Methods and systems for detecting and quantifying a large number of molecular biomarkers of a body fluid sample

Abstract

Methods for detecting and quantifying analytes in a sample can include (a) identifying and / or profiling the analytes; (b) identifying at least one partitioning analyte from the analytes; (c) partitioning the analytes into a plurality of groups using the at least one partitioning analyte; and (d) detecting and / or quantifying a first group of analytes ending with a first partitioning analyte by scanning and / or quantifying the first group of analytes until a first intensity threshold of the first partitioning analyte is reached. The method can further include (e) switching to detecting and quantifying a second group of analytes beginning with the first partitioning analyte by scanning and quantifying the second group of analytes until a second intensity threshold of the second partitioning analyte is reached. Step e) can be repeated until each group is scanned and / or quantified.

Description

[0001] Cross-reference to related applications

[0002] This application claims priority to U.S. Provisional Patent Application No. 63 / 162,894, filed March 18, 2021, which is incorporated herein by reference in its entirety for all purposes. Background Technology

[0003] This disclosure generally relates to methods and systems for detecting and quantifying a large number of molecular biomarkers (e.g., at least 500 or at least 1,000 molecular biomarkers, biomolecules (proteins or metabolites) and others) from bodily fluid samples.

[0004] Body fluids are an ideal resource for non-invasive molecular diagnostics. However, analyzing these analytes in body fluid samples is challenging because they often exhibit significant variations across many orders of magnitude in their detection properties, such as abundance and retention time. For example, in a unit volume of plasma, albumin (at a concentration of approximately 40–50 g / L) is a billion times more abundant than free triiodothyronine (at a concentration of approximately 1–6 ng / L), regardless of the subject's disease or health status. Generally, the detection of low-abundance analytes or molecules (e.g., free triiodothyronine) is always impossibly difficult or affected by the presence of high-abundance molecules. However, most high-abundance molecules are not disease biomarkers, and meaningful disease biomarkers are typically present in body fluids at lower abundances.

[0005] In targeted mass spectrometry-based detection, existing technologies limit the total number of target analytes or molecules that can be detected and / or quantified per scan cycle, thus restricting the number of measurable analytes or molecules to around a few thousand or less (e.g., n ~ = 500). Generally, bodily fluid samples contain at least several thousand or tens of thousands of analytes, exceeding the limitations of existing technologies.

[0006] Furthermore, as the number of target analytes or molecules increases in each scan cycle, data quality typically decreases. This is because if the cycle time is fixed, existing mass spectrometers have to spend less time on each molecule, thus reducing detection accuracy; or they have to run with longer cycle times to accommodate more analytes without reducing the time spent on each analyte. Therefore, since the number of scan cycles decreases due to the extended cycle time, fewer data points will be collected within a given time window.

[0007] Existing technologies (e.g., commercial mass spectrometers) are not suitable for or unable to detect and quantify large numbers (e.g., at least a thousand) of analytes in body fluid samples within a single scan cycle.

[0008] What is needed in the art is a method or system for detecting and quantifying large quantities (e.g., at least thousands) of analytes in a body fluid sample in each analytical assay. Summary of the Invention

[0009] This disclosure includes the understanding that using endogenous separating analytes to divide detection (typically exceeding the limitations of existing techniques) into multiple consecutive sub-detections over multiple time windows, with the detection of each endogenous separating analyte serving as a trigger to switch to the next sub-detection, represents a breakthrough in the detection and quantification of large numbers (e.g., at least thousands) of analytes in body fluid samples. In fact, if the analytes can be eluted into the mass spectrometer along a solvent gradient, the methods or systems of this disclosure can perform consecutive detection of as many analytes as possible.

[0010] In one aspect, the present invention relates to a method for detecting and quantifying an analyte in a sample of a subject. The method includes: (a) identifying and / or profiling the analyte; (b) identifying at least one separator analyte from the analyte; (c) dividing the analyte into multiple groups using the at least one separator analyte; and (d) detecting and / or quantifying the first group of analytes ending with a first of the at least one separator analyte by scanning and / or quantifying the first group of analytes until a first intensity threshold of the first of the at least one separator analyte is reached.

[0011] In one embodiment, the method further includes (e) switching to detecting and quantifying the second set of analytes, starting with the first of the at least one separating analyte, by scanning and quantifying the second set of analytes until a second intensity threshold of the second of the at least one separating analyte is reached.

[0012] In one implementation, the method includes (f) repeating step e) until each of the plurality of groups is scanned and quantified.

[0013] In one embodiment, the sample comprises bodily fluids.

[0014] In one embodiment, the sample is selected from the group consisting of saliva samples, plasma samples, sweat samples, tear samples, gastrointestinal fluid samples, pancreatic juice samples, serum samples, urine samples, and combinations thereof.

[0015] In one embodiment, at least one of steps (a) to (d) is performed using a mass spectrometer.

[0016] In one implementation, at least one of the plurality of groups includes at least 500 analytes.

[0017] In one implementation, at least one of the plurality of groups includes at least 1,000 analytes.

[0018] In one embodiment, at least one of steps (a) to (e) is performed using a mass spectrometer.

[0019] In one embodiment, at least one of steps (a) to (d) is performed using a mass spectrometer.

[0020] In one implementation, step (b) includes sorting the analytes according to their detectability to identify the at least one separating analyte.

[0021] In one embodiment, the at least one separating analyte has an abundance, and the abundance of the at least one separating analyte is used as a threshold for each group of analytes adjacent to the at least one separating analyte.

[0022] In one embodiment, quantifying the first set of analytes includes using the first of the at least one separating analyte as an internal standard.

[0023] In one embodiment, the quantification of the first set of analytes includes the addition of an exogenous separator.

[0024] In one implementation, step (e) includes changing the detection parameters according to the second set of analytes.

[0025] In one implementation, the detection parameter includes retention time.

[0026] In another aspect, the present invention relates to a system for detecting and quantifying analytes in a sample of a subject. The system includes: an input unit configured to receive the sample in a detection unit; the detection unit configured to detect and / or quantify the analytes in the sample; and a control unit including a storage unit and a processor, wherein the storage unit stores one or more instructions to cause the processor to perform steps including: (a) identifying and / or profiling the analytes, (b) identifying at least one separator analyte from the analytes, (c) dividing the analytes into multiple groups using at least one separator analyte, and (d) detecting and / or quantifying the first group of analytes ending with a first of the at least one separator analyte by scanning and / or quantifying the first group of analytes until a first intensity threshold of the first of the at least one separator analyte is reached.

[0027] In one embodiment, the step further includes (e) switching to detecting and quantifying the second set of analytes, starting with the first of the at least one separating analyte, by scanning and quantifying the second set of analytes until a second intensity threshold of the second of the at least one separating analyte is reached.

[0028] In one implementation, the step further includes (f) repeating step (e) until each of the plurality of groups is scanned and quantified.

[0029] In one embodiment, the system further includes an output component configured to generate a result report for the detection and / or quantification steps. Attached Figure Description

[0030] Figure 1 This is a diagram illustrating the logic of a method and system according to certain embodiments of the present invention. Figure 1 In this process, the identified endogenous molecules #1 to #9 (e.g., nine analytes / molecules) divide all analytes into ten detection segments (i.e., detection segments 0 to 9). The system, including the mass spectrometer, first scans the first group of analytes (hereinafter referred to as "detection segment #0"), which includes the high abundance molecule at the first endogenous boundary (i.e., "first boundary molecule"). When the instrument detects that the intensity of the first boundary molecule exceeds a certain threshold, the system, including the mass spectrometer, switches to detect the second group of analytes (i.e., "second boundary molecule"), which includes the high abundance molecule at the second endogenous boundary (i.e., "second boundary molecule"). When the instrument detects that the intensity of the second boundary molecule exceeds a certain threshold, the mass spectrometer switches to detect the third group of analytes (i.e., "third boundary molecule"), which includes the high abundance molecule at the third endogenous boundary (i.e., "third boundary molecule"), which includes the high abundance molecule at the third endogenous boundary (i.e., "third boundary molecule"), and so on, until the system has completed scanning all analytes.

[0031] Figure 2 This is a set of graphs showing the scanning of the same group of analytes in Example 1 using different HPLC methods or by running different gradient HPLC methods. For example... Figure 2 As shown, actual detection times may vary with different HPLC methods or by running different gradient HPLC methods, but the relationships (relative positions of analytes) represented in the table are stable, which has been confirmed by repeated determinations under different HPLC methods. Specifically, the retention times of HPLC chromatograms may fluctuate under different HPLC methods, but the stable retention relationships between adjacent peaks are reproducible. All graphs are derived from different organic solvent gradient methods, but the x-axis is the same, representing retention time (minutes).

[0032] Figure 3 This is a system diagram illustrating a method according to certain embodiments of the present invention.

[0033] Figure 4 This is a system diagram showing a system according to certain embodiments of the present invention. Detailed Implementation

[0034] definition

[0035] The following provides some definitions. However, the definitions can be found in the “Implementation” section below, and the title “Definitions” above does not mean that the disclosures in the “Implementation” section are not definitions.

[0036] Unless otherwise stated, all percentages expressed herein are weight percentages of the total weight of the composition. As used herein, “about,” “approximately,” and “substantially” should be understood to refer to numbers within a numerical range, for example, a range of -10% to +10% of a reference number, preferably a range of -5% to +5% of a reference number, more preferably a range of -1% to +1% of a reference number, and most preferably a range of -0.1% to +0.1% of a reference number. All numerical ranges herein should be understood to include all integers, non-negative integers, or fractions within the range. Furthermore, these numerical ranges should be construed as supporting claims involving any number or subset of numbers within that range. For example, disclosures of 1 to 10 should be construed as supporting ranges of 1 to 8, 3 to 7, 1 to 9, 3.6 to 4.6, 3.5 to 9.9, etc.

[0037] In this disclosure and the appended claims, unless the context clearly specifies otherwise, the singular forms “a,” “an,” and “the” include plural references. Thus, for example, “a component” or “the component” includes two or more components.

[0038] The terms “comprise,” “comprises,” and “comprising” should be interpreted as inclusive, not exclusive. Similarly, the terms “include,” “including,” and “or” should also be interpreted as inclusive unless the context explicitly prohibits such interpretation. However, the components disclosed herein may lack any elements not specifically disclosed herein. Therefore, the use of the term “comprising” in disclosure of embodiments includes disclosure of embodiments of the components identified as “substantially consisting of” and “consisting of”.

[0039] The term "and / or" used in the context of "X and / or Y" should be interpreted as "X", or "Y", or "X and Y". Similarly, "at least one of X or Y" should be interpreted as "X", or "Y", or "X and Y". For example, "at least a small molecule or peptide" should be interpreted as "small molecule", or "peptide", or "both small molecule and peptide".

[0040] As used herein, the terms “for example” and “such as”, especially when listed below, are merely exemplary and illustrative and should not be considered exclusive or comprehensive. As used herein, “related” or “affected” by one condition means that the two conditions occur simultaneously, preferably that the two conditions are caused by the same underlying condition, and most preferably that one of the identified conditions is caused by another identified condition.

[0041] As used herein, the term "subject" refers to a mammal that may or may not have a disease such as cancer. Mammals include, but are not limited to, rodents, aquatic mammals, livestock such as dogs and cats, farm animals such as sheep, pigs, cattle, and horses, and humans. In one embodiment, the mammal may be a cat, dog, or human. In some embodiments, the terms "subject" and "patient" may be used interchangeably when referring to a human subject herein.

[0042] As used herein, the term "body fluid" refers to any liquid sample from a subject, such as saliva, plasma, sweat, tears, gastrointestinal fluid, pancreatic juice, serum, or urine. However, this method / system is not limited to body fluids. For example, this method / system can also be used for other test substances, such as cell lysates or non-biological samples containing mixtures.

[0043] As used herein, the term "analyte" refers to a molecule or substance to be detected or quantified. For example, an analyte can be any known or unknown component in a sample (e.g., body fluids). In one embodiment, an analyte is a chemical molecule of interest, such as a biopolymer, i.e., an oligomer or polymer, such as oligonucleotides, peptides, polypeptides, antibodies, etc., any small molecule or any substance, or any metabolite.

[0044] As used herein, the terms "divider," "divider analyte," or "divider molecule" refer to endogenous substances or molecules whose characteristics have been predetermined. Typically, in mass spectrometry, these divider analytes are molecules present in the analyte sample (i.e., the test sample) at an intensity sufficient for easy detection and quantification by the instrument. Furthermore, each of a set of divider analytes may have a different, but predetermined, detection time. More specifically, the detection times of both dividers and analytes can be predetermined, and their detection order in different analytes can be predetermined to help establish sequential detection of different detection ranges and the dividers within them.

[0045] As used herein, the term "endogenous" refers to a substance or molecule that is naturally produced or generated within the body of a subject (e.g., a mammal). For example, an endogenous separator analyte used in this invention to detect all analytes from a subject's sample refers to a substance or molecule that is naturally produced or generated within the body of the subject along with other analytes in the sample. In one embodiment, this method / system uses only endogenous separators.

[0046] As used herein, the terms "non-endogenous" or "exogenous" refer to substances or molecules that a subject (e.g., a mammal) does not produce naturally or in its body. For example, an exogenous separator analyte used in this invention to detect all analytes from a subject's sample refers to a substance or molecule that is not produced naturally or in its body by the subject along with other analytes in the sample. In one embodiment, the method / system uses both endogenous and exogenous separators. In another embodiment, the method / system uses only exogenous separators.

[0047] As used herein, the term "biomarker" refers to a molecule or substance that is quantitatively or qualitatively associated with biological changes. Examples of biomarkers may include polypeptides, proteins, or fragments of polypeptides or proteins; polynucleotides, such as gene products, RNA, or fragments of RNA; and any other body metabolites.

[0048] As used herein, the term "metabolite" refers to any small chemical molecule physiologically present in a body fluid sample or other biological sample. Metabolites may or may not be related to the pharmaceutical agents used. For example, in one embodiment, a metabolite is a product of a physiological process and may or may not be related to one or more pharmaceutical agents, adjuvants, additives, or excipients used in the formulation, or combinations thereof.

[0049] As used herein, the term "biopolymer" refers to a polymer of one or more types of repeating units, regardless of their origin. In one embodiment, biopolymers may be present in biological systems and particularly include polypeptides and polynucleotides, as well as compounds containing amino acids, nucleotides, or their analogues.

[0050] As used herein, the term "peptide" refers to a polymer of amino acids of any length. Generally, a peptide can be of any length, such as greater than 2 amino acids, greater than 4 amino acids, greater than about 10 amino acids, greater than about 20 amino acids, greater than about 50 amino acids, greater than about 100 amino acids, greater than about 300 amino acids, and typically more than about 500 or 1000 or more amino acids. As used herein, the term "peptide" refers to a polymer of amino acids. For example, a peptide can generally be greater than 2 amino acids, greater than 4 amino acids, greater than about 10 amino acids, greater than about 20 amino acids, and typically more than about 50 amino acids. In some embodiments, the length of the peptide is between 5 and 30 amino acids.

[0051] In one embodiment, the terms "polypeptide," "peptide," and "protein" are used interchangeably to refer to a polymer of amino acid residues. That is, the description of a polypeptide applies equally to the description of a peptide and a protein, and vice versa. For example, these terms apply both to naturally occurring amino acid polymers and to amino acid polymers in which one or more amino acid residues are non-natural amino acids. In one embodiment, the term includes amino acid chains of any length, including full-length proteins, wherein the amino acid residues are linked by covalent peptide bonds. In one embodiment, the polypeptide of the present invention consists of all naturally occurring amino acids.

[0052] As used herein, the term "amino acid" refers to natural and / or non-natural or synthetic amino acids, as well as amino acid analogs and amino acid mimics that function similarly to naturally occurring amino acids. Naturally occurring amino acids are those encoded by the genetic code, as well as those that have been modified, such as hydroxyproline, α-carboxyglutamic acid, and O-phosphoserine. For example, there are 20 common naturally encoded amino acids (and their corresponding monoletter symbols) [alanine (A), arginine (R), asparagine (N), aspartic acid (D), cysteine ​​(C), glutamine (Q), glutamic acid (E), glycine (G), histidine (H), isoleucine (I), leucine (L), lysine (K), methionine (M), phenylalanine (F), proline (P), serine (S), threonine (T), tryptophan (W), tyrosine (Y), and valine (V)] as well as pyrrolidone and selenocysteine. Amino acid analogs are compounds that have the same basic chemical structure as naturally occurring amino acids, i.e., any carbon atom bound to a hydrogen, carboxyl, amino, or R group, such as homoserine, oroleucine, methionine sulfoxide, and methionine sulfone. These analogs have modified R groups (e.g., oroleucine) or modified peptide backbones, but retain the same basic chemical structure as naturally occurring amino acids. Amino acid mimics are compounds whose structure differs from the general chemical structure of amino acids, but whose function is similar to that of natural amino acids.

[0053] The term "non-naturally occurring amino acid" refers to an amino acid that is not one of the 20 common amino acids, or pyrrolidone, or selenocysteine. Other terms synonymous with "non-naturally occurring amino acid" include "non-naturally encoded amino acid," "non-naturally occurring amino acid," and their various hyphenated and unhyphenated versions. The term "non-naturally occurring amino acid" may include, but is not limited to, amino acids that are naturally occurring through modification of naturally encoded amino acids (including but not limited to the 20 common amino acids, or pyrrolidone and selenocysteine), but which are not themselves introduced into the growing polypeptide chain through the translation complex. Examples of non-naturally encoded natural amino acids include, but are not limited to, N-acetyl-glucosamine-L-serine, N-acetyl-glucosamine-L-threonine, and O-phosphotyrosine.

[0054] As used herein, the term "fragment of a polypeptide or protein" refers to a peptide chain.

[0055] As used herein, the term "polynucleotide" refers to a polymer of nucleotides or analogues of any length, including oligonucleotides of 10 to 100 nucleotides in length, polynucleotides of more than 100 nucleotides in length, polynucleotides of more than 1,000 nucleotides in length, polynucleotides of more than 10,000 nucleotides in length, or polynucleotides of more than 100,000 nucleotides in length.

[0056] As used herein, the term "BF-Quant" refers to Body Fluid Quantitative Mass Spectrometry, which is one embodiment of the method of this invention.

[0057] As used herein, the term "predetermined" means determined prior to the process of interest. This includes not only determination before processing begins according to the exemplary embodiment, but also determination at any point in time prior to the process of interest, based on the conditions / state at that time or up to that time, even after processing has begun according to the exemplary embodiment. If multiple "predetermined values" exist, each of these values ​​may be different, or two or more values ​​may be the same (including the case where all values ​​are the same).

[0058] As used herein, the term "high throughput" refers to a relatively large detection capacity of the present method / system compared to existing technologies. In one embodiment, the present method / system may have a detection capacity of 10 times (10X), 100 times (100X), 1000 times (1000X), 10000 times (10000X), 100000 times (100000X), or more than that of existing technologies.

[0059] As used herein, the term "retention time" refers to the time required for a given analyte, compound, or substance, or a portion thereof, to pass through a chemical analysis system, such as a chromatographic system. In one embodiment, the terms "retention time" and "detection time" may be used interchangeably.

[0060] As used herein, the term “abundance” refers to the amount or concentration of an analyte in a sample (such as body fluids).

[0061] As used herein, the term "detectability" refers to the signal strength at which an analyte is detected by a mass spectrometer. For example, "higher detectability" as used herein means that the mass spectrometer detects a stronger signal strength of an analyte when all analytes have the same abundance.

[0062] All components of the composition can be mixed together, or the composition can be supplied as a kit of parts, wherein the components or groups of components are supplied individually. These individual compositions can be consumed individually or together.

[0063] Implementation

[0064] One aspect of this disclosure is a method for detecting and quantifying a large number of analytes (e.g., at least 500 analytes, preferably at least 1000 analytes) in a subject sample (e.g., body fluids). For example, this method is capable of detecting and quantifying at least 500 analytes, preferably at least 1000 analytes from body fluids in each analytical assay.

[0065] In one embodiment, this method is suitable for detecting and quantifying large quantities of analytes, such as biopolymers or small molecules. In one embodiment, the analyte is a biopolymer, such as an oligomer, or a polymer, such as an oligonucleotide, peptide, polypeptide, antibody, etc. In another embodiment, the analyte is any small molecule, any substance, or any metabolite.

[0066] In one embodiment, the method is capable of detecting and quantifying at least 500 analytes, at least 1000 analytes, at least 1500 analytes, at least 2000 analytes, at least 2500 analytes, at least 3000 analytes, at least 3500 analytes, at least 4000 analytes, at least 4500 analytes, at least 5000 analytes, at least 5500 analytes, at least 6000 analytes, or even more in each analytical assay. At least 6,500 analytes, at least 7,000 analytes, at least 7,500 analytes, at least 8,000 analytes, at least 8,500 analytes, at least 9,000 analytes, at least 9,500 analytes, at least 10,000 analytes, at least 10,500 analytes, at least 11,000 analytes, at least 11,500 analytes, at least 12,000 analytes, at least 12,500 analytes, or at least 13,000 analytes.

[0067] The applicant has surprisingly discovered that by using a set of highly abundant and / or easily detectable endogenous analytes in body fluid samples as a set of boundaries (e.g., separator analytes) to divide existing continuous detection into multiple time windows, the limitations of existing methods can be overcome, thereby successfully detecting and quantifying a large number of analytes (e.g., at least 500 analytes, preferably at least 1000 analytes) in body fluid samples.

[0068] This method / system continuously detects each detection segment in all detection segments, and uses the detection of each separated analyte as an event that triggers changes in the detection parameters of different analyte groups in different detection segments, thereby analyzing each detection segment and achieving the detection and quantification of a large number of analytes.

[0069] In one implementation, detection in each detection segment is analyte-specific for that segment. For example, during detection of each segment, other remaining detection segments can be masked, allowing for single detection. Parameters such as detection time or abundance / detectability will be specific to the analyte in that segment.

[0070] Therefore, in one implementation, the results and related analyses are analyte-specific. For example, in the analysis Figure 1 When endogenous molecule #3 is detected, all other endogenous molecules are completely masked. Therefore, only the properties of endogenous molecule #3 are visible, while all other analytes located in detection segment 2 are in their dedicated detection segments.

[0071] One of the key steps of this method is to generate a time series of detection segments, each containing a subgroup of biomarkers or analytes to be detected. The switching of detection of biomarkers or analytes from segment 1 to segment 2 or from segment 2 to segment 3, or so on, is triggered by the successful detection of a “separator molecule” (e.g., a separator analyte), which is selected based on high and reproducible detectability from biological samples.

[0072] This method uses separator analytes (e.g., separators or separator molecules) to divide the detections of existing methods into a number of sub-detections of this invention. The separator analytes of this invention are essentially endogenous analytes, which are always present in high abundance in body fluid samples and are relatively stable in the sample. Therefore, this method significantly improves the detectability of the total number of target analytes and the quality of the data. For example, this method can sequentially detect as many molecules as possible from all analytes eluted into a spectrometer (e.g., a mass spectrometer) using a specific solvent gradient.

[0073] The applicant uses mass spectrometry as an exemplary detection technique in the following description and examples. The applicant envisions that other detection techniques may also be used in this invention. Other detection techniques may include, but are not limited to: detecting analytes using optical detection devices, such as UV-VIS detection, diode array detection (DAD), or photodiode array detection (PDA); detecting analytes using flow cytometry; detecting analytes using fluorescence detection; detecting analytes using isotope analysis and NMR; detecting analytes using atomic absorption and emission; detecting analytes using calorimetry; and detecting analytes using biosensors, such as physicochemical detectors.

[0074] In one aspect, this disclosure relates to a method for detecting and quantifying analytes in a sample of a subject. In one embodiment, the method includes: (a) identifying and / or dissecting each of the analytes; (b) sorting each of the analytes to identify a first plurality of separator analytes; (c) dividing the analytes into a second plurality of groups using the separator analytes; (d) detecting and / or quantifying a first group ending with a first separator analyte by scanning and / or quantifying each of the analytes in the first group until a threshold of the intensity of the first separator analyte is reached; (e) switching to detecting and quantifying a second group starting with a first separator analyte by scanning and quantifying each of the analytes in the second group (including the second separator analyte) until a threshold of the intensity of the second separator analyte is reached; (f) switching to detecting and quantifying a third group starting with a second separator analyte by scanning and quantifying each of the analytes in the third group (including the third separator analyte) until a threshold of the intensity of the third separator analyte is reached; and (g) repeating step f) until each of the second plurality of groups is scanned and quantified.

[0075] In one embodiment, the sample includes bodily fluids. In one embodiment, the sample includes any liquid sample from the subject. In another embodiment, the sample consists of any liquid sample from the subject. In yet another embodiment, the sample is any liquid sample from the subject.

[0076] In one embodiment, the body fluid includes one or more of the following: saliva samples, plasma samples, sweat samples, tear samples, gastrointestinal fluid samples, pancreatic juice samples, serum samples, and urine samples. In another embodiment, the body fluid is selected from the group consisting of saliva samples, plasma samples, sweat samples, tear samples, gastrointestinal fluid samples, pancreatic juice samples, serum samples, and urine samples.

[0077] In one embodiment, this method is not limited to bodily fluids. For example, this method / system can also be used for other test substances, such as cell lysates, non-biological samples containing mixtures, or any sample containing multiple analytes.

[0078] In a preferred embodiment, the sample is a body fluid.

[0079] The subjects to which this method is applied include any mammal. Mammals may or may not have diseases such as cancer. In one embodiment, mammals may include rodents, aquatic mammals, livestock such as dogs and cats, farm animals such as sheep, pigs, cattle, and horses, and humans. In one embodiment, the subject may be a cat, dog, or human. In a preferred embodiment, the subject may be a human.

[0080] It is well known that low-abundance analytes in the bodily fluids of mammals (such as humans) are important or meaningful disease-related biomarkers. However, the detection and quantification of large quantities (e.g., at least 500, at least 1000, at least 1500, or at least 2000) of low-abundance analytes in the bodily fluids of mammals (such as humans) remains a challenge with existing technologies.

[0081] In one implementation, the method is based on mass spectrometry detection.

[0082] In one implementation, the method is capable of detecting and quantifying at least 500 analytes in each analytical assay.

[0083] In one embodiment, the method is capable of detecting and quantifying at least 1,000 analytes in each analytical assay.

[0084] To address the aforementioned challenges and overcome the limitations of existing technologies, this method first processes bodily fluid samples to identify and analyze analytes, and further identifies analytes that separate the analytes into several detection segments.

[0085] In one embodiment, the separating analytes of the present invention possess superior detectability observed from detection techniques such as mass spectrometry. For each separating analyte, its specific detection time can be verified. Empirical detection times for each separating analyte can be observed from literature or other publicly available resources, or obtained by analyzing each separating analyte in a solvent of the same gradient.

[0086] In one implementation, it is important to establish retention time relationships for all analytes, including the target analyte of interest (i.e., biomarkers, such as protein / peptide analytes) and the separator analytes (e.g., protein / peptide analytes).

[0087] like Figure 1 As shown, this method can be used to analyze each segment of the detection segment by continuously detecting and quantitatively detecting each segment, while treating the detection of each separating analyte as an event that triggers changes in the detection parameters of different biomarker groups in different detection segments.

[0088] For example, such as Figure 1 As shown, by using detection techniques such as mass spectrometry, this method first scans the first analyte group (e.g., the first endogenous boundary high-abundance molecule, i.e., the first separating molecule) Figure 1 The detection segment #0); when an instrument such as a mass spectrometer detects that the intensity of the first separating molecule exceeds a certain predetermined threshold, the mass spectrometer switches to detecting molecules including the second endogenous boundary high-abundance molecule (i.e., Figure 1 The second analyte group (e.g., the second separator molecule) Figure 1 The detection segment #1); when an instrument such as a mass spectrometer detects that the intensity of the second separating molecule exceeds another predetermined threshold, the mass spectrometer switches to detecting molecules including the third endogenous boundary high-abundance molecule (i.e., Figure 1 The third analyte group (e.g., the third separator molecule) Figure 1 The detection segment #2), and so on.

[0089] This method involves continuous scanning to complete the analytical measurement (i.e., a complete run from detection segment 0 to detection segment 9), until... Figure 1 The last detection segment (i.e., detection segment 9) is scanned and analyzed. Therefore, during each analytical assay, this method detects and quantifies all analytes from each detection segment (i.e., detection segments 0 to 9).

[0090] like Figure 1 As shown, this method can achieve high-throughput detection. For example, Figure 1 Non-limiting embodiments show that if a total of 9 endogenous high-abundance separating analytes (i.e., Figure 1The "endogenous molecule #1", "endogenous molecule #2", ..., "endogenous molecule #9" in the sample can be used to increase the overall detection capability by as much as 10 times (10X). Specifically, the exemplary nine analytes can divide the detection count of a single group of X number of detections into 10 segments (i.e., ... Figure 1 The segments “Detection Segment 0”, “Detection Segment 1”, “Detection Segment 2”, ..., “Detection Segment 9” can each accommodate X detections. Therefore, the method with X detections can be changed to a method with 10X detections.

[0091] For example, a conventional mass spectrometer can perform a maximum of 500 detections in one detection cycle (limited by instrument capacity), but with this method, if we assume there are a total of 9 separating analytes (i.e., boundary molecules) in the above scenario, the same instrument can achieve up to 500 x 10 = 5000 detections.

[0092] Since even larger numbers of analytes can be used (e.g., 99, 999, 9999, 99999, 999999 or more), the total number of detections that can be achieved may be even greater (e.g., 100X, 1000X, 10000X, 100000X, 100000X or more).

[0093] See now Figure 3 An exemplary method 300 for detecting and quantifying a large number of analytes in body fluid samples is described.

[0094] like Figure 3 As shown, each analyte in the sample was identified and analyzed (302).

[0095] In one implementation, a detection technique such as mass spectrometry is used to rapidly scan the sample, identifying and profiling each analyte in the sample. Each analyte in the sample can be identified using parameters such as retention time and detectability.

[0096] In another implementation, each analyte in the sample is identified and profiled using external resources, such as existing publicly available data. Numerous databases contain both targeted and untargeted detection parameters for mass spectrometry detection of molecules or analytes. For example, some major databases containing targeted and untargeted detection parameters for mass spectrometry detection of molecules or analytes (such as peptides) include: 1. PeptideAtlas http: / / www.peptideatlas.org / builds / human / ; 2. SRMatlas http: / / www.srmatlas.org; and 3. PRIDE https: / / www.ebi.ac.uk / pride / archive / . Some major databases containing targeted and non-targeted detection parameters for mass spectrometry detection of molecules or analytes (such as metabolites) include: 1. HMDB https: / / hmdb.ca; 2. MetLin https: / / metlin.scripps.edu / ; 3. KEGG https: / / www.genome.jp / kegg / compound / ; 4. LipidMaps https: / / www.lipidmaps.org; and 5. ChEBI https: / / www.ebi.ac.uk / chebi / .

[0097] Back Figure 3 After identifying and analyzing each analyte in the sample (302), each analyte is sorted to identify the first plurality of separate analytes (304).

[0098] In one implementation, analytes are sorted according to their abundance, with analytes of higher abundance appearing at the top of the list. In another implementation, the analytes at the top of the list can be identified as the first plurality of separating analytes.

[0099] In one implementation, each analyte is sorted according to its different abundance to identify a first plurality of separator analytes that divide the analytes into a second plurality of groups, such that the number of analytes included in each group is below the detection limit of conventional methods.

[0100] In one implementation, the abundance of each separator analyte is set with a threshold in each pair of analytes adjacent to each separator analyte.

[0101] In one implementation, the separating analyte can belong to the same category as other analytes. For example, if the analyte is a peptide, the peptide analyte with the highest abundance or that is detectable can be selected as the separating analyte.

[0102] In one implementation, the separator analyte may belong to a different category than the target analyte. However, these separators, which belong to different categories than the target analyte, need to be able to be analyzed and detected using the same methods as biomarker detection.

[0103] For example, when analyzing peptide samples, in addition to selecting all peptide separators, small molecules such as dopamine or adrenaline can be chosen as separators along with other peptide separators if the separators and biomarkers can be analyzed in the same assay and environment. Specifically, both dopamine and adrenaline can be eluted from a C18 column to a mass spectrometer under organic conditions, and this analytical method is also applicable to the peptide biomarkers to be analyzed.

[0104] In one implementation, for detection techniques such as mass spectrometry, each of the separator analytes may have a unique but predetermined detection time (e.g., retention time). More specifically, the detection times of both the separator analytes and the analytes can be predetermined, and their detection order among the different analytes can be predetermined to help determine the sequential detection of different detection segments and the separators therein.

[0105] In one implementation, the separator analyte typically represents an analyte that is stably present in complex biological samples at moderate to high abundance. Therefore, in one implementation, the separator analyte itself can serve as a standardized internal control to represent the abundance of the sample quality.

[0106] In one implementation, since the analytes are endogenous, their detection time fluctuations are substantially the same as those of other target analytes of interest emerging from body fluid samples. Therefore, the detection by this method is unaffected by retention time fluctuations.

[0107] In one implementation, the separator analyte can represent the efficiency of sample preparation (such as the efficiency of digestion, chemical derivatization, and purification procedures), which cannot be achieved by adding an exogenous separator.

[0108] In one implementation, one or more exogenous separating analytes may be added to the sample.

[0109] In one implementation, exogenous separators can have several disadvantages. For example, exogenous separators can often cause significant ion suppression, hindering the detection of endogenous analytes of interest, and may reduce the detection sensitivity of biomarkers of interest that are co-eluted with the exogenous separator.

[0110] In one implementation, exogenous separators may still be used and mixed with the test sample. In another implementation, endogenous and exogenous separators may be mixed and used in the same sample.

[0111] In one implementation, the first plurality of separated analytes may depend on the number of analytes and / or the complexity of the downstream diagnostic methods.

[0112] For example, if there are about one hundred biomarkers that require further analysis, then a list of the top ten analytes as separators may be sufficient. If there are more than one thousand biomarkers that require further analysis, then a list of the top 50 analytes as separators may be sufficient to divide the detection of these one thousand biomarkers into many consecutive detection segments, each of which can be simplified to the range of the instrument's detection capabilities.

[0113] Back Figure 3 After sorting each analyte and identifying the first plurality of separator analytes (304), the analytes are divided into a second plurality of groups (306) using the separator analytes.

[0114] In one implementation, a series of consecutive segments can be divided into analytes having a list of detection parameters (e.g., retention time or detection time) using a segmentation analyte.

[0115] In one implementation, both the separating analyte and the target analyte can be extensively analyzed, and a dedicated segment for each analyte can be determined.

[0116] For example, for a list of 50 analytes to be analyzed by mass spectrometry using a 10-minute HPLC gradient (with a maximum detection limit of 12 analytes in the same assay), the same body fluid sample can be analyzed in 5 runs (each run containing 10 analytes, which is below the detection limit of 12 analytes). The detection time window for each analyte can be recorded, and then the four separating analytes eluted from HPLC at minutes 2, 4, 6, and 8 can be selected, dividing the list of 50 analytes into 5 segments. Therefore, the mass spectrometer can detect 10 analytes in each segment (assuming the 50 analytes are uniformly distributed across the gradient; if not, more separating analytes can be added to segments with more analytes).

[0117] like Figure 3 As shown, after the analytes are divided into a second plurality of groups (306) using the separator analyte, the first group ending with the first separator analyte is detected and / or quantified by scanning and / or quantifying each of the analytes in the first group until the intensity threshold (308) of the first separator analyte is reached.

[0118] In addition, such as Figure 3As shown, once the intensity threshold (308) of the first separator analyte is reached, the method switches to detecting and quantifying the second group of analytes starting from the first separator analyte by scanning and quantifying the second group of analytes until the intensity threshold (310) of the second separator analyte is reached.

[0119] Furthermore, such as Figure 3 As shown, once the intensity threshold (310) of the second separator analyte is reached, the method switches to detecting and quantifying the third group starting from the second separator analyte by scanning and quantifying each of the analytes in the third group until the intensity threshold (312) of the third separator analyte is reached.

[0120] In each of 310 and 312, once the intensity threshold of the relevant separating analyte is reached, the method switches its detection from the previous group to the next group with different parameters.

[0121] In one implementation, when one group is being detected, other groups can be completely shielded to avoid interference from them, and by simplifying the target detection list, the detection sensitivity and specificity of the instrument can be maximized.

[0122] like Figure 1 As shown, the first group corresponds to detection segment 0 ending with the first separating analyte (i.e., endogenous molecule #1); the second group corresponds to detection segment 1 ending with the second separating analyte (i.e., endogenous molecule #2); and the third group corresponds to detection segment 2 ending with the third separating analyte (i.e., endogenous molecule #3). This method first scans the first analyte group (e.g., the first analyte group) including the first endogenous boundary high-abundance molecule (i.e., the first separating molecule). Figure 1 The detection segment #0); when an instrument such as a mass spectrometer detects that the intensity of the first separating molecule exceeds a certain predetermined threshold, the mass spectrometer switches to detecting molecules including the second endogenous boundary high-abundance molecule (i.e., Figure 1 The second analyte group (e.g., the second separator molecule) Figure 1 The detection segment #1); when an instrument such as a mass spectrometer detects that the intensity of the second separating molecule exceeds another predetermined threshold, the mass spectrometer switches to detecting molecules including the third endogenous boundary high-abundance molecule (i.e., Figure 1 The third analyte group (e.g., the third separator molecule) Figure 1 (Detection segment #2).

[0123] Back Figure 3 After reaching the intensity threshold (312) of the third analyte, step 312 is repeated until each of the second plurality of groups is scanned and quantified. Figure 1 As shown, repeat step 312 until all remaining detection segments #2 to #9 are scanned and quantified.

[0124] In one embodiment, the step of quantifying the analyte in each group includes using the corresponding separator analyte as an internal standard. In another embodiment, the step of quantifying the analyte in each group includes adding an exogenous separator.

[0125] In one aspect, this disclosure relates to a system for detecting and quantifying an analyte in a sample from a subject. In one embodiment, the system includes an input system for introducing a subject sample into a detection system; a detection system for detecting and quantifying the analyte in the subject sample; a network system for communicating with external resources to analyze analyte data; and an output system for generating a report.

[0126] In one embodiment, the system includes a control system that includes a non-transitory computer-readable storage medium.

[0127] In one implementation, a computer-readable non-transitory storage medium or medium may include one or more semiconductor- or other types of integrated circuits (ICs) (e.g., field-programmable gate arrays (FPGAs) or application-specific integrated circuits (ASICs)), hard disk drives (HDDs), hybrid hard disk drives (HHDs), optical disks, optical disk drives (ODDs), magneto-optical disks, magneto-optical disk drives, floppy disks, floppy disk drives (FDDs), magnetic tape, solid-state drives (SSDs), RAM drives, secure digital cards or drives, any other suitable computer-readable non-transitory storage medium, or any suitable combination of two or more of these (as applicable). A computer-readable non-transitory storage medium may be volatile, non-volatile, or a combination of volatile and non-volatile (as applicable).

[0128] In one embodiment, a computer-readable non-transitory storage medium stores one or more computer programs suitable for causing a processor in a control system to execute the following steps: (a) identifying and profiling each analyte; (b) sorting each analyte to identify a first plurality of partitioned analytes; (c) dividing the analytes into a second plurality of groups using partitioned analytes; (d) detecting and quantifying analytes in the first group ending with a first partitioned analyte by scanning and quantifying each analyte in the first group until an intensity threshold of the first partitioned analyte is reached; (e) moving to detecting and quantifying analytes in the second group starting with a first partitioned analyte by scanning and quantifying each analyte in the second group until an intensity threshold of the second partitioned analyte is reached; (f) moving to detecting and quantifying analytes in the third group starting with a second partitioned analyte by scanning and quantifying each analyte in the third group until an intensity threshold of the third partitioned analyte is reached; and (g) repeating step (f) until each of the second plurality of groups is scanned and quantified.

[0129] In one implementation, the sample is a bodily fluid.

[0130] In one embodiment, the body fluid is selected from the group consisting of saliva samples, plasma samples, sweat samples, tear samples, gastrointestinal fluid samples, pancreatic juice samples, serum samples, and urine samples.

[0131] In one implementation, the detection system is a mass spectrometer.

[0132] In one embodiment, in each analytical assay, the system is used to detect and quantify at least 500 analytes, at least 1000 analytes, at least 1500 analytes, at least 2000 analytes, at least 2500 analytes, at least 3000 analytes, at least 3500 analytes, at least 4000 analytes, at least 4500 analytes, at least 5000 analytes, at least 5500 analytes, at least 6000 analytes, and so on. At least 6,500 analytes, at least 7,000 analytes, at least 7,500 analytes, at least 8,000 analytes, at least 8,500 analytes, at least 9,000 analytes, at least 9,500 analytes, at least 10,000 analytes, at least 10,500 analytes, at least 11,000 analytes, at least 11,500 analytes, at least 12,000 analytes, at least 12,500 analytes, or at least 13,000 analytes.

[0133] Now for reference Figure 4 An exemplary system 400 for detecting and quantifying a large number of analytes from body fluid samples is described.

[0134] like Figure 4 As shown, an exemplary system 400 for detecting and quantifying a large number of analytes from body fluid samples includes an input system 401, a detection system 402, a network system 403, a control system 404, and an output system 405.

[0135] The input system 401 includes a sample holder that communicates electronically with the detection system 402. Once a body fluid sample is added to the sample holder of the input system 401, the detection system 402 is able to scan and analyze all analytes in the body fluid sample.

[0136] In one embodiment, the detection system 402 is a mass spectrometer.

[0137] In one implementation, the analysis data of the analyte can be obtained from external resources such as publications or databases via network system 403.

[0138] For example, some major databases containing targeted and non-targeted detection parameters for mass spectrometry detection of molecules or analytes (e.g., peptides) include: 1. PeptideAtlas http: / / www.peptideatlas.org / builds / human / ; 2. SRMatlas http: / / www.srmatlas.org; and 3. PRIDE https: / / www.ebi.ac.uk / pride / archive / . Major databases containing targeted and non-targeted detection parameters for mass spectrometry detection of molecules or analytes such as metabolites include: 1. HMDB https: / / hmdb.ca; 2. MetLin https: / / metlin.scripps.edu / ; 3. KEGG https: / / www.genome.jp / kegg / compound / ; 4. LipidMaps https: / / www.lipidmaps.org; and 5. ChEBI https: / / www.ebi.ac.uk / chebi / .

[0139] The control system 404 controls each process of this method and / or any other component of system 400. In one embodiment, the control system 404 is a computer system.

[0140] The output system 405 receives the detection and quantification results and generates a report.

[0141] All the methods and systems described in this disclosure can be implemented using one or more computer programs or components. These components may be provided as a set of computer instructions on any conventional computer-readable or mechanically readable medium, including volatile and non-volatile memories such as RAM, ROM, flash memory, magnetic or optical disks, optical storage, or other storage media. The instructions may be provided as software or firmware and may be implemented in all or part of hardware components such as ASICs, FPGAs, DSPs, or any other similar devices. The instructions may be configured to execute via one or more processors, and when executed, to run or facilitate the operation of all or part of the disclosed methods and programs.

[0142] It should be understood that various changes and modifications to the embodiments described herein will be apparent to those skilled in the art. Such changes and modifications can be made without departing from the spirit and scope of this disclosure and without diminishing the intended advantages. Therefore, it is intended that such changes and modifications be included in the appended claims.

[0143] Example

[0144] Example 1. Establishment of a plasma proteomic biomarker detection and quantification platform using BF-Quant—detection and quantification of a large number of proteomic biomarkers.

[0145] The following non-limiting embodiments illustrate scientific data on the conception of methods and systems for the detection and quantification of a wide range of analytes (e.g., molecular biomarkers, biomolecules (proteins and metabolites) and other analytes) from bodily fluid samples.

[0146] Step 1. By analyzing the plasma proteome, the applicant identified two groups of peptides: Group 1 consists of peptide biomarkers indicating disease states, and Group 2 consists of peptide separators with good detectability observed in mass spectrometry. Specific detection times for each peptide are validated and are shown in the table. The validated detection times for each peptide can be observed in the literature and obtained by analyzing each peptide using the same organic solvent gradient. The key was to establish relationships between the retention times of all analytes, including the target of interest (i.e., biomarkers, such as the protein / peptide analyte sequences in Group 1 of Table 1) and separators (e.g., the protein / peptide analyte sequences in Group 2 of Table 1, highlighted).

[0147] Table 1. Biomarkers, such as protein / peptide analyte sequences (Group 1 shows protein / peptide analyte sequences and Group 2 shows separators (i.e. separator analytes)).

[0148]

[0149]

[0150]

[0151]

[0152] Step 2. Based on the retention times of each separator in Group 2 and each target molecule in Group 1, we roughly divide the detection of the targets in Group 1 into a certain number of detection segments. These segments are separated / divided by the peptide separators in Group 2, and the following table (Table 2) is created. A detection schedule (Table 2) is established using the actual detection times of each molecule of interest and each separator. The actual detection times may vary with different HPLC methods or by running different gradient HPLC methods, but the relationships (relative positions of the analytes) shown in the table are stable, as demonstrated by repeated assays using different HPLC methods. Figure 2 ).

[0153] Table 2. The peptide separators in Group 2 divide the detection of the target analytes in Group 1 into a certain number of detection segments.

[0154]

[0155]

[0156]

[0157]

[0158] Step 3. Therefore, a plasma proteomic biomarker detection and quantification platform was established using BF-Quant. Detailed progress for the detection of each biomarker is shown in Table 2.

[0159] also, Figure 2 The graphs show the fluctuations in liquid chromatography retention times for different methods, but the stable retention relationships between adjacent peaks are reproducible. All graphs were obtained using different organic solvent gradient methods, but the same x-axis is used to indicate retention times (minutes).

[0160] The actual retention time for each analyte can vary under different HPLC gradient conditions. For example, solvent A is water and 0.1% formic acid, and solvent B is acetonitrile and 0.1% formic acid. In a 30-minute run, one approach could start at 0 minutes with 100% solvent A and 0% solvent B, and then gradually change to 0% solvent A and 100% solvent B over 30 minutes. The concentration of solvent B is gradually increased from 0% to 100% over the 30-minute time span (while solvent A decreases from 100% to 0% over the same 30-minute period). Then, over the entire 30-minute run, analyte A can be eluted and detected at 10 minutes (the retention time of A in this method is 10 minutes), and analyte B can be eluted and detected at 20 minutes (the retention time of B in this method is 20 minutes).

[0161] Furthermore, when the 30-minute run is changed to a 15-minute run, that is, it can start at 0 minutes with 100% solvent A and 0% solvent B and then gradually change to 0% solvent A and 100% solvent B over 15 minutes, then during the entire 15-minute run, analyte A can be eluted and detected at 5 minutes and analyte B can be eluted and detected at 10 minutes.

[0162] Therefore, the actual retention time of each analyte may differ in different HPLC methods, but in an organic gradient environment, analyte B is always eluted after analyte A, and this relationship, rather than the actual time point, is reproducible.

[0163] Furthermore, if thousands of small peaks are detected by elution between analyte A and analyte B, the relationship between each of these peaks and A (i.e., they are all eluted after A) or between each of them and B (i.e., they are all eluted before B) is also reproducible. This relationship is also stable between randomly selected pairs of analytes in the determination of these 1002 molecules, unless the system is changed to something entirely different. For example, if a different column with different chemicals, such as a HILIC column instead of a C18 column, is used, where the different properties of the analytes are used to determine the retention times, then the retention times need to be reset, but their relationship remains stable within the system.

[0164] Example 2.

[0165] Research Design

[0166] The key is the generation of a time series of detection segments, each containing a subgroup of biomarkers to be detected. The switching of biomarker detection from segment 1 to segment 2, or from segment 2 to segment 3, etc., is triggered by "segmenting molecules" selected based on high and reproducible detectability in biological samples. For example, peptides from albumin, immunoglobulins, haptoglobin, etc., can be selected as segmenters for detecting proteomic biomarkers in plasma.

[0167] Different separation methods can be used to generate the retention time values ​​essential for constructing the BF-Quant method. For example, in proteomics assays, the applicant uses a C18 column to separate different peptides based on their varying hydrophobicity when interacting with the column and mobile phase; in nucleic acid assays, the applicant can use size exclusion columns to separate different nucleic acid sequences based on their varying molecular weights when interacting with the size exclusion matrix and mobile phase. Other methods can also be used. If a method can separate the analyte in a reproducible manner, it can be used to generate the retention time values ​​subsequently used to generate the BF-Quant worksheet. This document does not limit the separation methods used.

[0168] In the BF-Quant of this invention, each detection segment may include the number of target molecules up to the upper limit specified in the instrument manual. For example, a modern mass spectrometer may have a target detection list of 500 to 3000 switching elements within the same scan cycle, while with BF-Quant, this number can be increased indefinitely by adding more and more separators, and 500 to 3000 switching elements can be detected between each adjacent separator. When the required number of detections in a detection segment meets or exceeds the instrument's limitations, at least one more separator is provided within that detection segment to reduce the size of the target list for each newly formed detection segment to within the lower limit of the instrument.

[0169] Due to page limitations, the embodiments described herein do not exhaustively cover the throughput levels of modern mass spectrometry. The applicant intends to demonstrate the principles of BF-Quant, and therefore each detection segment in the applicant's tables (specifically Tables 2, 4, 6, and 8) typically contains dozens of molecules to be detected. However, the applicant could easily fit thousands of molecules into each segment to get closer to the limitations of a mass spectrometer, but that would require thousands of pages to show in a single table.

[0170] Detection and quantification of a large number of metabolomics biomarkers

[0171] In clinical settings, approximately 300 to 500 metabolites require routine detection and quantification. However, this represents only a small fraction of the entire pool of metabolites present in the human body fluid system. The BF-Quant of this invention can be used to detect large or virtually unlimited numbers of metabolites. To establish a high-throughput detection method for metabolomics biomarkers, we performed the following three steps:

[0172] Step 1. By analyzing body fluid samples, we identified two groups of metabolites: Group 1 consists of clinically significant metabolomics biomarkers, and Group 2 consists of well-detectable metabolic separators observed in mass spectrometry. The list of biomarkers can be derived from our own profiling analysis or from literature or databases. In the table below, the specific detection time for each metabolomics biomarker is validated and shown. Empirical detection times for each metabolite can be observed in the literature and obtained by analyzing each metabolite separately using the same organic solvent gradient. The key is to establish relationships between the retention times of all analytes, including the target of interest (i.e., metabolomics biomarkers representing disease states) and the separators (i.e., the highly stable and highly detectable metabolomics biomarkers in Group 2 of Table 3).

[0173] Table 3. Metabolomics biomarkers of the target substances to be detected in the method.

[0174]

[0175]

[0176]

[0177]

[0178]

[0179]

[0180] Step 2. Based on the retention times of each separator in Group 2 and the retention times of each target molecule in Group 1, we roughly divide the detection of the targets in Group 1 into a certain number of detection segments. These segments are separated by the metabolite separators in Group 2, and the following table (Table 4) is created. A detection schedule (Table 4) is established using the actual detection times of each molecule of interest and each separator. The actual detection times may vary with different HPLC methods or by running different gradient HPLC methods, but the relationships (relative positions of the analytes) shown in the table are stable, as demonstrated by repeated assays using different HPLC methods.

[0181] Table 4. Detection progress table after implementing the BF-Quant strategy

[0182]

[0183]

[0184]

[0185]

[0186]

[0187]

[0188] Step 3. Therefore, a fluid metabolomics biomarker detection and quantification platform was established using BF-Quant. Detailed progress for each biomarker detection is shown in Table 4. The switching (actual detection parameters to be implemented by the instrument) for each target (Table 3) can be collected and loaded into the instrument based on the detection schedule listed in Table 4.

[0189] Example 3. Detection and quantification of a large number of lipidomics biomarkers

[0190] In clinical settings, the routine detection and quantification of approximately 200 to 300 lipids is required. However, this represents only a small fraction of the entire liposome pool present in the human body's fluid systems. BF-Quant can be used to detect large or virtually unlimited quantities of lipids. To establish a high-throughput detection method for lipidomics biomarkers, we performed the following three steps:

[0191] Step 1. By analyzing body fluid samples, we identified two groups of lipids: Group 1 consists of clinically significant lipidomic biomarkers, and Group 2 consists of lipid separators with good detectability observed in mass spectrometry. The list of biomarkers can be derived from our own assays or from literature or databases. In the table below, the specific detection time for each lipidomic biomarker is validated and shown. Empirical detection times for each lipid can be observed in the literature and obtained by analyzing each metabolite separately using the same organic solvent gradient. The key is to establish relationships between the retention times of all analytes, including the target of interest (i.e., lipidomic biomarkers representing disease states) and the separators (i.e., the highly stable and highly detectable lipidomic biomarkers in Group 2 of Table 5).

[0192] Table 5. Target analytes and lipidomic biomarkers used in the method.

[0193]

[0194]

[0195]

[0196]

[0197]

[0198]

[0199] Step 2. Based on the retention times of each separator in Group 2 and each target molecule in Group 1, we roughly divided the detection of the targets in Group 1 into a certain number of detection segments. These segments were separated / divided by the lipid biomarker separators in Group 2, and the following table (Table 6) was created. A detection schedule was established using the actual detection times of each molecule of interest and each separator (Table 6). The actual detection times may vary with different HPLC methods or by running different gradient HPLC methods, but the relationships (relative positions of analytes) shown in the table are stable, as demonstrated by repeated assays under different HPLC methods.

[0200] Table 6. Detection progress table after implementing the BF-Quant strategy

[0201]

[0202]

[0203]

[0204]

[0205]

[0206] Step 3. Therefore, a platform for the detection and quantification of body fluid lipidomics biomarkers was established using BF-Quant. Detailed progress for the detection of each biomarker is shown in Table 6. Switching (actual detection parameters implemented by the instrument) for each target analyte (Table 5) can be collected and loaded into the instrument according to the detection schedule listed in Table 6.

[0207] Example 4. Detection and quantification of a large number of genomic biomarkers

[0208] Next-generation sequencing is the most commonly used method for identifying clinically significant genomic mutations. In addition, genomic mutations can also be detected by mass spectrometry. Pre-amplification of genomic regions that may contain the mutant of interest is usually required. However, mass spectrometry is limited in terms of the total number of nucleic acid sequences that can be analyzed in a single method. The BF-Quant of this invention can be used to detect large or virtually unlimited numbers of nucleic acid mutants. To establish a high-throughput detection method for genomic biomarkers, we performed the following three steps:

[0209] Step 1. By analyzing bodily fluid samples, we identified two groups of nucleic acid sequences: Group 1 consists of clinically significant genomic biomarkers, and Group 2 consists of genomic separators that are well-detectable through overamplification and readily observable in mass spectrometry. These biomarkers represent hotspot mutations in human cancers. They mutate frequently in different types of human cancers. The separators were selected from WT sequences in the Human Genome Database, and therefore are present in most (if not all) humans. The list of biomarkers may be derived from our own assays or from literature or databases. In the table below, the specific detection time for each genomic biomarker is validated and shown. Empirical detection times for each nucleic acid sequence can be observed in the literature and obtained by analyzing each nucleic acid sequence separately using the same mobile phase gradient. The key is to establish relationships between the retention times of all analytes, including the target of interest (i.e., genomic biomarkers representing disease states) and the separators (i.e., the highly stable and highly detectable genomic biomarkers in Group 2 of Table 7).

[0210] Table 7. Genomic biomarkers of the target organisms to be detected in the method

[0211]

[0212]

[0213]

[0214]

[0215]

[0216] Step 2. Based on the retention times of each separator in Group 2 and each target molecule in Group 1, we roughly divided the detection of the targets in Group 1 into a certain number of detection segments. These segments were separated / divided by the genomic biomarker separators in Group 2, and the results were presented in Table 8. A detection schedule was established using the actual detection times for each molecule of interest and each separator (Table 8). The actual detection times may vary with different HPLC methods or by running different gradient HPLC methods, but the relationships (relative positions of the analytes) shown in the table are stable, as demonstrated by repeated assays using different HPLC methods.

[0217] Table 8. Detection progress chart after implementing the BF-Quant strategy

[0218]

[0219]

[0220]

[0221]

[0222]

[0223] Step 3. Therefore, a humoral genomics biomarker detection and quantification platform was established using BF-Quant. Detailed progress for each biomarker detection is shown in Table 8. Switching (actual detection parameters implemented by the instrument) for each target can be collected and loaded into the instrument according to the detection schedule listed in Table 8 (Table 7).

Claims

1. A method for detecting and quantifying an analyte in a sample of a subject, the method comprising: (a) Identify and / or analyze the analyte; (b) Identify at least one endogenous septum analyte from the analyte; (c) Using the at least one endogenous separating analyte, the analyte is divided into multiple groups; (d) Detecting and quantifying the first group of analytes ending with the first of the at least one endogenous separator analytes by mass spectrometry scanning and quantification, while simultaneously masking the detection of the remaining groups, until a first intensity threshold of the first of the at least one separator analyte is reached; and (e) Switch to detecting and quantifying the second group of analytes, starting with the first of the at least one endogenous separating analytes, by mass spectrometry scanning and quantification until a second intensity threshold of the second of the at least one endogenous separating analytes is reached.

2. The method of claim 1, comprising: (f) Repeat step (e) until each of the plurality of groups has been scanned and quantified.

3. The method of claim 1, wherein, The sample includes bodily fluids.

4. The method of claim 1, wherein, The samples are selected from the group consisting of saliva samples, plasma samples, sweat samples, tear samples, gastrointestinal fluid samples, pancreatic juice samples, serum samples, urine samples, and combinations thereof.

5. The method of claim 1, wherein, At least one of steps (a) to (d) is performed using a mass spectrometer.

6. The method of claim 1, wherein, At least one of the multiple groups includes at least 500 analytes.

7. The method of claim 4, wherein, At least one of the multiple groups includes at least 1,000 analytes.

8. The method of claim 1, wherein, At least one of steps (a) to (e) is performed using a mass spectrometer.

9. The method of claim 1, wherein, Step (b) includes sorting the analytes according to their detectability to identify the at least one endogenous septum analyte.

10. The method of claim 1, wherein, The at least one endogenous separator analyte has an abundance, and the abundance of the at least one endogenous separator analyte is used as a threshold for each group of analytes adjacent to the at least one endogenous separator analyte.

11. The method of claim 1, wherein, The quantification of the first group of analytes includes using the first of the at least one endogenous separating analytes as an internal standard.

12. The method of claim 1, wherein, The quantitative analysis of the first group of analytes includes the addition of exogenous separators.

13. The method of claim 1, wherein, Step (e) involves changing the detection parameters based on the second set of analytes.

14. The method of claim 13, wherein, The detection parameters include retention time.

15. A system for detecting and quantifying an analyte in a sample of a subject, the system comprising: An input component configured to receive the sample in a detection component; The detection component is configured to detect and / or quantify analytes in the sample; and A control unit includes a storage unit and a processor, wherein the storage unit stores one or more instructions to cause the processor to perform steps including: (a) Identify and / or analyze the analyte; (b) Identify at least one endogenous septate analyte from the analyte; (c) Using the at least one endogenous separating analyte, the analyte is divided into multiple groups; (d) Detecting and quantifying the first group of analytes ending with the first of the at least one endogenous separator analytes by mass spectrometry scanning and quantification, while simultaneously masking the detection of the remaining groups, until a first intensity threshold of the first of the at least one endogenous separator analyte is reached; and (e) Switch to detecting and quantifying the second group of analytes, starting with the first of the at least one endogenous separating analytes, by mass spectrometry scanning and quantification until a second intensity threshold of the second of the at least one endogenous separating analytes is reached.

16. The system of claim 15, wherein, The steps further include: (f) Repeat step (e) until each of the plurality of groups has been scanned and quantified.

17. The system of claim 15, further comprising: The output component is configured to generate a result report for the detection and quantification steps.

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