panel has a negative surface charge. (Item 60) The method according to any one of items 1 to 58, wherein at least one distinct particle type of the particle panel has a positive surface charge. (Item 61) The method according to any one of items 1 to 58, wherein at least one distinct particle type of the particle panel has a neutral surface charge. (Item 62) The method according to any one of items 1 to 61, wherein the particle panel comprises one or more distinct particle types selected from Table 10. (Item 63) The method according to any one of items 1 to 62, wherein the particle panel includes two or more distinct particle types, three or more distinct particle types, four or more distinct particle types, five or more distinct particle types, six or more distinct particle types, seven or more distinct particle types, eight or more distinct particle types, nine or more distinct particle types, or all 10 distinct particle types selected from Table 10. (Item 64) The method according to any one of items 1 to 63, wherein the particle panel comprises one or more distinct particle types selected from Table 12. (Item 65) The method according to any one of items 1 to 64, wherein the particle panel includes two or more distinct particle types, three or more distinct particle types, four or more distinct particle types, five or more distinct particle types, six or more distinct particle types, seven or more distinct particle types, eight or more distinct particle types, nine or more distinct particle types, or all 10 distinct particle types selected from Table 12. (Item 66) A composition comprising three or more distinct magnetic particle types that differ in two or more physicochemical properties, wherein a subset of the three or more distinct magnetic particle types shares one of the two or more physicochemical properties, and such particle types of the subset bind to different proteins. (Item 67) The composition according to item 66, wherein the three or more distinct magnetic particle types adsorb proteins from the sample over a dynamic range of at least 7, at least 8, at least 9, or at least 10. (Item 68) The composition according to any one of items 66 to 67, wherein the three or more distinct magnetic particle types are capable of adsorbing a group of 1 to 20,000 proteins from a sample. (Item 69) The composition according to any one of items 66 to 68, wherein the three or more distinct magnetic particle types are capable of adsorbing 1,000 to 10,000 proteins from a sample. (Item 70) The composition according to any one of items 66 to 69, wherein the three or more distinct magnetic particle types are capable of adsorbing 1,000 to 5,000 proteins from a sample. (Item 71) The composition according to any one of items 66 to 70, wherein the three or more distinct magnetic particle types are capable of adsorbing 1,200 to 2,200 proteins from the sample. (Item 72) The composition according to any one of items 69 to 71, wherein the protein group comprises a peptide sequence having a minimum length of 7 amino acid residues. (Item 73) The composition according to any one of items 66 to 72, wherein the three or more distinct magnetic particle types are capable of adsorbing 1 to 20,000 proteins from a sample. (Item 74) The composition according to any one of items 66 to 73, wherein the three or more distinct magnetic particle types are capable of adsorbing 1,000 to 10,000 proteins from the sample. (Item 75) The composition according to any one of items 66 to 74, wherein the three or more distinct magnetic particle types are capable of adsorbing 1,800 to 5,000 proteins from the sample. (Item 76) A composition according to any one of items 66 to 75, comprising at least four distinct magnetic particle types, at least five distinct magnetic particle types, at least six distinct magnetic particle types, at least seven distinct magnetic particle types, at least eight distinct magnetic particle types, at least nine distinct magnetic particle types, at least ten distinct magnetic particle types, at least eleven distinct magnetic particle types, at least twelve distinct magnetic particle types, at least thirteen distinct magnetic particle types, at least fourteen distinct magnetic particle types, at least fifteen distinct magnetic particle types, at least twenty distinct magnetic particle types, or at least thirty distinct magnetic particle types. (Item 77) The composition according to item 76, comprising at least 10 distinct magnetic particle types. (Item 78) A composition according to any one of items 66 to 77, comprising a first distinct particle type and a second distinct particle type, wherein the first distinct particle type and the second distinct particle type share at least two physicochemical properties but differ in at least two physicochemical properties, and therefore the first distinct particle type and the second distinct particle type are different. (Item 79) A composition according to any one of items 66 to 78, comprising a first distinct particle type and a second distinct particle type, wherein the first distinct particle type and the second distinct particle type share at least two physicochemical properties but differ in at least one physicochemical property, and therefore the first distinct particle type and the second distinct particle type are different. (Item 80) The composition according to any one of items 66 to 79, wherein the physicochemical properties include size, charge, core material, shell material, porosity, or surface hydrophobicity. (Item 81) The composition according to item 80, wherein the size is a diameter or radius measured by dynamic light scattering, SEM, TEM, or any combination thereof. (Item 82) A composition according to any one of items 66 to 81, comprising a first distinct particle type and a second distinct particle type, wherein the first distinct particle type and the second distinct particle type comprise a carboxylate material, the first distinct particle is a microparticle, and the second distinct particle type is a nanoparticle. (Item 83) The composition according to any one of items 66 to 81, wherein the particle panel comprises a first distinct particle type and a second distinct particle type, the first distinct particle type and the second distinct particle type having surface charges of 0 mV and -50 mV, the first distinct particle type having a diameter of less than 200 nm, and the second distinct particle type having a diameter of greater than 200 nm. (Item 84) The composition according to any one of items 66 to 81, wherein the particle panel comprises a first distinct particle type and a second distinct particle type, the first distinct particle type and the second distinct particle type having a diameter of 100 to 400 nm, the first distinct particle type having a positive surface charge, and the second distinct particle type having a neutral surface charge. (Item 85) The composition according to any one of items 66 to 81, wherein the particle panel comprises a first distinct particle type and a second distinct particle type, the first distinct particle type and the second distinct particle type being nanoparticles, the first distinct particle type having a surface charge of less than -20 mV, and the second distinct particle type having a surface charge of greater than -20 mV. (Item 86) The composition according to any one of items 66 to 81, wherein the particle panel comprises a first distinct particle type and a second distinct particle type, the first distinct particle type and the second distinct particle type being microparticles, the first distinct particle type having a negative surface charge, and the second distinct particle type having a positive surface charge. (Item 87) A composition according to any one of items 66 to 81, comprising a subset of negatively charged nanoparticles, wherein each particle type of the subset differs in respect to at least one surface chemical group. (Item 88) A composition according to any one of items 66 to 81, comprising a first distinct particle type, a second distinct particle type, and a third distinct particle type, wherein the first distinct particle type, the second distinct particle type, and the third distinct particle type comprise an iron oxide core, a polymer shell, and have a diameter of less than approximately 500 nm, the first distinct particle type having a negative charge, the second distinct particle type having a positive charge, the third distinct particle type having a neutral charge, and the diameter being the average diameter measured by dynamic light scattering. (Item 89) The composition according to item 88, wherein the first distinct particle form comprises a silica coating, the second distinct particle form comprises poly(N-(3-(dimethylamino)propyl)methacrylamide) (PDMAPMA), and the third distinct particle form comprises a poly(oligo(ethylene glycol)methyl ether methacrylate) (POEGMA) coating. (Item 90) The composition according to any one of items 66 to 89, wherein the three or more distinct magnetic particle types include nanoparticles. (Item 91) The composition according to any one of items 66 to 90, wherein the three or more distinct magnetic particle types include microparticles. (Item 92) The composition according to any one of items 66 to 91, wherein at least one distinct particle type among the three or more distinct magnetic particle types is a superparamagnetic iron oxide particle. (Item 93) The composition according to any one of items 66 to 92, wherein at least one distinct particle type among the three or more distinct magnetic particle types comprises an iron oxide material. (Item 94) The composition according to any one of the three or more distinct magnetic particle types, wherein at least one distinct particle type has an iron oxide core. (Item 95) The composition according to any one of items 66 to 94, wherein at least one distinct particle type among the three or more distinct magnetic particle types has iron oxide crystals embedded in a polystyrene core. (Item 96) The composition according to any one of items 66 to 95, wherein each of the three or more distinct particle types is a superparamagnetic iron oxide particle. (Item 97) The composition according to any one of items 66 to 96, wherein each of the three or more distinct magnetic particle types comprises an iron oxide core. (Item 98) The composition according to any one of items 66 to 97, wherein each of the three or more distinct magnetic particle types has an iron oxide crystal embedded in a polystyrene core. (Item 99) A composition according to any one of items 66 to 98, wherein at least one of the three or more distinct magnetic particle types comprises a polymer coating. (Item 100) The composition according to any one of items 66 to 99, wherein the three or more distinct magnetic particle types include a carboxylated polymer, an amination polymer, a zwitterionic polymer, or any combination thereof. (Item 101) The composition according to any one of items 66 to 100, wherein at least one of the three or more distinct magnetic particle types comprises an iron oxide core having a silica shell coating. (Item 102) The composition according to any one of items 66 to 100, wherein at least one of the three or more distinct magnetic particle types comprises an iron oxide core having a poly(N-(3-(dimethylamino)propyl)methacrylamide) (PDMAPMA) coating. (Item 103) The composition according to any one of items 66 to 100, wherein at least one of the three or more distinct magnetic particle types comprises an iron oxide core having a poly(oligo(ethylene glycol) methyl ether methacrylate) (POEGMA) coating. (Item 104) The composition according to any one of items 66 to 103, wherein at least one of the three or more distinct magnetic particle types has a negative surface charge. (Item 105) The composition according to any one of items 66 to 103, wherein at least one of the three or more distinct magnetic particle types has a positive surface charge. (Item 106) The composition according to any one of items 66 to 103, wherein at least one of the three or more distinct magnetic particle types has a neutral surface charge. (Item 107) The composition according to any one of items 66 to 106, wherein the three or more distinct magnetic particle types include one or more particle types from Table 10. (Item 108) The composition according to any one of items 66 to 107, wherein the particle panel comprises two or more distinct particle types, three or more distinct particle types, four or more distinct particle types, five or more distinct particle types, six or more distinct particle types, seven or more distinct particle types, eight or more distinct particle types, nine or more distinct particle types, or all 10 distinct particle types selected from Table 10. (Item 109) The composition according to any one of items 66 to 108, wherein the three or more distinct magnetic particle types include one or more particle types from Table 12. (Item 110) The composition according to any one of items 66 to 109, wherein the particle panel comprises two or more distinct particle types, three or more distinct particle types, four or more distinct particle types, five or more distinct particle types, six or more distinct particle types, seven or more distinct particle types, eight or more distinct particle types, nine or more distinct particle types, or all ten distinct particle types selected from Table 12. (Item 111) The composition according to any one of items 1 to 64 or any one of items 67 to 110, wherein the sample is a biological sample. (Item 112) The method according to item 111, wherein the biological sample is plasma, serum, CSF, urine, tears, cell lysate, tissue lysate, cell homogenate, tissue homogenate, papillary aspirate, fecal sample, synovial fluid and whole blood, or saliva. (Item 113) The composition according to any one of items 1 to 64 or any one of items 67 to 110, wherein the sample is a non-biological sample. (Item 114) The method according to item 113, wherein the non-biological sample is water, milk, solvent, or homogenate sample.

Claims

[Claim 1] The invention described in the specification.

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