Methods for evaluating sample quality

Abstract

Provided are biomarkers, methods, devices, reagents, systems, and kits that can be used to assess the quality of a sample collected from a subject. Such biomarkers, methods, devices, reagents, systems, and kits can be useful in assessing sample handling acceptability and / or sample handling consistency across multiple samples.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of priority to U.S. Provisional Patent Application No. 63 / 301,654, filed January 21, 2022, which is incorporated by reference in its entirety and for all purposes.

[0002] Technical Field This application relates generally to methods for detecting biomarkers and for assessing the quality or suitability of a sample or sample set for use in the medical evaluation of a subject, such as biomarker discovery and diagnostic assays. [Background technology]

[0003] Blood contains various cellular and humoral systems to respond to injury or foreign and infectious agents. Small challenges can induce the innate immune system (cells such as the complement system and macrophages) to release signals and enzymes, cause platelet activation, and induce blood clotting. These signals are of interest because they are directly involved in defense and repair systems and can serve as markers of disease. However, signals of such processes may also respond to the effects of blood sample preparation and processing. Lysis of cells in the sample, degranulation of platelets, or activation of the complement system may cause changes in the concentration of analytes in the sample after collection that may be detected by "high fidelity" measurement techniques. Simply exposing blood to air can inadvertently activate these mechanisms. Thus, altering the time of sample processing steps can change the apparent composition of serum or plasma in such a way that physiological information is masked by preanalytical variations imparted to the sample during collection and processing. The sensitivity of these processes and proteins to subtle changes in sample handling may undermine their use as biomarkers.

[0004] Currently, multivariate biology researchers are concerned with pre-analytical sample variability (often referred to as "batch effects"). The extent to which sample quality can be determined is largely limited to visually obvious changes, e.g., red color indicates red blood cell lysis, and turbidity indicates high lipids or other contaminants. These relatively crude methods limit the reliability of all but the most robust protein measurements. Ostroff, R. et al. (2010), J. Proteomics 73:649-666, note that variability in serum and plasma preparation has complex, nonlinear effects.

[0005] Specific techniques for determining compliance with sample processing protocols are needed to monitor compliance, reject low quality samples, and / or correct the analytes of interest, improving the quality assessment of human or animal blood samples used in biomarker research, clinical diagnostic applications, biobanks, and drug development. Summary of the Invention

[0006] This application includes biomarkers, methods, reagents, devices, systems, and kits for assessing sample quality. The biomarkers of this application were identified using linear regression of measurements of a specific set of proteins that are affected by variations in sample processing protocols. In some embodiments, the panel of biomarkers includes proteins that are sensitive to sample handling.

[0007] In some embodiments, the method includes detecting the biomarkers using a multiplex slow off-rate aptamer-based assay described herein, for example, to assess the quality of the sample. In some embodiments, the sample is a blood sample, a plasma sample, a serum sample, and a urine sample. In some embodiments, the sample is a plasma sample or a serum sample.

[0008] In some embodiments, the time in hours between one or more sample processing steps is predicted or estimated, hi some embodiments, the sample processing steps include one or more of centrifugation of the sample, decanting or aspirating the centrifuged supernatant, and freezing the decanted or aspirated sample.

[0009] In some embodiments, there is provided a method of assessing the quality of a sample taken from a subject, the method comprising detecting a level of each of N biomarker proteins in the sample, where N is at least 3 and at least three of the N biomarker proteins are selected from LANC2, PKHM2, ENOA, cytoplasmic domain of TMEM9, PMM2, PGAM2, EFHD1, and THIK, and where the sample is a plasma sample. In some embodiments, the method comprises measuring the level of each of N biomarker proteins in a plasma sample from the subject, where N is at least 3 and at least three of the N biomarker proteins are selected from LANC2, PKHM2, ENOA, cytoplasmic domain of TMEM9, PMM2, PGAM2, EFHD1, and THIK, and identifying the sample as an analytical sample or a negative sample based on the levels of the N biomarker proteins, where an analytical sample is a sample that is suitable for use in one or more of the following: a protein biomarker discovery analysis, a protein expression level analysis, a diagnostic method, or a prognostic method, and a negative sample is a sample that is not suitable for use as an analytical sample. In some embodiments, the sample was frozen and thawed prior to detection. In some such embodiments, the method comprises determining an approximate duration of time that has elapsed from the completion of collection and processing of the sample to the time the sample is frozen.

[0010] In some embodiments, a method of assessing the quality of a sample taken from a subject is provided, the method comprising detecting the level of each of N biomarker proteins in the sample, where N is at least 2 and at least two of the N biomarker proteins are selected from LYAG, IL21R, C3b, IL36A, and GDF5, where the sample is a serum sample. In some embodiments, the method comprises measuring the level of each of N biomarker proteins in a serum sample from the subject, where N is at least 2 and at least two of the N biomarker proteins are selected from LYAG, IL21R, C3b, IL36A, and GDF5, and identifying the sample as an analytical sample or a negative sample based on the levels of the N biomarker proteins, where an analytical sample is a sample that is suitable for use in one or more of the following: a protein biomarker discovery analysis, a protein expression level analysis, a diagnostic method, or a prognostic method, and a negative sample is a sample that is not suitable for use as an analytical sample. In some embodiments, the sample is frozen and thawed prior to detection. In some such embodiments, the method includes determining an approximate duration of time that has elapsed from completion of collection and processing of the sample to the time the sample is frozen.

[0011] In some embodiments, a method of assessing the quality of a sample taken from a subject is provided, the method comprising detecting the level of each of N biomarker proteins in the sample, where N is at least 4 and at least four of the N biomarker proteins are selected from IHH, SHH, PGAM1, and ROA2. In some embodiments, the method comprises measuring the level of each of N biomarker proteins in a sample from the subject, where N is at least 4 and at least four of the N biomarker proteins are selected from IHH, SHH, PGAM1, and ROA2, and identifying the sample as an analytical sample or a negative sample based on the levels of the N biomarker proteins, where an analytical sample is a sample that is suitable for use in one or more of the following: a protein biomarker discovery analysis, a protein expression level analysis, a diagnostic method, or a prognostic method, and a negative sample is a sample that is not suitable for use as an analytical sample. In some embodiments, the sample was centrifuged prior to detection. In some such embodiments, the method comprises determining an approximate duration of time that has elapsed from the time the sample was taken to the time the sample was centrifuged.

[0012] In some embodiments, a method of assessing the quality of a sample collected from a subject is provided, the method comprising detecting a level of each of N biomarker proteins in the sample, where N is at least 9, and at least 9 of the N biomarker proteins are selected from APB, CFAD, PTN4, PGAM2, HPPD, C4A, IF4A2, IHH, SHH, ADAM9, PGAM1, IL18, and TMEM9. In some embodiments, the method includes measuring the level of each of N biomarker proteins in a sample from a subject, where N is at least 9, and where at least 9 of the N biomarker proteins are selected from APB, CFAD, PTN4, PGAM2, HPPD, C4A, IF4A2, IHH, SHH, ADAM9, PGAM1, IL18, and TMEM9, and identifying the sample as an analytical sample or a negative sample based on the levels of the N biomarker proteins, where an analytical sample is a sample that is suitable for use in one or more of the following: a protein biomarker discovery analysis, a protein expression level analysis, a diagnostic method, or a prognostic method, and a negative sample is a sample that is not suitable for use as an analytical sample. In some embodiments, the sample is centrifuged and decanted or aspirated prior to detection. In some such embodiments, the method includes determining an approximate duration of time that has elapsed from the time centrifugation is completed to the time the sample is decanted or aspirated.

[0013] In some embodiments, a method for assessing the quality of a sample taken from a subject comprises detecting N biomarker proteins, where one or more of the N biomarker proteins are associated with time to centrifugation, where one or more of the N biomarker proteins are associated with time to decanting or aspirating, and where one or more of the N biomarker proteins are associated with time to freezing of an appropriate sample type. In some embodiments, a method for assessing the quality of a sample taken from a subject comprises detecting N biomarker proteins, where one or more of the N biomarker proteins are associated with time to centrifugation, and where one or more of the N biomarker proteins are associated with time to decanting or aspirating. In some embodiments, a method for assessing the quality of a sample taken from a subject comprises detecting N biomarker proteins, where one or more of the N biomarker proteins are associated with time to centrifugation, and where one or more of the N biomarker proteins are associated with time to freezing of an appropriate sample type. In some embodiments, a method for assessing the quality of a sample taken from a subject comprises detecting N biomarker proteins, where one or more of the N biomarker proteins are associated with time to centrifugation, and where one or more of the N biomarker proteins are associated with time to freezing of an appropriate sample type.

[0014] In any of the foregoing corrections, N is 9, N is 10, N is 11, N is 12, N is 13, N is 14, N is 15, N is 16, N is 17, N is 18, N is 19, or N is 20. In some embodiments, N is 2, N is 3, N is 4, N is 5, N is 6, N is 7, or N is 8. In some embodiments, all of the N biomarker proteins are selected from the list provided herein.

[0015] In some embodiments, the subject is a human subject and the sample is a liquid sample. In some embodiments, the sample is plasma or serum obtained from a whole blood sample. In some embodiments, the approximate duration of time between sample processing steps determined by the method herein is 0, 0.5, 1, 1.5, 3, 3.5, 6, 9, or 24 hours, or more than 24 hours. In some embodiments, the method is carried out in vitro.

[0016] In some embodiments, the sample is identified as having passed the quality assessment or as having failed the quality assessment. In some such embodiments, the identification is based at least in part on the detected levels of the N biomarker proteins in the sample. In some embodiments, the identification is based at least in part on the determined approximate duration or durations of time between two or more sample processing steps. In some embodiments, if the sample is identified as having passed the quality assessment, it is subjected to further analysis, and if the sample is identified as having failed the quality assessment, it is discarded.

[0017] In some embodiments, the present disclosure provides a method for evaluating the quality of a plurality of samples, comprising detecting the respective levels of N biomarkers in a plurality of samples from a plurality of subjects.In some such embodiments, the approximate duration of time between two or more sample processing steps is determined and compared across a plurality of samples.In some such embodiments, the consistency of sample handling across a plurality of samples is determined.

[0018] In some embodiments, the method comprises contacting a biomarker protein of a sample from a subject with a set of capture reagents, each capture reagent of the set of capture reagents specifically binding to one biomarker protein to be detected. In some embodiments, the method comprises contacting a biomarker protein of a sample from a subject with a set of capture reagents, each capture reagent of the set of capture reagents specifically binding to a different biomarker protein to be detected. In some embodiments, each capture reagent is an antibody or an aptamer. In some embodiments, each biomarker capture reagent is an aptamer. In some embodiments, at least one aptamer is a slow off-rate aptamer. In some embodiments, at least one slow off-rate aptamer comprises at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 modified nucleotides. In some embodiments, each slow off-rate aptamer binds to its target protein with an off-rate (t1 / 2) of ≧30 minutes, ≧60 minutes, ≧90 minutes, ≧120 minutes, ≧150 minutes, ≧180 minutes, ≧210 minutes, or ≧240 minutes.

[0019] In some embodiments, a kit is provided, the kit comprising N biomarker protein capture reagents, where N is at least 3 and at least 3 of the capture reagents bind to LANC2, PKHM2, ENOA, the cytoplasmic domain of TMEM9, PMM2, PGAM2, EFHD1, or THIK; or N is at least 2 and at least 2 of the capture reagents bind to LYAG, IL21R, C3b, IL36A, or GDF5; or N is at least 4 and at least 4 of the capture reagents bind to IHH, SHH, PGAM1, or ROA2; or N is at least 9 and at least 9 of the capture reagents bind to APB, CFAD, PTN4, PGAM2, HPPD, C4A, IF4A2, IHH, SHH, ADAM9, PGAM1, IL18, or TMEM9. In some embodiments, each capture reagent binds to a different biomarker protein. In some embodiments, N is 2, N is 3, N is 4, N is 5, N is 6, N is 7, N is 8, N is 9, N is 10, N is 11, N is 12, N is 13, N is 14, N is 15, N is 16, N is 17, N is 18, N is 19, or N is 20. In some embodiments, the kit comprises capture reagents from multiple sample processing panels.

[0020] In some embodiments, each of the N biomarker protein capture reagents specifically binds to a biomarker protein selected from Tables 1, 2, 3, and / or 4. In some embodiments, each of the N biomarker capture reagents is an antibody or an aptamer. In some embodiments, each biomarker capture reagent is an aptamer. In some embodiments, at least one aptamer is a slow off-rate aptamer. In some embodiments, at least one slow off-rate aptamer comprises at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 modified nucleotides. In some embodiments, each slow off-rate aptamer binds to its target protein with an off-rate (t1 / 2) of ≧30 min, ≧60 min, ≧90 min, ≧120 min, ≧150 min, ≧180 min, ≧210 min, or ≧240 min. In some embodiments, the kit is used to detect the N biomarker proteins in a sample from a subject. In some embodiments, the kit is used to assess the quality of a sample or samples. [Brief description of the drawings]

[0021] [Figure 1] 1 illustrates a non-limiting exemplary computer system for use with the various computer-implemented methods described herein. [Diagram 2] 1 depicts non-limiting exemplary aptamer assays that can be used to detect one or more biomarkers in a biological sample. [Diagram 3] 1 provides certain exemplary modified pyrimidines that may be incorporated into aptamers, such as slow off-rate aptamers. [Figure 4] 1 provides certain exemplary modified pyrimidines that may be incorporated into aptamers, such as slow off-rate aptamers. [Diagram 5] 1 provides certain exemplary modified pyrimidines that may be incorporated into aptamers, such as slow off-rate aptamers. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0022] While the invention has been described in conjunction with specific exemplary embodiments, it will be understood that the invention is not limited to those embodiments, as defined by the claims.

[0023] One skilled in the art will recognize many methods and materials similar or equivalent to those described herein, which could be used in the practice of the present invention, and the present invention is in no way limited to the methods and materials described.

[0024] Unless otherwise defined, technical and scientific terms used herein have the meaning commonly understood by one of ordinary skill in the art to which this invention belongs. Although any methods, devices, and materials similar or equivalent to those described herein can be used in the practice of the present invention, specific methods, devices, and materials are described herein.

[0025] All publications, published patent documents, and patent applications cited in this specification are herein incorporated by reference to the same extent as if each individual publication, published patent document, or patent application was specifically and individually indicated to be incorporated by reference herein.

[0026] As used herein, the terms "comprises," "comprising," "includes," "including," "contains," "containing," and all variations thereof are intended to cover a non-exclusive inclusion whereby a process, method, product-by-process, or composition of matter that comprises, includes, or contains an element or set of elements may include other elements not expressly listed.

[0027] "Biological sample", "sample" and "test sample" are used interchangeably herein to mean any substance, biological fluid, tissue or cell obtained from or otherwise derived from an individual. Examples include blood (including, for example, whole blood, white blood cells, peripheral blood mononuclear cells, buffy coat, plasma and serum), sputum, tears, mucus, nasal washings, nasal aspirates, urine, saliva, peritoneal washings, ascites, cyst fluid, glandular fluid, lymphatic fluid, bronchial aspirates, synovial fluid, joint aspirates, organ secretions, cells, cell extracts and cerebrospinal fluid. Biological samples or biological matrices also include all of the aforementioned fractions separated in an experiment. For example, a blood sample can be fractionated into serum, plasma or into fractions containing specific types of blood cells, such as red blood cells or white blood cells (leukocytes). In some embodiments, the sample is a plasma sample. As used herein, a "plasma sample" comprises plasma and, optionally, one or more preservatives or additives. A plasma sample is separated from whole blood and is therefore substantially free of other blood components. In some embodiments, the blood sample is a serum sample. As used herein, a "serum sample" comprises serum and, optionally, one or more preservatives or additives. A serum sample is separated from whole blood and is therefore substantially free of other blood components. In some embodiments, the sample is a urine sample. As used herein, a "urine sample" comprises urine and, optionally, one or more preservatives or additives. In some embodiments, the blood sample is a dried blood spot. In some embodiments, the plasma sample is a dried plasma spot. In some embodiments, the sample can be a combination of samples from an individual, for example, a combination of a tissue sample and a liquid sample. The term "biological sample" also includes materials containing homogenized solid material, for example, from a stool sample, a tissue sample, or a tissue biopsy. The term "biological sample" also includes materials from tissue culture or cell culture. Any suitable method for obtaining a biological sample may be used, and exemplary methods include, for example, phlebotomy, swabs (e.g., buccal swabs), and fine needle aspiration cytology procedures. Exemplary tissues amenable to fine needle aspiration include lymph nodes, lung, thyroid, breast, pancreas, and liver.Samples can also be obtained, for example, by microdissection (e.g., laser capture microdissection (LCM) or laser microdissection (LMD)), bladder washing, smear (e.g., PAP smear), or ductal lavage. A "biological sample" obtained or derived from an individual includes any such sample that has been processed in any suitable manner after being obtained from an individual.

[0028] Further, in some embodiments, the biological sample may be obtained by taking biological samples from multiple individuals and pooling them, or pooling aliquots of each individual's biological sample. The pooled sample may be processed as described herein for samples from single individuals, and if, for example, a pooled sample is found to have poor sample quality, each individual biological sample may be re-examined to determine which samples need to be discarded, or the entire group of samples may be discarded if they are known to have been handled or processed in the same way.

[0029] For purposes of this specification, the phrase "data resulting from a biological sample from an individual" is intended to mean any form of data derived from or generated using a biological sample from an individual. After the data is generated, it may be reformatted, modified, or its numerical value may be altered to some extent, such as by conversion from units in one measurement system to units in another measurement system, but the data is understood to be derived from or generated using a biological sample.

[0030] "Target", "target molecule", and "analyte" are used interchangeably herein to refer to any molecule of interest that may be present in a biological sample. A "molecule of interest" includes any minor change in a particular molecule, such as, in the case of a protein, minor changes in amino acid sequence, disulfide bond formation, glycosylation, lipidation, acetylation, phosphorylation, or any other manipulation or modification, such as conjugation with a labeling moiety, that does not substantially change the identity of the molecule. A "target molecule", "target", or "analyte" refers to one or a set of copies of a molecule or multimolecular structure. Exemplary target molecules include proteins, polypeptides, nucleic acids, carbohydrates, lipids, polysaccharides, glycoproteins, hormones, receptors, antigens, antibodies, affibodies, antibody mimetics, viruses, pathogens, toxic substances, substrates, metabolites, transition state analogs, cofactors, inhibitors, drugs, dyes, nutrients, growth factors, cells, tissues, and any fragments or portions of any of the foregoing. In some embodiments, the target molecule is a protein, in which case the target molecule may be referred to as a "target protein."

[0031] As used herein, a "capture agent" or "capture reagent" refers to a molecule capable of specifically binding to a biomarker. A "target protein capture reagent" refers to a molecule capable of specifically binding to a target protein. Non-limiting exemplary capture reagents include aptamers, antibodies, adnectins, ankyrins, other antibody mimetics and other protein scaffolds, autoantibodies, chimeras, small molecules, nucleic acids, lectins, ligand-binding receptors, imprinted polymers, avimers, peptidomimetics, hormone receptors, cytokine receptors, synthetic receptors, as well as modified versions or fragments of any of the above capture reagents. In some embodiments, the capture reagent is selected from an aptamer and an antibody.

[0032] The term "antibody" refers to full-length antibodies of any species, as well as fragments and derivatives of such antibodies, including Fab fragments, F(ab')2 fragments, single chain antibodies, Fv fragments, and single chain Fv fragments. The term "antibody" also refers to synthetically derived antibodies, such as antibodies and fragments derived by phage display, affibodies, nanobodies, etc.

[0033] As used herein, "marker" and "biomarker" are used interchangeably to refer to a target molecule that is indicative of or indicative of a normal or abnormal process in an individual, or that is indicative of or indicative of a high or low quality of a sample. More specifically, a "marker" or "biomarker" is an anatomical, physiological, biochemical, or molecular parameter associated with the presence of a particular condition or process. Biomarkers can be detected and measured by a variety of methods, including laboratory assays and medical imaging.

[0034] As used herein, "biomarker level" and "level" refer to a measurement obtained using any analytical method to detect a biomarker in a biological sample and indicating the presence, absence, absolute amount or concentration, relative amount or concentration, titer, level, expression level, ratio of measured levels, etc. of, relating to, or corresponding to the biomarker in a biological sample. The exact nature of the "level" depends on the specific design and components of the particular analytical method used to detect the biomarker.

[0035] When a biomarker is indicative of or indicative of a poor quality sample, the biomarker is generally described as being either overexpressed or underexpressed relative to an expression level or value of the biomarker that is indicative of or indicative of a normal or high quality sample. "Upregulated," "upregulated," "overexpression," "overexpressed," and any variations thereof are used interchangeably to refer to a value or level of a biomarker in a biological sample that exceeds the value or level (or range of values ​​or levels) of that biomarker normally detected in a similar properly handled biological sample.

[0036] "Downregulated," "downregulated," "underexpressed," "underexpressed," and any variations thereof, are used interchangeably to refer to a value or level of a biomarker in a biological sample that is less than the value or level (or range of values ​​or levels) of that biomarker normally detected in a similar properly handled biological sample.

[0037] Additionally, a biomarker that is overexpressed or underexpressed may also be referred to as being "differentially expressed" or having a "differential level" or "differential value" compared to a "normal" expression level or value of the biomarker that is indicative of or indicative of normal processes or proper sample handling. Thus, the "differential expression" of a biomarker may also be referred to as a variation from the "normal" expression level of that biomarker.

[0038] A "control level" of a target molecule refers to the level of the target molecule in properly handled samples of the same sample type. A control level may refer to the average level of the target molecule in properly handled samples from a population of individuals.

[0039] As used herein, "individual," "subject," and "patient" are used interchangeably to refer to a mammal. A mammalian subject can be human or non-human. In various embodiments, the individual is human. A healthy or normal individual is one in which no disease or condition of the subject (including, for example, chronic heart failure and cardiovascular events such as myocardial infarction, stroke, and hospitalization for heart failure) is detected by conventional diagnostic methods.

[0040] As used herein, "detecting" or "determining" with respect to a biomarker value includes the use of both the instrumentation used to observe and record a signal corresponding to the biomarker level as well as the substance / substances required to generate that signal. In various embodiments, biomarker levels are detected using any suitable method, including fluorescence, chemiluminescence, surface plasmon resonance, surface acoustic waves, mass spectrometry, infrared spectroscopy, Raman spectroscopy, atomic force microscopy, scanning tunneling microscopy, electrochemical detection methods, nuclear magnetic resonance, quantum dots, and the like.

[0041] As used herein, "sample processing" and "sample handling" refer to steps or procedures performed to prepare a sample, such as a blood sample, for storage or analysis after the sample is collected. In some embodiments, the sample processing steps include centrifugation of the sample and decanting or aspirating the supernatant. In some embodiments, the quality of the sample is assessed by determining the approximate duration of time elapsed between sample processing steps. A sample processing time of zero or near zero means that each sample processing step was performed immediately, with minimal time elapsed between sample processing steps.

[0042] As used herein, "time to centrifuge" refers to the time elapsed from the moment a blood sample is drawn from a subject into a test tube to the moment the test tube begins to spin in a centrifuge. In some embodiments, the time to centrifuge is measured in hours. In some embodiments, the ideal centrifugation time for optimal sample quality is 2 hours or less. In some embodiments, the time to centrifuge is rounded to the nearest hour. In some embodiments, the time to centrifuge is rounded to the nearest half hour. Thus, in some embodiments, if the time to centrifuge is less than 30 minutes or less than 15 minutes, it is rounded up to zero.

[0043] As used herein, "time to decant" refers to the time elapsed from the moment centrifugation of a sample is completed to the moment the supernatant of the centrifuged sample begins to be decanted or aspirated from the sediment. In some embodiments, the time to decant is measured in hours. In some embodiments, the ideal time to decant for optimal sample quality is less than 1 hour, or less than 30 minutes, or less than 15 minutes. In some embodiments, the time to decant is rounded to the nearest hour. In some embodiments, the time to decant is rounded to the nearest half hour. Thus, in some embodiments, times to decant less than 30 minutes or less than 15 minutes are rounded up to zero.

[0044] As used herein, "time to freeze" refers to the time elapsed from the moment decanting or aspirating the centrifuged sample is completed to the moment the decanted or aspirated sample is placed in conditions below -20°C. In some embodiments, the time to freeze is measured in hours. In some embodiments, the ideal time to decant for optimal sample quality is less than 1 hour, or less than 30 minutes, or less than 15 minutes. In some embodiments, the time to freeze is rounded to the nearest hour. In some embodiments, the time to freeze is rounded to the nearest half hour. Thus, in some embodiments, times less than 30 minutes or less than 15 minutes to freeze are rounded up to zero.

[0045] As used herein, a "solid support" refers to any substrate having a surface to which molecules can be directly or indirectly attached, either covalently or non-covalently. A "solid support" can have a variety of physical forms, including, for example, a membrane; a chip (e.g., a protein chip); a slide (e.g., a glass slide or a cover slip); a column; a hollow, solid, semi-solid, particle containing holes or cavities, such as beads; a gel; a fiber, including fiber optic materials; a matrix; and a sample container. Exemplary sample containers include sample wells, tubes, capillaries, vials, and any other container, groove, or depression that can hold a sample. Sample containers can be mounted on multi-sample platforms, such as microtiter plates, glass slides, microfluidic devices, and the like. Supports can be composed of natural or synthetic materials, organic or inorganic materials. The composition of the solid support to which the capture reagent is attached generally depends on the method of attachment (e.g., covalent attachment). Other exemplary containers include microdroplets, microfluidic controlled, or bulk oil-in-water emulsions in which assays and related operations can be performed. Suitable solid supports include, for example, plastics, resins, polysaccharides, silica or silica-based materials, functionalized glass, modified silicon, carbon, metals, inorganic glass, membranes, nylon, natural fibers (e.g., silk, wool, and cotton), polymers, etc. The material constituting the solid support may contain reactive groups, such as, for example, carboxy, amino, or hydroxyl groups, which are used to bind the capture reagent. Polymeric solid supports include, for example, polystyrene, polyethylene glycol tetraphthalate, polyvinyl acetate, polyvinyl chloride, polyvinylpyrrolidone, polyacrylonitrile, polymethylmethacrylate, polytetrafluoroethylene, butyl rubber, styrene-butadiene rubber, natural rubber, polyethylene, polypropylene, (poly)tetrafluoroethylene, (poly)vinylidene fluoride, polycarbonate, and polymethylpentene. Suitable solid support particles that may be used include, for example, coded particles, such as Luminex® type coded particles, magnetic particles, and glass particles.

[0046] Exemplary Uses of Biomarkers In various exemplary embodiments, a method is provided for evaluating or assessing the quality of a sample by detecting one or more biomarker values ​​corresponding to one or more biomarkers present in a sample from an individual, such as a blood, serum or plasma sample, by any number of analytical methods, including any of the analytical methods described herein. For example, these biomarkers are present at different levels in samples of different quality. In some embodiments, the differences in sample quality are due to differences in sample processing. Detection of different levels of biomarkers in a sample can be used to estimate the time elapsed between sample processing steps, such as, for example, time to centrifugation, time to decant, time to freeze.

[0047] In addition to detecting biomarkers to assess the quality of a sample, the biomarkers can be used in diagnostic applications or to determine whether a disease or condition is present in the subject from whom the sample was taken, in some embodiments, only samples that pass the quality assessment are further analyzed for diagnostic applications.

[0048] DETECTION AND DETERMINATION OF BIOMARKERS AND BIOMARKER LEVELS The levels of the biomarkers described herein can be detected using any of a variety of known analytical methods. In one embodiment, a capture reagent is used to detect the biomarker levels. In various embodiments, the capture reagent can be exposed to the biomarker in solution, or the capture reagent can be exposed to the biomarker while immobilized on a solid support. In other embodiments, the capture reagent includes a feature that is reactive to a secondary feature on the solid support. In these embodiments, the capture reagent can be exposed to the biomarker in solution, and then the feature on the capture reagent can be used in combination with the secondary feature on the solid support to immobilize the biomarker on the solid support. The capture reagent is selected based on the type of analysis to be performed. Capture reagents include, but are not limited to, aptamers, antibodies, adnectins, ankyrins, other antibody mimetics and other protein scaffolds, autoantibodies, chimeras, small molecules, F(ab')2 fragments, single chain antibody fragments, Fv fragments, single chain Fv fragments, nucleic acids, lectins, ligand binding receptors, affibodies, nanobodies, imprinted polymers, avimers, peptidomimetics, hormone receptors, cytokine receptors, and synthetic receptors, as well as modified versions and fragments thereof.

[0049] In some embodiments, biomarker levels are detected using a biomarker / capture reagent complex.

[0050] In some embodiments, the biomarker level is obtained from a biomarker / capture reagent complex and is detected indirectly, e.g., as a result of a reaction following biomarker / capture reagent interaction, but is dependent on the formation of a biomarker / capture reagent complex.

[0051] In some embodiments, the biomarker level is detected directly from the biomarker in a biological sample.

[0052] In some embodiments, biomarkers are detected using a multiplexing format that allows for simultaneous detection of two or more biomarkers in a biological sample. In some embodiments of the multiplexing format, the capture reagent is directly or indirectly immobilized at a separate location on a solid support by covalent or non-covalent binding. In some embodiments, the multiplexing format uses separate solid supports, where each solid support has a unique capture reagent bound to the solid support, e.g., quantum dots. In some embodiments, a separate device is used for the detection of each of the multiple biomarkers to be detected in a biological sample. The separate device can be configured to allow each biomarker in a biological sample to be processed simultaneously. For example, a microtiter plate can be used, such that each well in the plate is used to uniquely analyze one or more biomarkers to be detected in a biological sample.

[0053] In one or more of the foregoing embodiments, a component of the biomarker / capture reagent complex may be labeled using a fluorescent tag to allow detection of the biomarker level. In various embodiments, a fluorescent label may be conjugated to a capture reagent specific for any of the biomarkers described herein using known techniques, and the corresponding biomarker level may then be detected using the fluorescent label. Suitable fluorescent labels include rare earth chelates, fluorescein and its derivatives, rhodamine and its derivatives, dansyl, allophycocyanin, PBXL-3, Qdot 605, Lissamine, phycoerythrin, Texas Red, and other similar compounds.

[0054] In some embodiments, the fluorescent label is a fluorescent dye molecule. In some embodiments, the fluorescent dye molecule comprises at least one substituted indolium ring system, in which the substituent at the carbon at the 3-position of the indolium ring contains a chemically reactive group or a conjugated substance. In some embodiments, the dye molecule comprises an AlexaFluor molecule, such as, for example, AlexaFluor488, AlexaFluor532, AlexaFluor647, AlexaFluor680, or AlexaFluor700. In other embodiments, the dye molecule comprises a first type of dye molecule and a second type of dye molecule, for example, two different Alexafluor molecules. In some embodiments, the dye molecule comprises a first type of dye molecule and a second type of dye molecule, in which the two types of dye molecules have different emission spectra.

[0055] Fluorescence can be measured by a variety of instrumentation adaptable to a wide range of assay formats. For example, spectrofluorometers are designed to analyze microtiter plates, microscope slides, printed arrays, cuvettes, etc. See Principles of Fluorescence Spectroscopy by J.R. Lakowicz, Springer Science + Business Media, Inc., 2004. See Bioluminescence Chemiluminescence: Progress Current Applications; Philip E. Stanley Larry J. Kricka editors, World Scientific Publishing Company, January, 2002.

[0056] In one or more embodiments, a chemiluminescent tag may optionally be used to label components of the biomarker / capture complex to allow detection of biomarker levels. Suitable chemiluminescent materials include oxalyl chloride, rhodamine 6G, Ru(bipy)3, 2+, TMAE (tetrakis(dimethylamino)ethylene), pyrogallol (1,2,3-trihydroxybenzene), lucigenin, peroxyoxalates, aryloxalates, acridinium esters, and dioxetanes.

[0057] In some embodiments, the detection method involves an enzyme / substrate combination that generates a detectable signal corresponding to the biomarker level. In general, the enzyme catalyzes a chemical change of a chromogenic substrate, which can be measured using a variety of techniques, including spectrophotometry, fluorescence, and chemiluminescence. Suitable enzymes include, for example, luciferase, luciferin, malate dehydrogenase, urease, horseradish peroxidase (HRPO), alkaline phosphatase, β-galactosidase, glucoamylase, lysozyme, glucose oxidase, galactose oxidase, and glucose-6-phosphate dehydrogenase, uricase, xanthine oxidase, lactoperoxidase, microperoxidase, and the like.

[0058] In some embodiments, the detection method may be a combination of fluorescent, chemiluminescent, radionuclide, or enzyme / substrate combinations that generate a measurable signal. In some embodiments, multiplexed signal generation may have unique and advantageous features in biomarker assay formats.

[0059] In some embodiments, biomarker levels of the biomarkers described herein may be detected using any analytical method, including singleplex aptamer assays, multiplex aptamer assays, singleplex or multiplex immunoassays, mRNA expression profiling, miRNA expression profiling, mass spectrometry, histological / cytological methods, etc., as described below.

[0060] Determining biomarker levels using aptamer-based assays Assays aimed at the detection and quantification of biomarker molecules in biological and other samples are important tools in scientific research and the health care field. One class of such assays involves the use of microarrays that contain one or more aptamers immobilized on a solid support. Each aptamer can bind to a target molecule in a highly specific manner and with extremely high affinity. See, for example, U.S. Patent No. 5,475,096, entitled "Nucleic Acid Ligands"; also see, for example, U.S. Patent Nos. 6,242,246, 6,458,543, and 6,503,715, each entitled "Nucleic Acid Ligand Diagnostic Biochip." When the microarray is contacted with a sample, the aptamers bind to the respective target molecules present in the sample, thereby allowing the measurement of the biomarker levels corresponding to the biomarkers.

[0061] As used herein, "aptamer" refers to a nucleic acid that has a specific binding affinity to a target molecule. It is recognized that affinity interactions are a matter of degree, however, in this context, the "specific binding affinity" of an aptamer to its target generally means that the aptamer binds to its target with a much higher degree of binding affinity than it binds to other components in the test sample. An "aptamer" is a set of copies of one type or species of nucleic acid molecule that contains a specific nucleotide sequence. An aptamer can contain any suitable number of nucleotides, including any number of chemically modified nucleotides. An "aptamer" refers to a set of two or more such molecules. Different aptamers can have either the same or different number of nucleotides. Aptamers can be DNA or RNA or chemically modified nucleic acids, and can be single-stranded, double-stranded, or contain double-stranded regions and can include higher order structures. The aptamer may be a photoaptamer, which comprises a photoreactive or chemically reactive functional group to allow the aptamer to be covalently linked to its corresponding target. Any of the aptamer methods disclosed herein may comprise the use of two or more aptamers that specifically bind to the same target molecule. As will be further described below, the aptamer may comprise a tag. When an aptamer comprises a tag, it is not necessary that all copies of the aptamer have the same tag. Furthermore, when different aptamers each comprise a tag, these different aptamers can have either the same tag or different tags.

[0062] Aptamers can be identified using any known method, including the SELEX process. Once identified, aptamers can be prepared or synthesized according to any known method, including chemical and enzymatic synthesis.

[0063] The terms "SELEX" and "SELEX process" are generally used interchangeably herein to refer to the combination of (1) the selection of aptamers that interact with a target molecule in a desired manner, e.g., bind to a protein with high affinity, and (2) the amplification of those selected nucleic acids. The SELEX process can be used to identify aptamers with high affinity for a specific target or biomarker.

[0064] SELEX generally involves preparing a mixture of candidate nucleic acids, binding the mixture to a desired target molecule to form an affinity complex, separating the affinity complex from unbound candidate nucleic acids, separating and isolating the nucleic acids from the affinity complex, purifying the nucleic acids, and identifying specific aptamer sequences. The process may include multiple rounds to further increase the affinity of the selected aptamers. The process may include an amplification step at one or more points in the process. See, for example, U.S. Patent No. 5,475,096, entitled "Nucleic Acid Ligands." The SELEX process may be used to generate aptamers that bind covalently to targets as well as aptamers that bind non-covalently to targets. See, for example, U.S. Patent No. 5,705,337, entitled "Systematic Evolution of Nucleic Acid Ligands by Exponential Enrichment: Chemi-SELEX."

[0065] The SELEX process can be used to identify high affinity aptamers containing modified nucleotides that confer improved properties to the aptamer, such as improved in vivo stability or improved delivery properties. Examples of such modifications include chemical substitutions at the ribose and / or phosphate and / or base positions. Aptamers containing modified nucleotides identified by the SELEX process are described in U.S. Patent No. 5,660,985, entitled "High Affinity Nucleic Acid Ligands Containing Modified Nucleotides," which describes oligonucleotides containing nucleotide derivatives chemically modified at the 5' and 2' positions of the pyrimidines. U.S. Patent No. 5,580,737 (see above) describes highly specific aptamers containing one or more nucleotides modified with 2'-amino (2'-NH2), 2'-fluoro (2'-F), and / or 2'-O-methyl (2'-Ome). See also U.S. Patent Application Publication No. 20090098549, entitled "SELEX and PHOTOSELEX," which describes nucleic acid libraries with enhanced physical and chemical properties and their use in SELEX and photoSELEX.

[0066] SELEX can also be used to identify aptamers with desirable off-rate properties. See US Patent Publication No. 20090004667, entitled "Method for Generating Aptamers with Improved Off-Rates," which describes an improved SELEX process for generating aptamers capable of binding to target molecules. A method is described for generating aptamers and photoaptamers with slower off-rates from their respective target molecules. The method includes contacting a candidate mixture with a target molecule, forming a nucleic acid-target complex, and performing a process of enriching for aptamers with slow dissociation rates, where the nucleic acid-target complexes with fast dissociation rates dissociate and do not reform, while the complexes with slow dissociation rates remain intact. In addition, the method includes using modified nucleotides in the generation of the candidate nucleic acid mixture to generate aptamers with improved off-rate performance. Non-limiting exemplary modified nucleotides include, for example, the modified pyrimidines shown in Figures 3-5. In some embodiments, the aptamer comprises at least one nucleotide with a modification, for example, a base modification. In some embodiments, the aptamer comprises at least one nucleotide with a hydrophobic modification, such as a hydrophobic base modification, that allows hydrophobic contact with the target protein. In some embodiments, such hydrophobic contact contributes to more hydrophilic and / or slower off-rate binding by the aptamer. Non-limiting exemplary nucleotides with hydrophobic modifications are shown in FIG. 3. In some embodiments, the aptamer comprises at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 nucleotides with hydrophobic modifications, where each hydrophobic modification may be the same as or different from each other. In some embodiments, the at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 hydrophobic modifications in the aptamer may be independently selected from the hydrophobic modifications shown in FIG. 3.

[0067] In some embodiments, the aptamer is a slow off-rate aptamer. In some embodiments, the slow off-rate aptamer (including aptamers that contain at least one nucleotide with a hydrophobic modification) has an off-rate (t1 / 2) of ≧30 minutes, ≧60 minutes, ≧90 minutes, ≧120 minutes, ≧150 minutes, ≧180 minutes, ≧210 minutes, or ≧240 minutes.

[0068] In some embodiments, the assay employs aptamers that contain photoreactive functional groups that allow the aptamer to covalently bind or "photocrosslink" to its target molecule. See, e.g., U.S. Patent No. 6,544,776, entitled "Nucleic Acid Ligand Diagnostic Biochip." These photoreactive aptamers are also called photoaptamers. See, e.g., U.S. Patent Nos. 5,763,177, 6,001,577, and 6,291,184, entitled "Systematic Evolution of Nucleic Acid Ligands by Exponential Enrichment: Photoselection of Nucleic Acid Ligands and Solution SELEX," respectively. See also, e.g., U.S. Patent No. 6,458,539, entitled "Photoselection of Nucleic Acid Ligands." After the microarray is contacted with the sample and the photoaptamers are given an opportunity to bind to their target molecules, the photoaptamers are photoactivated and the solid support is washed to remove any non-specifically bound molecules. Stringent washing conditions may be used because target molecules bound to the photoaptamers are not typically removed due to the covalent bond generated by the photoactivated functional group(s) on the photoaptamers. In this manner, the assay allows for detection of biomarker levels corresponding to the biomarker in the test sample.

[0069] In some assay formats, the aptamer is immobilized on a solid support before contacting with the sample. However, under certain circumstances, immobilizing the aptamer before contacting with the sample may not provide an optimal assay. For example, pre-immobilization of the aptamer may result in inefficient mixing of the aptamer with the target molecule on the solid support surface, which may lead to prolonged reaction time, and thus extended incubation time allows the aptamer to efficiently bind to its target molecule. Furthermore, when photoaptamers are used in the assay, depending on the material utilized as the solid support, the solid support may tend to scatter or absorb the light used to affect the formation of covalent bonds between the photoaptamer and its target molecule. Furthermore, depending on the method used, the surface of the solid support may also be exposed to and affected by any labeling agent used, which may lead to inaccurate detection of the target molecule bound to the aptamer. Finally, immobilization of aptamers on a solid support generally involves a preparation step of the aptamer (ie, immobilization) prior to exposure of the aptamer to a sample, which may affect the activity or functionality of the aptamer.

[0070] Aptamer assays have also been described that allow aptamers to capture their targets in solution and then employ a separation step designed to remove certain components of the aptamer-target mixture prior to detection (see U.S. Patent Application Publication No. 20090042206, entitled "Multiplexed Analyses of Test Samples"). The aptamer assay methods described allow for the detection and quantification of non-nucleic acid targets (e.g., protein targets) in a test sample by detecting and quantifying nucleic acids (i.e., aptamers). The methods described create nucleic acid surrogates (i.e., aptamers) for the detection and quantification of non-nucleic acid targets, thereby allowing a wide variety of nucleic acid technologies, including amplification, to be applied to a wider range of desired targets, including protein targets.

[0071] Aptamers can be constructed to facilitate separation of assay components from the aptamer-biomarker complex (or covalent complex of photoaptamer-biomarker) and allow isolation of the aptamer for detection and / or quantification. In one embodiment, these constructs can include a cleavable or releasable element within the aptamer sequence. In other embodiments, additional functionality can be introduced into the aptamer, such as a label or detectable moiety, a spacer moiety, or a specific binding tag or immobilization element. For example, an aptamer can include a tag linked to the aptamer via a cleavable moiety, a label, a spacer component separating the labels, and a cleavable moiety. In one embodiment, the cleavable element is a photocleavable linker. The photocleavable linker can be attached to a biotin moiety and a spacer moiety and can include an NHS group for derivatization of an amine and can be used to introduce a biotin group into the aptamer, thereby allowing release of the aptamer later in the assay.

[0072] Homogeneous assays, performed with all assay components in solution, do not require separation of samples and reagents prior to signal detection. These methods are rapid and easy to use. These methods generate signals based on molecular capture or binding reagents that react with specific targets. In some embodiments, the molecular capture reagents include one or more aptamers and / or antibodies, etc., and the specific targets of each of the one or more aptamers and / or antibodies, etc., may be biomarkers shown in Table 1, Table 2, Table 3, or Table 4.

[0073] In some embodiments, the signal generation method utilizes anisotropic signal changes resulting from the interaction of a fluorophore-labeled capture reagent with its specific biomarker target. When the labeled capture reagent reacts with its target, the increased molecular weight causes the rotational motion of the fluorophore bound to the complex to become very slow, resulting in a change in anisotropy value. By monitoring the anisotropy change, the binding event may be used to quantitatively measure the biomarker in solution. Other methods include fluorescence polarization assays, molecular beacon techniques, time-resolved fluorescence quenching, chemiluminescence, fluorescence resonance energy transfer, and the like.

[0074] An exemplary solution-based aptamer assay that can be used to detect biomarker levels in a biological sample includes the following steps: (a) preparing a mixture by contacting the biological sample with an aptamer that includes a first tag and has a specific affinity for the biomarker, whereby if the biomarker is present in the sample, an aptamer affinity complex is formed; (b) exposing the mixture to a first solid support that includes a first capture element, causing the first tag to associate with the first capture element; (c) removing any components of the mixture that are not associated with the first solid support; and (d) binding a second tag to an aptamer affinity complex. (e) releasing the aptamer affinity complex from the first solid support; (f) exposing the released aptamer affinity complex to a second solid support comprising a second capture element and associating the second tag with the second capture element; (g) removing any uncomplexed aptamer from the mixture by separating it from the aptamer affinity complex; (h) eluting the aptamer from the solid support; and (i) detecting the biomarker by detecting the aptamer component of the aptamer affinity complex.

[0075] Any means known in the art can be used to detect the biomarker value by detecting the aptamer component of the aptamer affinity complex. Many different detection methods can be used to detect the aptamer component of the affinity complex, such as hybridization assays, mass spectrometry, or QPCR. In some embodiments, nucleic acid sequencing can be used to detect the aptamer component of the aptamer affinity complex, and thus detect the biomarker value. In summary, a test sample can be subjected to any type of nucleic acid sequencing to identify and quantify one or more aptamer sequences or sequences present in the test sample. In some embodiments, the sequence includes the entire aptamer molecule or any part of the molecule that can be used to uniquely identify the molecule. In other embodiments, the identification sequence is a specific sequence added to the aptamer, and such sequences are often referred to as "tags," "barcodes," or "zip codes." In some embodiments, the sequencing method includes an enzymatic step to amplify the aptamer sequence or to convert any type of nucleic acid, including RNA and DNA containing chemical modifications at any position, into any other type of nucleic acid suitable for sequencing.

[0076] In some embodiments, the sequencing method comprises one or more cloning steps, while in other embodiments, the sequencing method comprises a direct sequencing method without cloning.

[0077] In some embodiments, the sequencing method comprises a directed approach using specific primers that target one or more aptamers in the test sample. In other embodiments, the sequencing method comprises a shotgun approach that targets all aptamers in the test sample.

[0078] In some embodiments, the sequencing method includes an enzymatic step to amplify the molecule targeted for sequencing. In other embodiments, the sequencing method directly sequences a single molecule. An exemplary nucleic acid sequencing-based method that can be used to detect biomarker values ​​corresponding to biomarkers in biological samples includes: (a) converting a mixture of aptamers containing chemically modified nucleotides into unmodified nucleic acids by an enzymatic step; (b) shotgun sequencing the resulting unmodified nucleic acids using a massively parallel sequencing platform, such as the 454 Sequencing System (454 Life Sciences / Roche), Illumina Sequencing System (Illumina), ABI SOLiD Sequencing System (Applied Biosystems), HeliScope 1 Molecular Sequencer (Helicos Biosciences), or Pacific BioSciences Real-Time 1 Molecule Sequencing System (Pacific BioSciences) or Polonator G Sequencing Systems (Dover Systems); and (c) identifying and quantifying the aptamers present in the mixture by specific sequences and sequence counts.

[0079] A non-limiting exemplary method for detecting biomarkers in biological samples using aptamers is described in Example 1. See also Kraemer et al., 2011, PloS One, 6(10):e26332.

[0080] Determining Biomarker Levels Using Immunoassays Immunoassays are based on the reaction of antibodies with their corresponding targets or analytes, and can detect the analytes in a sample depending on the specific assay format. 22 To improve the specificity and sensitivity of assay methods based on ffibo-reactivity, monoclonal antibodies and their fragments are often used due to their specific epitope recognition. Polyclonal antibodies have also been successfully used in various immunoassays due to their higher affinity for targets compared to monoclonal antibodies. Immunoassays are designed for use with a wide range of biological sample matrices. Immunoassay formats are designed to provide qualitative, semi-quantitative, and quantitative results.

[0081] Quantitative results are obtained by using a standard curve, which is constructed using known concentrations of the specific analyte to be detected. The response or signal from the unknown sample is plotted against the standard curve, and the amount or level corresponding to the target in the unknown sample is determined.

[0082] A large number of immunoassay formats have been designed. ELISA or EIA can be quantitative for the detection of an analyte. The method is based on the binding of a label to either the analyte or to an antibody, the label component comprising an enzyme, either directly or indirectly. ELISA tests can be in a format for direct, indirect, competitive, or sandwich detection of an analyte. Other methods include, for example, the use of radioisotopes (I 125 ) or based on labels such as fluorescence. Additional techniques include, for example, agglutination, nephelometry, turbidimetry, Western blot, immunoprecipitation, immunocytochemistry, immunohistochemistry, flow cytometry, Luminex assays, etc. (see ImmunoAssay: A Practical Guide, edited by Brian Law, published by Taylor & Francis, Ltd., 2005 edition).

[0083] Exemplary assay formats include enzyme-linked immunosorbent assays (ELISAs), radioimmunoassays, fluorescence, chemiluminescence, and fluorescence resonance energy transfer (FRET) or time-resolved FRET (TR-FRET) immunoassays. Exemplary procedures for detecting biomarkers include biomarker immunoprecipitation followed by quantitative methods that allow size and peptide level discrimination, such as gel electrophoresis, capillary electrophoresis, planar electrochromatography, and the like.

[0084] The method of detecting and / or quantifying a detectable label or a signal generating substance depends on the nature of the label. The products of the reaction catalyzed by a suitable enzyme (wherein the detectable label is an enzyme; see above) can be, but are not limited to, fluorescent, luminescent, or radioactive, or they can absorb visible or ultraviolet light. Examples of detectors suitable for detecting such detectable labels include, but are not limited to, X-ray film, radioactivity counters, scintillation counters, spectrophotometers, colorimeters, fluorometers, luminometers, and densitometers.

[0085] Any of the detection methods can be carried out in any format that allows any suitable preparation, processing, and analysis of the reaction.The detection methods can be carried out, for example, in multi-well assay plates (e.g., 96-well or 386-well) or using any suitable array or microarray.Stock solutions of various agents can be made manually or robotically, and all subsequent pipetting, dilution, mixing, dispensing, washing, incubation, sample reading, data collection, and analysis can be carried out robotically using commercially available analysis software, robots, and detection equipment that can detect detectable labels.

[0086] Determining Biomarker Levels Using Gene Expression Profiling In some embodiments, measuring mRNA in a biological sample may be used as a surrogate to detect the level of the corresponding protein in the biological sample. Thus, in some embodiments, a biomarker or panel of biomarkers described herein may be detected by detecting the appropriate RNA.

[0087] In some embodiments, mRNA expression levels are measured by reverse transcription quantitative polymerase chain reaction (RT-PCR followed by qPCR). RT-PCR is used to generate cDNA from mRNA. The cDNA may be used in a qPCR assay to generate fluorescence as the DNA amplification process proceeds. qPCR allows absolute measurements, such as the number of copies of mRNA per cell, to be obtained by comparison with a standard curve. Northern blots, microarrays, invader assays, and a combination of RT-PCR and capillary electrophoresis have all been used to measure mRNA expression levels in samples. See Gene Expression Profiling: Methods and Protocols, Richard A. Shimkets, editor, Humana Press, 2004.

[0088] Detection of biomarkers using in vivo molecular imaging techniques In some embodiments, the biomarkers described herein may be used in molecular imaging studies, for example, imaging agents may be conjugated to capture agents that can be used to detect the biomarkers in vivo.

[0089] In vivo imaging techniques provide a non-invasive method for determining the state of certain diseases within an individual's body. For example, all parts of the body or the entire body may be displayed as a three-dimensional image, thereby providing useful information regarding the morphology and structure of the body. Such techniques may be combined with the detection of biomarkers described herein to provide information regarding the biomarkers in vivo.

[0090] Various technological advances have led to the development of in vivo molecular imaging technology. These advances include the development of new imaging agents or labels, such as radiolabels and / or fluorescent labels, that can generate strong signals within the body, and the development of powerful new imaging technologies that can detect and analyze these signals from outside the body with sufficient sensitivity and accuracy to provide useful information. The imaging agent can be visualized with a suitable imaging system, thereby providing an image of the part or parts of the body in which the imaging agent is present. The imaging agent can be bound to or associated with, for example, a capture agent such as an aptamer or an antibody, and / or a peptide or protein or oligonucleotide (e.g., for detecting gene expression), or a complex that includes any of these together with one or more macromolecules and / or other particulate forms.

[0091] Contrast agents may be characterized by radioactive atoms useful in imaging. Suitable radioactive atoms for scintigraphy studies include technetium-99m or iodine-123. Other easily detectable moieties include, for example, spin labels for magnetic resonance imaging (MRI), such as iodine-123, iodine-131, indium-111, fluorine-19, carbon-13, nitrogen-15, oxygen-17, gadolinium, manganese, or iron. Such labels are well known in the art and can be easily selected by those skilled in the art.

[0092] Standard imaging techniques include, but are not limited to, magnetic resonance imaging, computed tomography, positron emission tomography (PET), single photon emission computed tomography (SPECT), etc. In in vivo imaging, the type of detection instrument available is an important factor in the selection of a given imaging agent, for example, a given radionuclide and the specific biomarker (protein, mRNA, etc.) that is targeted with it. The radionuclide typically selected exhibits a certain type of decay that is detectable by a given type of instrument. In addition, when selecting a radionuclide for in vivo diagnosis, its half-life should be long enough to allow detection when maximally taken up by the target tissue, and short enough to minimize harmful radiation to the host.

[0093] Exemplary imaging techniques include, but are not limited to, PET and SPECT, which are imaging techniques in which radionuclides are administered synthetically or locally to an individual. Subsequent radioactive tracer uptake is measured over time and used to obtain information about the target tissue and biomarkers. Depending on the high-energy (gamma-ray) emission of the particular isotope used, as well as the sensitivity and sophistication of the instrument used to detect it, the two-dimensional distribution of radioactivity can be estimated from outside the body.

[0094] Commonly used positron-emitting nuclides in PET include, for example, carbon-11, nitrogen-13, oxygen-15, and fluorine-18. SPECT uses isotopes that decay by electron capture and / or gamma emission, including, for example, iodine-123 and technetium-99m. An exemplary method for labeling amino acids with technetium-99m is the reduction of pertechnetate ions in the presence of a chelating precursor to form an unstable technetium-99m-precursor complex, which then reacts with the metal binding group of a bifunctionally modified chemotactic peptide to form a technetium-99m-chemotactic peptide conjugate.

[0095] In such in vivo imaging diagnostic methods, antibodies are frequently used. The preparation and use of antibodies for in vivo diagnosis is well known in the art. Similarly, aptamers may be used in such in vivo imaging diagnostic methods. For example, the aptamers used to identify the specific biomarkers described herein may be appropriately labeled and injected into an individual to detect the biomarker in vivo. The labels used are selected according to the imaging technique used, as described above. Aptamer-directed imaging agents may have unique and advantageous properties compared to other imaging agents in terms of tissue penetration, biodistribution, kinetics, clearance, efficacy, and selectivity.

[0096] Such techniques may optionally be carried out using labeled oligonucleotides to detect gene expression, for example, by imaging with antisense oligonucleotides.These methods are used, for example, in situ hybridization, using fluorescent molecules or radionuclides as labels.Other methods for detecting gene expression include, for example, detecting the activity of reporter genes.

[0097] Another common type of imaging technique is optical imaging, in which fluorescent signals within a subject are detected by optical devices external to the subject. These signals can be due to actual fluorescence and / or bioluminescence. Improvements in the sensitivity of optical detection equipment have increased the usefulness of optical imaging for in vivo diagnostic assays.

[0098] For a review of other techniques, see N. Blow, Nature Methods, 6, 465-469, 2009.

[0099] Determining Biomarker Levels Using Mass Spectrometry Mass spectrometers of various configurations can be used to detect biomarker levels. Several types of mass spectrometers are available or can be manufactured in various configurations. Generally, mass spectrometers have the following main components: sample inlet, ion source, mass analyzer, detector, vacuum system, and instrument control system, and data system. The differences in the sample inlet, ion source, and mass analyzer generally define the type of instrument and its capabilities. For example, the inlet can be a capillary column liquid chromatography source, or a direct probe or stage such as used in matrix-assisted laser desorption. Common ion sources are electrospray, including nanospray and microspray, or matrix-assisted laser desorption. Common mass analyzers include quadrupole mass filters, ion trap mass analyzers, and time-of-flight mass analyzers. Additional mass spectrometry methods are well known in the art (see Burlingame et al., Anal. Chem. 70:647R-716R (1998); Kinter and Sherman, New York (2000)).

[0100] Protein biomarkers and biomarker levels can be detected and measured by any of the following: electrospray ionization mass spectrometry (ESI-MS), ESI-MS / MS, ESI-MS / (MS)n, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS), surface-enhanced laser desorption / ionization time-of-flight mass spectrometry (SELDI-TOF-MS), silicon-assisted desorption / ionization (DIOS), secondary ion mass spectrometry (SIMS), quadrupole time-of-flight (Q-TOF), a tandem time-of-flight (TOF / TOF) technology called UltraFlex III TOF / TOF, atmospheric pressure chemical ionization mass spectrometry (APCI-MS), APCI-MS / MS, APCI-(MS)N, atmospheric pressure photoionization mass spectrometry (APPI-MS), APPI-MS / MS, and APPI-(MS)N, quadrupole mass spectrometry, Fourier transform mass spectrometry (FTMS), quantitative mass spectrometry, and ion trap mass spectrometry.

[0101] Sample preparation and processing strategies are used to label and enrich samples prior to characterization of protein biomarkers and determination of biomarker levels by mass spectrometry. Labeling methods include, but are not limited to, iso-mass tags for relative or absolute quantification (iTRAQ) and stable isotope labeling with amino acids in cell culture (SILAC). Capture reagents used to selectively enrich samples for potential biomarker proteins prior to mass spectrometry analysis include, but are not limited to, aptamers, antibodies, nucleic acid probes, chimeras, small molecules, F(ab')2 fragments, single-chain antibody fragments, Fv fragments, single-chain Fv fragments, nucleic acids, lectins, ligand-binding receptors, affibodies, nanobodies, ankyrin, domain antibodies, alternative antibody scaffolds (e.g., diabodies, etc.), imprinted polymers, avimers, peptidomimetics, peptoids, peptide nucleic acids, threose nucleic acids, hormone receptors, cytokine receptors, and synthetic receptors, as well as modified versions and fragments thereof.

[0102] Determining biomarker levels using proximity ligation assays Proximity ligation assays can be used to determine biomarker values. In summary, a test sample is contacted with a pair of affinity probes, which can be a pair of antibodies or a pair of aptamers, with each member of the pair extended with an oligonucleotide. The targets of the pair of affinity probes can be two different determinants on one protein, or one determinant each on two different proteins that can exist as homo- or heteromultimeric complexes. When the probes bind to the target determinants, the free ends of the oligonucleotide extensions are close enough to hybridize together. Hybridization of the oligonucleotide extensions is facilitated by a common connector oligonucleotide that serves to bridge the oligonucleotide extensions together if they are placed close enough together. Once the oligonucleotide extensions of the probes are hybridized, the ends of the extensions are linked together by enzymatic DNA ligation.

[0103] Each oligonucleotide extension contains a primer site for PCR amplification. When the oligonucleotide extensions are ligated together, the oligonucleotides form a continuous DNA sequence, and through PCR amplification, information about the identity and amount of the target protein, as well as information about protein-protein interactions when the target determinants are on two different proteins, is revealed. Proximity ligation can provide a highly sensitive and specific assay for real-time protein concentration and interaction information by using real-time PCR. Probes that do not bind to the determinants of interest will not bring the corresponding oligonucleotide extensions into proximity, and ligation or PCR amplification cannot proceed, resulting in no signal generation.

[0104] The aforementioned assays allow for the detection of biomarker values ​​useful in methods of assessing the quality of a sample, comprising detecting in a biological sample from an individual at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, or all 8 biomarkers selected from the biomarkers in Table 1; or detecting at least 2, at least 3, at least 4, or all 5 biomarkers selected from the biomarkers in Table 2; detecting at least 2, at least 3, or at least 4 biomarkers selected from the biomarkers in Table 3; detecting at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, or all 13 biomarkers selected from the biomarkers in Table 4; or a combination of the biomarkers shown in Table 1 and the biomarkers shown in Table 3 and / or Table 4; or a combination of the biomarkers shown in Table 2 and the biomarkers shown in Table 3 and / or Table 4. As described below, the biomarker levels are used to classify whether the sample is of acceptable quality for use in subsequent analyses. According to any of the methods described herein, biomarker values ​​can be detected and classified individually, or can be detected and classified collectively, such as in a multiplex assay format.

[0105] Biomarker classification and sample processing time calculation In some embodiments, a biomarker "signature" for a given sample quality test contains a set of biomarkers, each with a characteristic level in samples of acceptable quality or poor quality. The characteristic level may, in some embodiments, refer to the average or mean of the biomarker levels for samples in a particular group. In some embodiments, the methods described herein can be used to assign samples to one of two groups, either those that pass the quality assessment or those that fail the quality assessment.

[0106] The assignment of a sample to one of two or more groups is known as classification, and the procedure used to achieve this assignment is known as a classifier or classification method. Classification methods may also be referred to as scoring methods. There are numerous classification methods that can be used to build a classifier from a set of biomarker levels. In some cases, classification is performed using supervised learning techniques, where a data set is collected using samples from two (or more in the case of multiple classification situations) separate groups that one wishes to distinguish. Since the class (group or population) to which each sample belongs is known in advance for each sample, the classifier can be trained to produce the desired classification response. It is also possible to use unsupervised learning techniques to generate quality classifiers.

[0107] Common approaches for developing classifiers include decision trees; bagging + boosting + forests; learning based on rule inference; Parzen windows; linear models; logistic curves; neural network methods; unsupervised clustering; K-means; hierarchical ascending / descending classification; semi-supervised learning; prototype methods; nearest neighbor methods; kernel density estimation; support vector machines; hidden Markov models; Boltzmann learning, and classifiers may be combined simply or in a way that minimizes a particular objective function. For a review, see, e.g., Pattern Classification, R.O.Duda, et al., editors, John Wiley & Sons, 2nd edition, 2001; see also The Elements of Statistical Learning-Data Mining, Inference, and Prediction, T.Hastie, et al., editors, Springer Science+Business Media, LLC, 2nd edition, 2009.

[0108] To generate a classifier using supervised learning techniques, a set of samples, called training data, is obtained. For quality testing, the training data includes samples from different groups (classes) to which unknown samples will be assigned later. For example, samples that have undergone processing with different set times between processing steps can constitute the training data for developing a classifier that can classify unknown samples as either passing or failing a quality assessment based on the elapsed time between sample processing steps. The development of a classifier from the training data is known as training a classifier. The specific details regarding training a classifier depend on the nature of the supervised learning technique. Training a naive Bayes classifier is one example of such a supervised learning technique (see, for example, Pattern Classification, R.O.Duda, et al., editors, John Wiley & Sons, 2nd edition, 2001; see also The Elements of Statistical Learning - Data Mining, Inference, and Prediction, T.Hastie, et al., editors, Springer Science+Business Media, LLC, 2nd edition, 2009). Training a Naive Bayes classifier is described, for example, in U.S. Patent Publication Nos. 2012 / 0101002 and 2012 / 0077695.

[0109] Usually, there may be many higher biomarker levels compared to the samples in the training set, so care must be taken to avoid overfitting.Overfitting occurs when statistical model represents random error or noise instead of the underlying relationship.Overfitting can be avoided in various ways, including, for example, limiting the number of biomarkers used in classifier development, assuming that the responses of biomarkers are independent of each other, limiting the complexity of the underlying statistical model used, and ensuring that the underlying statistical model fits the data.

[0110] A specific example of the development of a test using a set of biomarkers is the application of the naive Bayes classifier, which is a simple probabilistic classifier based on Bayes' theorem with strict independent processing of biomarkers. Each biomarker is described by a class-dependent probability density function (pdf) for the measured RFU value or log RFU (relative fluorescence unit) value in each class. The joint pdf for a set of biomarkers in a class is estimated to be the product of the individual class-dependent pdfs for each biomarker. Training a naive Bayes classifier in this context is equivalent to assigning parameters ("parameterization") to characterize the class-dependent pdf. Any underlying model can be used for the class-dependent pdf, but the model must generally fit the data observed in the training set.

[0111] The performance of a naive Bayes classifier depends on the number and quality of biomarkers used to build and train the classifier. A single biomarker will perform according to the KS (Kolmoborov-Smirnov) distance. Subsequent addition of biomarkers with good KS distance (e.g., >0.3) will generally improve the classification performance, provided that the subsequently added biomarkers are independent of the first biomarker. By using specificity in addition to sensitivity as the classifier score, a large number of highly scoring classifiers can be generated using a type of greedy method. (A greedy method is any algorithm that follows a metaheuristic for problem solving, making locally optimal choices at each stage with the goal of finding a globally optimal solution).

[0112] Another way to portray classifier performance is through the receiver operating characteristic (ROC), or simply the ROC curve or ROC plot. The ROC is a graphical plot of sensitivity (true positive rate) versus false positive rate (1-specificity or 1-true negative rate) as the discrimination threshold of a binary classifier system is varied. This ROC can be equivalently represented by plotting the proportion of true positives among positives (TPR=true positive rate) against the proportion of false positives among negatives (FPR=false positive rate). It is also known as the relative operating characteristic curve, since it is a comparison of two operating characteristics (TPR and FPR) as the criteria are varied. The area under the ROC curve (AUC) is commonly used as a summary measure of diagnostic accuracy. It can take values ​​between 0.0 and 1.0. The AUC has important statistical properties. That is, the AUC of a classifier is equal to the probability that the classifier will rank a randomly selected positive case higher than a randomly selected negative case (Fawcett T, 2006. An introduction to ROC analysis. Pattern Recognition Letters. 27: 861-874). It is equivalent to the Wilcoxon rank test (Hanley, JA, McNeil, BJ, 1982. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143, 29-36). Another way to express the performance of a diagnostic test against a known reference standard is the net reclassification improvement, which is the ability of a new test to accurately increase or decrease risk compared to the reference standard test. See, for example, Pencina et al., 2011, Stat. Med. 30: 11-21. While the AUC under the ROC curve is optimal for assessing the performance of a two-class classifier, stratification and personalized medicine rely on the inference that a population contains more than two classes. For such comparisons, hazard ratios for the upper versus lower quartile (or other stratifications such as deciles) may be more appropriately used.

[0113] kit For example, any combination of biomarkers described herein can be detected using a suitable kit for use in carrying out the methods disclosed herein. Additionally, any kit can contain one or more detectable labels, such as fluorescent moieties, as described herein.

[0114] In some embodiments, the kit includes (a) one or more capture reagents (e.g., at least one aptamer or antibody) for detecting one or more biomarkers in a biological sample, where the biomarkers are at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, or all 8 biomarkers selected from the biomarkers in Table 1; or at least 2, at least 3, at least 4, or all 5 biomarkers selected from the biomarkers in Table 2; at least 2, at least 3, or at least 4 biomarkers selected from the biomarkers in Table 3; or at least 2, at least 3, or at least 4 biomarkers selected from the biomarkers in Table 4. or a combination of the biomarkers shown in Table 1 with the biomarkers shown in Table 3 and / or Table 4, or a combination of the biomarkers shown in Table 2 with the biomarkers shown in Table 3 and / or Table 4; the kit optionally (b) includes one or more software or computer program products for classifying the obtained samples as either passing or failing a quality assessment, or for determining the approximate time or number of sample processing steps, as further described herein. Alternatively, one or more instructions for a human to manually carry out the above steps rather than one or more computer program products may be provided.

[0115] In some embodiments, the kit comprises a solid support, at least one capture reagent, and a substance that generates a signal. The kit may also comprise instructions for using the device and the reagents, sample handling, and data analysis. Additionally, the kit may be used with a computer system or software for analyzing biological samples and reporting the results of the analysis.

[0116] The kit may further include reagents for diagnostic analysis of the samples, particularly those that have passed quality assessment.

[0117] The kits can also contain one or more reagents for processing the biological sample (e.g., solubilization buffer, detergent, wash solution, or buffer). Any of the kits described herein can also include, for example, buffers, blocking agents, matrix materials for mass spectrometry, antibody capture agents, positive control samples, negative control samples, software, and information, such as protocols, guidelines, and reference data.

[0118] In some embodiments, the kit includes PCR primers for one or more aptamers specific for the biomarkers described herein. In some embodiments, the kit may further include instructions for use of the biomarkers and correlation with an estimate of sample processing time and / or sample quality. In some embodiments, the kit may also include a DNA array containing the complement of one or more aptamers specific for the biomarkers described herein, reagents, and / or enzymes for amplifying or isolating sample DNA. In some embodiments, the kit may include reagents for real-time PCR, e.g., TaqMan probes and / or primers, and enzymes.

[0119] For example, the kit may include (a) reagents including at least one capture reagent for determining the level of one or more biomarkers in a test sample, and, optionally, (b) one or more algorithms or computer programs for carrying out the steps of comparing the amount of each quantified biomarker in the test sample to one or more predefined cutoff values. In some embodiments, the algorithm or computer program assigns a score to each quantified biomarker based on the comparison, and in some embodiments, combines the assigned scores of each quantified biomarker to obtain a total score. Further, in some embodiments, the algorithm or computer program compares the total score to a predefined score and uses this comparison to determine whether the subject passes or fails the quality assessment. Alternatively, one or more instructions for a person to manually carry out the above steps rather than one or more algorithms or computer programs may be provided.

[0120] Biomarker Panels In some embodiments, one or more of the biomarkers listed in Table 1 are detected. In some embodiments, one or more of the biomarkers listed in Table 1 are detected in a plasma sample from a subject. In some embodiments, all of the biomarkers listed in Table 1 are detected. In some embodiments, the level of each protein listed in Table 1 is detected. In some embodiments, detection of one or more or all of the biomarkers is performed to determine the length or approximate length of time between sample processing and sample freezing, i.e., time to freezing. In some embodiments, sample processing includes or consists of centrifugation of the sample and removal of the sample supernatant by decanting or aspiration. In some such embodiments, sample processing was performed immediately after sample collection from the subject. [Table 1]

[0121] In some embodiments, one or more of the biomarkers listed in Table 2 are detected. In some embodiments, one or more of the biomarkers listed in Table 2 are detected in a serum sample from a subject. In some embodiments, all of the biomarkers listed in Table 2 are detected. In some embodiments, the level of each protein listed in Table 2 is detected. In some embodiments, detection of one or more or all of the biomarkers is performed to determine the length or approximate length of time between sample processing and sample freezing, i.e., time to freezing. In some embodiments, sample processing includes or consists of centrifugation of the sample and removal of the sample supernatant by decanting or aspiration. In some such embodiments, sample processing was performed immediately after sample collection from the subject. [Table 2]

[0122] In some embodiments, one or more of the biomarkers listed in Table 3 are detected. In some embodiments, one or more of the biomarkers listed in Table 3 are detected in a plasma sample from a subject. In some embodiments, all of the biomarkers listed in Table 3 are detected. In some embodiments, the level of each protein listed in Table 3 is detected. In some embodiments, detection of one or more or all of the biomarkers is performed to determine the length or approximate length of time between sample collection and sample centrifugation, i.e., time to centrifugation. [Table 3]

[0123] In some embodiments, one or more of the biomarkers listed in Table 4 are detected. In some embodiments, one or more of the biomarkers listed in Table 4 are detected in a plasma sample from a subject. In some embodiments, all of the biomarkers listed in Table 4 are detected. In some embodiments, the level of each protein listed in Table 4 is detected. In some embodiments, detection of one or more or all of the biomarkers is performed to determine the length or approximate length of time between sample centrifugation and removal of the supernatant by decantation or aspiration, i.e., time to decant. [Table 4]

[0124] Computer Law and Software A method for assessing sample quality, such as the length of time between sample processing and freezing, can include: 1) obtaining a biological sample, such as a sample that has already undergone sample processing; 2) performing an analytical method to detect and measure a panel of biomarkers or a set of biomarkers in the biological sample; 3) optionally performing any data normalization or standardization; 4) determining the level of each biomarker; and 5) reporting the results. In some embodiments, the results are adjusted for the sample type. In some embodiments, the biomarker levels are combined in some way and a single value for the combined biomarker levels is reported. In this approach, in some embodiments, the score is a single number or uniqueness determined from the integration of all biomarkers and compared to a pre-set threshold indicating a satisfactory (pass) or unsatisfactory (fail) quality. Alternatively, the predicted score may be a series of bars, each representing a biomarker value, and the pattern of responses compared to a pre-set pattern for a determination of satisfactory (pass) or unsatisfactory (fail) quality.

[0125] At least some embodiments of the methods described herein may be implemented using a computer. An example of a computer system 100 is shown in FIG. 1. Referring to FIG. 1, the system 100 is shown to be comprised of hardware elements electrically connected via a bus 108, including a processor 101, an input device 102, an output device 103, a storage device 104, a computer-readable storage medium reader 105a, a communication system 106, an accelerated processing device (e.g., a DSP or special purpose processor) 107, and a memory 109. The computer-readable storage medium reader 105a is further connected to a computer-readable storage medium 105b, which collectively represents a storage medium, memory, etc., in addition to remote, local, fixed, and / or removable storage devices for temporarily and / or persistently containing computer-readable information, and which may include the storage device 104, the memory 109, and / or any other such accessible system 100 resources. System 100 also includes software elements (shown here as residing in working memory 191) that include an operating system 192 and other code 193, eg, programs, data, and the like.

[0126] With reference to FIG. 1, system 100 has a wide range of flexibility and configurability. Thus, for example, a single structure may be utilized to implement one or more servers that may be further configured according to currently desired protocols, protocol variations, extensions, and the like. However, it will be apparent to one skilled in the art that embodiments may be fully utilized according to more specific application requirements. For example, one or more system elements may be implemented as sub-elements within the components of system 100 (e.g., within communication system 106). Custom hardware may also be utilized, and / or specific elements may be implemented in hardware, software, or both. Additionally, while connections to other computing devices such as network input / output devices (not shown) may be employed, it should be understood that wired, wireless, modem, and / or other connections or multiple connections to other computing devices may be utilized.

[0127] In one embodiment, the system can include a database containing biomarker features characteristic of the quality of the sample. The biomarker data (or biomarker information) can be utilized as input to a computer for use as part of a computer-implemented method. The biomarker data can include data described herein.

[0128] In one embodiment, the system further comprises one or more devices for providing input data to the one or more processors.

[0129] The system further comprises a memory for storing the dataset of ranked data elements.

[0130] In another embodiment, the device for providing input data includes a detector for detecting characteristics of the data elements, such as, for example, a mass spectrometer or a gene chip reader.

[0131] The system may additionally include a database management system. A user's request or query may be formatted in an appropriate language understood by the database management system, which processes the query and extracts relevant information from the training set database.

[0132] The system may be connectable to a network to which a network server and one or more clients are connected. The network may be a local area network (LAN) or a wide area network (WAN), as is known in the art. Preferably, the server includes the necessary hardware to execute a computer program product (e.g., software) that accesses database data to process user requests.

[0133] The system may include an operating system (e.g., UNIX or Linux) for executing instructions from a database management system. In one embodiment, the operating system operates on a global communications network, such as the Internet, and may connect to such a network using a global communications network server.

[0134] The system may include one or more devices with a graphical display interface that includes interface elements such as buttons, pull-down menus, scroll bars, fields for entering text, and the like, commonly found in graphical user interfaces known in the art. Requests entered into the user interface are transmitted to application programs in the system and formatted to search for relevant information in one or more system databases. User-entered requests or queries may be formulated in a suitable database language.

[0135] A graphical user interface may be generated by graphical user interface code as part of an operating system and may be used to input data and / or display input data. The results of processed data may be displayed in the interface, printed on a printer in communication with the system, stored on a storage device, and / or transmitted over a network, or provided in the form of a computer readable medium.

[0136] The system can be in communication with an input device for providing data regarding the data elements (e.g., values ​​of expressions) to the system. In one aspect, the input device can include a gene expression profiling system, including, for example, a mass spectrometer, a gene chip, or an array reader.

[0137] According to various embodiments, the method and apparatus for analyzing sample quality biomarker information may be implemented in any suitable manner, for example, using a computer program running on a computer system. Conventional computer systems including a processor and random access memory may be used, such as a remotely accessible application server, network server, personal computer, or workstation. Additional computer system elements may include a storage or information storage system, such as a mass storage system, and a user interface, such as a conventional monitor, keyboard, and tracking device. The computer system may be a stand-alone system or part of a network of computers, including a server and one or more databases.

[0138] A sample quality biomarker analysis system can provide functions and operations to complete data analysis, such as data collection, processing, analysis, reporting, and / or identification of sample quality. For example, in one embodiment, a computer system can execute a computer program that may receive, store, retrieve, analyze, and report information related to biomarkers of sample quality assessment. The computer program may include multiple modules that perform various functions or operations, such as a processing module that processes raw data and generates supplemental data, and an analysis module that analyzes the raw data and supplemental data to generate an estimated calculation of sample quality status and / or sample processing time. Calculation of sample processing time may optionally include generation or collection of additional information.

[0139] Some embodiments described herein may be implemented to include a computer program product, which may include a computer readable medium having computer readable program code embodied therein for executing an application program on a computer with a database.

[0140] As used herein, a "computer program product" refers to a set of instructions organized in the form of natural language statements or programming language statements that can be contained in a physical medium of any nature (e.g., written, electronic, magnetic, optical, etc.) and used by a computer or other automated data processing system. Such programming language statements, when executed by a computer or data processing system, cause the computer or data processing system to operate according to the specific content of the statements. Computer program products include, but are not limited to, source and object code embedded in a computer readable medium and / or programs in test or data libraries. Furthermore, computer program products that enable a computer system or data processing device to operate in a preselected manner may be provided in a number of forms, including, but not limited to, original source code, assembly code, object code, machine language, encrypted or compressed versions of the foregoing, and any equivalents.

[0141] In one embodiment, a computer program product for assessing sample quality is provided. The computer program product includes a computer readable medium having program code embodied therein executable by a processor of a computing device or computing system, the program code including code for retrieving data originating from a biological sample from an individual, the data including biomarker levels each corresponding to one of the biomarkers in Table 1 or Table 2, Table 3, and / or Table 4; and code for performing a classification method that indicates a state of sample quality as a function of the biomarker values.

[0142] In yet another aspect, a computer program product for determining the time or frequency of sample processing is provided. The computer program product includes a computer readable medium having program code embodied therein executable by a processor of a computing device or computing system, the program code including code for retrieving data originating from a biological sample from an individual, the data including a biomarker value corresponding to at least one biomarker in the biological sample selected from the biomarkers provided in Tables 1 or 2, 3, and / or 4; and code for performing a classification method indicating a state of sample quality as a function of the biomarker value.

[0143] While various embodiments are described as methods or apparatus, it should be understood that the embodiments may be implemented through code used in conjunction with a computer, e.g., code resident in or accessible by a computer. For example, software and databases may be utilized to implement many of the methods described above. Thus, in addition to hardware-implemented embodiments, it should also be noted that these embodiments may be implemented through the use of an article of manufacture consisting of a computer usable medium having computer readable program code embodied therein that enables the functionality disclosed herein. Thus, the embodiments are desirably considered to be protected by this patent in their program code means as well. Furthermore, the embodiments may be embodied as code stored in virtually any type of computer readable memory, including, but not limited to, RAM, ROM, magnetic media, optical media, or magneto-optical media. More generally, the embodiments may be implemented in software, including, but not limited to, software running on a general purpose processor, microcode, programmable logic arrays (PLAs), or application specific integrated circuits (ASICs), or in any combination thereof.

[0144] It is further contemplated that embodiments may be achieved as a computer signal embodied in a carrier wave and a signal propagated through a transmission medium (e.g., electrical and optical). Thus, the various types of information described above may be formatted in structures, such as data structures, and transmitted as electrical signals over a transmission medium or stored on a computer-readable medium.

[0145] It should also be noted that many of the structures, materials, and acts recited herein may be recited as a means for performing a function or a step for performing a function, and therefore such language should be understood to be entitled to cover all structures, materials, or acts disclosed within this specification, including those incorporated by reference, and equivalents thereof.

[0146] The use of the biomarkers disclosed herein and various methods for determining biomarker values ​​are detailed above with respect to assessing sample quality and suitability for further analysis, such as diagnostic analysis. In some embodiments, the biomarkers, methods, and kits described herein are used to assess the absolute sample quality of one or more samples, or the relative consistency of sample quality across multiple samples. In some such embodiments, the method includes identifying samples that pass or fail a quality assessment. In some embodiments, samples that pass the quality assessment are analyzed, and samples that fail the quality assessment are discarded. In some embodiments, information obtained using the biomarkers, methods, and kits herein can be used to determine whether a sample collection and processing method or facility is suitable. EXAMPLES

[0147] The following examples are provided for illustrative purposes only and are not intended to limit the scope of this application, which is defined by the appended claims. Routine molecular biology techniques described in the following examples are described in detail in Sambrook et al., Molecular Cloning: A Laboratory Manual, 3rd The procedure can be carried out as described in standard laboratory manuals, such as Theorem 1990, ed., Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY, (2001).

[0148] Example 1: Detection of Exemplary Biomarkers Using Aptamers Exemplary methods for detecting one or more biomarker proteins in a sample are described, for example, in Kraemer et al., PloS One 6(10):e26332, and described below. Three different quantification methods are described: microarray-based hybridization, Luminex bead-based methods, and qPCR.

[0149] reagent HEPES, NaCl, KCl, EDTA, EGTA, MgCl2, and Tween-20 can be purchased, for example, from Fisher Biosciences. Dextran sulfate sodium salt (DxSO4), nominally 8000 molecular weight, can be purchased, for example, from AIC and dialyzed against deionized water for at least 20 hours with one change. KOD EX DNA polymerase can be purchased, for example, from VWR. Tetramethylammonium chloride and CAPSO can be purchased, for example, from Sigma-Aldrich, and streptavidin-phycoerythrin (SAPE) can be purchased, for example, from Moss Inc. 4-(2-aminoethyl)-benzenesulfonyl fluoride hydrochloride (AEBSF) can be purchased, for example, from Gold Biotechnology. Streptavidin coated 96-well plates can be purchased, for example, from Thermo Scientific (Pierce Streptavidin Coated Plates HBC, clear, 96-well, product number 15500 or 15501). NHS-PEO4-Biotin can be purchased, for example, from Thermo Scientific (EZ-Link NHS-PEO4-Biotin, product number 21329), dissolved in anhydrous DMSO, and stored frozen in single-use aliquots. IL-8, MIP-4, lipocalin-2, RANTES, MMP-7, and MMP-9 can be purchased, for example, from R&D Systems. Resistin and MCP-1 can be purchased, for example, from PeproTech, and tPA can be purchased, for example, from VWR.

[0150] nucleic acid Conventional oligodeoxynucleotides (including amine and biotin substitutions) can be purchased, for example, from Integrated DNA Technologies (IDT). Z-Blocks are single-stranded oligodeoxynucleotides of the sequence 5'-(AC-BnBn)7-AC-3', where Bn represents a benzyl-substituted deoxyuridine residue. Z-Blocks can be synthesized using conventional phosphoramidite chemistry. Aptamer capture reagents can be synthesized by conventional phosphoramidite chemistry and purified, for example, on a 21.5 x 75 mm PRP-3 column operated at 80°C on a Waters Autopurification 2767 system (or Waters 600 series semi-automated system) using, for example, a Timberline TL-600 or TL-150 heater and a gradient of triethylammonium bicarbonate (TEAB) / I to elute the product. Detection is performed at 260 nm, and the best fractions are pooled after collecting fractions across the main peak.

[0151] buffer solution Buffer SB18 is composed of 40 mM HEPES, 101 mM NaCl, 5 mM KCl, 5 mM MgCl2, and 0.05% (v / v) Tween-20, adjusted to pH 7.5 with NaOH. Buffer SB17 is SB18 supplemented with 1 mM trisodium EDTA. Buffer PB1 is composed of 10 mM HEPES, 101 mM NaCl, 5 mM KCl, 5 mM MgCl2, 1 mM trisodium EDTA, and 0.05% (v / v) Tween-20, adjusted to pH 7.5 with NaOH. CAPSO elution buffer is composed of 100 mM CAPSO (pH 10.0) and 1 M NaCl. Neutralization buffer contains 500 mM HEPES, 500 mM HCl, and 0.05% (v / v) Tween-20. Agilent Hybridization Buffer is a proprietary formulation supplied as part of the kit (Oligo aCGH / ChIP-on-chip Hybridization Kit). Agilent Wash Buffer 1 is a proprietary formulation (Oligo aCGH / ChIP-on-chip Wash Buffer 1, Agilent). Agilent Wash Buffer 2 is a proprietary formulation (Oligo aCGH / ChIP-on-chip Wash Buffer 2, Agilent). TMAC Hybridization Solution consists of 4.5 M tetramethylammonium chloride, 6 mM trisodium EDTA, 75 mM Tris-HCl (pH 8.0), and 0.15% (v / v) Sarkosyl. KOD buffer (10x concentrated) consisted of 1200 mM Tris-HCl, 15 mM MgSO4, 100 mM KCl, 60 mM (NH4)2SO4, 1% v / v Triton-X100, and 1 mg / mL BSA.

[0152] Sample preparation Serum (stored at -80°C in 100 μL aliquots) is thawed in a 25°C water bath for 10 minutes and then stored on ice prior to sample dilution. Samples are mixed by gently vortexing for 8 seconds. 6% serum sample solutions are prepared by diluting in 0.94x SB17 supplemented with 0.6 mM MgCl2, 1 mM trisodium EGTA, 0.8 mM AEBSF, and 2 μM Z-Block. A portion of the 6% serum stock solution is diluted 10-fold in SB17 to create a 0.6% serum stock solution. In some embodiments, the 6% and 0.6% stock solutions are used to detect high and low abundance analytes, respectively.

[0153] Preparation of capture reagents (aptamers) and streptavidin plates Aptamers are sorted into two mixtures according to the relative abundance of their associated analytes (or biomarkers). Stock concentrations are 4 nM for each aptamer, with final concentrations of each aptamer of 0.5 nM. The aptamer stock mixture is diluted 4-fold in SB17 buffer and heated to 95°C for 5 min and cooled to 37°C for 15 min before use. This denaturation-renaturation cycle aims to normalize the conformational isomer distribution of the aptamers, thereby ensuring reproducible aptamer activity despite historical variations. Streptavidin plates are washed twice with 150 μL of buffer PB1 before use.

[0154] Incubation and capture on plates The heated-cooled 2x aptamer mix (55 μL) is combined with an equal volume of 6% or 0.6% serum dilution to generate mixes containing 3% and 0.3% serum. The plate is sealed with a silicone sealing mat (Axymat silicone sealing mat, VWR) and incubated at 37°C for 1.5 hours. The mix is ​​then transferred to the wells of a washed 96-well streptavidin plate and incubated for an additional 2 hours with shaking at 800 rpm on an Eppendorf Thermomixer set at 37°C.

[0155] Manual Assay Unless otherwise stated, the liquid is removed by discarding it followed by tapping twice on a stack of paper towels. The wash volume is 150 μL and all shaking incubations are performed on an Eppendorf Thermomixer set at 25° C. and 800 rpm. The mixture is removed by pipetting and the plate is washed twice for 1 min with Buffer PB1 supplemented with 1 mM dextran sulfate and 500 μM biotin, then washed four times for 15 s with Buffer PB1. A freshly made solution of 1 mM NHS-PEO4-biotin in Buffer PB1 (150 μL / well) is added and the plate is incubated for 5 min with shaking. The NHS-biotin solution is removed and the plate is washed three times with Buffer PB1 supplemented with 20 mM glycine and three times with Buffer PB1. Then, 85 μL of Buffer PB1 supplemented with 1 mM DxSO4 is added to each well and the plate is irradiated under a BlackRay ultraviolet lamp (nominal wavelength 365 nm) at a distance of 5 cm with shaking for 20 minutes. The samples are transferred to a new washed streptavidin-coated plate or to unused wells of an existing washed streptavidin plate, combining the high and low dilution sample mixtures in a single well. The samples are incubated for 10 minutes at room temperature with shaking. Unadsorbed material is removed and the plate is washed 8 times for 15 seconds each with Buffer PB1 supplemented with 30% glycerol. The plate is then washed once with Buffer PB1. The aptamers are eluted using 100 μL of CAPSO elution buffer for 5 minutes at room temperature. 90 μL of the eluate is transferred to a 96-well HybAid plate and 10 μL of neutralization buffer is added.

[0156] Semi-automated assay The streptavidin plate with the adsorbed equilibrated mixture is placed on the deck of a BioTek EL406 plate washer. The washer is programmed to carry out the following steps: Unadsorbed material is removed by aspiration and the wells are washed four times with 300 μL of Buffer PB1 supplemented with 1 mM dextran sulfate and 500 μM biotin. The wells are then washed three times with 300 μL of Buffer PB1. 150 μL of a freshly prepared (from a 100 mM stock in DMSO) solution of 1 mM NHS-PEO4-biotin in Buffer PB1 is added. The plate is incubated for 5 minutes with shaking. The liquid is aspirated and the wells are washed eight times with 300 μL of Buffer PB1 supplemented with 10 mM glycine. 100 μL of Buffer PB1 supplemented with 1 mM dextran sulfate is added. After these automated steps, the plate was removed from the plate washer and placed 5 cm away on a thermoshaker mounted under a UV light source (BlackRay, nominal wavelength 365 nm) for 20 minutes. The thermoshaker was set at 800 rpm and 25° C. After 20 minutes of irradiation, the samples were manually transferred to a new washed streptavidin plate (or to an unused well of an existing washed plate). The high abundance (3% serum + 3% aptamer mix) and low abundance reaction mixes (0.3% serum + 0.3% aptamer mix) were now combined in a single well. This “Catch-2” plate was placed on the deck of a BioTek EL406 plate washer. The washer was programmed to perform the following steps: The plate was incubated for 10 minutes with shaking. The liquid was aspirated and the wells were washed 21 times with 300 μL of buffer PB1 supplemented with 30% glycerol. The wells are washed five times with 300 μL of Buffer PB1 and the final wash is aspirated. 100 μL of CAPSO Elution Buffer is added and the aptamers are eluted for 5 minutes with shaking. After these automated steps, the plate is then removed from the plate washer deck and 90 μL aliquots of the samples are manually transferred to wells of a HybAid 96-well plate containing 10 μL of Neutralization Buffer.

[0157] Hybridization to custom Agilent 8x15k microarrays 24 μL of neutralized eluate is transferred to a new 96-well plate and 6 μL of 10× Agilent Block (Oligo aCGH / ChIP-on-chip Hybridization Kit, Large Capacity, Agilent 5188-5380) containing a set of hybridization controls consisting of 10 Cy3 aptamers is added to each well. 30 μL of 2× Agilent Hybridization Buffer is added to each sample and mixed. 40 μL of the resulting hybridization solution is manually added using a pipette to each "well" of a Hybridization Gasket Slide (8 microarrays per slide format, Agilent). Custom Agilent microarray slides with 10 probes per array complementary to a 40 nucleotide random region of each aptamer with 20× dT linkers are placed on the gasket slide according to the manufacturer's protocol. The assembly (Hybridization Chamber Kit, SureHyb compatible, Agilent) is clamped and incubated for 19 hours at 60° C. with rotation at 20 rpm.

[0158] Post-hybridization washes Place approximately 400 mL of Agilent Wash Buffer 1 into each of two separate glass staining dishes. Disassemble and separate the slides (no more than two at a time) while immersed in Wash Buffer 1, then transfer to the slide rack in the second staining dish, also containing Wash Buffer 1. Incubate the slides in Wash Buffer 1 for an additional 5 minutes with agitation. Transfer the slides to Wash Buffer 2, pre-equilibrated to 37°C, and incubate for 5 minutes with agitation. Transfer the slides to a fourth staining dish containing acetonitrile and incubate for 5 minutes with agitation.

[0159] Microarray imaging Microarray slides are imaged using an Agilent G2565CA microarray scanner system with a resolution of 5 μm, Cy3-channel with PMT settings of 100% and XRD option enabled at 0.05. The resulting TIFF images are processed using Agilent's feature extraction software (version 10.5.1.1) with the GE1_105_Dec08 protocol.

[0160] Luminex Probe Design The bead-immobilized probes have 40 deoxynucleotides complementary to a random 40-nucleotide region at the 3' end of the target aptamer. The aptamer-complementary region is attached to a Luminex microsphere via a hexaethylene glycol (HEG) linker with a 5' amino terminus. The biotinylated detector deoxyoligonucleotides contain 17-21 deoxynucleotides complementary to the 5' primer region of the target aptamer. A biotin moiety is added to the 3' end of the detector oligo.

[0161] Binding of probes to Luminex microspheres The probes were coupled to Luminex microspheres essentially according to the manufacturer's instructions, with the following modifications: the amount of amino-terminal oligonucleotide was 2.5 × 10 6 The EDC addition is 0.08 nanol per microsphere at 10 mg / mL and the second EDC addition is 5 μL at 10 mg / mL. The coupling reaction is carried out in an Eppendorf thermoshaker set at 25° C. and 600 rpm.

[0162] Microsphere Hybridization The microsphere stock solution (approximately 40,000 microspheres / μL) is vortexed and sonicated for 60 seconds in a Health Sonics ultrasonic cleaner (model: T1.9C) to suspend the microspheres. The suspended microspheres are diluted to 2000 microspheres per reaction in 1.5×TMAC hybridization solution and mixed by vortexing and sonication. 33 μL of the bead mixture per reaction is transferred to a 96-well HybAid plate. 7 μL of 15 nM biotinylated detection oligonucleotide stock in 1×TE buffer is added to each reaction and mixed. 10 μL of neutralized assay sample is added and the plate is sealed with a silicone cap matte seal. The plate is first incubated at 96° C. for 5 minutes and then incubated overnight at 50° C. without agitation in a conventional hybridization oven. A filter plate (Durapore, Millipore part number MSBVN1250, 1.2 μm pore size) is pre-wetted with 75 μL of 1×TMAC hybridization solution supplemented with 0.5% (w / v) BSA. The entire sample volume from the hybridization reaction is transferred to the filter plate. The hybridization plate is rinsed with 75 μL of 1×TMAC hybridization solution containing 0.5% BSA to transfer any remaining material to the filter plate. The sample is filtered using 150 μL of buffer under slow vacuum and evacuated for approximately 8 seconds. The filter plate is washed once with 75 μL of 1×TMAC hybridization solution containing 0.5% BSA, and the microspheres in the filter plate are resuspended in 75 μL of 1×TMAC hybridization solution containing 0.5% BSA. The filter plate is protected from light and incubated at 1000 rpm for 5 minutes on an Eppendorf thermal mixer R. The filter plate is then washed once with 75 μL of 1×TMAC hybridization solution containing 0.5% BSA. 75 μL of 10 μg / mL streptavidin phycoerythrin (SAPE-100, MOSS, Inc.) in 1×TMAC hybridization solution is added to each reaction and incubated at 25° C., 1000 rpm on an Eppendorf Thermal Mixer R for 60 minutes.The filter plate is washed twice with 75 μL of 1×TMAC hybridization solution containing 0.5% BSA, and the microspheres in the filter plate are resuspended in 75 μL of 1×TMAC hybridization solution containing 0.5% BSA. The filter plate is then incubated for 5 minutes at 1000 rpm on an Eppendorf Thermal Mixer R and protected from light. The filter plate is then washed once with 75 μL of 1×TMAC hybridization solution containing 0.5% BSA. The microspheres are resuspended in 75 μL of 1×TMAC hybridization solution supplemented with 0.5% BSA, and analyzed on a Luminex 100 instrument running Xponent 3.0 software. At least 100 microspheres per bead type are counted under high PMT calibration and doublet rejection instrument settings of 7500-18000.

[0163] QPCR readout Prepare qPCR standard curve samples in 10-fold dilutions using water, ranging from 108 to 102 copies, and prepare a no template control. Dilute neutralized assay samples 40-fold in diH2O. Prepare qPCR master mix at 2x final concentration (2x KOD buffer, 400 μM dNTP mix, 400 nM forward and reverse primer mix, 2x SYBR Green I, and 0.5 U KOD EX). Add 10 μL of 2x qPCR master mix to 10 μL of diluted assay sample. Run qPCR using a BioRad MyIQ iCycler at 96°C for 2 min, followed by 40 cycles of 96°C for 5 s and 72°C for 30 s.

[0164] Example 2. Time to plasma freezing model The plasma sample obtained from the subject may be first obtained as a whole blood sample and then centrifuged. The resulting plasma supernatant may then be collected by decanting or aspirating from the sediment. To ensure its quality, the plasma sample should be analyzed immediately after centrifugation and decanting or aspirating, or frozen at -20°C or below. This is because the time elapsed between the completion of the sample collection and processing steps at room temperature and the time the sample is frozen can be a source of pre-analytical variability.

[0165] To assess the quality of plasma samples, a linear regression elastic net model was developed containing a panel of eight biomarker proteins listed in Table 1. The model provides a predicted value in hours, which is the time from completion of centrifugation and decanting or aspiration of the sample to the time of freezing the sample. Training and validation datasets were obtained from the analysis of adult volunteer samples, as described in model development below. Table 5 shows the performance metrics of the model. "CCC" is the concordance correlation coefficient. "R 2 " is the degree of linear correlation, or goodness of fit. "CI" is the confidence interval. CCC and R 2 indicates the predictive performance of the model. [Table 5]

[0166] The plasma time to freezing model is an elastic net linear regression model. The model features eight aptamers that bind to eight different biomarker proteins listed in Table 1. The output of the model is an untransformed prediction of time to freezing in hours, with values ​​less than zero rounded up to zero. Time to freezing is the amount of time in hours that the plasma sample is left at room temperature after the whole blood sample is immediately centrifuged and decanted or aspirated. Thus, the output is a number equal to or greater than zero, with zero being the immediate transfer of the decanted or aspirated plasma sample to freezing conditions.

[0167] Developing the model With a tourniquet applied, blood was collected from each of 10 adult human volunteers using multiple K2-EDTA plasma tubes (purple top, BD#367863) using a 21G butterfly needle set. After collection, the tubes were inverted 8-10 times and immediately centrifuged at 2200 x g for 15 or 30 min. The resulting plasma was carefully aspirated from each tube and pooled for a given donor.

[0168] Independent aliquots of plasma were taken from each donor pool and incubated at room temperature for 0, 0.5, 1, 1.5, 3, 6, or 24 hours. After the appropriate incubation period, 90 μL aliquots were made into matrix tubes and stored frozen at -80°C, and 750 μL aliquots were made and stored at -20°C. After 4-6 days, the -20°C stored aliquots were thawed and 90 μL portions were transferred to matrix tubes and stored at -80°C until thawed on the day that aptamer-based analysis was to be performed, e.g., according to the protocol described in Example 1.

[0169] The data was split 80% / 20% into training / validation datasets such that two samples were randomly selected from each of the seven time points for the validation set (14 samples in validation set) and the remaining samples were the training set (56 samples in training). For validation, a parallel study of time to freezing was performed where samples were stored at -20°C instead of -80°C. Due to the small number of samples, the hold-out test dataset was not used for model development. Models were developed on the training dataset and then evaluated on the validation set, but were not used as a hold-out validation or test set. Sample numbers are shown in Table 6 below. [Table 6]

[0170] After running the aptamer assays, the data were normalized. Control samples were internally median normalized, plate scaled, calibrated, and samples were normalized using adaptive normalization by maximum likelihood (ANML). All samples were within the pass / fail criteria of the normalization scale factor, so no samples were excluded from the analysis. The normalization scale factor did not correlate with the endpoints. POC results

[0171] The POC results showed that a number of analytes were significant at various false discovery rate (FDR) levels. Table 7 below shows the number and percentage of analytes (after excluding red list analytes) that were significant at various type I error cutoffs for the univariate outcome of time to plasma freezing using Student's t-test. [Table 7]

[0172] Improvement and confirmation For the models developed in the refinement, the training and validation sets described above were used.

[0173] Feature selection was performed starting with 62 features of the initial elastic net model. Various combinations of aptamers with 1 to 30 features were examined using best subset selection. Results showed that model performance plateaued above 10 features, with adjusted R 2The results showed that 5 or more features were required to achieve a β of 0.95. From this, a series of elastic net models with feature counts of 5 to 15 analytes were generated using five rounds of 10-fold cross-validation. The final selected model had high performance in the training set and a low coefficient of variation (CV) with predictions of time to freezing made on samples from other sample handling studies, ensuring that predictions of time to freezing were independent of other sample handling conditions. Model predictions were examined on the training and validation data sets, and Lin's correlation coefficients were calculated for the training and validation data (Table 8). No significant degradation in model performance was observed between the training and validation data, although sample size was limited to only a few samples per condition in the validation data.

[0174] The model trained on samples frozen at -80°C was used to predict time to freezing in a parallel study in which samples were treated in the same way but frozen at -20°C. The model performed well on the validation set and was shown to be stable in R 2 was 0.934 and the root mean square error (RMSE) was 2.04 (Table 8). [Table 8]

[0175] We evaluated the effect of winsorization and feature removal to correct for outliers on other datasets. For the original data, the RMSE was 2.612, whereas with winsorization it was 4.227 and with feature removal it was 3.037. Therefore, in this model, outliers were replaced by zeros. The results are shown in Tables 9 and 10 below. [Table 9] [Table 10]

[0176] Example 3: Model for time to serum freezing The serum sample obtained from the subject may be first obtained as a whole blood sample, which is then allowed to clot and centrifuged. The resulting serum supernatant may then be collected by decanting or aspirating from the sediment. To ensure its quality, the serum sample should be analyzed immediately after centrifugation and decanting or aspirating, or frozen at -20°C or below. This is because the time elapsed between the completion of the sample collection and processing steps at room temperature and the time the sample is frozen can be a source of pre-analytical variability.

[0177] To assess serum sample quality, a linear regression model was developed containing a panel of five biomarker proteins listed in Table 2. The model provides a predicted value in hours, which is the time from completion of centrifugation and decanting or aspiration of the sample to the time of freezing the sample. Training and validation data sets were obtained from the analysis of adult volunteer samples, as described in model development below. Table 11 shows the performance metrics of the model. "CCC" is the concordance correlation coefficient. "R 2 " is the degree of linear correlation, or goodness of fit. "CI" is the confidence interval. CCC and R 2 indicates the predictive performance of the model. [Table 11]

[0178] The serum time to freezing model is a linear regression model. The model features five aptamers that bind to five different biomarker proteins listed in Table 2. The output of the model is an untransformed prediction of time to freezing in hours, with values ​​less than zero rounded up to zero. Time to freezing is the time in hours that the serum sample is allowed to sit at room temperature after the whole blood sample is immediately clotted, centrifuged, and decanted or aspirated. Thus, the output is a number equal to or greater than 0, with 0 being the immediate transfer of the decanted or aspirated serum sample to freezing conditions.

[0179] Developing the model With a tourniquet applied, blood was collected in multiple serum tubes (red top, BD#367814) using a 21G butterfly needle set from each of 20 adult volunteers. After collection, the tubes were inverted five times and allowed to clot for 50-95 minutes at room temperature. After clot formation, the blood tubes were immediately centrifuged at 2200 x g for 15 minutes. The resulting serum was carefully aspirated from each tube and pooled for a given donor.

[0180] Independent aliquots of serum were taken from each donor pool and incubated at room temperature for 0, 0.5, 1, 1.5, 3, 3.5, 6, or 24 hours. After the appropriate incubation period, 90 μL aliquots were made into matrix tubes and stored frozen at -80°C, and 550 μL aliquots were made and stored at -20°C. After 4-6 days, the -20°C stored aliquots were thawed and 90 μL portions were transferred to matrix tubes and stored at -80°C until thawed on the day that aptamer-based analysis was to be performed, e.g., according to the protocol described in Example 1.

[0181] Data was split 80% / 20% into training / validation. Individual donors were not assigned to training / validation, but each time point was randomly assigned for each donor. For validation, a parallel study of time to freezing was performed where samples were stored at -20°C instead of -80°C. The data set was derived from 12 donors with 8 time points to freezing (there are 8 unique points, but each donor was only measured at 7 points), but were frozen at -20°C instead of -80°C. Sample numbers are shown in Table 12 below. An "*" indicates that one sample was excluded from the analysis due to sample failure. [Table 12]

[0182] After running the aptamer assay, the data were normalized: control samples were internally median normalized, plate scaled, calibrated, and samples were normalized using adaptive normalization by maximum likelihood (ANML). One sample did not pass the check, leaving a total of 83 samples for training and validation.

[0183] POC results The POC results showed that a number of analytes were significant at various false discovery rate (FDR) levels. Table 13 below shows the number and percentage of analytes (after excluding red list analytes) that were significant at various type I error cutoffs for the univariate outcome of time to serum freezing using Student's t-test. [Table 13]

[0184] Improvement and confirmation Feature selection was performed by starting with the top 200 features by rank identified in the POC univariate analysis. This list was further refined via a series of elastic net regressions with alpha and lambda both set to 0.5, the optimal values ​​identified in the POC analysis. After each round of elastic net regression, the feature with the smallest coefficient was removed from the feature list and a new model was trained. The performance of this new model was evaluated on the validation set, and features were removed until performance on the validation set significantly decreased. This process resulted in seven features.

[0185] The final step in the feature selection process was to use a log2 fold change univariate analysis performed using the time to decant and time to clot data from version 4.0 to remove analytes with significant (defined as FDR<0.05) fold change at 24 hours for either of these endpoints. This filtering was performed because we wanted a time to freeze model that was as independent as possible from other sample handling time endpoints. This resulted in the removal of two features from the final feature list, bringing the total to five features. Lin's correlation coefficients were calculated for the training and validation data (Table 14). [Table 14]

[0186] We evaluated the effect of winsorization and feature removal to correct for outliers on other datasets. For the original data, the RMSE was 1.374, whereas with winsorization it was 4.470 and with feature removal it was 2.245. Therefore, we replaced the outliers with zeros in this model. The results are shown in Tables 15 and 16 below. [Table 15] [Table 16]

[0187] Example 4: Time to Centrifugation Model Plasma and serum samples obtained from subjects may be first obtained as whole blood samples and then centrifuged. The time between collection and centrifugation (time to centrifugation) should ideally be less than 2 hours. Deviations from this processing time are known to activate platelets in the collection tube and shift the overall analyte signal for most of the menu as the time to centrifugation is increased, thereby contributing to preanalytical variation.

[0188] To assess the quality of the plasma samples, an elastic net linear regression model was developed containing a panel of four biomarker proteins listed in Table 3. The model provides a predicted value in hours, which is the time from blood collection to the time the sample is centrifuged. Training and validation data sets were obtained from the analysis of adult volunteer samples, as described in the model development below. Table 17 shows the performance metrics of the model. "CCC" is the concordance correlation coefficient. "R 2 " is the degree of linear correlation, or goodness of fit. "CI" is the confidence interval. CCC and R 2 indicates the predictive performance of the model. [Table 17]

[0189] The time to centrifugation model is an elastic net linear regression model. The model features four aptamers that bind to four different biomarker proteins listed in Table 3. The output of the model is the estimated time to centrifugation in hours, with values ​​less than zero rounded up to zero. Thus, the output is a number equal to or greater than 0, with 0 being immediate centrifugation of the collected sample.

[0190] Developing the model Blood was collected from 18 adult volunteers in multiple K2-EDTA plasma tubes and centrifuged at 0, 0.5, 1.5, 3, 9, or 24 hours. Plasma was obtained from each sample and frozen at -80°C prior to analysis by the aptamer assay, e.g., according to the protocol described in Example 1.

[0191] Data was split 80% / 20% into training / validation. Individual donors were not assigned to training / validation, but each time point was randomly assigned for each donor. Due to the small number of samples, a separate validation holdout set was not used. Sample numbers are shown in Table 18 below. [Table 18]

[0192] After running the aptamer assays, the data were normalized. Control samples were internally median normalized, plate scaled, calibrated, and samples were normalized using adaptive normalization by maximum likelihood (ANML). All samples were within the pass / fail criteria of the normalization scale factor, so no samples were excluded from the analysis. The normalization scale factor was partially correlated with the time to centrifugation, which was expected given the large changes seen for most of the menus, especially at 24 hours.

[0193] POC results The POC results showed that a large number of analytes were significant at various false discovery rate (FDR) levels. Table 19 below shows the number and percentage of analytes (after excluding red list analytes) that were significant at various type I error cutoffs for the univariate outcome of time to centrifugation using the Kruskal-Wallis test. [Table 19]

[0194] Improvement and confirmation The focus of refinement was on reducing the number of features without significantly compromising model performance, reproducibility of predictions from replicate samples, and orthogonality with other sample handling studies.To reduce model complexity and obtain a set of analytes with high resolution between time points, further feature selection was performed using prior information in combination with manual selection.

[0195] The final model included the four analytes in an elastic net regression model and showed good predictive performance in the training and validation sets (Table 20). "MAE" is the mean absolute error. [Table 20]

[0196] We evaluated the effect of winsorization and feature removal to correct outliers for other data sets. For the original data, the RMSE was 1.619, whereas with winsorization it was 1.619 and with feature removal it was 1.704. Hence, the outliers were winsorized. The results are shown in Tables 21 and 22 below. [Table 21] [Table 22]

[0197] Example 5: Time to Decant Model Plasma and serum samples obtained from subjects may be first obtained as whole blood samples, then centrifuged and decanted or aspirated. The time between centrifugation and decanting or aspirating into a new tube (time to decant) should ideally be less than 2 hours. Deviations from this processing time are known to activate platelets in the collection tube and shift the overall analyte signal for most of the menu as the time to decant is increased, thereby contributing to preanalytical variation.

[0198] To assess the quality of plasma samples, an elastic net linear regression model was developed containing a panel of 13 biomarker proteins listed in Table 4. The model provides a predictive value in hours, which is the time from centrifugation of the sample to transfer (e.g., by decanting or aspirating) into a new tube. Training and validation datasets were obtained from the analysis of adult volunteer samples, as described in model development below. Table 23 shows the performance metrics of the model. "CCC" is the concordance correlation coefficient. "R 2 " is the degree of linear correlation, or goodness of fit. "CI" is the confidence interval. CCC and R 2 indicates the predictive performance of the model. [Table 23]

[0199] The model for time to decant is an elastic net linear regression model. The model features 13 aptamers that bind to 13 different biomarker proteins, listed in Table 4. The output of the model is the estimated time to decant in hours, with values ​​less than zero rounded up to zero. Thus, the output is a number equal to or greater than 0, with 0 being immediate decant of the centrifuged sample.

[0200] Developing the model Multiple K2-EDTA plasma tubes of blood were collected from each of 18 adult volunteers and immediately centrifuged. Plasma was transferred to new tubes at 0, 0.5, 1.5, 3, 9, or 24 hours after centrifugation. Plasma samples were frozen at -80°C before analysis by the aptamer assay, e.g., according to the protocol described in Example 1.

[0201] Data was split 80% / 20% into training / validation. Individual donors were not assigned to training / validation, but each time point was randomly assigned for each donor. Due to the small number of samples, a separate validation holdout set was not used. Sample numbers are shown in Table 24 below. [Table 24]

[0202] After running the aptamer assays, the data were normalized. Control samples were internally median normalized, plate scaled, calibrated, and samples were normalized using adaptive normalization by maximum likelihood (ANML). All samples were within the pass / fail criteria for the normalization scale factor, so no samples were excluded from the analysis. The normalization scale factor did not correlate significantly with the time to decant.

[0203] POC results The POC results showed that a number of analytes were significant at various false discovery rate (FDR) levels. Table 25 below shows the number and percentage of analytes (after excluding red list analytes) that were significant at various type I error cutoffs for the univariate outcome of time to decant using the Kruskal-Wallis test. [Table 25]

[0204] Improvement and confirmation The focus of refinement was on reducing the number of features without significantly compromising model performance, reproducibility of predictions from replicate samples, and orthogonality with other sample handling studies.To reduce model complexity and obtain a set of analytes with high resolution between time points, further feature selection was performed using prior information in combination with manual selection.

[0205] These 13 analytes were used in elastic net regression for time to decant and showed good predictive performance in the training and validation sets. (Table 26) "MAE" is the mean absolute error.

[0206] There was no loss associated with the validation set. Predictions of longer times to decant were not as well captured; however, at longer times to decant, there is less concern about obtaining more accurate predictions at shorter times to decant. [Table 26]

[0207] We evaluated the effect of winsorization and feature removal to correct outliers for other data sets. For the original data, the RMSE was 2.468, whereas with winsorization it was 2.487 and with feature removal it was 3.653. Hence, the outliers were winsorized. Additional validation results are shown in Tables 27 and 28 below. [Table 27] [Table 28]

[0208] Example 6: Biomarker Directionality The table below shows the directionality of each biomarker in each model. Negative log2 fold change values ​​mean that the levels of the biomarker protein decrease over time, and positive log2 fold change values ​​mean that the levels of the biomarker protein increase over time. In the table below, "N / A" means that the biomarker did not meet the FDR significance threshold of 0.05. [Table 29] [Table 30] [Table 31] [Table 32]

Claims

1. 1. A method for assessing the quality of a sample collected from a subject, comprising detecting the levels of each of N biomarker proteins in the sample, wherein N is at least 4, and at least four of the N biomarker proteins are IHH, SHH, PGAM1, and ROA2.

2. 1. A method comprising: a) measuring the level of each of N biomarker proteins in a sample from the subject, wherein N is at least 4 and at least 4 of the N biomarker proteins are selected from IHH, SHH, PGAM1, and ROA2; b) identifying the sample as an analytical sample or a negative sample based on the levels of the N biomarker proteins; In this case, the analytical sample is a sample suitable for use in one or more of a protein biomarker discovery analysis, a protein expression level analysis, a diagnostic method, or a prognostic method, and the negative sample is a sample not suitable for use as an analytical sample.

3. 3. The method of claim 1 or 2, wherein N is 4.

4. The method of claim 1 or 2, wherein the subject is a human subject.

5. The method of claim 1 or 2, wherein the sample is a plasma sample, a serum sample or a urine sample.

6. 3. The method of claim 1 or 2, wherein the sample is treated prior to said detecting, said treating comprising centrifugation and decanting or aspirating the resulting supernatant, and wherein said detecting is performed on the supernatant.

7. determining an approximate time that has elapsed from the time the sample was taken to the time the sample was centrifuged; At that time, determining the approximate time is based on comparing the detected levels of the N biomarker proteins to reference levels; the reference level is the average level of the N biomarker proteins present in samples having zero or near zero treatment time; A detection level for one or both of PGAM1 and ROA2 higher than the reference level, or a detection level for one or both of IHH and SHH lower than the reference level, indicates that the approximate time elapsed from sample collection to the start of sample centrifugation was greater than 0 hours, greater than 0.5 hours, greater than 1.0 hours, greater than 1.5 hours, greater than 3 hours, greater than 6 hours, greater than 9 hours, or greater than 24 hours.

3. The method according to claim 1 or 2.

8. and identifying whether the sample passed or failed a quality assessment, wherein the identifying is based, at least in part, on the detected levels of the N biomarker proteins. (i) the sample is identified as passing if the approximate time elapsed from sample collection to the start of sample centrifugation is determined to be 0 hours, less than 0.5 hours, less than 1.0 hours, less than 1.5 hours, less than 3 hours, less than 6 hours, or less than 24 hours; or (ii) if the approximate time elapsed from sample collection to the start of sample centrifugation is determined to be greater than 1.0 hour, greater than 1.5 hours, greater than 3 hours, greater than 6 hours, or greater than 24 hours, the sample is identified as failing; 3. The method according to claim 1 or 2.

9. The method of claim 8, further comprising: identifying whether the sample passed or failed a quality assessment, wherein the identifying is based, at least in part, on the detected levels of the N biomarker proteins: (i) the sample is identified as passing if the approximate time elapsed from sample collection to the start of sample centrifugation is determined to be less than three hours; or (ii) if the approximate time elapsed from sample collection to the start of sample centrifugation is determined to be more than 3 hours, the sample is identified as failing; 3. The method according to claim 1 or 2. (i) detecting the levels of each of N biomarkers in a plurality of samples from a plurality of subjects; (ii) a) determining the approximate time elapsed between sample collection and sample centrifugation for each sample; and b) comparing the approximate time determined for each of the plurality of samples; and (iii) identifying the plurality of samples as consistently handled or inconsistently handled, wherein the consistently handled samples all have a determined approximate time between sampling and centrifugation within 1, 2, or 3 hours of each other; 3. The method according to claim 1 or 2.

11. 1. A method for assessing the quality of a sample collected from a subject, comprising detecting in the sample the level of each of N biomarker proteins, wherein N is at least 9, and at least 9 of the N biomarker proteins are selected from APB, CFAD, PTN4, PGAM2, HPPD, C4A, IF4A2, IHH, SHH, ADAM9, PGAM1, IL18, and the cytoplasmic domain of TMEM9.

12. 1. A method comprising: a) measuring in a sample from the subject the level of each of N biomarker proteins, wherein N is at least 9 and at least 9 of the N biomarker proteins are selected from APB, CFAD, PTN4, PGAM2, HPPD, C4A, IF4A2, IHH, SHH, ADAM9, PGAM1, IL18, and TMEM9 cytoplasmic domain; b) identifying the sample as an analytical sample or a negative sample based on the levels of the N biomarker proteins; In this case, the analytical sample is a sample suitable for use in one or more of a protein biomarker discovery analysis, a protein expression level analysis, a diagnostic method, or a prognostic method, and the negative sample is a sample not suitable for use as an analytical sample.

13. 13. The method of claim 11 or 12, wherein N is 9, N is 10, N is 11, N is 12, or N is 13, and all of the N biomarker proteins are selected from APB, CFAD, PTN4, PGAM2, HPPD, C4A, IF4A2, IHH, SHH, ADAM9, PGAM1, IL18, and TMEM9.

14. 13. The method of claim 11 or 12, wherein the subject is a human subject.

15. 13. The method of claim 11 or 12, wherein the sample is a plasma sample, a serum sample or a urine sample.

16. 13. The method of claim 11 or 12, wherein the sample is treated prior to said detecting, said treating comprising centrifugation and decanting or aspirating the resulting supernatant, and wherein said detecting is performed on the supernatant.

17. and identifying whether the sample passed or failed a quality assessment, wherein the identifying is based, at least in part, on the detected levels of the N biomarker proteins. (ii) the sample is identified as passing if the approximate time elapsed from the completion of sample centrifugation to the start of sample decantation or aspiration is determined to be 0 hours, less than 0.5 hours, less than 1.0 hours, less than 1.5 hours, less than 3 hours, less than 6 hours, or less than 24 hours; or (iii) if the approximate time elapsed from sample collection to completion of sample centrifugation to the start of sample decanting or aspiration is determined to be greater than 1.0 hour, greater than 1.5 hours, greater than 3 hours, greater than 6 hours, or greater than 24 hours, the sample is identified as failing; 13. The method of claim 11 or 12.

18. The method of claim 17, further comprising: identifying whether the sample passed or failed a quality assessment, wherein the identifying is based, at least in part, on the detected levels of the N biomarker proteins: (ii) the sample is identified as passing if the approximate time elapsed from the completion of sample centrifugation to the start of sample decanting or aspiration is determined to be less than 1.0 hour; or (iii) if the approximate time elapsed from sample collection to completion of sample centrifugation to the initiation of sample decantation or aspiration is determined to be greater than 1.0 hour, the sample is identified as failing; 13. The method of claim 11 or 12.

19. A method for assessing the quality of a sample collected from a subject, comprising detecting the levels of each of N biomarker proteins in the sample, wherein N is at least 3, at least three of the N biomarker proteins are selected from LANC2, PKHM2, ENOA, the cytoplasmic domain of TMEM9, PMM2, PGAM2, EFHD1, and THIK, and the sample is a plasma sample.

20. A method comprising: a) measuring in a plasma sample from the subject the level of each of N biomarker proteins, wherein N is at least 3 and at least three of the N biomarker proteins are selected from LANC2, PKHM2, ENOA, the cytoplasmic domain of TMEM9, PMM2, PGAM2, EFHD1, and THIK; b) identifying the sample as an analytical sample or a negative sample based on the levels of the N biomarker proteins; The method, wherein the analytical sample is a sample suitable for use in one or more of a protein biomarker discovery analysis, a protein expression level analysis, a diagnostic method, or a prognostic method, and the negative sample is a sample not suitable for use as an analytical sample.

21. The method of claim 19 or 20, wherein N is 3, N is 4, N is 5, N is 6, N is 7, or N is 8, or all of the N biomarker proteins are selected from LANC2, PKHM2, ENOA, the cytoplasmic domain of TMEM9, PMM2, PGAM2, EFHD1, and THIK.

22. The method of claim 19 or 20, comprising determining the approximate time elapsed from decanting or aspirating a sample to the time the sample is frozen, wherein said determining is based on comparing the detected levels of the N biomarker proteins to a reference level, the reference level being the average level of the N biomarker proteins present in samples having a processing time of zero or near zero.

23. A method for assessing the quality of a sample collected from a subject, comprising detecting the levels of each of N biomarker proteins in the sample, wherein N is at least 2, at least two of the N biomarker proteins are selected from LYAG, IL21R, C3b, IL36A, and GDF5, and the sample is a serum sample.

24. A method comprising: a) measuring a level of each of N biomarker proteins in a serum sample from the subject, wherein N is at least 2 and at least two of the N biomarker proteins are selected from LYAG, IL21R, C3b, IL36A, and GDF5; b) identifying the sample as an analytical sample or a negative sample based on the levels of the N biomarker proteins; In this case, the analytical sample is a sample suitable for use in one or more of a protein biomarker discovery analysis, a protein expression level analysis, a diagnostic method, or a prognostic method, and the negative sample is a sample not suitable for use as an analytical sample.

25. The method of claim 23 or 24, wherein N is 2, N is 3, N is 4, or N is 5, or all of the N biomarker proteins are selected from LYAG, IL21R, C3b, IL36A, and GDF5.

26. A method as described in claim 23 or 24, comprising determining the approximate time elapsed from decanting or aspirating a sample to the time the sample is frozen, said determining being based on comparing the detected levels of the N biomarker proteins with a reference level, said reference level being the average level of the N biomarker proteins present in samples having a processing time of zero or near zero.

27. ​​The method of claim 23 or 24, comprising identifying whether the sample has passed or failed quality assessment, wherein the identifying is based, at least in part, on the detected levels of the N biomarker proteins.

28. A method described in claim 23 or 24, comprising detecting the levels of each of N biomarkers in multiple samples from multiple subjects.

29. 24. The method of claim 1, 11, 19, or 23, wherein the detecting comprises performing mass spectrometry, an aptamer-based assay, and / or an antibody-based assay.

30. 25. The method of any one of claims 1, 2, 11, 12, 19, 20, 23, and 24, wherein the method comprises contacting biomarker proteins of the sample from the subject with a set of capture reagents, each capture reagent of the set specifically binding to one biomarker protein to be detected, and each of the capture reagents binding to a different biomarker protein.

31. A kit comprising capture reagents for N biomarker proteins, wherein N is at least 3, and at least three of the capture reagents bind to proteins selected from LANC2, PKHM2, ENOA, the cytoplasmic domain of TMEM9, PMM2, PGAM2, EFHD1, and THIK.

32. 1. A kit comprising capture reagents for N biomarker proteins, wherein N is at least 2, and at least two of the capture reagents bind to a protein selected from LYAG, IL21R, C3b, IL36A, and GDF5.

33. A kit comprising capture reagents for N biomarker proteins, where N is at least 4, and four of the capture reagents bind to proteins IHH, SHH, PGAM1, and ROA2.

34. 1. A kit comprising capture reagents for N biomarker proteins, wherein N is at least 9, and at least 9 of the capture reagents bind to a protein selected from APB, CFAD, PTN4, PGAM2, HPPD, C4A, IF4A2, IHH, SHH, ADAM9, PGAM1, IL18, and TMEM9.

35. The kit of any one of claims 31 to 34, wherein each of the capture reagents binds to a different protein.

36. The kit of any one of claims 31 to 34, wherein each of the capture reagents is an antibody or an aptamer.

37. The kit of any one of claims 31 to 34, wherein each capture reagent is an aptamer and at least one aptamer is an aptamer with a slow off-rate.

38. The kit of claim 37, wherein at least one aptamer with a slow off-rate contains at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 modified nucleotides, and each aptamer with a slow off-rate binds to its target protein with an off-rate (t 1 / 2 ) of ≥ 30 minutes, ≥ 60 minutes, ≥ 90 minutes, ≥ 120 minutes, ≥ 150 minutes, ≥ 180 minutes, ≥ 210 minutes, or ≥ 240 minutes.

39. A kit according to any one of claims 31 to 34 for use in detecting the N biomarker proteins in a sample from a subject.

40. 40. The kit of claim 39, used to assess the quality of the sample based at least in part on the levels of the N biomarker proteins detected in the sample.

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