Simultaneous analysis of multiple analytes

Abstract

For simultaneous analysis of multiple analytes. [Solution] Methods, devices and systems are described that can simultaneously determine the presence, identity or level of multiple analytes present in a single sample by performing steps including denaturation, standardization, extraction, mixed-mode liquid chromatography and mass spectrometry, thereby determining the presence, identity or level of the analytes in a single sample.

Description

Technical Field

[0001] Cross - reference to Related Applications This application claims the benefit of U.S. Provisional Patent Application No. 62 / 825,610, filed Mar. 28, 2019, which is hereby incorporated by reference in its entirety for all purposes.

Background Art

[0002] It is common knowledge in analytical chemistry that the chemical diversity found within biomolecules is vast and too large to be studied using a single method. Thus, efforts have mainly been focused on the analysis of subsets of homogenous biomolecules. Among these methods, liquid chromatography (LC - MS) combined with mass spectrometry has emerged as an essential analytical platform for many of these analyses and is used to characterize many small and large molecules. Despite this versatility, LC - MS is still commonly used for the analysis of a single class of biochemicals. There is a growing interest in combining different layers of biological information in multi - omics studies. Multi - omics provides an opportunity to integrate various analyses such as genomics, epigenomics, transcriptomics, proteomics, glycoproteomics, glycomics, lipidomics, metabolomics, fluxomics, ionomics, microbiomics, phenomics, metallomics, and exposomics onto one platform. However, these methods are aimed at integrating separate datasets using computational methods and the possibility of combining these different classes of analytes prior to data acquisition has remained unexamined.

[0003] The present invention aims to provide the ability to perform the combined analysis of diverse biomolecules in a single instrument run.

Summary of the Invention

[0004] In one aspect, a method is provided for substantially simultaneously determining the presence, identity, or amount of multiple analytes present in a single sample, the method comprising: a. treating the sample with a denaturing reagent or treatment that denatures the biopolymers in the sample, thereby forming a denatured sample, wherein the denaturing reagent or treatment does not denature the components of the normalization reagent when added to the normalization reagent; b. treating the denatured sample with a normalization reagent, wherein the normalization agent converts the multiple analytes in the denatured sample into normalized analyte species and normalized analyte fragment species, which can be separated by multiple modes of chromatography and individually identified by mass spectrometry, thereby forming a normalized sample; c. treating the normalized sample with an extraction reagent, retaining soluble analyte species and analyte fragment species therein, thereby forming an extracted sample; d. subjecting the extracted sample to multiple modes of chromatography followed by mass spectrometry (MS), thereby generating data therefrom for each of the normalized analyte species and normalized analyte fragment species present therein; e. computationally determining from the data the presence, identity, or level of the multiple analytes present in the extracted sample.

[0005] In some embodiments of methods for substantially simultaneously determining the presence, identity, or amount of multiple analytes present in a single sample, the multiple analytes include chemically related analytes and chemically unrelated analytes. In some embodiments, analytes include proteins, carbohydrates, nucleic acids, lipids, electrolytes, metals, metabolites, volatile compounds, exogenous chemicals, or any combination thereof. In some embodiments, analytes include proteins, carbohydrates, nucleic acids, lipids, electrolytes, metals, small molecules, volatile compounds, exogenous chemicals, or any combination thereof. In some embodiments, the sample is a biological sample. In some embodiments, the sample is whole blood, plasma, serum, urine, feces, cerebrospinal fluid, saliva, sweat, saliva, cell or tissue sample. In some embodiments, the sample includes an aqueous solution, aqueous suspension or aqueous tissue. In some embodiments, the sample can be homogenized before or during step (a). In some embodiments, the denaturing reagent or treatment includes one or more solvents, one or more chaotropic agents, heat, pressure, irradiation, one or more reference standards, or any combination thereof. In some embodiments, the solvent can be methanol, ethanol or acetonitrile. In some embodiments, the solvent is methanol. In some embodiments, the aqueous sample is homogenized in approximately equal amounts of methanol. In some embodiments, the denaturing reagent or treatment further includes a metal chelating agent. In some embodiments, the metal chelating agent is EDTA, EGTA or DMSA. In some embodiments, the metal chelating agent is EDTA. In some embodiments, the denaturing step and treating the denatured sample with a standardizing reagent are performed substantially simultaneously. In some embodiments, the standardizing reagent includes one or more enzymes that depolymerize biopolymers in the sample. In some embodiments, the standardizing reagent includes a pancreatic endolytic enzyme. In some embodiments, the standardizing reagent includes one or more proteases, one or more nucleases, one or more glycosidases, one or more lipases, one or more chelating agents, one or more buffers, one or more reducing agents, one or more derivatizing agents, or any combination thereof.In some embodiments, the derivatizing agent is iodoacetamide. In some embodiments, the standardizing reagent includes trypsin. In some embodiments, the standardizing reagent includes trypsin, ribonuclease A, EDTA, ammonium bicarbonate, or amylase. In some embodiments, when the standardizing reagent includes DNAse I, EDTA is not present simultaneously. In some embodiments, the proteinase is added to the denatured sample simultaneously with or after incubation of the denatured sample with other components of the standardizing reagent. In some embodiments, treatment with the standardizing agent includes incubation at a temperature of about 37 °C for 0 to about 24 hours. In some embodiments, the extraction reagent is added to the standardized sample at 1:1 (v / v). In some embodiments, the extraction reagent includes acetonitrile and acetone. In some embodiments, acetonitrile and acetone are present at 1:1 (v / v). In some embodiments, retaining soluble species internally is achieved by centrifugation or filtration. In some embodiments, the multiple-mode chromatography includes reverse-phase separation, cation-exchange separation, anion-exchange separation, ion-pair separation, normal-phase separation, ion-mobility separation, size-exclusion separation, chiral separation, affinity separation, ligand-exchange separation, polar nonionic separation, or any combination thereof. In some embodiments, the mobile phase further includes one or more ionized adducts. In some embodiments, the ionized adducts are selected from ammonium, proton, sodium, acetate, formate, propionate, phosphate, medronate, urea, biuret, triuret, or any combination thereof. In some embodiments, the mass spectrometry data acquisition includes high-resolution full-scan with low or high-resolution MS2 data non-dependency or data-dependency acquisition with dynamic exclusion in both positive and negative ion modes. In some embodiments, computationally determining the presence, identity, or amount of multiple analytes present in the extracted sample from the LC-MS data includes comparing the LC-MS data of the standardized analyte species or standardized analyte fragment species to the mass spectrometry of those species generated from known amounts of known analytes.

[0006] In one aspect, a method is provided for substantially simultaneously determining the presence, identity or amount of a plurality of chemically related and chemically unrelated analytes present in a single biological sample, the sample being an aqueous sample, and the method comprising: a. treating the sample with an equal volume of a denaturing reagent comprising methanol, wherein the denaturing agent denatures the biopolymers in the sample, thereby forming a denatured sample, and the denaturing reagent or treatment does not denature the components of the normalization reagent when added to the normalization reagent; b. treating the denatured sample with a normalization reagent comprising 50 mM ammonium bicarbonate, 5 mM EDTA, 1:20 m / m trypsin: sample protein at pH 7.8 and 37° C. for 4 hours, wherein the normalizing agent converts a plurality of analytes in the denatured sample into normalized analyte species and normalized analyte fragment species, which species can be separated by multimode chromatography and individually identified by mass spectrometry, thereby forming a normalized sample; c. treating the normalized sample with an equal volume of an extraction reagent comprising 1:1 acetonitrile:acetone, centrifuging the sample, and retaining the supernatant containing soluble analyte species and analyte fragment species therein, thereby forming an extracted sample; d. subjecting the extracted sample to multimode chromatography followed by mass spectrometry (MS), thereby generating data therefrom for each normalized analyte species and normalized analyte fragment species present therein; e. computationally determining from the data the presence, amount or identity of a plurality of analytes present in the extracted sample.

[0007] In one aspect, a method for determining the pathological state of a biological source from which a biological sample is derived comprises: a. substantially simultaneously determining the presence, identity or amount of a plurality of analytes present in the biological sample according to the method described above; b. Comparing the presence, identity or amount of an internal analyte with that of a biological sample from a biological source sample without the medical condition, wherein the medical condition is identifiable from changes in the presence, identity or level of a plurality of analytes. c. Determining the medical condition of the biological source.

[0008] In some embodiments of a method for determining the medical condition of a biological source, the results from determining the medical condition are used to select or monitor an intervention on the biological source.

[0009] In one aspect, an apparatus is provided for substantially simultaneously determining the presence, identity or amount of a plurality of analytes present in a single sample, the apparatus comprising: a. Means for denaturing biopolymers in the sample, thereby forming a denatured sample, wherein the denaturation does not denature the components of the normalization reagent when added to the normalization reagent; b. Means for normalizing the sample, wherein the plurality of analytes in the denatured sample are converted into normalized analyte species and normalized analyte fragment species, which can be separated by multiple modes of chromatography and individually identified by mass spectrometry; c. Means for extracting the normalized sample and retaining soluble analyte species and analyte fragment species therein; d. Means for subjecting the extracted sample to multiple modes of chromatography followed by mass spectrometry (MS), thereby generating data therefrom for each normalized analyte species and normalized analyte fragment species present therein; e. Means for computationally determining the presence, identity or amount of a plurality of analytes present in the extracted sample from the data.

[0010] In some embodiments of the device, the plurality of analytes include chemically related analytes and chemically unrelated analytes. In some embodiments, analytes include proteins, carbohydrates, nucleic acids, lipids, electrolytes, metals, small molecules, volatile compounds, exogenous chemicals, or any combination thereof. In some embodiments, the sample is a biological sample. In some embodiments, the sample is whole blood, plasma, serum, urine, feces, cerebrospinal fluid, saliva, sweat, saliva, cell or tissue sample. In some embodiments, the sample includes an aqueous solution, aqueous suspension or aqueous tissue. In some embodiments, means for homogenizing the sample are provided before or during step (a). In some embodiments, means for denaturing biopolymers in the sample include the use of a denaturing reagent or treatment that includes one or more solvents, one or more chaotropic agents, heat, pressure, irradiation, one or more reference standards, or any combination thereof. In some embodiments, the solvent is methanol, ethanol or acetonitrile. In some embodiments, the solvent is methanol. In some embodiments, the aqueous sample is homogenized in approximately equal amounts of methanol. In some embodiments, denaturing further includes metal chelation. In some embodiments, metal chelation is provided by EDTA, EGTA or DMSA. In some embodiments, chelating the metal is provided by EDTA. In some embodiments, denaturing and normalizing are performed substantially simultaneously. In some embodiments, normalizing is achieved using a normalization reagent that includes one or more enzymes that depolymerize biopolymers in the sample. In some embodiments, the normalization reagent includes pancreatic internal lysing enzymes. In some embodiments, the normalization reagent includes one or more proteases, one or more nucleases, one or more glycosidases, one or more lipases, one or more chelating agents, one or more buffers, one or more reducing agents, one or more derivatizing agents, or any combination thereof. In some embodiments, the derivatizing agent is iodoacetamide. In some embodiments, the normalization reagent includes trypsin.In some embodiments, the normalization reagent includes trypsin, ribonuclease A, EDTA, ammonium bicarbonate, or amylase. In some embodiments, when the normalization reagent includes DNAse I, EDTA is not present simultaneously. In some embodiments, proteinase is added to the denatured sample simultaneously with or after incubation of the denatured sample with other components of the normalization reagent. In some embodiments, the means for normalization includes incubation at a temperature of about 37°C for 0 to about 24 hours. In some embodiments, the means for extraction is achieved using an extraction reagent added to the normalized sample at 1:1 (v / v). In some embodiments, the extraction reagent includes acetonitrile and acetone. In some embodiments, acetonitrile and acetone are present at 1:1 (v / v). In some embodiments, the means for retaining soluble species internally is achieved by centrifugation or filtration. In some embodiments, the means for multi-mode chromatography includes reverse-phase separation, cation-exchange separation, anion-exchange separation, ion-pair separation, normal-phase separation, ion mobility separation, size-exclusion separation, chiral separation, affinity separation, ligand-exchange separation, polar non-ionic separation, or any combination thereof. In some embodiments, the mobile phase used in chromatography further includes one or more ionized adducts. In some embodiments, the ionized adducts are selected from ammonium, proton, sodium, acetate, formate, propionate, phosphate, medronate, urea, biuret, triuret, or any combination thereof. In some embodiments, mass spectrometry data acquisition includes high-resolution full-scan with low or high-resolution MS2 data non-dependence or data-dependence acquisition with dynamic exclusion in both positive and negative ion modes. In some embodiments, the means for computationally determining the presence, identity, or amount of a plurality of analytes present in the extracted sample from LC-MS data includes comparing the LC-MS data of the normalized analyte species or the normalized analyte fragment species to the mass spectrometry of those species generated from known amounts of known analytes.

[0011] In one aspect, a method for determining the state of a biological source from which a biological sample is derived comprises: a. substantially simultaneously determining the presence, identity, or amount of a plurality of analytes present in the biological sample using the apparatus described above; b. comparing the presence, identity, or amount of the internal analytes with those of the biological sample from a biological source sample without the disease state, wherein the disease state is identifiable from changes in the presence, identity, or amount of the plurality of analytes; c. determining the disease state of the biological source.

[0012] In some embodiments of the method of the immediately preceding paragraph, determining the state is used to select or monitor an intervention on the biological source.

[0013] In one aspect, a system is provided for substantially simultaneously determining the presence, identity, or amount of a plurality of chemically related and chemically unrelated analytes present in a single sample, the system comprising: a. a denaturing reagent or treatment that denatures the biopolymers in the sample, thereby forming a denatured sample, the denaturing reagent or treatment not denaturing the components of the normalization reagent when added to the normalization reagent; b. a normalization reagent that converts the plurality of analytes in the sample into standardized analyte species or standardized analyte fragment species that can be separated by mixed-mode liquid chromatography and individually identified by tandem mass spectrometry; c. an extraction reagent that extracts the soluble analyte species and analyte fragment species; d. a separation process that internally retains the soluble analyte species and analyte fragment species, thereby forming an extracted sample; e. a multi-mode chromatography that degrades the soluble analyte species and analyte fragment species; f. a mass spectrometry that generates data regarding the individual species. g. one or more algorithms for computationally determining the presence, identity, or level of a plurality of analytes present in a sample from data of each standardized analyte species and standardized analyte fragment species, and the like.

[0014] In some embodiments of the system, the plurality of analytes includes chemically related analytes and chemically unrelated analytes. In some embodiments, the analytes include proteins, carbohydrates, nucleic acids, lipids, electrolytes, metals, small molecules, volatile compounds, exogenous chemicals, or any combination thereof. In some embodiments, the sample is a biological sample. In some embodiments, the sample is whole blood, plasma, serum, urine, feces, cerebrospinal fluid, saliva, sweat, saliva, cell or tissue sample. In some embodiments, the sample includes an aqueous solution, aqueous suspension or aqueous tissue. In some embodiments, before or during step (a), the sample is homogenized. In some embodiments, the denaturing reagent or treatment includes one or more solvents, one or more chaotropic agents, heat, pressure, irradiation, one or more reference standards, or any combination thereof. In some embodiments, the solvent is methanol, ethanol or acetonitrile. In some embodiments, the solvent is methanol. In some embodiments, the aqueous sample is homogenized in approximately equal amounts of methanol. In some embodiments, the denaturing reagent or treatment further includes a metal chelating agent. In some embodiments, the metal chelating agent is EDTA, EGTA or DMSA. In some embodiments, the metal chelating agent is EDTA. In some embodiments, the denaturing step and treating the denatured sample with a standardizing reagent are performed substantially simultaneously. In some embodiments, the standardizing reagent includes one or more enzymes that depolymerize biopolymers in the sample. In some embodiments, the standardizing reagent includes pancreatic endolysin. In some embodiments, the standardizing reagent includes one or more proteases, one or more nucleases, one or more glycosidases, one or more lipases, one or more chelating agents, one or more buffers, one or more reducing agents, one or more derivatizing agents, or any combination thereof. In some embodiments, the derivatizing agent is iodoacetamide. In some embodiments, the standardizing reagent includes trypsin. In some embodiments, the standardizing reagent includes trypsin, ribonuclease A, EDTA, ammonium bicarbonate or amylase. In some embodiments, when the standardizing reagent includes DNAse I, EDTA is not present simultaneously.In some embodiments, the protease is added to the denatured sample either simultaneously with or after incubation of the denatured sample with other components of the normalization reagent. In some embodiments, treatment with the normalization agent includes incubation at a temperature of about 37° C. for 0 to about 24 hours. In some embodiments, the extraction reagent is added to the normalized sample at 1:1 (v / v). In some embodiments, the extraction reagent includes acetonitrile and acetone. In some embodiments, acetonitrile and acetone are present at 1:1 (v / v). In some embodiments, retaining soluble species internally is achieved by centrifugation or filtration. In some embodiments, multi-mode chromatography includes reverse phase separation, cation exchange separation, anion exchange separation, ion pair separation, normal phase separation, ion mobility separation, size exclusion separation, chiral separation, affinity separation, ligand exchange separation, polar non-ionic separation, or any combination thereof. In some embodiments, the mobile phase used in chromatography further includes one or more ionized adducts. In some embodiments, the ionized adducts are selected from ammonium, proton, sodium, acetate, formate, propionate, phosphate, medronate, urea, biuret, triuret, or any combination thereof. In some embodiments, mass spectrometry data acquisition includes high resolution full scan with low or high resolution MS2 data non-dependency or data dependency acquisition with dynamic exclusion in both positive and negative ion modes. In some embodiments, computationally determining the presence, identity, or amount of a plurality of analytes present in the extracted sample from LC-MS data includes comparing the LC-MS data of the normalized analyte species or normalized analyte fragment species to the mass spectrometry of those species generated from a known amount of a known analyte.

[0015] In one aspect, a method for determining the pathological state of a biological source from which a biological sample is derived comprises a. substantially simultaneously determining the presence, identity, or amount of a plurality of analytes present in the biological sample according to the system described above; and b. Comparing the presence, identity, or amount of an internal analyte with that of a biological sample from a biological source without the medical condition, wherein the medical condition is identifiable from changes in the presence, identity, or amount of a plurality of analytes; c. Determining the medical condition of the biological source.

[0016] In some embodiments of the methods of the immediately preceding paragraph, the medical condition is used to select or monitor a therapeutic intervention for the biological source.

[0017] In one aspect, the disclosure provides a method for identifying a plurality of different types of analytes from a sample, the method comprising: (a) subjecting a sample that contains or is suspected of containing a plurality of different types of analytes to conditions sufficient to produce a solution that includes the plurality of analytes or derivatives thereof; (b) processing the solution using an instrument to identify the plurality of analytes or derivatives thereof, thereby identifying the plurality of analytes, wherein the plurality of analytes or derivatives thereof are identified in a single run of the instrument.

[0018] In some embodiments of a method for identifying multiple different types of analytes from a sample described above or elsewhere in this specification, the multiple analytes include at least three types of analytes selected from the group consisting of proteins, nucleic acids, small molecules, lipids, carbohydrates, electrolytes, and metals. In some embodiments, the multiple analytes include at least one type of analyte selected from small molecules, lipids, and carbohydrates and at least two types of analytes selected from proteins, nucleic acids, electrolytes, and metals. In some embodiments, the multiple analytes include at least one type of analyte selected from proteins and nucleic acids and at least two types of analytes selected from small molecules, lipids, and carbohydrates. In some embodiments, the multiple analytes include small molecules, lipids, and proteins. In some embodiments, the multiple analytes include small molecules, lipids, carbohydrates, and proteins. In some embodiments, the multiple analytes include small molecules, lipids, carbohydrates, and electrolytes. In some embodiments, the multiple analytes include small molecules, lipids, carbohydrates, electrolytes, and metals. In some embodiments, the multiple analytes further include one or both of proteins and nucleic acids. In some embodiments, the small molecules are endogenous small molecules, exogenous small molecules, or a combination thereof. In some embodiments, the multiple analytes include exogenous chemical substances. In some embodiments, at least one of the multiple analytes is a volatile compound.

[0019] In some embodiments of methods for identifying multiple different types of analytes from a sample described above or elsewhere in this specification, the multiple analytes or their derivatives in solution have a molecular size or mass distribution that is different from the multiple analytes contained in or suspected of being contained in the sample. In some embodiments, the multiple analytes or their derivatives in solution contain a different amount of charged molecules, hydrophilic molecules, molecules with hydrophobic functional groups, or any combination thereof, from the multiple analytes contained in or suspected of being contained in the sample. In some embodiments, the multiple analytes or their derivatives in solution have a greater mass percentage of molecules within a predetermined range than the multiple analytes contained in or suspected of being contained in the sample. In some embodiments, the multiple analytes or their derivatives in solution have a narrower molecular size or mass distribution than the multiple analytes contained in or suspected of being contained in the sample. In some embodiments, the multiple analytes or their derivatives in solution have a greater mass percentage of charged molecules than the multiple analytes contained in or suspected of being contained in the sample. In some embodiments, the multiple analytes or their derivatives in solution have a greater mass percentage of hydrophilic molecules when measured by the octanol-water partition coefficient than the multiple analytes contained in or suspected of being contained in the sample. In some embodiments, the multiple analytes or their derivatives in solution have a narrower mass-to-charge ratio (m / z) distribution than the multiple analytes contained in or suspected of being contained in the sample, such that the multiple analytes or their derivatives in solution have a greater mass percentage of molecules within the range detectable by mass spectrometry than the multiple analytes contained in or suspected of being contained in the sample. In some embodiments, the range detectable by mass spectrometry is from 100 Daltons per electron charge (Da / e) to 2,000 Da / e. In some embodiments, the multiple analytes or their derivatives in solution each have a mass-to-charge ratio (m / z) of from 15 Da / e to 4,000 Da / e.

[0020] In some embodiments of a method for identifying a plurality of different types of analytes from a sample described above or elsewhere in this specification, (a) includes one or any combination of the following to obtain a processed sample: (i) homogenizing the sample; (ii) contacting the sample with a denaturing agent to thereby change the conformation of at least one of the plurality of analytes; (iii) contacting the sample with a chelating agent to thereby form a chelate complex with at least one of the plurality of analytes; (iv) contacting the sample with a derivatizing agent to thereby form a derivative of at least one of the plurality of analytes; (v) contacting the sample with a reducing agent to thereby modify at least one of the plurality of analytes; (vi) contacting the sample with an enzyme to thereby generate a fragment of at least one of the plurality of analytes. In some embodiments, (iii) is performed one or more times. In some embodiments, (vi) is performed one or more times. In some embodiments, (i) is performed before or substantially simultaneously with (ii). In some embodiments, (ii) is performed substantially simultaneously with (iii). In some embodiments, (ii) is performed before or substantially simultaneously with (vi). In some embodiments, (vi) is performed substantially simultaneously with one or more of (iii), (iv), and (v). In some embodiments, (vi) is performed substantially simultaneously with (iv). In some embodiments, (vi) is performed substantially simultaneously with (iii) and (iv). In some embodiments, (iii) is performed at least twice, once substantially simultaneously with (i) and again substantially simultaneously with (vi). In some embodiments, (vi) is performed at least twice, once substantially simultaneously with (ii) and again substantially simultaneously with (iv). In some embodiments, the enzyme used in (vi) performed substantially simultaneously with (ii) is a protease. In some embodiments, (i) is performed in a mixture of water and methanol at a volume ratio of 1:5 to 5:1. In some embodiments, the denaturing agent includes a solvent, a chaotropic agent, and a reference standard.In some embodiments, the solvent is selected from methanol, ethanol, and acetonitrile. In some embodiments, the solvent is methanol. In some embodiments, the chelating agent, independently of each presence of (iii), is selected from ethylenediaminetetraacetic acid (EDTA), ethylene glycol-bis(β-aminoethyl ether)-N,N,N’,N’-tetraacetic acid (EGTA), and dimercaptosuccinic acid (DMSA). In some embodiments, the chelating agent is EDTA. In some embodiments, the derivatizing agent is a peptide alkylating agent. In some embodiments, the derivatizing agent is iodoacetamide. In some embodiments, the enzyme, independently of each presence of (vi), comprises one or any combination selected from the group consisting of nuclease, protease, glycosidase, and lipase. In some embodiments, the glycosidase is amylase. In some embodiments, the protease is trypsin. In some embodiments, the nuclease comprises one or both of DNAse I and ribonuclease A. In some embodiments, DNAse I and EDTA do not coexist simultaneously in (vi). In some embodiments, (vi) is carried out in an ammonium bicarbonate buffer. In some embodiments, (vi) comprises incubating at about 37 °C for about 24 hours or less.

[0021] In some embodiments of a method for identifying a plurality of different types of analytes from a sample as described above or elsewhere in this specification, (a) further comprises extracting a plurality of analytes or their derivatives from the processed sample with an extraction reagent, thereby obtaining a solution. In some embodiments, extracting comprises adding the extraction reagent to the processed sample in a 1:1 volume ratio. In some embodiments, the extraction reagent comprises acetonitrile and acetone in a 1:1 volume ratio. In some embodiments, (a) further comprises removing insoluble impurities from the solution by centrifugation or filtration.

[0022] In some embodiments of a method for identifying a plurality of different types of analytes from a sample described above or elsewhere in this specification, (b) comprises subjecting the plurality of analytes or derivatives thereof to an electron beam, thereby generating at least one ionized form or fragment of at least one of the plurality of analytes or derivatives thereof. In some embodiments, (b) further comprises contacting the plurality of analytes or derivatives thereof with a mixed-mode chromatography matrix comprising at least three orthogonal chromatography modes, each of the at least three orthogonal chromatography modes being configured to separate a given type of the plurality of analytes or derivatives thereof from a solution. In some embodiments, at least one of the orthogonal chromatography modes separates at least one derivative of the analyte from a solution. In some embodiments, the mixed-mode chromatography matrix comprises at least three properties selected from the group consisting of cation exchange properties, anion exchange properties, ion exclusion properties, ligand exchange properties, size exclusion properties, chiral properties, reverse phase properties, affinity properties, hydrophilic properties, multidentate properties, or any combination thereof. In some embodiments, (b) further comprises eluting the plurality of analytes and derivatives thereof with at least three mobile phases. In some embodiments, a first fraction of the plurality of analytes and derivatives thereof is eluted by a first mobile phase, the first mobile phase comprising water and 0.1% formic acid (v / v), a second fraction of the plurality of analytes and derivatives thereof is eluted by a second mobile phase, the second mobile phase comprising acetonitrile and 0.1% formic acid (v / v), and a third fraction of the plurality of analytes and derivatives thereof is eluted by a third mobile phase, the third mobile phase comprising 1:1 (v / v) methanol / water, 200 mM ammonium acetate, and formic acid.

[0023] In some embodiments of a method for identifying a plurality of different types of analytes from a sample described above or elsewhere in this specification, at least one of at least three mobile phases, or at least one of a first mobile phase, a second mobile phase, and a third mobile phase, comprises one or more ionization adducts selected from the group consisting of ammonium, proton, sodium, acetate, formate, propionate, phosphate, medronate, urea, biuret, and triuret. In some embodiments, (b) comprises determining the mass of each of the plurality of analytes or their derivatives by mass spectrometry. In some embodiments, (b) comprises determining the amount of each of the plurality of analytes or their derivatives by mass spectrometry. In some embodiments, the mass spectrometry is low or high resolution full scan or fragment scan mass spectrometry generated by data non-dependent or data-dependent acquisition, with or without dynamic exclusion in both positive and negative ion modes. In some embodiments, determining the mass or amount comprises comparing low or high resolution mass spectrometry characteristics to one or more reference mass full or spectra. In some embodiments, the solution is an aqueous solution, optionally containing a water-miscible organic solvent. In some embodiments, the sample is a biological sample.

[0024] In one aspect, the present disclosure provides a method for determining a disease or condition in a subject, the method comprising: (i) identifying, individually or collectively, from a single sample of the subject, a plurality of analytes known to be associated with the disease or condition, wherein the plurality of analytes includes other types of analytes, in order to obtain the amount identified for each of the plurality of analytes; (ii) determining, for each of the plurality of analytes, the difference between the identified amount and a reference amount in order to obtain a plurality of difference values; and (iii) using a trained machine learning algorithm to determine the disease or condition based on the plurality of difference values.

[0025] In some embodiments of a method for determining a disease or condition in a subject described above or elsewhere in this specification, (i) comprises (a) subjecting a single sample that contains or is suspected of containing a plurality of analytes to conditions sufficient to produce a solution that contains the plurality of analytes or derivatives thereof, and (b) using the solution to identify the plurality of analytes or derivatives thereof, thereby identifying the plurality of analytes. In some embodiments, a trained machine learning algorithm is trained using a plurality of training samples that include 170 or fewer training samples. In some embodiments, the trained algorithm is trained using a plurality of training samples that include 30 or fewer training samples. In some embodiments, the trained algorithm is trained using a plurality of training samples that include at least 30 training samples. In some embodiments, a trained machine learning algorithm is trained using a plurality of training samples that include at least 170 training samples. In some embodiments, a method for determining a disease or condition in a subject further comprises identifying a plurality of analytes from each of the plurality of training samples prior to (i). In some embodiments, the disease or condition is determined with at least 80% accuracy. In some embodiments, the accuracy is at least 90%. In some embodiments, the disease or condition is selected from the group consisting of aging, cardiovascular disease, inflammation, heart failure, and dementia. In some embodiments, the plurality of analytes includes one or more analytes selected from the group consisting of apolipoprotein B (apoB), cortisol, C-reactive protein (CRP), and N-terminal pro-brain natriuretic peptide (NT-ProBNP), and derivatives thereof. In some embodiments, the plurality of analytes includes two or more analytes selected from the group consisting of ribonucleotides, deoxyribonucleotides, polypeptides, and metabolites. In some embodiments, the plurality of analytes includes three or more analytes selected from the group consisting of ribonucleotides, deoxyribonucleotides, polypeptides, and metabolites. In some embodiments, the plurality of analytes includes ribonucleotides, deoxyribonucleotides, and metabolites.In some embodiments, the plurality of analytes includes polypeptides and metabolites.

[0026] These and other aspects of the invention will be understood from the following description of the drawings and the detailed description of the invention.

[0027] Brief Description of the Drawings The subject matter regarded as the invention is particularly pointed out and distinctly claimed in the concluding portion of this specification. However, the invention, together with its objects, features, and advantages, may best be understood by reference to the following detailed description, read in conjunction with the accompanying drawings, regarding both the construction and the method of operation thereof.

Brief Description of the Drawings

[0028]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Figure 9

Figure 10

Figure 11

Figure 12A

Figure 12B

Figure 12C

Figure 12D

Figure 13A

Figure 13B

Figure 13C

Figure 13D

Figure 14A

Figure 14B

Figure 14C

Figure 14D

Figure 15A

Figure 15B

Figure 15C

Figure 15D

Figure 16A

Figure 16B

Figure 16C

Figure 16D

Figure 17

[0029] This subject matter can be more readily understood by reference to the following detailed description, which forms a part of this disclosure. The present invention is not limited to the specific products, methods, conditions or parameters described and / or shown herein, and the terms used herein are for the purpose of describing specific embodiments by way of example only and are not intended to limit the claimed invention.

[0030] Unless otherwise defined herein, scientific and technical terms used in connection with this application shall have the meanings commonly understood by one of ordinary skill in the art. Further, unless specifically required by the context, singular terms shall include the plural, and plural terms shall include the singular.

[0031] As used above and throughout this disclosure, the following terms and abbreviations shall be understood to have the following meanings unless otherwise specified.

[0032] In the present disclosure, the singular forms "a", "an", and "the" include plural references, and a reference to a particular numerical value includes at least that particular value unless the context clearly indicates otherwise. Thus, for example, a reference to "a compound" is a reference to one or more such compounds and equivalents thereof known to those skilled in the art. As used herein, the term "plural" means two or more. When a range of values is recited, another embodiment includes from one particular value and / or to another particular value.

[0033] Similarly, by using the antecedent "about", when a value is expressed as an approximation, it is understood that the particular value forms another embodiment. All ranges are inclusive and combinable. In the context of the present disclosure, "about" a particular amount means that the amount is within ±20% of the recited amount, preferably within ±10% of the recited amount, or more preferably within ±5% of the recited amount.

[0034] As used herein, the terms "treat," "treatment," or "therapy" (and their different forms) refer to therapeutic treatment, including prophylactic or preventative measures, the purpose of which is to prevent or alleviate (reduce) undesirable physiological changes associated with a disease or condition. Beneficial or desirable clinical outcomes include, but are not limited to, alleviation of symptoms, whether detectable or undetectable, diminishment of the degree of a disease or condition, stabilization of a disease or condition (i.e., where the disease or condition does not worsen), delay or slowing of the progression of a disease or condition, amelioration or improvement of a disease or condition, and remission, whether partial or total, of a disease or condition. Those in need of treatment include those already having a disease or condition, as well as those who are predisposed to having a disease or condition, or in whom a disease or condition should be suppressed. As used herein, the terms "component," "composition," "formulation," "composition of compounds," "compound," "drug," "pharmacologically active agent," "active agent," "therapeutic agent," "therapy," "therapeutic," or "medicament" are used interchangeably herein as the context dictates, and refer to a compound(s) or composition of substances that, when administered to a subject (human or animal), induces the desired pharmacological and / or physiological effect(s) by local and / or systemic action. A personalized composition or method refers to a product or the use of a product in a regimen that has been tailored or individualized to meet the specific needs identified or contemplated in a subject.

[0035] The terms "subject", "individual", and "patient" are used interchangeably herein and refer to an animal, such as a human, to which treatment with a composition or formulation according to the invention is provided. The term "subject" or "biological source" as used herein refers to humans and non-human animals. The terms "non-human animal" and "non-human mammal" are used interchangeably herein and include all vertebrates, such as non-human primates (especially higher primates), sheep, dogs, rodents (e.g., guinea pigs or rats), guinea pigs, goats, pigs, cats, rabbits, cows, horses, etc., mammals, and reptiles, amphibians, birds, butterflies, etc. non-mammals. In certain embodiments, "biological source" includes non-animal organisms such as microorganisms, fungi, moss liverworts, and plants including other human and animal edibles such as grains, fruits, vegetables, and foods made therefrom. The compositions described herein can be used to treat any suitable mammal including primates such as monkeys and humans, horses, cows, cats, dogs, rabbits, and rodents such as rats and mice. In one embodiment, the mammal being treated is a human. The human can be any human of any age. In one embodiment, the human is an adult. In another embodiment, the human is a child. The human can be male, female, pregnant, middle-aged, young, or elderly. According to any of the methods of the invention, in one embodiment, the subject is a human. In another embodiment, the subject is a non-human primate. In another embodiment, the subject is murine, which in one embodiment is a mouse and in another embodiment is a rat. In another embodiment, the subject is a dog, cat, cow, horse, laprine or pig. In another embodiment, the subject is a mammal.

[0036] The states and disorders for which a particular drug, compound, composition, formulation (or combination thereof) is said to be "indicated" herein are not limited to those states and disorders for which the drug or compound or composition or formulation has been explicitly approved by a regulatory authority, but also include other states and disorders that are known or reasonably believed by a physician or other health or nutrition professional to be suitable for treatment with the drug or compound or composition or formulation or combination thereof.

[0037] There is great interest in deciphering the vast amount of molecular information present in biological systems. Because even simple biological samples exhibit overwhelming chemical diversity, highly specialized analytical methods are required to detect and measure various subsets of analytes found in nature. The inventors describe a method for quantifying a diverse range of molecular species in a single analytical run in various biological specimens using LC-MS. This assay provides a direct approach for analyzing complex biochemical systems and offers unique opportunities in biomedical research. This framework integrates the analysis of diverse biomolecules into a single instrument run by combining orthogonal mixing mode (or multiple modes of) chromatography with various steps and reagents aimed at standardizing and encompassing chemicals prior to introducing the sample into the mass spectrometer. Using this approach, simultaneous quantification of thousands of proteins, lipids, small molecules, electrolytes, and even oligosaccharides and oligonucleotides is provided (Figure 1).

[0038] In one embodiment, the biological sample is first homogenized and denatured in a methanol solution. Next, the analytes are chemically normalized to improve LC-MS compatibility, for example, polymers are hydrolyzed by enzymes into fragments such as small molecules, and molecules with insufficient detection such as neutral lipids and electrolytes are paired with ionization adducts. Fragments that are incompatible with LC-MS are precipitated using a water-miscible organic solvent, and a comprehensive mixed polarity extract is obtained after centrifugal clarification. The analytes are separated using orthogonal mixed mode (or multiple modes of) chromatography before acquiring mass spectrometry data. Full MS scans and MS / MS fragmentation scans are acquired for these multi-omics preparations with polarity switching to capture both positively and negatively ionized molecules. A more detailed description of the sample processing method can be found further below and in the examples.

[0039] As shown in the following examples, a representative set of analytes including proteins, lipids, metabolites, and electrolytes was selected for analysis to demonstrate that this method enables the separation and quantification of chemically diverse molecules. With the exception of polar neutral compounds such as sugars and oligosaccharides, most compounds were retained by chromatography. These analytes were detectable in human plasma preparations at endogenous levels (Figure 2).

[0040] To demonstrate the reproducibility of this method in quantification, five fasting plasma sample preparations were analyzed five times. Examination of a small set of representative analytes showed that the measurements had sufficient precision to distinguish between samples (Figure 3). Unlabeled feature quantification of these data using the KNIME (v3.6.2) OpenMS (v2.4.0) plugin yielded a total of 98,797 deisotoped features, of which 12,150 had an average within-sample coefficient of variation (CV) of less than 20% and an average within-sample CV smaller than the between-sample CV. Using a narrowed precursor scan window over 10 runs of 60 minutes each in a semi-exhaustive BoxCar-like data-dependent acquisition fragmentation scan analysis of pooled plasma, peptide matches were found for 2,559 protein families using Thermo Proteome Discoverer (v2.2.0) and compound matches were found for 270 compounds using Thermo Compound Discoverer (v2.0.0).

[0041] To demonstrate the feasibility of simultaneous bottom-up high-molecular analysis of multiple biochemical classes, pancreatic endogenous enzymes α-amylase, RNase A, and DNase I were used to digest serum albumin, glycogen, RNA, and DNA. Digestion over 18 hours yielded detectable products corresponding to oligosaccharides, oligonucleotides, and oligodeoxynucleotides even in the presence of trypsin and methanol. To test this approach in biological matrices, mouse liver homogenate was digested with RNase A and trypsin, enabling the detection of metabolites, oligosaccharides, oligonucleotides, and tryptic peptides (Figures 5 and 6). Even without enzymes, low-molecular-weight components were clearly separated and identifiable and quantifiable (Figure 4). Similar results were obtained in cultured human liver cancer cells (Figure 7), human plasma (Figure 8), bovine adipose tissue (Figure 9), and human urine (Figure 10).

[0042] Each of the components and steps of the method is described in further detail below. In one embodiment, a method for performing an analysis is provided. In one embodiment, an apparatus or device is provided that includes all of the equipment and reagents necessary for performing the analysis. In one embodiment, a fluidic or microfluidic pathway is provided for processing an input sample as preparation for analysis by LC-MS. In one embodiment, individual samples can be loaded into a cassette or similar device for sequential or automated processing of multiple samples in any order programmed into the device or apparatus. In one embodiment, the apparatus or device can be programmed to provide data regarding the presence, identity, or amount of an analyte present in a sample. In one embodiment, the apparatus or device can be programmed to provide a diagnosis of a disease or health profile based on the presence or absence or relative amounts of various analytes in a sample. In one embodiment, the output from the device leads to a therapeutic intervention or prevention of a condition or disease and then to monitoring of the progression of the condition or disease and the effectiveness or lack thereof of the therapeutic intervention. In one embodiment, the apparatus or device can be programmed to classify a sample based on a set of predetermined parameters regarding an individual or group of analytes present in the sample. In one embodiment, a database is created that includes results from samples from a large population of individuals having various conditions or diseases for use in identifying health or disease in a new sample. These embodiments are in no way intended to limit the usefulness of the invention, the apparatus and devices, methods, or systems that utilize the invention for its intended purposes.

[0043] Samples and Internal Analytes. In one embodiment, the method is provided for substantially simultaneously determining the presence, identity, or levels of multiple analytes present in a single sample. Any sample for which a soluble extract can be prepared for analysis by LC-MS can be used. The sample can be, by way of non-limiting example, a biological sample, a water sample, an oil sample, a soil sample, a mineralogical sample, a contaminated water sample, an agricultural product sample, a meat sample, etc. The sample can be a solid, a liquid, or a combination thereof. The sample can be a gas sample in which the components are dissolved in an aqueous medium. Non-limiting examples of biological samples include samples from humans or other animals, or plants. Non-limiting examples of samples from animals include whole blood, plasma, serum, urine, feces, cerebrospinal fluid, saliva, sweat, saliva, semen, biopsy specimens, cell or tissue samples. Other samples can be hair, nails, shed skin, dandruff, etc. The sample can be an aqueous solution, an aqueous suspension, or an aqueous tissue. The sample can be homogenized from a solid or semi-solid sample. Other biological samples include biofilms, food and beverage preparations and derivatives. Non-biological samples can include, but are not limited to, industrial materials such as raw materials, in-process samples, process intermediates, and production batches. In one embodiment, the method is used to determine the presence of biological components in non-biological materials such as rock and mineral samples, fossils including fossilized humans and other animals, and rock samples from or on extraterrestrial sources or locations.

[0044] The sample may contain a plurality of analytes. In one embodiment, the analytes are chemically related. In one embodiment, the analytes are not chemically related. In one embodiment, the analytes are biopolymers, for example, any biomolecule or its repeating unit containing similar subunits such as polysaccharides, nucleic acids, and proteins. Other analytes are small molecule compounds such as metabolites, electrolytes, sugars, amino acids, metals, drugs, etc. Other biological components include polar and nonpolar lipids, volatile compounds, other exogenous chemical substances, etc. The foregoing and other descriptions herein mainly describe analytes from biological sources, particularly from the human body, but also include any veterinary or livestock-derived analytes such as water pollutants, industrial chemicals, and industrial products, as well as non-living whole organisms. Sources from plants are embodied herein.

[0045] In one embodiment, the sample is homogenized before or during Step 1. The sample can be homogenized in an aqueous solution such as water or an aqueous buffer.

[0046] Step 1: Denaturation. The sample is treated with a denaturing reagent that denatures the biopolymers in the sample, thereby forming a denatured sample, and the denaturing reagent does not denature the components of the normalization reagent when added to the normalization reagent.

[0047] In one embodiment, the denaturing reagent includes one or more solvents, one or more chaotropic agents, one or more reference standards, or any combination thereof. In one embodiment, the solvent is methanol, ethanol, acetonitrile, or another volatile organic solvent or any combination thereof. In one embodiment, the solvent is methanol. In one embodiment, the aqueous sample is homogenized in approximately equal amounts of methanol. In one embodiment, the denaturing reagent further includes a metal chelating agent. In one embodiment, the metal chelating agent is EDTA, EGTA, or DMSA. In one embodiment, the metal chelating agent is EDTA. In one embodiment, the metal chelating agent is present in the sample, such as when EDTA is used as an anticoagulant for blood samples.

[0048] In one embodiment, the denaturation of the sample as described above is carried out together with normalization, as will be explained in the following steps. In one embodiment, the components of the denatured sample are compatible with the normalization step in that the enzymes and other components of the normalization step can perform their desired functions in the presence of any components from the denaturation step. In one embodiment, the denaturation step results in a solution having a methanol content of about 40-60%.

[0049] Step 2: Normalization. The denatured sample is treated with a normalization reagent that converts a plurality of analytes in the denatured sample into normalized analyte species and normalized analyte fragment species. In one embodiment, the species thus formed can be separated by multi-mode chromatography and individually identified by mass spectrometry, thereby forming a normalized sample.

[0050] In one embodiment, the normalization reagent includes one or more enzymes that depolymerize biopolymers in the sample into analyte fragment species. In one embodiment, the normalization reagent includes pancreatic internal lysing enzymes. In one embodiment, the normalization reagent includes one or more proteases, one or more nucleases, one or more glycosidases, one or more lipases, one or more chelating agents, one or more buffers, one or more reducing agents, one or more derivatizing agents, or any combination thereof.

[0051] In an embodiment, the protease is trypsin, chymotrypsin, proteinase K, LysC, LysN, AspN, GluC or ArgC. In one embodiment, the protease is trypsin. In one embodiment, the derivatizing agent is β-mercaptoethanol, dithiothreitol, tris(2-carboxyethyl)phosphine-HCl, or iodoacetamide. In one embodiment, the derivatizing agent is iodoacetamide.

[0052] In one embodiment, the buffer is phosphate, citrate, bicarbonate, sulfonate, formate, acetate or ammonia. In one embodiment, the pH of the sample being standardized is provided to maximize the standardization of the sample. In one embodiment, the pH is about 7.8.

[0053] In one embodiment, the standardization reagent comprises a combination of trypsin, ribonuclease A, EDTA, ammonium bicarbonate, and amylase.

[0054] In one embodiment, the standardization reagent comprises 50 mM ammonium bicarbonate, 5 mM EDTA, and 1:20 m / m trypsin: sample protein.

[0055] In one embodiment, the standardization includes successive steps where specific reagents are not compatible, such as when the optimal pH of different enzymes is different, and as a result, the conditions during the standardization process change to maximize the standardization of the sample. In one embodiment, when the standardization reagent includes DNAse I, EDTA is not present simultaneously.

[0056] In one embodiment, the proteinase is added to the denatured sample simultaneously with or after incubation with the other components of the standardization reagent for the denatured sample.

[0057] In one embodiment, treatment with the standardizing agent includes incubation at a temperature of about 37 °C for about 0 - 24 hours.

[0058] These guidelines regarding the desired results of the standardization step do not mean restricting the hydrolysis of the biopolymer into fragments that can be analyzed by LC-MS, which may be difficult to analyze without hydrolyzing the biopolymer, and derivatizing any components so that they can also be analyzed by LC-MS.

[0059] Step 3: Extraction. The standardized sample is treated with an extraction reagent, retaining soluble analyte species and analyte fragment species therein, thereby forming the extracted sample.

[0060] The addition of organic solvents is a procedure commonly used in metabolomics to substantially simultaneously remove proteins from a sample and extract their metabolites. The combination of mixed polar solvents is utilized to provide sample clean-up while extracting both polar and non-polar molecules. The sample can optionally be concentrated, but injection of this relatively crude preparation enables the detection of volatile analytes such as isopropanol or ethanol.

[0061] As described herein, the components of analytes in a sample that can be analyzed by LC-MS are made soluble by denaturation and normalization steps. These soluble components are extracted and then analyzed by LC-MS. In one embodiment, the extraction reagent is added to the normalized sample at 1:1 (v / v). In one embodiment, the extraction reagent contains acetonitrile and acetone. In one embodiment, acetonitrile and acetone are present at 1:1 (v / v).

[0062] In one embodiment, the sample after the extraction step contains soluble components and fragments of analytes in the original sample. In one embodiment, separation of the soluble components in the extracted sample is achieved by centrifugation or filtration.

[0063] The foregoing methods of denaturation, normalization, and extraction have been described as separate steps, but in one embodiment, the addition of reagents, mixing, incubation, heating, separation, and other operations may be performed continuously in a fluid path that can be achieved in any suitable order to prepare a sample for LC-MS. In one embodiment where a fluid path is used, a sample is introduced, a denaturing reagent is added to the fluid, and the combined solution is mixed in a mixing chamber or turbulent region of a provided diameter, length, and flow rate to optimize the desired results of denaturation. Next, a normalization reagent is added to the reagent as the sample moves through the fluid path or is immobilized along the fluid path. In the case of non-compatible normalization reagents and conditions simultaneously, the sample may be treated with nuclease, for example, in the absence of EDTA, and then subsequently diluted and treated with protease and glycosidase among other components in the presence of EDTA. The design of the fluid path, including length, diameter, flow rate, smooth regions or turbulent / mixing regions, is designed to maximize the efficiency of the sample processing steps. After normalization, an extraction reagent is added to the fluid path, and the soluble components of the fluid are separated from the insoluble components by in-line filtration, continuous or zone centrifugation, or any other means for separating soluble components for further processing. After processing, the extracted sample can enter the LC-MS, or the processed sample can be continuously stored and then injected into the LC-MS when it is ready to analyze the next programmed sample. The foregoing conditions are merely exemplary in the form of a single fluid path means for sample processing and are not intended to be limiting in any way.

[0064] In some embodiments, fluid processing is gravity-dependent. In some embodiments, the fluid processing path is gravity-independent, for example, for use in space environments and exploration.

[0065] Step 4 LC-MS. The extracted sample is subjected to multi-mode chromatography followed by mass spectrometry, thereby generating MS data for each normalized analyte species and normalized analyte fragment species present internally.

[0066] In one embodiment, electrospray ionization is used. In other embodiments, atmospheric pressure chemical ionization (APCI), atmospheric pressure photoionization (APPI) or electrospray ionization (ESI) is used. These are examples of atmospheric pressure ionization (API) methods that can be used but are not intended to be limiting.

[0067] The use of chemical additives to improve the LC-MS response of poorly detected analytes is well established. Volatile salts such as ammonium acetate are used to elute the ion exchange phase and improve the ionization of neutral compounds such as lipids. Metal chelators have been used to improve the chromatography and sensitivity of phosphorylated compounds and metal ions using medronic acid and EDTA. Combinations of such strategies are used herein to elute analytes with commercially available tri-modal mixed mode columns characterized by reverse phase, cation and anion exchange properties. Using aqueous starting conditions, this column appears to retain most compounds except for polar neutral molecules such as oligosaccharides and sugars.

[0068] Multi-mode chromatography enables chromatographic separation of diverse species, but in chromatography characterized by single-mode chromatography, analytes that do not participate in chemical interactions with the stationary phase cannot be separated. Multi-mode separation is commonly used to resolve complex mixtures of analytes in various formats, including offline fractionation, or online one-dimensional mixed bed / phase columns, or online sequential multi-dimensional LC. Here, commercially available trimodal columns are described, but increasing the number of modes may improve separation based on other chemical properties, including ion mobility, chirality, size, and non-ionic polarity.

[0069] In one embodiment, the multi-mode chromatography includes reverse-phase separation, cation-exchange separation, anion-exchange separation, ion-pair separation, normal-phase separation, ion mobility separation, size-exclusion separation, chiral separation, affinity separation, ligand-exchange separation, polar non-ionic separation, or any combination thereof.

[0070] In one embodiment, the mobile phase used in the chromatography further includes one or more ionizable additives, which are not limited to these, such as ammonium, proton, sodium, acetate, formate, propionate, phosphate, medronate, urea, biuret, triuret, or any combination thereof.

[0071] In one embodiment, the mass spectrometry data acquisition includes high-resolution full-scan with low or high-resolution MS2 data non-dependency or data-dependency acquisition with dynamic exclusion in both positive and negative ion modes.

[0072] Step 5: Analysis. The presence, identity, or level of multiple analytes present in the extracted sample is computationally determined from the MS data.

[0073] In some embodiments, the LC-MS data includes chromatographic retention time, ion mobility collision cross-section, mass-to-charge ratios of different forms of precursor and product ions, states and ratios of ion adducts, losses, multimers, isotope abundances, charge states, or any combination thereof.

[0074] In one embodiment, computationally determining the presence, identity, or level of multiple analytes present in the extracted sample from the MS data includes comparing the MS data of the standardized analyte species or standardized analyte fragment species with the MS data of those species generated from known amounts of known analytes.

[0075] In one embodiment, computationally determining the presence, identity, or level of a plurality of analytes present in an extracted sample from MS data involves comparing the MS data of a standardized analyte species or a standardized analyte fragment species to the MS data generated from known amounts of isotopic molecular species of those species.

[0076] In one embodiment, computationally determining the presence, identity, or level of a plurality of analytes present in an extracted sample from MS data involves comparing the MS data of a standardized analyte species or a standardized analyte fragment species to the computationally simulated MS data of a known or theoretical analyte.

[0077] In one embodiment, the computational simulation of MS data for a biopolymer fragment involves generating all possible or conceivable combinations and permutations of subunits for the biopolymer fragment and calculating the MS data expected based on the subunit chemical composition of the theoretical biopolymer fragment.

[0078] In one embodiment, computationally determining the identity of a plurality of analytes present in an extracted sample from MS data involves pre - identifying the biochemical class of a standardized analyte species or a standardized analyte fragment species by comparing the isotope distribution of precursor ions to the computationally simulated isotope distribution of a known biochemical class, and subsequently comparing the MS data of the standardized analyte species or the standardized analyte fragment species to the MS data of a known or theoretical analyte from the matched biochemical class.

[0079] In one embodiment, computationally determining the presence or level of a plurality of analytes present in an extracted sample from MS data involves preparing the intensity peak area of a standardized analyte species or a standardized analyte fragment species in proportion to the intensity peak area of a known amount of a known analyte added to the sample or already present in the sample.

[0080] In one embodiment, computationally determining the identity of a plurality of analytes present in an extracted sample from MS data involves matching a standardized analyte species or a standardized analyte fragment species based on relative abundances of chromatographic retention times, expected isotope masses, or their precursor ions, their fragment ions, their alternative charge state ions, their alternative positively or negatively charged ions, or chemically related ions such as derivatives or analogs of such characteristics recorded or simulated for known analytes.

[0081] In some embodiments, software, algorithms, and other methods for identifying species in MS data and therefrom identifying the identity of analytes are achieved by use of the KNIME (v3.6.2) OpenMS (v2.4.0) plugin, Thermo Proteome Discoverer (v2.2.0), and Thermo Compound Discoverer (v2.0.0), but these are merely exemplary and not intended to be limiting.

[0082] In one example of this method, the following steps can be performed.

[0083] Step 1. Treat the sample with an equal volume of a denaturing reagent containing methanol, where the denaturing agent denatures the biopolymers in the sample, thereby forming a denatured sample, and the denaturing reagent does not denature the components of the standardizing reagent when added to the standardizing reagent.

[0084] Step 2. Treat the denatured sample with a standardizing reagent containing 50 mM ammonium bicarbonate, 5 mM EDTA, 1:20 m / m trypsin: sample protein at pH 7.8 and 37°C for 4 hours, where the standardizing agent converts the plurality of analytes in the denatured sample into standardized analyte species and standardized analyte fragment species, which can be separated by multi-mode chromatography and individually identified by mass spectrometry, thereby forming a standardized sample.

[0085] Step 3. The standardized sample is treated with an equal volume of extraction reagent containing 1:1 acetonitrile:acetone, the sample is centrifuged, and the supernatant containing soluble analyte species and analyte fragment species therein is retained, thereby forming the extracted sample.

[0086] Step 4. The extracted sample is subjected to multi-mode chromatography followed by mass spectrometry, thereby generating MS data for each of the standardized analyte species and standardized analyte fragment species present therein.

[0087] Step 5. The presence, level, and / or identity of the plurality of analytes present in the extracted sample are computationally determined from the MS data.

[0088] The foregoing method, apparatus, device, or system can be used for various purposes. In one embodiment, a method for determining the state of a biological source from which a biological sample is derived comprises a. substantially simultaneously determining the presence, identity, or level of a plurality of analytes present in a biological sample according to the method described herein; b. comparing the presence, identity, or level of internal analytes with that of a biological sample from a biological source sample without the condition, the condition being identifiable from a change in the presence, identity, or level of the plurality of analytes; c. determining the state of the biological source.

[0089] In one embodiment, the condition is a pathological condition or disease and the result of the determination is used to guide a therapeutic intervention or to monitor the effectiveness of treatment or progression of the disease or condition. In one embodiment, the condition is a dietary or metabolic imbalance and the result of the determination leads to a dietary or lifestyle change and is used to monitor the effect and progression of the change. In one embodiment, this method can be used to determine a molecule or pattern of molecules associated with protection from or risk of onset of a condition such as a pathological condition. In one non-limiting example, the risk of developing type 2 diabetes (T2D) can be evaluated, and a sample of T2D has a high or low level of one or more compounds or biomarkers that are not currently associated with an increased risk of diabetes, and a sample of a healthy body has a low or high level of such compounds or biomarkers that do not currently show protection against diabetes. By comparing an analyte or pattern or an analyte in a sample from a subject having T2D with a sample of a healthy individual, new biomarkers can be discovered.

[0090] In another embodiment of the present invention, the method described herein is implemented by an apparatus or device capable of performing all method steps. In one embodiment, the apparatus is a. means for denaturing a biopolymer in a sample, thereby forming a denatured sample, wherein the denaturing reagent does not denature the components of the standardizing reagent when added to the standardizing reagent; b. means for standardizing a sample, wherein a plurality of analytes in the denatured sample are converted into standardized analyte species and standardized analyte fragment species, and these species can be separated by multiple modes of chromatography and individually identified by mass spectrometry; c. means for extracting the standardized sample and retaining soluble analyte species and analyte fragment species therein; d. Means for subjecting the extracted sample to multi-mode chromatography followed by mass spectrometry, thereby generating MS data for each of the internal standardized analyte species and standardized analyte fragment species present therein. e. Means for computationally determining the presence, identity or level of a plurality of analytes present in the extracted sample from the MS data.

[0091] In one embodiment, a system for implementing the above-described method herein is provided. In one embodiment, the system is provided for substantially simultaneously determining the presence, identity or level of a plurality of chemically related and chemically unrelated analytes present in a single sample, and the system comprises: a. A denaturing reagent for denaturing the biopolymers in the sample, thereby forming a denatured sample, wherein the denaturing reagent does not denature the components of the standardizing reagent when added to the standardizing reagent. b. A standardizing reagent for converting a plurality of analytes in the sample into standardized analyte species or standardized analyte fragment species that can be separated by mixed-mode liquid chromatography and individually identified by tandem mass spectrometry. c. An extraction reagent for extracting soluble analyte species and analyte fragment species. d. A separation process for retaining soluble analyte species and analyte fragment species therein, thereby forming the extracted sample. e. Multi-mode chromatography for degrading soluble analyte species and analyte fragment species. f. Mass spectrometry for generating data on individual species. g. One or more algorithms for computationally determining the presence, identity or level of a plurality of analytes present in the sample from the data of each standardized analyte species and standardized analyte fragment species.

[0092] Some embodiments provide a method for identifying a plurality of different types of analytes from a sample, the method comprising: (a) subjecting the sample, which contains or is suspected of containing the plurality of different types of analytes, to conditions sufficient to produce a solution comprising the plurality of analytes or derivatives thereof; and (b) using an instrument to process the solution to identify the plurality of analytes or derivatives thereof, thereby identifying the plurality of analytes, wherein the plurality of analytes or derivatives thereof are identified in a single run of the instrument. In some embodiments, (b) is performed in a single run of a single instrument. Those skilled in the art will understand that the term "single run of an instrument" as used herein generally refers to a single analytical process performed in a single loading of a single sample using a single instrument. Those skilled in the art will understand that an "analyte derivative" can be a fragmented, ionized, oxidized, reduced, chemically functionalized, chemically defunctionalized, isomerized, coordinated, polymerized, or multimerized form of the analyte, or a combination thereof. In some embodiments, the plurality of analytes includes at least three types of analytes selected from the group consisting of proteins, nucleic acids, small molecules, lipids, carbohydrates, electrolytes, and metals. Those skilled in the art will understand that non-limiting examples of "electrolytes" include Na+, Cl−, K+, bicarbonate, phosphate, sulfate, bromide, acetate, formate, and ammonium. Those skilled in the art will also understand that non-limiting examples of small molecules include molecules having a molecular weight of less than 900 daltons, less than 1500 daltons, or less than 2000 daltons. In some embodiments, the plurality of analytes includes at least one type of analyte selected from small molecules, lipids, and carbohydrates and at least two types of analytes selected from proteins, nucleic acids, electrolytes, and metals. In some embodiments, the plurality of analytes includes at least one type of analyte selected from proteins and nucleic acids and at least two types of analytes selected from small molecules, lipids, and carbohydrates.In some embodiments, the plurality of analytes includes small molecules, lipids, and proteins. In some embodiments, the plurality of analytes includes small molecules, lipids, carbohydrates, and proteins. In some embodiments, the plurality of analytes includes small molecules, lipids, carbohydrates, and electrolytes. In some embodiments, the plurality of analytes includes small molecules, lipids, carbohydrates, electrolytes, and metals. In some embodiments, the plurality of analytes further includes one or both of proteins and nucleic acids. In some embodiments, the small molecules are endogenous small molecules, exogenous small molecules, or a combination thereof. In some embodiments, the plurality of analytes includes exogenous chemical substances. In some embodiments, at least one of the plurality of analytes is a volatile compound. In some embodiments, the plurality of analytes or their derivatives in the solution have a molecular size or mass distribution different from that of the plurality of analytes contained in or suspected of being contained in the sample. In some embodiments, the plurality of analytes or their derivatives in the solution include a different amount of charged molecules, hydrophilic molecules, molecules with hydrophobic functional groups, or any combination thereof from the plurality of analytes contained in or suspected of being contained in the sample. In some embodiments, the charged molecules include negatively charged molecules, positively charged molecules, or both. In some embodiments, the plurality of analytes or their derivatives in the solution have a distribution of pKa constant values of molecules with acidic and basic functional groups different from that of the plurality of analytes contained in or suspected of being contained in the sample. In some embodiments, the plurality of analytes or their derivatives in the solution have a distribution of octanol-water partition coefficients of molecules different from that of the plurality of analytes contained in or suspected of being contained in the sample. In some embodiments, the plurality of analytes or their derivatives in the solution have a larger mass percentage of molecules within a predetermined range than the plurality of analytes contained in or suspected of being contained in the sample.In some embodiments, the plurality of analytes or their derivatives in the solution have a molecular size or mass distribution that is narrower than that of the plurality of analytes contained in or suspected of being contained in the sample. In some embodiments, the plurality of analytes or their derivatives in the solution have a greater mass percentage of charged molecules than the plurality of analytes contained in or suspected of being contained in the sample. In some embodiments, the plurality of analytes or their derivatives in the solution have a greater mass percentage of hydrophilic molecules when measured by the octanol-water partition coefficient than the plurality of analytes contained in or suspected of being contained in the sample. In some embodiments, the plurality of analytes or their derivatives in the solution have a narrower mass-to-charge ratio (m / z) distribution than the plurality of analytes contained in or suspected of being contained in the sample, such that the plurality of analytes or their derivatives in the solution have a greater mass percentage of molecules that fall within the range detectable by mass spectrometry than the plurality of analytes contained in or suspected of being contained in the sample. In some embodiments, the range detectable by mass spectrometry is from 100 Daltons per electron charge (Da / e) to 2,000 Da / e. In some embodiments, the plurality of analytes or their derivatives in the solution each have a mass-to-charge ratio (m / z) from 15 Da / e to 4,000 Da / e.

[0093] In some embodiments of a method for identifying a plurality of different types of analytes from a sample described above or elsewhere in this specification, (a) includes one or any combination of the following to obtain a processed sample: (i) homogenizing the sample; (ii) contacting the sample with a denaturing agent to thereby change the conformation of at least one of the plurality of analytes; (iii) contacting the sample with a chelating agent to thereby form a chelate complex with at least one of the plurality of analytes; (iv) contacting the sample with a derivatizing agent to thereby form a derivative of at least one of the plurality of analytes; (v) contacting the sample with a reducing agent to thereby modify at least one of the plurality of analytes; (vi) contacting the sample with an enzyme to thereby generate a fragment of at least one of the plurality of analytes. In some embodiments, (iii) is performed one or more times. In some embodiments, (vi) is performed one or more times. In some embodiments, (i) is performed before or substantially simultaneously with (ii). In some embodiments, (ii) is performed substantially simultaneously with (iii). In some embodiments, (ii) is performed before or substantially simultaneously with (vi). In some embodiments, (vi) is performed substantially simultaneously with one or more of (iii), (iv), and (v). In some embodiments, (vi) is performed substantially simultaneously with (iv). In some embodiments, (vi) is performed substantially simultaneously with (iii) and (iv). In some embodiments, (iii) is performed at least twice, once substantially simultaneously with (i) and again substantially simultaneously with (vi). In some embodiments, (vi) is performed at least twice, once substantially simultaneously with (ii) and again substantially simultaneously with (iv). In some embodiments, the enzyme used in (vi) performed substantially simultaneously with (ii) is a protease.One of ordinary skill in the art will understand that the term "substantially simultaneously" as used herein generally means that each of the relevant steps is not longer than necessary to achieve the objectives of the relevant method and is within a range of being temporally close to each other (i.e., within 1 hour, within 30 minutes, within 10 minutes, within 5 minutes, or within 1 minute). In some embodiments, (i) is carried out in a mixture of water and methanol at a volume ratio of 1:5 to 5:1. In some embodiments, (i) is carried out in a mixture of water and methanol at a volume ratio of 1:2 to 2:1. In some embodiments, the mixture is a 1:1 mixture by volume of water and methanol. In some embodiments, the denaturing agent includes a solvent, a chaotropic agent, and a reference standard. In some embodiments, the solvent is selected from methanol, ethanol, and acetonitrile. In some embodiments, the solvent is methanol. In some embodiments, the chelating agent is independently selected from ethylenediaminetetraacetic acid (EDTA), ethylene glycol-bis(β-aminoethyl ether)-N,N,N',N'-tetraacetic acid (EGTA), and dimercaptosuccinic acid (DMSA) for each presence of (iii). In some embodiments, the chelating agent is EDTA. In some embodiments, the derivatizing agent is a peptide alkylating agent. In some embodiments, the derivatizing agent is iodoacetamide. In some embodiments, the enzyme independently for each presence of (vi) includes one or any combination selected from the group consisting of nuclease, protease, glycosidase, and lipase. In some embodiments, the glycosidase is amylase. In some embodiments, the protease is trypsin. In some embodiments, the nuclease includes one or both of DNase I and ribonuclease A. In some embodiments, DNase I and EDTA are not present simultaneously in (vi). In some embodiments, (vi) is carried out in an ammonium bicarbonate buffer. In some embodiments, (vi) includes incubating at about 37°C for about 24 hours or less.In some embodiments, (a) further comprises extracting the plurality of analytes or derivatives thereof from the processed sample with an extraction reagent, thereby obtaining the solution. In some embodiments, the extracting comprises adding the extraction reagent to the processed sample in a 1:1 volume ratio. In some embodiments, the extraction reagent comprises acetonitrile and acetone in a 1:1 volume ratio. In some embodiments, (a) further comprises removing insoluble impurities from the solution by centrifugation or filtration.

[0094] In some embodiments of a method for identifying a plurality of different types of analytes from a sample described above or elsewhere in this specification, (b) comprises subjecting the plurality of analytes or derivatives thereof to an electron beam, thereby generating at least one ionized form or fragment of at least one of the plurality of analytes or derivatives thereof. In some embodiments, (b) further comprises contacting the plurality of analytes or derivatives thereof with a mixed-mode chromatography matrix comprising at least three orthogonal chromatography modes, each of the at least three orthogonal chromatography modes being configured to separate a given type of the plurality of analytes or derivatives thereof from the solution. In some embodiments, at least one of the orthogonal chromatography modes separates at least one derivative of the analyte from the solution. In some embodiments, the mixed-mode chromatography matrix comprises at least three properties selected from the group consisting of cation exchange properties, anion exchange properties, ion exclusion properties, ligand exchange properties, size exclusion properties, chiral properties, reverse phase properties, affinity properties, hydrophilic properties, multi-site properties, or any combination thereof. One of ordinary skill in the art will understand that "cation exchange" can include strong cation exchange (SCX) and weak cation exchange (WCX). One of ordinary skill in the art will also understand that "anion exchange" can include strong anion exchange (SAX) and weak anion exchange (WAX). In some embodiments, (b) further comprises eluting the plurality of analytes and their derivatives with at least three mobile phases. In some embodiments, a first fraction of the plurality of analytes and their derivatives is eluted by a first mobile phase, the first mobile phase comprising water and 0.1% formic acid (v / v), a second fraction of the plurality of analytes and their derivatives is eluted by a second mobile phase, the second mobile phase comprising acetonitrile and 0.1% formic acid (v / v), and a third fraction of the plurality of analytes and their derivatives is eluted by a third mobile phase, the third mobile phase comprising 1:1 (v / v) methanol / water, 200 mM ammonium acetate, and formic acid.In some embodiments, at least one of the at least three mobile phases, or at least one of the first mobile phase, the second mobile phase, and the third mobile phase, comprises one or more ionized adducts selected from the group consisting of ammonium, proton, sodium, acetate, formate, propionate, phosphate, medronate, urea, biuret, and triuret. In some embodiments, (b) comprises determining the mass of each of the plurality of analytes or their derivatives by mass spectrometry. In some embodiments, (b) comprises determining the amount of each of the plurality of analytes or their derivatives by mass spectrometry. One of ordinary skill in the art will understand that the mass or amount of each of the plurality of analytes or derivatives can be identified from LC-MS data including chromatographic retention time, ion mobility collision cross section, mass-to-charge ratio of different forms of precursor and product ions, state and ratio of ion adducts, losses, multimers, isotope abundances, charge state, or any combination thereof. In some embodiments, the mass spectrometry is low or high resolution full scan or fragment scan mass spectrometry generated by data non-dependent or data-dependent acquisition with or without dynamic exclusion in both positive and negative ion modes. In some embodiments, determining the mass or the amount comprises comparing the low or high resolution mass spectrometry characteristics to one or more reference mass full or spectra. In some embodiments, the solution is an aqueous solution and optionally comprises a water-miscible organic solvent. In some embodiments, the sample is a biological sample.

[0095] Some embodiments provide a method for determining a disease or condition in a subject, the method comprising: (i) identifying, individually or together, a plurality of analytes known to be associated with the disease or condition from a single sample of the subject, to obtain the amounts identified for each of the plurality of analytes, the plurality of analytes including other types of analytes; (ii) determining, for each of the plurality of analytes, the difference between a reference amount and the identified amount to obtain a plurality of difference values; and (iii) using a trained machine learning algorithm to determine the disease or condition based on the plurality of difference values. One of ordinary skill in the art will understand that the term "reference amount" as used herein generally refers to an amount that enables determination of whether a subject has a disease or condition. In some embodiments of the method for determining a disease or condition in a subject described above or elsewhere herein, (i) comprises: (a) subjecting the single sample that contains or is suspected of containing the plurality of analytes to conditions sufficient to produce a solution comprising the plurality of analytes or derivatives thereof; and (b) using the solution to identify the plurality of analytes or derivatives thereof, thereby identifying the plurality of analytes. In some embodiments of the method for determining a disease or condition in a subject described above or elsewhere herein, (b) can be performed in a single instrument run. The trained algorithm can be trained using 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, or 170, or about (i.e., ±1, ±2, or ±3) of those training samples, or a plurality of training samples in a range between any two of the foregoing values. The trained algorithm can be trained using a plurality of training samples that include 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, or 170 or less, or about (i.e., ±1, ±2, or ±3) or less of those training samples. The trained algorithm can be trained using a plurality of training samples that include 170 or less training samples.The trained algorithm can be trained using a plurality of training samples including 30 or fewer training samples. The trained algorithm can be trained using a plurality of training samples including at least, or at least about (i.e., ±1, ±2, or ±3), 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, or 170 training samples. The trained algorithm can be trained using a plurality of training samples including at least 30 training samples. The trained algorithm can be trained using a plurality of training samples including at least 170 training samples. This method can further include identifying the plurality of analytes from each of the plurality of training samples prior to (i). The disease or condition can be determined with an accuracy of 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, or 0.8, or about (i.e., ±0.01, ±0.02, or ±0.03), or within a range between any two of the aforementioned values. The disease or condition can be determined with an accuracy of at least, or at least about (i.e., ±1%, ±2%, ±3%, ±4%, or ±5%), 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 99%. The disease or condition can be determined with a specificity of at least, or at least about (i.e., ±1%, ±2%, ±3%, ±4%, or ±5%), 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 99%. The disease or condition can be determined with a sensitivity of at least, or at least about (i.e., ±1%, ±2%, ±3%, ±4%, or ±5%), 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 99%. The disease or condition can be determined with an exactness of at least, or at least about (i.e., ±1%, ±2%, ±3%, ±4%, or ±5%), 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 99%. The disease or condition can be selected from the group consisting of aging, cardiovascular disease, inflammation, heart failure, and dementia.The plurality of analytes may include one or more selected from the group consisting of apolipoprotein B (apoB), cortisol, C-reactive protein (CRP), and N-terminal pro-brain natriuretic peptide (NT-ProBNP), and derivatives thereof. The plurality of analytes may include two or more analytes selected from the group consisting of ribonucleotides, deoxyribonucleotides, polypeptides, and metabolites. The plurality of analytes may include three or more analytes selected from the group consisting of ribonucleotides, deoxyribonucleotides, polypeptides, and metabolites. The plurality of analytes may include all four analytes selected from the group consisting of ribonucleotides, deoxyribonucleotides, polypeptides, and metabolites. The plurality of analytes may include ribonucleotides, deoxyribonucleotides, and metabolites. The plurality of analytes may include polypeptides and metabolites. The plurality of analytes may include at least three types of analytes selected from the group consisting of proteins, nucleic acids, small molecules, lipids, carbohydrates, electrolytes, and metals. The plurality of analytes may include at least one type of analyte selected from small molecules, lipids, and carbohydrates and at least two types of analytes selected from proteins, nucleic acids, electrolytes, and metals. The plurality of analytes may include at least one type of analyte selected from proteins and nucleic acids and at least two types of analytes selected from small molecules, lipids, and carbohydrates.

[0096] Computer system The present disclosure provides a computer system programmed to implement the methods disclosed herein or otherwise configured. FIG. 17 shows a computer control system 401 programmed to process data from a mass spectrometer or otherwise configured. The computer control system 401 can regulate various aspects of the methods of the present disclosure, such as, for example, methods of analyzing vital sign data. The computer control system 401 can be implemented in a user's electronic device or a computer system located remotely from the electronic device. The electronic device can be a mobile electronic device.

[0097] The computer system 401 includes a central processing unit (CPU, also referred to herein as "processor" and "computer processor") 405, which can be a single-core or multi-core processor, or multiple processors for parallel processing. The computer control system 401 also includes a memory or memory location 410 (e.g., random access memory, read-only memory, flash memory), an electronic storage unit 415 (e.g., hard disk), a communication interface 420 (e.g., network adapter) for communicating with one or more other systems, and peripheral devices 425 such as a cache, other memory, data storage, and / or an electronic display adapter. The memory 410, storage unit 415, interface 420, and peripheral devices 425 communicate with the CPU 405 via a communication bus (solid lines), such as a motherboard. The storage unit 415 can be a data storage unit (or data repository) for storing data. The computer control system 401 can be operably connected to a computer network ("network") 430 using the assistance of the communication interface 420. The network 430 can be the Internet, the Internet and / or an extranet, or an intranet and / or an extranet communicating with the Internet. The network 430 can be, in some cases, a telecommunications and / or data network. The network 430 can include one or more computer servers that can enable distributed computing, such as cloud computing. The network 430 can, in some cases, implement a peer-to-peer network using the computer system 401, thereby enabling devices connected to the computer system 401 to operate as clients or servers.

[0098] The CPU 405 can execute a series of machine-readable instructions, which can be embodied in a program or software. The instructions can be stored in a memory location such as the memory 410. The instructions can be directed to the CPU 405, which can then program or otherwise configure the CPU 405 to implement the method of the present disclosure. Examples of operations performed by the CPU 405 can include fetch, decode, execute, and write-back.

[0099] The CPU 405 can be part of a circuit, such as an integrated circuit. One or more other components of the system 401 can be included in the circuit. In some cases, the circuit is an application-specific integrated circuit (ASIC).

[0100] The storage unit 415 can store files such as drivers, libraries, and saved programs. The storage unit 415 can store user data, such as user settings and user programs. In some cases, the computer system 401 can include one or more additional data storage units external to the computer system 401, such as being located on a remote server that communicates with the computer system 401 via an intranet or the Internet.

[0101] Computer system 401 can communicate with one or more remote computer systems via network 430. For example, computer system 401 can communicate with a remote computer system of a user (e.g., a user who controls a smart wearable product). Examples of remote computer systems include personal computers (e.g., portable PCs), slates or tablet PCs (e.g., Apple® iPad, Samsung® Galaxy Tab), telephones, smartphones (e.g., Apple® iPhone, Android-compatible devices, Blackberry®), or portable information terminals. A user can access computer system 401 via network 430.

[0102] The methods described herein can be implemented by machine (e.g., computer processor) executable code stored in an electronic memory location of computer system 401, such as memory 410 or electronic storage unit 415. The machine executable code or machine readable code can be provided in the form of software. In use, the code can be executed by processor 405. In some cases, the code can be retrieved from storage unit 415 and stored in memory 410 for immediate access by processor 405. In some situations, electronic storage unit 415 can be excluded and machine executable instructions can be stored in memory 410.

[0103] The code can be pre-compiled and configured for use on a machine having a processor adapted to execute the code, or can be compiled at runtime. The code can be supplied in a programming language selected to enable the code to be executed in a pre-compiled or just-in-time fashion.

[0104] Aspects of the systems and methods provided herein, such as computer system 401, can be embodied in programming. Various aspects of the technology can typically be considered a "product" or "manufacture" in the form of machine (or processor) executable code and / or associated data that is executed or embodied in a type of machine-readable medium. The machine executable code can be stored in an electronic storage unit such as a memory (e.g., read-only memory, random access memory, flash memory) or a hard disk. A "memory" type of medium can include any or all of tangible memories such as computers, processors, or associated modules such as various semiconductor memories, tape drives, disk drives, etc., which can provide non-transitory storage at any time for software programming. All or part of the software may be communicated via the Internet or various other electrical communication networks. Such communication can enable, for example, the loading of software from one computer or processor to another, such as from an administrative server or host computer to an application server computer platform. Thus, another type of medium that can hold software elements includes light, electrical, and electromagnetic waves such as those used via a physical interface between local devices, via wired and optical terrestrial links networks, and via various air links. Physical elements that carry waves such as wired or wireless links, optical links, etc., can also be considered media that hold software. As used herein, unless limited to non-transitory tangible "memory" media, terms such as "readable medium" of a computer or machine generally refer to any medium involved in providing instructions to a processor for execution.

[0105] Thus, machine-readable media such as computer-executable code can take many forms including, but not limited to, tangible storage media, carrier wave media, or physical transmission media. Non-volatile storage media includes, for example, optical or magnetic disks such as any of the storage devices in any computer that can be used to implement, for example, a database shown in the drawings. Volatile storage media includes dynamic memory such as the main memory of such a computer platform. Tangible transmission media includes coaxial cables, copper wire, and fiber optics including the wires that make up a bus within a computer system. Carrier wave transmission media can take the form of electrical or electromagnetic signals, or acoustic or light waves such as those generated during radio frequency (RF) and infrared (IR) data communications. Thus, common forms of computer-readable media include, for example, floppy disks, flexible disks, hard disks, magnetic tape, any other magnetic media, CD-ROM, DVD or DVD-ROM, any other optical media, punch cards, perforated tapes, any other physical storage media with patterns of holes, RAM, ROM, PROM, and EPROM, FLASH-EPROM, any other memory chip or cartridge, carrier waves that carry data or instructions, cables or links that carry such carrier waves, 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 carrying one or more sequences of one or more instructions to a processor for execution.

[0106] Computer system 401 can include, or be in communication with, for example, an electronic display 435 that includes a user interface (UI) 440 for providing parameters for generating a slurry and / or for applying the slurry to a substrate. Examples of UIs include, but are not limited to, graphical user interfaces (GUIs) and web-based user interfaces.

[0107] The methods and systems of the present disclosure can be implemented by one or more algorithms. The algorithms can be implemented by software when executed by a central processing unit 405. The algorithms can, for example, collect data from smart wearable products and analyze the data for changes in the data over a defined period of time.

Example

[0108] Materials and Methods Sample Preparation A chemical reference standard was prepared at a concentration of 1 μM in the extraction solution and analyzed by LC-MS to confirm the retention time and characteristic ion mass.

[0109] Approximately 100 mg of the sample was homogenized in 20 μL of aqueous additive and 100 μL of methanol to bring the mixture to a final concentration of 50 mM ammonium bicarbonate, 5 mM EDTA, trypsin (1:20 m / m trypsin:protein), and approximately 10% of the dry sample mass. The sample digestion mixture was incubated at 37 °C for 4 hours while rotating. After digestion, the sample was treated with 100 μL of acetonitrile and 100 μL of acetone and equilibrated at 4 °C for 15 minutes. The sample was centrifuged at 15000 xg for 5 minutes, and the supernatant was transferred to a new vial and stored at 4 °C until LC-MS analysis.

[0110] Liquid Chromatography The analytes are separated using mixed-mode chromatography featuring cation-exchange, anion-exchange, and reversed-phase properties on a Dionex Trinity P1 2.1 mm × 150 mm equipped with a pre-column filter and guard column. Solvent programming consists of two linear gradients with the following mobile phases: Mobile Phase A - acetonitrile containing 95:5 water:0.1% formic acid and 5 mM ammonium acetate, Mobile Phase B - water containing 95:5 acetonitrile:0.1% formic acid and 5 mM ammonium acetate, and Mobile Phase C - methanol containing 50:50 water:200 mM formic acid and 200 mM ammonium acetate. 5 μM of medronic acid is added to all mobile phases to improve the chromatography and detection of phosphorylated compounds. The linear gradient programming proceeds forward at 200 μL / min and 30 °C from 100% A to 100% B to 100% C, and in reverse at 0, 20, 40, 45, 50, and 60 minutes respectively. The run is extended by lengthening the forward portion of the gradient programming. Depending on the instrument used for analysis, either a Dionex Ultimate 3000RS UHPLC or a Thermo Surveyor HPLC autosampler and LC stack are used.

[0111] Mass spectrometry detection The eluent is introduced into the MS by an electrospray ionization source of either a Thermo Q Exactive Plus or a Thermo LTQ Orbitrap XL. External calibration is performed using standard positive and negative ESI calibration solutions prior to analysis. The mass spectrometer is set to acquire high-resolution full scans with low or high-resolution MS2 data-dependent acquisition with dynamic exclusion in both positive and negative ion modes using mass spectrometer parameters optimized to maximize sensitivity for the MRFA peptide in both positive and negative ion modes.

[0112] Enzymatic digestion Prepare the enzyme and trypsin stock solutions at 10 mg / mL according to the supplier's instructions. Add the enzyme solution (10 μL) and the trypsin solution (10 μL) to the substrate solution (100 μL) and incubate at 37 °C for 18 h with rotation. Optionally, substitute an equivalent solution without substrate or enzyme as a negative control. Stop the reaction by adding an organic solvent (200 μL of 1:1 acetonitrile:acetone).

[0113] Data analysis Perform qualitative data analysis using the Thermo Qual browser, which was selected based on chemical diversity and recognized biomedical importance, to quantify the selected compounds. Measure the peak areas in full scan and normalize them by the added internal standard (to adjust the injection volume) and a constant contaminant ion (to adjust the ionization efficiency).

[0114] Convert the raw LC-MS data to the mzML format using proteowizard for processing in OpenMS. Match the MS / MS fragmentation spectra to the tryptic peptide sequences generated from human, reversed decoy, and cRAP proteins using the!Tandem peptide mapping algorithm. Also match the fragmentation spectra to metabolites using the Sirius adapter. Features are identified using a mass tracking approach and retained if detected in more than 20% of the samples. Features are exported to tab-delimited text for analysis in the R software environment. Transform the intensities to log10 and normalize the data for total amount, digestion and extraction efficiency, and total injected amount and instrument sensitivity by linearly adjusting for the median intensities of peptides, lipids, and metabolites.

[0115] Using the foregoing method, selected analytes presented as detected in human plasma are shown in Figure 2. The peak areas of time and intensity are shown on the horizontal and vertical axes, respectively. The selected analytes are sodium, cholesterol, phenylalanine, creatinine, fructosamine (and isomers), triglyceride isomer (52:2), trypsin albumin peptide, and bilirubin. Figure 3 shows box-and-whisker plots of selected analytes across five different plasma samples over five repetitions. The name of each analyte is described above each dot plot, and the Kruskal-Wallis p-value indicates whether any difference between the medians of the samples is statistically significant. The sample number and the peak area of intensity on a log10 scale are shown on the horizontal and vertical axes, respectively. The dots represent five measurements of each sample, and the horizontal lines represent the quartiles of all repetitions.

[0116] Figure 4 shows spectra obtained from mouse liver homogenates digested without enzymes and analyzed according to the described procedure. Time and mass-to-charge ratio are shown on the horizontal and vertical axes, respectively, and an increase in the brightness of the color represents a higher ion intensity. The approximate positions of the selected analyte classes are labeled according to previous observations.

[0117] Figure 5 shows spectra obtained from mouse liver homogenates digested with RNase A and analyzed according to the described procedure. Time and mass-to-charge ratio are shown on the horizontal and vertical axes, respectively, and an increase in the brightness of the color represents a higher ion intensity. The approximate positions of the selected analyte classes are labeled according to previous observations.

[0118] Figure 6 shows spectra obtained from mouse liver homogenates digested with RNase A and trypsin and analyzed according to the described procedure. Time and mass-to-charge ratio are shown on the horizontal and vertical axes, respectively, and an increase in the brightness of the color represents a higher ion intensity. The approximate positions of the selected analyte classes are labeled according to previous observations.

[0119] Figure 7 shows spectra obtained from cultured human liver cancer cells digested with trypsin and analyzed according to the described procedure. Time and mass-to-charge ratio are shown on the x-axis and y-axis, respectively, and an increase in the color brightness represents a higher ion intensity. The approximate positions of the selected analyte classes are labeled according to previous observations.

[0120] Figure 8 shows spectra obtained from human plasma digested with trypsin and analyzed according to the described procedure. Time and mass-to-charge ratio are shown on the x-axis and y-axis, respectively, and an increase in the color brightness represents a higher ion intensity. The approximate positions of the selected analyte classes are labeled according to previous observations.

[0121] Figure 9 shows spectra obtained from bovine fat digested with trypsin and analyzed according to the omni-MS procedure. Time and mass-to-charge ratio are shown on the x-axis and y-axis, respectively, and an increase in the color brightness represents a higher ion intensity. The approximate positions of the selected analyte classes are labeled according to previous observations.

[0122] Figure 10 shows spectra of human urine obtained, digested with trypsin, and analyzed according to the omni-MS procedure. Time and mass-to-charge ratio are shown on the x-axis and y-axis, respectively, and an increase in the color brightness represents a higher ion intensity. The approximate positions of the selected analyte classes are labeled according to previous observations.

[0123] Figure 11, left panel: Representative pairwise scatter plots of representative plasma samples and ion bins over repeats. The x-axis and y-axis correspond to the log10 intensity counts seen within the ion bins. Right panel: Hierarchical clustering based on the correlation relationships of plasma samples and repeats. The y-axis is the distance in the correlation between profiles, and the samples are ordered approximately according to the shortest distance by correlation.

[0124] Identification of Known Clinical Applications from Multi-omic Data Multi-omic data was obtained from biobanked clinical plasma or serum samples using the multi-omic methods described above or elsewhere in this specification. The obtained multi-omic data (i.e., the spectrograms of FIGS. 12B, 13B, 14B, 15B, and 16B) was grouped into pixel “bins” each representing a detected analyte based on the mass-to-charge ratio and retention time of the detected analytes. The obtained multi-omic data was then analyzed using bioinformatics such as a Manhattan plot with a false discovery rate (FDR) to identify statistical associations with at least one clinical metric. The selected clinical metrics were known to be associated with diseases or conditions such as age or aging (FIGS. 12A-12B), cardiovascular disease risk (FIGS. 13A-13B), heart failure (FIGS. 14A-14B), inflammation (FIGS. 15A-15B), or dementia (FIGS. 16A-16B). In particular, Manhattan plots (i.e., FIGS. 12A, 13A, 14A, 15A, and 16A) were used to show statistically significant analytes.

[0125] FIGS. 12A, 13A, 14A, 15A, and 16A each show Manhattan plots indicating the statistical associations between various multi-omically detected analytes from a single sample and clinical variables associated with age, cardiovascular disease, heart failure, inflammation, and dementia. The results of the statistical associations were filtered with false discovery rate (FDR) thresholds: <0.2 (red triangles), 0.2 (inclusive)-0.5 (green squares), and ≧0.5 (gray circles).

[0126] FIGS. 12B, 13B, 14B, 15B, and 16B each show the multi-omic spectrograms of the blood samples analyzed in the corresponding Manhattan plots. Analytes falling within the 0.3 false discovery rate (FDR) threshold were marked with squares.

[0127] Training and Validation of Models Using Multi-omic Data Using an elastic net regression model with cross-validation as the prediction model (such as those described in Zou and Hastie (Journal of the Royal Statistical Society: Series B (Statistical Methodology) 67.2 (2005): 301 - 320), which is incorporated by reference in its entirety), it was demonstrated that multi-omic data obtained using the multi-omic methods described above or elsewhere in this specification can be used to train the prediction model. Those skilled in the art will understand that other machine learning algorithms (such as linear regression (e.g., Lasso or Ridge) or support vector machines) can be trained with the multi-omic data obtained using the multi-omic methods described above or elsewhere in this specification. In the cross-validation method (such as 10-fold cross-validation), the leave one out strategy was implemented. Those skilled in the art will understand that in one 10-fold cross-validation approach, the obtained multi-omic data can be randomly divided into 10 subsets, 9 of which are used for training and 1 is used for testing over all permutations. The training of the model further included adjusting the hyperparameter settings to minimize the cross-validation test set error. Next, the trained prediction model was tested on the remaining subset to predict clinical markers (indicators) from the multi-omic data. Using the aforementioned prediction model and training strategy, one indicator was predicted at a time.

[0128] Figure 12C shows the mean absolute error (MAE) of a series of predictions regarding age using leave-one-out cross-validation while varying the degrees of freedom of the machine learning model. The bottom axis of Figure 12C shows the parameter setting in units of the elastic net regression model that controls the degrees of freedom accessible to the model. The top axis of Figure 12C shows the number of variables selected from the data used for prediction. The left vertical axis of Figure 12C shows the mean absolute error of the predicted age (in years) compared to the measured age in the above-described 10-fold cross-validation.

[0129] Figure 13C shows the mean absolute error (MAE) of a series of predictions regarding leave-one-out cross-validation of ApoB while varying the degrees of freedom of the machine learning model. The lower axis of Figure 13C shows the parameter setting of the elastic net regression model without units that controls the degrees of freedom accessible to the model. The upper axis of Figure 13C shows the number of variables selected from the data used for prediction. The left vertical axis of Figure 13C shows the mean absolute error of the predicted ApoB levels (mg / dL) compared with the measured values in the 10-fold cross-validation described above.

[0130] Figure 14C shows the mean absolute error (MAE) of a series of predictions regarding N-terminal pro-brain natriuretic peptide (NT-ProBNP) using leave-one-out cross-validation while varying the degrees of freedom of the machine learning model. The lower axis of Figure 14C shows the parameter setting of the elastic net regression model without units that controls the degrees of freedom accessible to the model. The upper axis of Figure 14C shows the number of variables selected from the data used for prediction. The left vertical axis of Figure 14C shows the mean absolute error of the predicted NT-ProBNP levels (pg / mL) compared with the measured values in the 10-fold cross-validation described above.

[0131] Figure 15C shows the mean absolute error (MAE) of a series of predictions regarding leave-one-out cross-validation of C-reactive protein (CRP) while varying the degrees of freedom of the machine learning model. The lower axis of Figure 15C shows the parameter setting of the elastic net regression model without units that controls the degrees of freedom accessible to the model. The upper axis of Figure 15C shows the number of variables selected from the data used for prediction. The left vertical axis of Figure 15C shows the mean absolute error of the predicted CRP levels (mg / L) compared with the measured values in the 10-fold cross-validation described above.

[0132] FIG. 16C shows the mean absolute error (MAE) of a series of predictions regarding the classification of clinical dementia versus controls using leave-one-out cross-validation while varying the degrees of freedom of the machine learning model. The bottom axis of FIG. 16C shows the unitless parameter settings of the elastic net regression model that controls the degrees of freedom accessible to the model. The top axis of FIG. 16C shows the number of variables selected from the data used to make the predictions. The left vertical axis of FIG. 16C shows the mean absolute error (binary, misclassification error) of the predicted dementia compared to dementia measured by 10-fold cross-validation (as described above).

[0133] FIGS. 12D, 13D, 14D, 15D, and 16D each show the prediction accuracy by comparing the predicted clinical biomarker (using the methods described herein) with the actual clinical biomarker. The correlation coefficient (r) is shown above the plot and the sample size (n) is shown below the plot.

[0134] Although specific features of the invention have been illustrated and described herein, many modifications, substitutions, changes, and equivalents will occur to those skilled in the art. Accordingly, it is to be understood that the appended claims are intended to cover all such modifications and changes as fall within the true spirit of the invention.

[0135] 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 by way of example only. The present invention is not intended to be limited by the specific examples provided within this specification. Although the present invention has been described with reference to the foregoing specification, the description and illustration of the embodiments herein are not intended to be construed in a limiting sense. Numerous variations, modifications, and substitutions will occur to those skilled in the art without departing from the present invention. Furthermore, it should be understood that all aspects of the present invention are not limited to the specific depictions, configurations, or relative ratios described herein, which depend on various conditions and variables. It should be understood that various alternatives to the embodiments of the present invention described herein may be used in practicing the present invention. Accordingly, the present invention is considered to cover any such alternatives, modifications, variations, or equivalents. The following claims define the scope of the present invention, and it is intended that methods and structures within the scope of these claims and their equivalents be covered thereby.

Claims

**Claim 1** A method for substantially simultaneously determining the presence, identity or amount of a plurality of analytes including small molecules, lipids and proteins present in a single sample, comprising: a. treating the sample with a denaturation treatment to thereby form a denatured sample, wherein the denaturation treatment does not denature the components of the normalization reagent when added to the normalization reagent; b. treating the denatured sample with the normalization reagent to thereby form a normalized sample; c. treating the normalized sample with an extraction reagent to retain soluble analyte species and analyte fragment species therein, thereby forming an extracted sample; d. subjecting the extracted sample to an instrument for identifying the plurality of analytes or derivatives thereof, thereby identifying the plurality of analytes; e. computationally determining from the data the presence, identity or amount of the plurality of analytes present in the extracted sample. **Claim 2** The method according to claim 1, wherein the plurality of analytes or derivatives thereof are identified in a single run of the instrument. **Claim 3** The method according to claim 1, wherein after step c and before subjecting the extracted sample to the instrument, the extracted sample is subjected to one or more chromatographic separations. **Claim 4** The method according to claim 1, wherein the instrument is a mass spectrometry (MS) instrument. **Claim 5** The method according to claim 1, wherein the plurality of analytes further comprises one or more analytes selected from the group consisting of proteins, carbohydrates, nucleic acids, lipids, electrolytes, metals, small molecules, volatile compounds, and exogenous chemicals. **Claim 6** The method according to claim 1, wherein the sample is a biological sample. **Claim 7** The method according to claim 1, wherein the denaturation treatment comprises one or more solvents, one or more chaotropic agents, heat, pressure, irradiation, one or more reference standards, or any combination thereof. **Claim 8** The method according to claim 7, wherein the denaturation treatment further comprises a metal chelating agent. **Claim 9** The method according to claim 1, wherein the denaturation step and the treating of the denatured sample with the normalization reagent are performed substantially simultaneously. **Claim 10** The method according to claim 1, wherein the normalization reagent comprises one or more proteases, one or more nucleases, one or more glycosidases, one or more lipases, one or more chelating agents, one or more buffers, one or more reducing agents, one or more derivatizing agents, or any combination thereof.

11. The method according to claim 10, wherein the normalization reagent comprises (a) trypsin, ribonuclease A, EDTA, ammonium bicarbonate or amylase, (b) pancreatic endolytic enzyme, or (c) DNase I, or any combination thereof, and when DNase I is included, EDTA is not present simultaneously.

12. The method according to claim 1, wherein the one or more chromatographic separations are selected from the group consisting of reverse phase separation, cation exchange separation, anion exchange separation, ion pair separation, normal phase separation, ion mobility separation, size exclusion separation, chiral separation, affinity separation, ligand exchange separation, polar nonionic separation, or any combination thereof.

13. The method according to claim 12, wherein the mobile phase used in the chromatography further comprises one or more ionized adducts selected from ammonium, proton, sodium, acetate, formate, propionate, phosphate, medronate, urea, biuret, triuret, or any combination thereof.

14. The method according to claim 1, wherein the device is a mass spectrometer, and the data acquisition of the mass spectrometer comprises high-resolution full-scan with low or high-resolution MS2 data non-dependency or data-dependency acquisition with dynamic exclusion in both positive and negative ion modes.

15. The method according to claim 1, wherein computationally determining the presence, identity or amount of the plurality of analytes present in the extracted sample from the data comprises comparing the data of the standardized analyte species or standardized analyte fragment species with the mass spectrometry of known amounts of known analytes.

16. A system for substantially simultaneously determining the presence, identity or amount of a plurality of analytes, including small molecules, lipids and proteins, present in a single sample, (i) a denaturing reagent that does not denature the components of the normalization reagent when added to the normalization reagent, (ii) a normalization reagent that converts the analyte in the sample into a normalized analyte species or a normalized analyte fragment species; (iii) an extraction reagent; (iv) a separation device; (v) a first instrument configured to generate data regarding the normalized analyte species and the normalized analyte fragment species; (vi) a second instrument configured to computationally determine the presence, identity, or amount of the plurality of analytes present in the sample from the data; A system comprising.

17. The system further includes means for treating the sample with the denaturing reagent and treating the denatured sample with the normalization reagent substantially simultaneously, (a) the denaturing reagent includes one or more solvents, one or more chaotropic agents, heat, pressure, irradiation, one or more reference standards, or any combination thereof; or (b) the normalization reagent includes one or more proteases, one or more nucleases, one or more glycosidases, one or more lipases, one or more chelating agents, one or more buffers, one or more reducing agents, one or more derivatizing agents, or any combination thereof; or (c) the normalization reagent includes (i) trypsin, ribonuclease A, EDTA, ammonium bicarbonate or amylase, (ii) pancreatic endolytic enzyme, or (iii) DNase I, and when including DNase I, EDTA is not present simultaneously, The system according to claim 16.

18. A method for providing information on a disease or condition of a subject from which a biological sample is derived, comprising: (i) using the system according to claim 16 or 17 to substantially simultaneously determine the presence, identity, or amount of a plurality of analytes present in the biological sample; (ii) comparing the presence, identity, or amount of one or more of the plurality of analytes with the presence, identity, or amount of the one or more analytes of the reference; (iii) providing information on the disease or condition of the subject. A method comprising.

19. A method for providing information on a disease or condition of a subject from which a biological sample is derived, comprising: (i) using the method according to any one of claims 1 to 15 to substantially simultaneously determine the presence, identity, or amount of a plurality of analytes present in the biological sample; (ii) comparing the presence, identity or amount of one or more of the plurality of analytes in the biological sample with the presence, identity or amount of the one or more analytes in the reference; (iii) providing information about the disease or condition of the subject; A method comprising:

20. The method according to claim 19, wherein the comparing step comprises determining one or more differences between the amount of the one or more analytes in the reference and the amount of the one or more analytes in the biological sample to obtain a plurality of difference values, and the providing step comprises using a trained machine learning algorithm to provide information about the disease or condition based on the plurality of difference values.

Images