Determining the appropriate blood dilution of bone marrow aspirate using biomarkers

Abstract

A method, system, and machine-readable medium for determining hemodilution of a bone marrow sample. In one embodiment, the method includes analyzing the bone marrow sample to determine the number of cells in the bone marrow sample that express one or more cell surface markers (e.g., CD13 and a complementary marker). The method further includes correlating the number of cells that express one or more cell surface markers (e.g., CD13 and a complementary marker) with the level of hemodilution of the bone marrow sample.

Description

[Technical Field]

[0001] Cross-reference of related applications This application claims priority to U.S. Provisional Application No. 63 / 056,504, filed on 24 July 2020, which is incorporated herein by reference in its entirety.

[0002] Methods, systems, and kits for determining or measuring hemodilution in bone marrow aspirate or bone marrow samples from subjects are provided herein. More specifically, methods, systems, and kits for determining or measuring hemodilution in bone marrow samples by measuring or determining the amount (or level) of one or more proteins or cell surface markers are provided herein. [Background technology]

[0003] Bone marrow aspiration is the process of extracting (extracting) a small amount of bone marrow fluid from the bone using a needle. Bone marrow aspiration is essential for diagnosis, testing, and investigation, including the diagnosis of cancer or monitoring the response to the procedure in patients with hematological malignancies. Often, multiple bone marrow samples are aspirationed for various purposes during a single visit. For example, a first aspiration may be performed to provide a bone marrow sample for morphological assessment, while a second aspiration may be performed to provide a bone marrow sample for flow cytometry. However, sequential bone marrow aspiration can gradually increase the amount of blood contamination in these bone marrow samples, thereby causing hemodilution. This type of hemodilution can reduce the reliability of data obtained from analyses performed using bone marrow samples. In some cases, bone marrow samples with a large amount of hemodilution may need to be excluded from analysis. Therefore, methods, systems, and kits for accurately measuring the amount of hemodilution in bone marrow samples are desirable. [Overview of the project]

[0004] Embodiments described herein provide various methods, systems, and computer program products for determining the level of blood dilution in bone marrow samples.

[0005] In some embodiments, a method is provided for determining hemodilution of a bone marrow sample. The method comprises analyzing the bone marrow sample to determine the number of cells in the bone marrow sample expressing CD13 and complementary markers. The method further comprises correlating the number of cells expressing CD13 and complementary markers with the hemodilution level of the bone marrow sample.

[0006] In some embodiments, a non-temporary computer-readable medium is provided for storing computer instructions for determining hemodilution of a bone marrow sample. One or more processors receive data obtained from the bone marrow sample. One or more processors analyze the data to determine the number of cells in the bone marrow sample that express CD13 and complementary markers. One or more processors correlate the number of cells expressing CD13 and complementary markers with the hemodilution level of the bone marrow sample.

[0007] In some embodiments, a method is provided for monitoring the progression of a disease or disorder in a subject by analyzing a bone marrow sample obtained from the subject. The method includes determining the number of cells in the bone marrow sample that express CD13 and complementary markers. The method includes correlating the number of cells expressing CD13 and complementary markers with the blood dilution level of the bone marrow sample. The method further includes assessing whether the blood dilution level passes a predetermined blood dilution standard, and performing an assessment or analysis of the bone marrow sample in response to a blood dilution level that passes the predetermined blood dilution standard.

[0008] In some embodiments, a method is provided for monitoring the progression of a disease or disability in a subject by analyzing a bone marrow sample obtained from the subject. The method includes extracting targeted mRNA molecules associated with CD13 and complementary markers from the bone marrow sample. The method includes sequencing the targeted mRNA molecules extracted from the bone marrow sample. The method includes determining the expression levels of CD13 and complementary markers in the bone marrow sample using sequence data obtained from the targeted mRNA molecules. The method includes assessing whether the expression levels of CD13 and complementary markers meet a predefined complementary marker expression criterion. The method also includes performing an assessment or analysis of the bone marrow sample in response to the expression levels of CD13 and complementary markers that meet the predefined complementary marker expression criterion.

[0009] In some embodiments, a method is provided for monitoring the effectiveness of a therapeutic regimen in treating a disease or disorder by analyzing a bone marrow sample obtained from a subject being treated with the therapeutic regimen. The method includes determining the number of cells in the bone marrow sample expressing CD13 and complementary markers. The method includes correlating the number of cells with the hemodilution level of the bone marrow sample. The method includes assessing whether the hemodilution level passes a predetermined hemodilution standard. The method also includes performing an assessment or analysis of the bone marrow sample for at least one of monitoring or verifying the therapeutic effectiveness of the therapeutic regimen in response to the hemodilution level passing the predetermined hemodilution standard.

[0010] In some embodiments, a method is provided for monitoring the effectiveness of a therapeutic regimen in treating a disease or disorder by analyzing bone marrow samples obtained from a subject being treated with the therapeutic regimen. The method includes extracting targeted mRNA molecules associated with CD13 and complementary markers from the bone marrow sample. The method includes sequencing the targeted mRNA molecules extracted from the bone marrow sample. The method includes determining the expression levels of CD13 and complementary markers in the bone marrow sample using sequence data obtained from the targeted mRNA molecules. The method includes assessing whether the expression levels of CD13 and complementary markers meet a predefined complementary marker expression criterion. The method also includes performing an assessment or analysis of the bone marrow sample for at least one of monitoring or validating the therapeutic effectiveness of the therapeutic regimen in response to the expression levels of CD13 and complementary markers meeting the predefined complementary marker expression criterion.

[0011] In some embodiments, methods are provided for determining hemodilution of a bone marrow sample. The method includes analyzing the bone marrow sample to identify the cell distribution of cells in the bone marrow sample that express one or more cell surface markers. The method includes calculating a statistical distance score for the cell distribution. The method also includes correlating the statistical distance score with the hemodilution level of the bone marrow sample.

[0012] In some embodiments, a non-temporary computer-readable medium is provided for storing computer instructions for determining hemodilution of a bone marrow sample. One or more processors receive data obtained from the bone marrow sample. One or more processors analyze the data to identify the cell distribution of cells in the bone marrow sample expressing one or more cell surface markers. One or more processors calculate a statistical distance score for the cell distribution. One or more processors correlate the statistical distance score with the hemodilution level of the bone marrow sample.

[0013] In some embodiments, a method is provided for monitoring the progression of disease or disability in a subject by analyzing a bone marrow sample obtained from the subject. The method includes calculating a statistical distance score for the cellular distribution of cells in the bone marrow sample expressing one or more cell surface markers. The method includes correlating the statistical distance score with the blood dilution level of the bone marrow sample. The method includes assessing whether the blood dilution level meets a predetermined blood dilution standard. The method also includes, if the blood dilution level meets the predetermined blood dilution standard, confirming the progression of disease or disability in the subject using the bone marrow sample.

[0014] In some embodiments, a method is provided for monitoring the effectiveness of a therapeutic regimen in treating a disease or disorder by analyzing bone marrow samples obtained from subjects being treated with the therapeutic regimen. The method includes calculating a statistical distance score for the cellular distribution of cells in the bone marrow sample expressing one or more cell surface markers. The method includes correlating the statistical distance score with the blood dilution level of the bone marrow sample. The method includes assessing whether the blood dilution level meets a predetermined blood dilution standard. The method also includes verifying improvement in the disease or disorder in the subject if the blood dilution level meets the predetermined blood dilution standard.

[0015] In some embodiments, methods are provided for determining hemodilution of a bone marrow sample. The method includes analyzing the bone marrow sample to identify the cell distribution for a cell subpopulation expressing at least one cell surface marker of interest. The method includes calculating a statistical distance score for the cell distribution relative to the control cell distribution for a cell subpopulation in a control bone marrow sample without hemodilution. The method also includes correlating the statistical distance score with the hemodilution level of the bone marrow sample.

[0016] In one or more embodiments, a kit is provided for performing one or more of the methods described above or elsewhere in this specification.

[0017] Some embodiments of the present disclosure include a system including one or more data processors. In some embodiments, the system is a non-transitory computer-readable storage medium storing instructions that, when executed on the one or more data processors, cause the one or more data processors to perform some or all of one or more of the methods disclosed herein and / or some or all of one or more processes. Some embodiments of the present disclosure include a computer program product tangibly embodied in a non-transitory machine-readable storage medium including instructions configured to cause one or more data processors to perform some or all of one or more of the methods disclosed herein and / or some or all of one or more processes.

[0018] The terms and expressions used are used as terms of description and not of limitation, and there is no intention, in the use of such terms and expressions, to exclude any equivalents or any portion of the features shown and described, but it is recognized that various modifications are possible within the scope of the invention as set forth in the claims. Accordingly, while the invention as set forth in the claims is specifically disclosed by embodiments and optional features, it is to be understood that modifications and variations of the concepts disclosed herein may be employed by those skilled in the art, and such modifications and variations are considered to be within the scope of the invention as defined by the appended claims.

[0019] The content of this application file contains at least one drawing created in color. A copy of this patent or this patent application with color drawings will be provided by the Patent Office upon request and payment of the necessary fees.

[0020] This disclosure is described in conjunction with the following accompanying drawings.

Brief Description of the Drawings

[0021] [Figure 1]This is a block diagram of an analysis system according to an exemplary embodiment. [Figure 2] This flowchart shows methods for determining the blood dilution level in a bone marrow sample according to various embodiments. [Figure 3] This flowchart shows a method for obtaining data for use in determining the blood dilution level of a bone marrow sample using flow cytometry in various embodiments. [Figure 4] This flowchart shows a method for determining the number of cells expressing CD13 and complementary markers using immunohistochemistry with a fluorometer according to various embodiments. [Figure 5] This flowchart shows a method for determining the number of cells expressing CD13 and complementary markers using immunohistochemistry with cell counting in various embodiments. [Figure 6] This flowchart shows a method for determining the expression level of a marker in a single cell for hemodilution assessment using single-cell sequencing in various embodiments. [Figure 7] This flowchart shows a method for determining the expression levels of multiple cell markers for hemodilution assessment using bulk cell sequencing according to various embodiments. [Figure 8] This flowchart shows a method for determining blood dilution in a bone marrow sample using statistical distance according to various embodiments. [Figure 9] This is a block diagram of a computer system in various embodiments. [Figure 10] This flowchart shows a method for identifying a target set of expression profiles using computationally mixed data from various embodiments. [Figure 11] This plot shows the relationship between cell subpopulations matching the expression profile CD13+CD11c- and blood dilution levels, based on simulation samples generated by computational mixing using various embodiments. [Figure 12]This plot shows the relationship between the expression profile CD13+CD11c-, based on samples generated by experimental mixing of bone marrow and blood using various embodiments, and the level of blood dilution. [Figure 13] This is a series of plots demonstrating that the expression profile CD13+CD11c- has a strong correlation with blood dilution levels in experimentally mixed samples according to various embodiments. [Figure 14] This plot shows the relationship between the cell subpopulation matching the expression profile CD13+CD15+ and the blood dilution level, based on simulation samples generated by computational mixing using various embodiments. [Figure 15] This plot shows the relationship between the expression profile CD13+CD15+, based on samples generated by experimental mixing of bone marrow and blood using various embodiments, and the level of blood dilution. [Figure 16] This plot shows the relationship between the expression profile CD13+CD16+, based on simulation samples generated by computational mixing using various embodiments, and the cell subpopulation matching that profile and the blood dilution level. [Figure 17] This plot shows the relationship between the expression profile CD13+CD16+-matching cell subpopulation and blood dilution levels, based on samples generated by experimental mixing of bone marrow and blood using various embodiments. [Figure 18] This plot shows the relationship between a subpopulation of cells matching the expression profile CD13+HLA-DR- and blood dilution levels, based on simulation samples generated by computational mixing using various embodiments. [Figure 19] This plot shows the relationship between the expression profile CD13+HLA-DR-, based on samples generated by experimental mixing of bone marrow and blood using various embodiments, and the level of blood dilution. [Figure 20]This plot shows the relationship between cell subpopulations matching the expression profile CD13-HLA-DR- and blood dilution levels, based on simulation samples generated by computational mixing using various embodiments. [Figure 21] This plot shows the relationship between cell subpopulations matching the expression profile CD13-HLA-DR- and blood dilution levels, based on samples generated by experimental mixing of bone marrow and blood using various embodiments. [Figure 22] Plot 2200 shows the relationship between statistical distance scores and blood dilution levels for cell subpopulations expressing CD71 according to various embodiments. [Figure 23] Plot 2300 shows the relationship between statistical distance scores and blood dilution levels for various embodiments of CD33-expressing cell subpopulations. [Figure 24] Plot 2400 shows the relationship between statistical distance scores and blood dilution levels for cell subpopulations expressing CD33 and CD117 in various embodiments. [Figure 25] Plot 2500 shows the relationship between statistical distance scores and blood dilution levels for cell subpopulations expressing CD56 and CD13 according to various embodiments. [Figure 26] Plot 2600 shows the relationship between statistical distance scores and blood dilution levels for various embodiments of CD19-expressing cell subpopulations. [Figure 27] This is a series of plots, number 2700, illustrating exemplary gating strategies implemented in AutoGate to identify target cell populations in various embodiments. [Modes for carrying out the invention]

[0022] In the accompanying drawings, similar components and / or features may have the same reference label. Furthermore, different components of the same type may be distinguished by following the reference label with a dash and a second label to distinguish similar components. Where only the first reference label is used herein, the description is applicable to any one of the similar components having the same first reference label, regardless of the second reference label.

[0023] I. Overview This disclosure describes various exemplary embodiments of methods, kits, and systems for assessing the amount of hemodilution in bone marrow samples. However, this disclosure is not limited to these exemplary embodiments and uses, or the manner in which the exemplary embodiments and uses operate or are described herein. Furthermore, drawings may show simplified or partial figures, and the dimensions of elements in the drawings may be exaggerated or disproportionate.

[0024] Embodiments described herein provide methods and systems for assessing the quality of a bone marrow sample based, for example, on the quantity of cells expressing complementary markers, including CD13 and one or more cell surface markers. For example, embodiments described herein provide methods and systems for analyzing a bone marrow sample to determine the quantity of cells expressing CD13 and complementary markers, and for correlating the quantity of cells with a hemodilution level. This hemodilution level can be used to assess the quality of the bone marrow sample. For example, a hemodilution level above a selected threshold (e.g., about 50%, about 40%, about 30%, about 20%, etc.) may be considered a low-quality bone marrow sample. A hemodilution level above a selected threshold may be considered a high-quality (or sufficiently high-quality) bone marrow sample that can be used to carry out further analysis (e.g., assessment of the effectiveness of a therapeutic agent).

[0025] In some embodiments, a report is generated based on the blood dilution level. The report may, for example, identify the blood dilution level, include an assessment of the quality of the bone marrow sample based on the blood dilution level, or both. In some examples, the report may identify one or more recommended actions to be taken by a human operator based on the assessed quality of the bone marrow sample. For example, if the blood dilution level exceeds some selected threshold (e.g., about 50%, about 40%, about 30%, about 20%, etc.), the report may include a warning indicating that the level exceeds the selected threshold and may identify one or more recommended actions. One or more recommended actions may include, for example, discarding the bone marrow sample, entering user input to perform one or more software adjustments and / or measurement adjustments for further analysis to be performed using the bone marrow sample, one or more other types of actions, or a combination thereof.

[0026] II. Exemplary Definitions and Considerations It should be understood that the technical terms used herein are merely for the purpose of describing specific embodiments and are not intended to limit them.

[0027] Unless otherwise defined, all technical terms, notations, and other scientific, technical, or specialized terms used herein are intended to have the same meaning as those generally understood by those skilled in the art in the field relating to the subject matter described in the claims. In some cases, terms that have a generally understood meaning are defined herein for clarity and / or easy reference, and the inclusion of such definitions herein should not necessarily be interpreted as representing a substantial difference from what is generally understood in the art. Generally, nomenclature and techniques used in relation to chemistry, biochemistry, molecular biology, pharmacology, and toxicology are described herein, are well known and commonly used in the art.

[0028] Where used herein, the singular forms “a,” “an,” and “the” are intended to include the plural forms similarly unless the context explicitly indicates otherwise. Where used herein, the terms “and / or” should be understood to refer to and include any possible combination of one or more of the enumerated items relating to each other. Where used herein, the terms “includes,” “including,” “comprises,” and / or “comprising” specify the presence of the described features, integers, steps, actions, elements, components, and / or units, but should be understood not to exclude the presence or addition of one or more other features, integers, steps, actions, elements, components, units, and / or groups thereof.

[0029] Throughout this disclosure, various aspects are presented in range form. It should be understood that the use of range form is for convenience and brevity only and should not be interpreted as an inflexible limitation to this disclosure. Therefore, range descriptions should be considered to specifically disclose all possible sub-ranges and the individual numerical values ​​within those ranges. For example, where a range of values ​​is provided, it should be understood that each intermediate value between the upper and lower limits of that range, and any other described or intermediate values ​​within that described range, are included in this disclosure. These smaller upper and lower limits may independently be included in smaller ranges and are also included in this disclosure, according to any specifically excluded limits within the described range. If a described range includes one or both limits, the range excluding either or both of those limits is also included in this disclosure. This applies regardless of the breadth of the range.

[0030] As used herein, the term “about” refers to the normal margin of error for each value that is readily known. References to values ​​or parameters following “about” herein include (and describe) embodiments relating to that value or parameter itself. For example, a statement referring to “about X” includes a statement relating to “X”. In some embodiments, “about” may mean ±15%, ±10%, ±5%, or ±1%, as understood by those skilled in the art.

[0031] In addition, wherever the terms “communicate with” or “communicately associated with” or similar terms are used herein, one element may be able to communicate directly, indirectly, or both, with another element by one or more wired communication links, one or more wireless communication links, one or more optical communication links, or a combination thereof. In addition, wherever a list of elements (e.g., elements a, b, c) is referred to, such a reference is intended to include any one of the enumerated elements, any combination of fewer elements than all of the enumerated elements, and / or all combinations of the enumerated elements.

[0032] As used herein, “substantially” means sufficient to function for the intended purpose. Therefore, the term “substantially” allows for minor, slight variations from absolute or perfect conditions, dimensions, measurements, results, etc., which are expected by those skilled in the art but do not significantly affect the overall performance. When used in relation to numerical values, or parameters or characteristics that can be expressed numerically, “substantially” means within 10 percent.

[0033] As used herein, the term "ones" means two or more things.

[0034] As used herein, the term “multiple” or “group” may mean two, three, four, five, six, seven, eight, nine, ten, or more.

[0035] As used herein, the term “set” means one or more.

[0036] As used herein, the phrase “at least one of” means, when used with a list of items, that one or more different combinations of the enumerated items may be used, and only one of the items in the list may be required. An item can be a specific object, thing, step, action, process, or category. In other words, “at least one of” means that any combination or any number of items from the list may be used, but not all of the items in the list may be required. For example, though not limited to, “at least one of item A, item B, or item C” or “at least one of item A, item B, and item C” could mean item A; item A and item B; item B; item A, item B, and item C; item B and item C; or items A and C. In some cases, "at least one of item A, item B, or item C" or "at least one of item A, item B, and item C" may mean, but are not limited to, two item A, one item B, and ten item C; four item B and seven item C; or several other suitable combinations.

[0037] As used herein, the terms “bone marrow sample,” “bone marrow aspirate,” and “bone marrow sample aspirate” are interchangeable and refer to a certain amount of bone marrow fluid aspirated from (extracted from) the bone in question.

[0038] As used herein, the terms “whole blood” and “peripheral blood” may be used interchangeably and refer to blood in which red blood cells have not been separated from white blood cells.

[0039] The terms “individual,” “subject,” or “patient” are mammals. Mammals include, but are not limited to, domesticated animals (e.g., cattle, sheep, cats, dogs, and horses), primates (e.g., humans and non-human primates, e.g., monkeys), rabbits, and rodents (e.g., mice and rats). In certain embodiments, the individual or subject is a human.

[0040] As used herein, “blood dilution” refers to an increase in the concentration of peripheral blood cells in a bone marrow sample.

[0041] As used herein, the term “CD13” refers to any native CD13 protein from any vertebrate source, including mammals such as primates (e.g., humans) and rodents (e.g., mice and rats), unless otherwise indicated. The term “CD13” encompasses “full-length” unprocessed CD13, as well as any form of CD13 resulting from cellular processing. The term “CD13” also encompasses naturally occurring variants of CD13, such as splice variants or allele variants. CD13 is located within the microvilli of the small intestine and kidneys, and also within other plasma membranes. In the small intestine, CD13 plays a role in the final digestion of peptides produced from protein hydrolysis by proteases in the stomach and pancreas. CD13 is also known as alanine aminopeptidase (AAP)N, alanylaminopeptidase, aminopeptidase M, microsomal aminopeptidase, bone marrow plasma membrane glycoprotein CD13, gp150, or membrane alanylaminopeptidase. CD13 is encoded by the ANPEP gene (also known as APN, CD13, or PEPN gene). (For further information on human CD13 and common isoforms, see https: / / www.uniprot.org / uniprot / P15144.)

[0042] As used herein, the term “CD11c” refers to any native CD11c protein from any vertebrate source, including mammals such as primates (e.g., humans) and rodents (e.g., mice and rats), unless otherwise indicated. The term “CD11c” encompasses “full-length” unprocessed CD11c, as well as any form of CD11c resulting from processing in cells. The term “CD11c” also encompasses naturally occurring variants of CD11c, such as splice variants or allele variants. CD11c is a receptor for fibrinogen. CD11c recognizes the sequence GPR in fibrinogen. CD11c mediates intercellular interactions during inflammatory responses and is used in monocyte adhesion and chemotaxis. CD11c is also known as integrin, alpha-X (complement component 3 receptor 4 subunit), or ITGAX. CD11c is encoded by the CD11c gene (integrin, alpha-X, also known as the complement component 3 receptor 4 subunit gene (ITGAX)). (For further information on human CD11c and common isoforms, see https: / / www.uniprot.org / uniprot / P20702.)

[0043] As used herein, the term “CD15” refers, unless otherwise indicated, to a tetrasaccharide carbohydrate that normally adheres to O-glycans on the surface of cells from any vertebrate source, including mammals such as primates (e.g., humans) and rodents (e.g., mice and rats). CD15 is a tetrasaccharide composed of sialic acid, fucose, and N-acetyllactosamine. Its systematic name is 5-acetylneuraminyl-(2-3)-galactosyl-(1-4)-(fucopyranosyl-(1-3))-N-acetylglucosamine (Neu5Acα2-3Galβ1-4[Fucα1-3]GlcNAcβ). CD15 is sialyl-Lewis XCD15 is also known as SLE, stage-specific embryonic antigen 1 ("SSEA-1"), or cluster of differentiation antigens 15s ("CD15s"). CD15 is also a blood group antigen and is expressed at the terminals of glycolipids present on the cell surface. The CD15 determinant, an E-selectin ligand carbohydrate structure, is constitutively expressed on granulocytes and monocytes and mediates the inflammatory migration of these cells. The term "CD15" encompasses "full-length" unprocessed CD15, as well as any form of CD15 resulting from processing in cells. The term "CD15" also encompasses naturally occurring variants of CD15, such as splice variants or allele variants.

[0044] As used herein, the term “CD16” refers to any native CD16 protein from any vertebrate source, including mammals such as primates (e.g., humans) and rodents (e.g., mice and rats), unless otherwise indicated. The term “CD16” encompasses “full-length” unprocessed CD16, as well as any form of CD16 resulting from processing in cells. The term “CD16” also encompasses naturally occurring variants of CD16, such as splice variants or allele variants. CD16, also known as FcγRIII, is a group of differentiation antigen molecules found on the surface of natural killer cells, neutrophils, monocytes, and macrophages. CD16 is a type III Fcγ receptor. In humans, CD16 exists in two distinct forms: FcγRIIIa (CD16a) and FcγRIIIb (CD16b), which share 96% sequence similarity in their extracellular immunoglobulin-binding domains. CD16a is encoded by the FCGR3A gene (also known as CD16A, FCG3, FCGR3, and IGFR3), and CD16b is encoded by the FCGR3B gene (also known as CD16B, FCG3, FCGR3, and IGFR3). (For further information on human CD16a and CD16b and common isoforms, see https: / / www.uniprot.org / uniprot / P08637 and https: / / www.uniprot.org / uniprot / O75015.)

[0045] As used herein, the term “HLA-DR” (or “HLADR”) refers, unless otherwise indicated, to any native HLA-DR protein from any vertebrate source, including mammals such as primates (e.g., humans) and rodents (e.g., mice and rats). The term “HLA-DR” encompasses “full-length” unprocessed HLA-DR, as well as any form of HLA-DR resulting from processing in cells. The term “HLA-DR” also encompasses naturally occurring variants of HLA-DR, such as splice variants or allele variants. HLA-DR is also known as human leukocyte antigen-DR isotype. HLA-DR helps present potentially exogenous peptide antigens to the immune system to induce or suppress a (helper) T cell response that ultimately leads to the production of antibodies against the same peptide antigen. HLA-DR is an αβ heterodimer, a cell surface receptor, whose each subunit contains two extracellular domains, a transmembrane domain, and a cytoplasmic tail. Both the α and β chains are fixed to the membrane. The N-terminal domain of the mature protein forms an alpha-helix that constitutes the exposed portion of the binding groove, and the C-terminal cytoplasmic region interacts with other chains to form a beta sheet beneath the binding groove that extends to the cell membrane. HLA-DR has five subunits: HLA-DRα, HLA-DRβ1, HLA-DRβ3, HLA-DRβ4, and HLA-DRβ5, which are encoded by HLA-DRA, HLA-DRB1, HLA-DRB3, HLA-DRB4, and HLA-DRB5, respectively. (For further information on human HLA-DRα, HLA-DRβ1, HLA-DRβ3, HLA-DRβ4, HLA-DRβ5 and common isoforms, see https: / / www.uniprot.org / uniprot / P01903, https: / / www.uniprot.org / uniprot / P01911, https: / / www.uniprot.org / uniprot / P79483, https: / / www.uniprot.org / uniprot / 13762, and https: / / www.uniprot.org / uniprot / Q30154.)

[0046] As used herein, “expression level” of a cell surface marker such as a protein refers to the measurable detection of that cell surface marker in individual cells, tissue samples, liquid samples, or some other types of samples. When used in relation to flow cytometry, for example, the expression level of a protein corresponds to the presence or absence of that protein on or embedded in a cell, determined by whether fluorescence corresponding to that protein is detected for the cell and / or whether that fluorescence has a low (or weak), medium, or high intensity level. When used in relation to immunohistochemistry (IHC) or mRNA sequencing, the expression level of a protein may refer to the presence or absence of that protein on or embedded in a cell relative to the overall expression level of that protein in the sample being analyzed or described above.

[0047] As used herein, the positive sign (+) associated with a cell surface marker (e.g., CD13+, CD15+, CD16+, HLA-DR+) refers to the positive expression of that cell surface marker on a cell (e.g., a cell contained in a blood or bone marrow sample). As used herein, positive expression may refer to a detectable expression level, such as an expression level above a detection threshold based on the detection method used, such as gene expression analysis by flow cytometry, IHC, or RNA sequencing. For example, a CD13+ cell is a cell that expresses CD13 or has CD13 on its surface.

[0048] As used herein, a negative sign (-) associated with a cell surface marker (e.g., CD13-, CD11c-, HLA-DR-) refers to the negative expression of that cell surface marker on a cell (e.g., a cell contained in a blood or bone marrow sample). As used herein, negative expression may refer to an undetectable expression level, such as an expression level below the detection threshold based on the detection method used, such as gene expression analysis by flow cytometry, IHC, or RNA sequencing. For example, a CD13- cell is a cell that does not express CD13 or does not have CD13 on its surface.

[0049] As used herein, “expression profile” is defined as the desired expression levels for each of two or more cell surface markers (e.g., proteins, carbohydrates) to enable the characterization of a cell in a manner that distinguishes it as a specific type of cell (e.g., bone marrow cells, peripheral blood cells) when the desired expression levels for each of the two or more cell surface markers are detected together on the cell. In these examples, the two or more cell surface markers include CD13 and at least one other complementary marker.

[0050] As used herein, “complementary markers” means a set of (one or more) cell surface markers that, when a target expression level for one or more of those cell surface markers on cells in a bone marrow sample is observed in combination with a target expression level for CD13 on those same cells in the bone marrow sample, enable the characterization of cells in a manner that allows for the mathematical correlation of the number of cells expressing CD13 and the complementary marker with the blood dilution level. For example, identifying specific cells in a bone marrow sample that have a target first expression level for CD13 (e.g., + or -) in combination with a target second expression level for the complementary marker (e.g., + or -) allows for the characterization of those specific cells as bone marrow cells or peripheral blood cells. In other words, the number of cells in a bone marrow sample having a target first expression level for CD13 and a target second expression level for the complementary marker can be linearly or otherwise correlated with the blood dilution level using a mathematical function (e.g., logarithmic, exponential, etc.). For example, an increase in the number of these cells may indicate an increase in the blood dilution level, or conversely, a decrease in the number of these cells may indicate an increase in the blood dilution level.

[0051] As used herein, "DNA (deoxyribonucleic acid)" is a chain of nucleotides consisting of four types of nucleotides: A (adenine), T (thymine), C (cytosine), and G (guanine), while "RNA (ribonucleic acid)" consists of four types of nucleotides: A, U (uracil), G, and C. Certain pairs of nucleotides bind specifically to each other in a complementary manner (called complementary base pairing). That is, adenine (A) pairs with thymine (T) (however, in the case of RNA, adenine (A) pairs with uracil (U)), and cytosine (C) pairs with guanine (G). When a first nucleic acid chain binds to a second nucleic acid chain consisting of nucleotides complementary to the nucleotides in the first chain, the two chains join to form a double helix.

[0052] As used herein, “nucleic acid sequencing data,” “nucleic acid sequencing information,” “nucleic acid sequence,” “genome sequence,” “gene sequence,” or “fragment sequence,” or “nucleic acid sequencing read” means any information or data indicating the order of nucleotide bases (e.g., adenine, guanine, cytosine, and thymine / uracil) in a DNA or RNA molecule (e.g., whole genome, whole transcriptome, exome, oligonucleotide, polynucleotide, fragment, etc.). It should be understood that this instruction intends to refer to sequence information obtained using all available techniques, platforms, or technologies, including but not limited to capillary electrophoresis, microarrays, ligation-based systems, polymerase-based systems, hybridization-based systems, direct or indirect nucleotide identification systems, pyrosequencing, ion or pH-based detection systems, and electronic signature-based systems.

[0053] "Polynucleotide," "nucleic acid," or "oligonucleotide" refers to a linear polymer of nucleosides (including deoxyribonucleosides, ribonucleosides, or their analogues) joined by nucleoside linkages. Typically, a polynucleotide contains at least three nucleosides. Oligonucleotides usually range in size from a few monomer units, e.g., 3-4 units, to several hundred monomer units. Whenever a polynucleotide, such as an oligonucleotide, is represented by a sequence of letters such as "ATGCCTG," unless otherwise noted, the nucleotides are in the 5'→3' order from left to right, with "A" meaning deoxyadenosine, "C" meaning deoxycytidine, "G" meaning deoxyguanosine, and "T" meaning thymidine. The letters A, C, G, and T may be used to refer to the base itself, a nucleoside, or a nucleotide containing a base, as is standard in the art.

[0054] As used herein, the term “cell” is interchangeable with the term “living cell.” Non-limiting examples of living cells include eukaryotic cells, plant cells, animal cells, e.g., mammalian cells, reptile cells, avian cells, fish cells, etc., prokaryotic cells, bacterial cells, fungal cells, protozoan cells, etc., cells dissociated from tissues, e.g., muscle, cartilage, fat, skin, liver, lung, nerve tissue, etc., immune cells, e.g., T cells, B cells, natural killer cells, macrophages, etc., embryos (e.g., zygotes), oocytes, eggs, spermatids, hybridomas, cultured cells, cells from cell lines, cancer cells, infected cells, transfected and / or transformed cells, reporter cells, etc. Mammalian cells may be from, for example, humans, mice, rats, horses, goats, sheep, cattle, primates, etc.

[0055] As used herein, genome refers to the genetic material of a cell or organism, including mammals, such as humans. In humans, the genome includes all DNA, such as genes, non-coding DNA, and mitochondrial DNA. The human genome typically contains 23 pairs of linear chromosomes: 22 pairs of autosomes, in addition to the sex-determining X and Y chromosomes. The 23 pairs of chromosomes each contain one copy from each parent. The DNA that makes up the chromosomes is called chromosomal DNA and is located in the nucleus of human cells (nuclear DNA). Mitochondrial DNA is located in mitochondria as a circular chromosome, is inherited only from the mother, and is often referred to as the mitochondrial genome in contrast to the nuclear genome, which is the DNA located in the nucleus.

[0056] The term "gene expression analysis" refers to any step or technique that can study the development or activity of gene product formation from its coding gene. This can be a useful indicator of biological activity, where changes in gene expression patterns reflect changes in biological processes. Gene expression analysis can include the measurement of gene expression at the mRNA level or the protein level. Gene expression analysis can include, but is not limited to, array-based methods (e.g., DNA microarrays), methods using real-time / digital / quantitative PCR instruments, and whole or targeted nucleic acid sequencing systems (e.g., NGS systems, capillary electrophoresis systems). Non-exclusive examples of gene expression analysis include Northern blotting, PCR, reverse transcription-quantitative PCR ("RT-qPCR"), fluorescence in situ hybridization ("FISH"), Taq Man analysis, FRET detection, hybridization with oligonucleotide arrays, hybridization with cDNA arrays, hybridization with polynucleotide arrays, hybridization with liquid microarrays, hybridization with microelectrical arrays, molecular beacons, clonal hybridization, cDNA fragment fingerprinting, sequential analysis of gene expression ("SAGE"), subtractive hybridization, differential display and / or differential screening, RNA sequencing ("RNA-seq"), and any combination thereof.

[0057] The term "sequencing" refers to any technique known in the art that enables the identification of a sequence of nucleotides of at least a portion of a nucleic acid. Non-exclusive exemplary sequencing techniques include RNA-seq (also known as whole transcriptome sequencing), Illumina® sequencing, direct sequencing, random shotgun sequencing, Sanger dideoxytermination sequencing, whole genome sequencing, massively parallel signature sequencing (MPSS), hybridization sequencing, pyrosequencing, capillary electrophoresis, gel electrophoresis, duplex sequencing, cycle sequencing, and single-nucleotide extension sequencing. This includes single-phase sequencing, high-throughput sequencing, ultra-parallel signature sequencing, emulsion PCR, reversible dye-terminator sequencing, paired-end sequencing, near-term sequencing, exonuclease sequencing, ligation sequencing, short-read sequencing, single-molecule sequencing, synthesis sequencing, real-time sequencing, reverse-terminator sequencing, nanopore sequencing, 454 sequencing, Solexa Genome Analyzer sequencing, SOLiD® sequencing, MS-PET sequencing, mass spectrometry, and any combination thereof.

[0058] The term "RNA-seq (RNA sequencing)" refers to any step or technique that uses sequencing, such as next-generation sequencing (NGS), to determine the presence, quantity, or sequence of RNA in a biological sample. RNA-seq can analyze the transcriptome of gene expression patterns encoded within RNA.

[0059] The term "next-generation sequencing" (NGS) refers to sequencing techniques that offer increased throughput compared to traditional Sanger and capillary electrophoresis-based methods, such as the ability to generate hundreds of thousands of relatively small sequence reads at once. Examples of next-generation sequencing techniques include, but are not limited to, synthesis sequencing, ligation sequencing, and hybridization sequencing. More specifically, Illumina's MISEQ, HISEQ, and NEXTSEQ systems, as well as Life Technologies Corp.'s Personal Genome Machine (PGM) and SOLiD sequencing systems, provide massively parallel sequencing of whole or targeted genomes. The SOLiD system and related workflows, protocols, chemistry, etc., are described in detail in PCT Publication WO2006 / 084132, international filing date February 1, 2006, entitled "Reagents, Methods, and Libraries for Bead-Based Sequencing," U.S. Patent Application No. 12 / 873,190, filed August 31, 2010, entitled "Low-Volume Sequencing System and Method of Use," and U.S. Patent Application No. 12 / 873,132, filed August 31, 2010, entitled "Fast-Indexing Filter Wheel and Method of Use," each of these applications in whole being incorporated herein by reference.

[0060] III. Conventional methods for determining hemodilution in bone marrow aspirate Assessing the quality of bone marrow samples obtained by aspiration is crucial for evaluating their reliability. For example, hemodiluted bone marrow samples, i.e., bone marrow samples contaminated with some amount of whole blood, can make the results of analyses using these bone marrow samples unreliable. Several conventional methods for assessing the amount of hemodilution in bone marrow samples are qualitative, overly complex for routine implementation, and cannot distinguish between lower hemodilution levels (e.g., approximately 25% hemodilution, approximately 50% hemodilution, etc.) or combinations thereof.

[0061] A conventional method involves a morphological assessment of hemodilution in a bone marrow sample before performing flow cytometry. Morphological assessment may include evaluating the morphology of cells observed in a smear of the bone marrow sample. While morphological assessment is a qualitative assessment that can be used to identify bone marrow samples with high levels of hemodilution, it does not provide the information necessary to quantify lower levels of hemodilution. For example, morphological assessment generally cannot distinguish between hemodilution of approximately 25%, approximately 50%, and approximately 75%.

[0062] Another method for assessing hemodilution involves measuring the number of white blood cells (WBCs) in a bone marrow sample using a blood analyzer. While this type of analysis provides a relatively accurate indicator of hemodilution, white blood cell counts are generally not a reliable marker for routine use. For example, white blood cell counts can vary considerably from subject to subject. Furthermore, white blood cell counts can be particularly unreliable in subjects with hematological disorders.

[0063] Another method for assessing hemodilution involves analyzing the percentages of plasma cells and cells expressing the cell surface marker CD34 (i.e., CD34+ cells) and granulocytes expressing the cell surface marker (maker) CD10 (i.e., CD10+ G) in a bone marrow sample. Plasma cells and CD34+ cells are two cell populations that are almost absent in whole blood, while CD10+ G constitute the majority of the granulocyte population in whole blood. A sample that shows a decrease in the percentages of plasma cells and CD34+ cells while simultaneously showing an increase in the percentage of CD10+ G is considered "contaminated". (See Jose Antonio Delgado, Francisco Guillen-Grima, Cristina Moreno, Carlos Panizo, Carmen Perez-Robles, Juan Jose Mata, Laura Moreno, Paula Arana, Silvia Chocarro, and Juana Merino. A simple-flow cytometry method to evaluate peripheral blood contamination of bone marrow aspirates, Journal of Immunological Methods 442, 54-58 (2017) (the entire work is incorporated herein by reference).) A peripheral blood contamination index (PCBI) is calculated based on the percentages of plasma cells, CD34+ cells, and CD10+ G cells found in the bone marrow sample and compared to a predetermined threshold. Bone marrow samples with a PCBI above the threshold are considered "contaminated," while bone marrow samples with a PCBI below the threshold are considered to be of good quality. While this method can identify samples with lower levels of hemodilution than can be assessed morphologically (e.g., approximately 75% hemodilution), it cannot provide the ability to distinguish between even lower levels of hemodilution (e.g., between approximately 25% hemodilution, approximately 50% hemodilution, and approximately 75% hemodilution, etc.).

[0064] Therefore, conventional methods for assessing hemodilution in bone marrow samples do not provide the desired level of accuracy for determining or measuring hemodilution in bone marrow samples, especially at lower levels of hemodilution.

[0065] IV. Exemplary Methods for Determining Hemodilution in Bone Marrow Aspirate Embodiments of various methods, kits, and systems described herein enable repeatable, simple, and reliable quantitative assessment of hemodilution in bone marrow samples. In particular, embodiments described herein enable the distinction of different hemodilution levels between 0% and about 100%, including but not limited to about 0%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, and 100%. Disclosure herein provides several proteins (e.g., biomarkers) used to determine or measure hemodilution. More specifically, embodiments described herein provide methods for analyzing bone marrow samples to determine the number of cells in the bone marrow sample expressing CD13 and complementary markers, and for correlating the number of cells with the hemodilution level of the bone marrow sample. Methods for analyzing bone marrow samples may include, for example, flow cytometry, cell counting, mRNA sequencing, or a combination thereof. The exemplary methods described below establish standards for hemodilution of bone marrow samples, or for determining or measuring purity / quality, for use in investigations, diagnostics, or clinical studies. The methods described herein are robust and reproducible.

[0066] Figure 1 is a block diagram of an analytical system 100 according to an exemplary embodiment. The analytical system 100 includes a computer system 102, an expression measurement system 104, a data storage 105, a display system 106, and a kit 108. The expression measurement system 104 can communicate with the computer system 102. In some examples, the expression measurement system 104 and the computer system 102 may be integrated together. The data storage 105 can communicate with the expression measurement system 104. The data storage 105 and the display system 106 can communicate with the computer system 102. In some examples, the data storage 105, the display system 106, or both may be considered part of the computer system 102, or otherwise integrated. Thus, in some examples, the computer system 102, the expression measurement system 104, the data storage 105, and the display system 106 may be separate components that communicate with each other, while in other examples, some combination of these components may be integrated together.

[0067] The analysis system 100 can be used to analyze bone marrow samples to determine or measure hemodilution based on the expression or non-expression of one or more combinations of cell surface markers. These cell surface markers may include molecules (e.g., proteins, receptors, carbohydrates, etc.). As an example, kit 108 includes one or more conjugates 110 for labeling various cell surface markers. These cell surface markers may include, but are not limited to, CD5, CD10, CD11c, CD13, CD15, CD16, CD19, CD33, CD34, CD38, CD45, CD56, CD57, CD71, CD117, HLA-DR, several other cell surface markers, several other types of proteins, or any combination thereof.

[0068] One or more conjugates 110 may include one or more fluorophore-conjugate antibodies, one or more fluorophore-conjugate peptides, or a combination thereof. More specifically, one of the conjugates 110 may be an antibody labeled with a label selected from the group consisting of fluorescent labels, enzyme labels, radioisotopes, quantum dots, colorimetric molecules, magnetic particles, or any other suitable detectable molecule or compound. One of the conjugates 110 may be a barcode used in RNA sequencing according to various embodiments. The barcode may be part of the analyte. The barcode may be independent of the analyte to be bound and detected. In addition to the intrinsic characteristics of the analyte (e.g., size or terminal sequence of the analyte), the barcode may be a tag attached to the analyte (e.g., a nucleic acid molecule) or a combination of tags. The barcode may be unique. The barcode may have a variety of different forms. For example, the barcode may include barcode sequences such as polynucleotide barcodes; random nucleic acids and / or amino acid sequences; and synthetic nucleic acids and / or amino acid sequences. Barcodes can be attached to analytes in a reversible or irreversible manner. For example, barcodes can be attached to fragments of deoxyribonucleic acid (DNA) or ribonucleic acid (RNA) samples before, during, and / or after the sequencing of the sample. Barcodes can enable the identification and / or quantification of individual sequencing reads.

[0069] The expression measurement system 104, in conjunction with kit 108, analyzes the bone marrow sample 112. The expression measurement system 104 may include a flow cytometry system, an IHC system (which may include a fluorometer), a gene sequencing system, an imaging system, or a combination thereof.

[0070] In one or more examples, the bone marrow sample 112 is a sample of bone marrow (e.g., bone marrow fluid, bone marrow tissue, etc.) taken from the bone of interest. Kit 108 is used to prepare (e.g., stain) the bone marrow sample 112 for use with the expression measurement system 104. The expression measurement system 104 measures the expression levels of cells in the bone marrow sample 112 based on different cell surface markers labeled using Kit 108. In particular, for each cell surface marker of interest, the expression measurement system 104 generates data 114 that can provide a count of substantially each cell in the bone marrow sample 112 having that cell surface marker.

[0071] Data 114 from the expression measurement system 104 can be processed by the computer system 102 to identify a specific cell subpopulation within the bone marrow sample 112. For example, the computer system 102 may include software, firmware, hardware, or a combination thereof for determining or measuring hemodilution in a bone marrow sample such as the bone marrow sample 112. As an example, the computer system 102 may include one or more processors that are integrated as part of the expression measurement system 104, communicate with the expression measurement system 104, or otherwise related to it.

[0072] Cells in a bone marrow sample that can be characterized by a specific expression profile of interest form a “cell subpopulation.” The expression profile of interest is one that has been shown to have a calculable correlation with the level of hemodilution in the bone marrow sample. For example, Equation 118 can define this correlation. The nature of this correlation depends on the specific cell surface markers included in the expression profile. For example, in some cases, an increase in the number (or percentage) of cells with a particular expression profile correlates substantially linearly with an increase in the level (or percentage) of hemodilution. In other cases, a decrease in the number (or percentage) of cells with a particular expression profile correlates substantially linearly with an increase in the level (or percentage) of hemodilution. Thus, Equation 118 can be used to linearly correlate the number of cells in a bone marrow sample 112 having a particular expression profile with a specific level or amount of hemodilution (e.g., approximately 25% hemodilution, approximately 50% hemodilution, etc.). In other cases, the correlation is with a range of hemodilution levels (e.g., between approximately 20-30%, between approximately 40-60%, between approximately 60-90%, etc.).

[0073] The computer system 102 uses data 114 from the expression measurement system 104 to measure one or more cell subpopulations having the desired expression profile from the expression profiles 116 stored in the data storage 105 (e.g., measuring the number of cells in each cell subpopulation). The computer system 102 then correlates this number of cells with the blood dilution level based on formula 118. Various simulation / experimental results show that the expression profile CD13+CD11c- strongly correlates linearly with the blood dilution level and can be used to reliably determine or measure the blood dilution level. Furthermore, the expression profiles CD13+CD15+, CD13-HLA-DR-, CD13+HLA-DR-, and CD13+CD16+ also show strong linear correlations with the blood dilution level and can similarly be used to reliably measure the blood dilution level. These expression profiles are identified in Table 1 below (further discussion of these expression profiles is provided in Section V below): JPEG0007874610000001.jpg59170

[0074] While the embodiments described herein relate to substantially linear correlations between the cell subpopulations and blood dilution levels identified above, it should be understood that other cell subpopulations may reveal different kinds of mathematical functions or other mathematically quantifiable relationships / correlations with blood dilution levels.

[0075] The computer system 102 can generate a report 119 showing the level or amount of blood dilution determined or measured for the bone marrow sample 112 for use by the operator 120 to assess the quality of the bone marrow sample 112. In other examples, the computer system 102 provides an assessment of the quality of the bone marrow sample 112 in the report 119. The computer system 102 can display the report 119 or at least some portion of the report 119 on the display system 106. In some cases, the computer system 102 displays a cell subpopulation plot generated by the expression measurement system 104 on the display system 106.

[0076] Operator 120 can take any of several different forms. For example, Operator 120 may be an engine (e.g., hardware, firmware, software, or a combination thereof) within the computer system 102 or another computer system. In other examples, Operator 120 may be a human operator, such as an analyst, medical professional, technician, or another type of human operator.

[0077] Based on the quality assessment of the bone marrow sample 112, operator 120 may determine whether certain actions are required. Such actions may include, but are not limited to, disqualifying the bone marrow sample 112 for use if the blood dilution level exceeds several selected thresholds (e.g., approximately 50%, approximately 40%, approximately 30%, approximately 20%, etc.). In some cases, operator 120 may determine that adjustments may be required to explain the reasons for the assessed blood dilution level (e.g., with respect to blast cell count) if the bone marrow sample 112 is to be used for diagnostic, procedural, or some other laboratory or medical purpose.

[0078] In some cases, hypodilution of a bone marrow sample is determined when the blood dilution percentage is less than approximately 100%. In some cases, hypodilution of a bone marrow sample is determined when the blood dilution percentage is less than approximately 75%. In some cases, hypodilution of a bone marrow sample is determined when the blood dilution percentage is less than approximately 50%. In some cases, hypodilution of a bone marrow sample is determined when the blood dilution percentage is less than approximately 25%.

[0079] In various embodiments, report 119 includes the identification of a blood dilution level, an assessment of the quality of the bone marrow sample 112, one or more recommended actions to be taken based on the assessment, or a combination thereof. Recommended actions may include, for example, performing software adjustments, analytical adjustments, measurement adjustments (e.g., with respect to blast cell counts), several other types of adjustments, or a combination thereof, to explain the reasons for the assessed blood dilution level. Recommended actions may include, for example, discarding the bone marrow sample. Recommended actions may include, for example, making a note in a record or file related to the bone marrow sample or its analysis.

[0080] In some embodiments, the computer system 102 is configured to display the report 119 on the display system 106 so that the assessed blood dilution level and one or more corresponding recommended actions are displayed simultaneously. This type of visual representation of the report allows the operator 120 to quickly and efficiently understand or identify the quality of the bone marrow sample along with one or more corresponding recommended actions.

[0081] Figure 2 is a flowchart illustrating methods for determining the blood dilution level of a bone marrow sample according to various embodiments. Method 200 can be implemented, for example, using the analytical system 100 described with respect to Figure 1, or a similar analytical system.

[0082] Step 202 includes obtaining a bone marrow sample from the subject. One technique for obtaining a bone marrow sample involves bone marrow aspiration. Bone marrow aspiration involves extracting bone marrow fluid from within the bone of the subject. In some cases, the bone marrow may be a previously collected sample that is stored or transported for use from a laboratory, hospital, testing facility, or other location. Another technique for obtaining a bone marrow sample includes performing a bone marrow biopsy to obtain a bone marrow tissue sample.

[0083] Step 204 includes analyzing a bone marrow sample to determine the number of cells in the bone marrow sample expressing CD13 and a complementary marker. The complementary marker may be, for example, CD11c, CD15, CD16, or HLA-DR. As an example, step 204 includes determining the number of cells in a bone marrow sample having a desired expression profile, such as CD13+, CD11c-, CD13+CD15+, CD13+CD16+, CD13-HL-ADR-, CD13+HLA-DR-, or one of the other expression profiles. Step 204 may be carried out using any number of techniques or any combination of techniques, including but not limited to flow cytometry, cell counting, IHC with fluoroscopy, and single-cell sequencing methods.

[0084] Step 206 involves correlating the number of cells with the hemodilution level of the bone marrow sample. As an example, the analysis system 100 in Figure 1 correlates the number of cells with the hemodilution level based on a linear correlation previously identified between the expression profile of interest and the hemodilution level.

[0085] In some embodiments, Method 200 includes step 208, which includes generating a report based on the blood dilution level. The report may include, for example, the identification of the blood dilution level, an assessment of the quality of the bone marrow sample 112, one or more recommended actions to be taken based on the assessment, or a combination thereof. Recommended actions may include, for example, making software adjustments, analytical adjustments, measurement adjustments (e.g., with respect to blast counts), several other types of adjustments, or a combination thereof to explain the reason for the assessed blood dilution level; discarding the bone marrow sample; making notes in a record or file related to the bone marrow sample or its analysis; or a combination thereof. In various embodiments, Method 200 (e.g., step 208) includes displaying the report on a display system to enable a human operator to easily and quickly understand the quality of the bone marrow sample.

[0086] The blood dilution level determined by Method 200 may be used to assess a disease or disorder. For example, the blood dilution level may be used to assess whether it meets a predetermined blood dilution standard. If the blood dilution level meets the predetermined blood dilution standard, the blood dilution level may be used to confirm the progression of the disease or disorder in the subject using a bone marrow sample, or to verify the improvement of the disease or disorder in the subject, thereby assessing the efficacy of a treatment regimen. If the blood dilution level does not meet the predetermined blood dilution standard, the bone marrow sample may be considered unreliable and unusable, and may be discarded or deemed unsuitable.

[0087] IV.A Analysis using flow cytometry IV.A.1. General Methodology Figure 3 is a flowchart illustrating a method for obtaining data for use in determining the blood dilution level of a bone marrow sample using flow cytometry according to various embodiments. Method 300 can be implemented using the analytical system 100 described with respect to Figure 1, or a similar analytical system.

[0088] Step 302 includes mixing the bone marrow sample with one or more conjugates for labeling CD13 and at least one complementary marker, which includes one or more cell surface markers selected from the group consisting of CD11c, CD15, CD16, and HLA-DR. In some cases, the complementary markers may include one or more proteins or other cell surface markers other than those listed herein. This mixing may be carried out by staining the bone marrow sample with one or more conjugates. In some examples, a single conjugate is used to label CD13 and at least one complementary marker. In other examples, multiple conjugates are required. The conjugates may include, for example, fluorophore-conjugated antibodies. The conjugates may include, for example, peptides conjugated with fluorophores (i.e., fluorophore-conjugated peptides). The conjugates may include antibodies labeled with, for example, fluorescent labels, enzyme labels, radioisotopes, quantum dots, or other types of labels. Examples of conjugates (or reagents) that may be used in Step 302 are shown in Table 3 of Section IV.E below.

[0089] Step 304 includes analyzing the bone marrow sample mixed with one or more binders using flow cytometry to generate data characterizing the cells of the bone marrow sample. For example, step 304 includes generating a measure of the number of cells in the bone marrow sample expressing each cell surface marker of interest (e.g., protein, carbohydrate, etc.). In this example, step 304 also includes generating a count of the total number of cells in the analyzed bone marrow sample.

[0090] Step 306 includes gating cells based on the data to determine the percentage of cells in the bone marrow sample having an expression profile selected from the group consisting of CD13+CD11c-, CD13+CD15+, CD13+CD16+, CD13+HLA-DR-, and CD13-HLA-DR-. As an example, step 306 includes automating the gating using the flowDensity software package so that the analysis of the flow cytometry data generated in step 304 is automated. (See Malek, M. et al. flowDensity: reproducing manual gating of flow cytometry data by automated density-based cell population identification, Bioinformatics 31, 606-607 (2015) (the whole is incorporated herein by reference).) In various embodiments, prior to step 306, the data is processed and “cleaned” to exclude outlier events based on signal stability over acquisition time. Furthermore, only data about “singlets” is processed. A singlet is a single particle / cell. Filtering of the singlet may be performed in step 306, in step 304, or between steps 304 and 306.

[0091] IV.A.2. Example Protocol for Obtaining Flow Cytometry Data Table 2 below shows an example of a flow cytometry protocol that can be used to analyze bone marrow samples and obtain flow cytometry data: JPEG0007874610000002.jpg80170

[0092] The protocol shown in Table 2 is for performing 8-color flow cytometry on all bone marrow samples (e.g., hemodilution specimens) from all donors using a FACSCanto II flow cytometer in conjunction with a 4-tube antibody panel designed to characterize hemodilution levels. From each hemodilution specimen, transfer four 100 μL aliquots into 5 mL test tubes. Add the corresponding antibody cocktail to each test tube. Then, incubate these test tubes in the dark at room temperature for 30 minutes. After the incubation period, continue sample preparation using Biolegend RBC Lysis solution, following the standard whole blood lysis procedure below: Dilute 10× RBC Lysis solution to 1× solution, add 3 ml of 1× solution to each test tube, then vortex these test tubes vigorously. Incubate the samples in the dark at room temperature for 15 minutes. After incubation with 1× RBC Lysis solution, centrifuge the samples at 1600 rpm for 5 minutes. Decant the supernatant and vortex the test tubes slightly to break up the pellet. Next, wash the sample with 3 ml of BD Biosciences Stain Buffer (FBS). Then, centrifuge the sample at 1600 rpm for 5 minutes. Repeat this cell washing and decantation procedure once more. After the final decantation of the supernatant, gently vortex the test tube and add 100 μL of 1% formaldehyde aqueous solution (16% formaldehyde aqueous solution of VWR diluted in water). Incubate the sample in the dark at 4°C for 15 minutes before acquisition. Acquire the sample on a FACSCanto II flow cytometer and acquire data using FACS Diva software (available from Becton Dickinson Biosciences, San Jose, CA). Perform manual gating using the FCS Express software program (e.g., De Novo Software, Los Angeles, CA).

[0093] IV.B Analysis using IHC IV.B.1. IHC using a Fluorescence Photometer Figure 4 is a flowchart of Method 400 for determining the number of cells expressing CD13 and complementary markers using immunohistochemistry with a fluorometer in various embodiments. The complementary markers may be selected from the group consisting of CD11c, CD15, CD16, and HLA-DR.

[0094] Step 402 involves staining the bone marrow sample with a fluorophore-conjugated antibody for complementary binding to CD13 and a complementary marker. In these examples, the bone marrow sample is a bone marrow tissue sample. Examples of antibodies (or reagents) and fluorescent dyes that can be conjugated for use in Step 402 are shown in Table 3 in Section IV.E below.

[0095] Step 404 involves adding excitation energy to the bone marrow sample.

[0096] Step 406 includes measuring the fluorescence emission level from the bone marrow sample to determine the number of cells expressing CD13 and complementary markers.

[0097] IV.B.2. IHC using cell counting Figure 5 is a flowchart illustrating a method 500 for determining the number of cells expressing CD13 and complementary markers using immunohistochemistry with cell counting according to various embodiments. The complementary markers may be selected from the group consisting of CD11c, CD15, CD16, and HLA-DR.

[0098] Step 502 involves staining the bone marrow sample with a fluorophore conjugate antibody for complementary binding to CD13 and a complementary marker. In these examples, the bone marrow sample is a bone marrow tissue sample. In step 502, staining the bone marrow sample involves using at least two different types or colors of fluorophore conjugate antibodies. Examples of antibodies (or reagents) and fluorescent dyes that can be conjugated for use in step 502 are shown in Table 3 in Section IV.E below.

[0099] Step 504 involves adding excitation energy to the bone marrow sample.

[0100] Step 506 involves imaging the bone marrow sample using a set of wavelength filters corresponding to the fluorophore conjugate antibody.

[0101] Step 508 includes counting the cells captured by imaging to determine the number of cells expressing CD13 and complementary markers. This counting may be performed manually or automated.

[0102] Analysis using IV.C gene expression In some embodiments, methods and systems for analyzing nucleic acid expression in a sample, such as a bone marrow sample, are provided herein.

[0103] IV.C.1. Single-cell sequencing Figure 6 is a flowchart illustrating a method 600 for determining the expression level of a marker in a single cell for hemodilution assessment using single-cell sequencing according to various embodiments. The marker may include CD13, as well as one or more complementary markers such as CD11c, CD15, CD16, and HLA-DR or any combination thereof.

[0104] In step 602, for example, cells from which RNA should be extracted can be isolated or selected from a bone marrow sample. The bone marrow sample may be readily available from storage or may be isolated before cell isolation. In various embodiments, the method may include providing a number of single cells by separating a population of cells (e.g., by flow cytometry, microfluidic fractionation or separation, etc.) and separating them into individual compartments, e.g., individual wells of a plate or individual droplets. In some embodiments, barcode-based multiplexing may be provided to allow tracing of sequenced cDNA from a subset of cells to specific cells. Any of the foregoing (or any of the nucleic acids, reagents, kits, and methods described herein) may be provided and / or used individually or in any combination. To mitigate batch effects, all laboratory procedures may be performed on the same day, in the same laboratory, and by the same person for both the control and experimental groups.

[0105] In step 604, mRNA can be extracted from lysed cells, particularly from targeted mRNA molecules associated with the expression of any target marker (e.g., CD13, and one or more complementary markers such as CD11c, CD15, CD16, and HLA-DR or any combination thereof). In various embodiments, total RNA can be extracted from cell or tissue samples, e.g., bone marrow samples. Novel classes of RNA (ncRNA), such as ribosomal RNA (rRNA) and microRNA (miRNA), can be removed from the total RNA. Targeted mRNA molecules associated with the expression of CD13 and one or more complementary markers can be extracted using probes complementary to one or more target markers. In some embodiments, a Unique Molecular Identifier (UMI) (e.g., a polynucleotide containing a UMI) may be provided, thereby serving as a robust defense against amplification bias. The UMI can specifically tag individual cDNA species when they are constructed from mRNA. Each UMI can enable the tracing of sequenced cDNA back to a single specific mRNA molecule that was present in the cell.

[0106] In step 606, the targeted mRNA molecule can be sequenced using any available sequencing method to obtain sequencing data. Sequencing methods may include, but are not limited to, next-generation sequencing ("NGS"), microarray sequencing, or RT-PCR. The targeted mRNA molecule can be used to produce cDNA by cDNA synthesis. Following cDNA synthesis, library preparation, PCR amplification, and sequencing such as NGS sequencing can be performed to obtain single-ended or paired-ended reads.

[0107] In step 608, sequencing data obtained from the targeted mRNA molecule can be used to determine the expression levels of CD13 and one or more complementary cell surface markers for each isolated cell. Sequence reads can be inspected for quality. Short and low-quality reads can be removed to improve quality. Sequence reads can be aligned to transcripts by reference-based mapping. The abundance of reads can be mapped to each target marker to obtain the expression levels of each target marker.

[0108] In step 610, cells are grouped into different populations based on the expression levels of CD13 and one or more complementary cell surface markers. For example, cells expressing both CD13 and CD11c can be separated from cells that express CD13 but not CD11c. The number of cells in each group can then be counted. The expression patterns of CD13 and one or more complementary markers in the cells, for example, the number of cells expressing the markers, can be used as an indicator of hemodilution.

[0109] A method is also provided for monitoring the progression of disease or disability in a subject by analyzing a bone marrow sample obtained from the subject. The method may include determining the number of cells expressing CD13 and complementary markers in the bone marrow sample and correlating the number of cells with the blood dilution level of the bone marrow sample. The method may further include assessing whether the blood dilution level passes a predetermined blood dilution standard and, if the blood dilution level passes the predetermined blood dilution standard, using the bone marrow sample to confirm the progression of disease or disability in the subject. A bone marrow sample having a blood dilution level that passes the predetermined blood dilution standard indicates that the bone marrow sample can be analyzed and used to accurately track the progression of disease or disability. In some embodiments, the predetermined blood dilution standard is a blood dilution percentage value for the bone marrow sample. For example, pre-set blood dilution criteria may include, but are not limited to, 0%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, or 50% blood dilutions of the bone marrow sample.

[0110] Methods for determining the effectiveness of a treatment regimen in treating a disease or disorder in a subject are also provided. The method may include analyzing a bone marrow sample obtained from a subject being treated with the treatment regimen. The method may include determining the number of cells expressing CD13 and complementary markers in the bone marrow sample and correlating the number of cells with the hemodilution level of the bone marrow sample. The method may further include assessing whether the hemodilution level passes a predetermined hemodilution standard. A bone marrow sample having a hemodilution level that passes a predetermined hemodilution standard indicates that the bone marrow sample may be analyzed and used to evaluate the effectiveness of a treatment regimen in treating a disease or disorder. The method may further include verifying improvement in the disease or disorder in a subject based on whether the expression levels of CD13 and complementary markers pass a predetermined hemodilution standard.

[0111] IV.C.2. Bulk Cell Sequencing The foregoing is also applicable to cell populations, cell lysates, tissue lysates, and / or extracted / purified RNA. For example, nucleic acids, kits, and methods for sequencing extracted / purified RNA (bulk RNA sequencing) or analysis of isolated populations of cells (e.g., from isolated populations of cells or tissues; analysis of cell or tissue lysates) may also be provided. In certain embodiments, any of the compositions, reagents, and methods described herein as applicable to single cells are also applicable to other starting material sources such as extracted RNA, purified RNA, cell lysates, or tissue lysates, and such applications are intended. In certain embodiments, any of the compositions, reagents, and methods described herein as applicable to extracted RNA, purified RNA, cell lysates, or tissue lysates are also applicable to single cells, and such applications are intended, such as extracting targeted mRNA molecules associated with the expression of CD13 and complementary cell surface markers (e.g., cell surface proteins) from bone marrow samples.

[0112] Figure 7 is a flowchart illustrating a method for determining the expression levels of multiple markers in cells for hemodilution assessment using bulk cell sequencing according to various embodiments. The markers may include CD13, as well as one or more complementary markers such as CD11c, CD15, CD16, and HLA-DR or any combination thereof.

[0113] In step 702, targeted mRNA molecules associated with the expression of CD13 and complementary markers can be extracted from a bone marrow sample containing multiple cells. The bone marrow sample may be from a single individual or from multiple individuals.

[0114] In step 704, the targeted mRNA molecule can be sequenced using any available sequencing method to obtain sequencing data. The sequencing method may include, but is not limited to, next-generation sequencing ("NGS"), microarray sequencing, or RT-PCR.

[0115] In step 706, the expression levels of CD13 and one or more complementary markers in the bone marrow sample can be determined using sequence data obtained from the targeted mRNA molecule. The expression levels of CD13 and one or more complementary markers can be used as an indicator for counting the number of cells expressing CD13 and one or more complementary markers, as well as an indicator of hemodilution. In various embodiments, a method can be provided for determining the hemodilution level of a bone marrow sample by correlating standard curves showing multiple expression levels of CD13 and one or more complementary markers and multiple hemodilution levels in the bone marrow sample.

[0116] Methods are also provided for monitoring the progression of disease or disability in subjects by analyzing bone marrow samples obtained from subjects. The methods may include extracting targeted mRNA molecules associated with CD13 and complementary markers from bone marrow samples. The methods may further include sequencing the targeted mRNA molecules extracted from bone marrow samples using any available sequencing method to obtain sequencing data. The sequencing methods may include, but are not limited to, next-generation sequencing ("NGS"), microarray sequencing, or RT-PCR. The methods may further include determining the expression levels of CD13 and complementary markers in the bone marrow samples using the sequence data obtained from the targeted mRNA molecules. The methods may further include assessing whether the expression levels of CD13 and complementary markers meet pre-defined complementary marker expression criteria. If the expression levels of CD13 and complementary markers meet the pre-defined complementary marker expression criteria, the methods may further include confirming the progression of disease or disability in the subject using the bone marrow samples. CD13 and complementary marker expression levels that meet pre-defined complementary marker expression criteria indicate that the bone marrow sample can be analyzed and used to accurately track the progression of disease or disability.

[0117] Methods may be provided for monitoring the effectiveness of a therapeutic regimen in treating a disease or disorder by analyzing bone marrow samples obtained from subjects being treated with the therapeutic regimen. The method may include extracting targeted mRNA molecules associated with CD13 and complementary markers from the bone marrow sample. The method may further include sequencing the targeted mRNA molecules extracted from the bone marrow sample using any available sequencing method to obtain sequencing data. The sequencing method may include, but is not limited to, next-generation sequencing ("NGS"), microarray sequencing, or RT-PCR. The method may further include determining the expression levels of CD13 and complementary markers in the bone marrow sample using the sequence data obtained from the targeted mRNA molecules. The method may further include assessing whether the expression levels of CD13 and complementary markers meet a pre-defined complementary marker expression criterion. The pre-defined complementary marker expression criterion may be determined by any appropriate statistical method as a cutoff value for determining hemodilution. The expression levels of CD13 and complementary markers that meet pre-defined complementary marker expression criteria indicate that bone marrow samples may be analyzed and used to evaluate the effectiveness of treatment regimens in the treatment of a disease or disorder. The method may further include verifying improvement in the disease or disorder in a subject based on whether the expression levels of CD13 and complementary markers meet pre-defined complementary marker expression criteria.

[0118] IV.D Analysis using statistical distance Figure 8 is a flowchart of Method 800 for determining blood dilution of bone marrow samples using statistical distances according to various embodiments.

[0119] Step 802 includes analyzing the bone marrow sample to identify the cell distribution of cells in the bone marrow sample that express one or more cell surface markers. The one or more cell surface markers may be, for example, a single cell surface protein or a combination of multiple cell surface proteins that, when expressed on a cell, provide several indicators of whether the cell is a bone marrow cell or a peripheral blood cell. For example, the cells may be a subpopulation of cells expressing CD71, a subpopulation of cells expressing CD33 and CD117, a subpopulation of cells expressing CD19, a subpopulation of cells expressing CD33, or a subpopulation of cells expressing CD56 and CD13.

[0120] Step 804 involves calculating a statistical distance score for the cell distribution. The statistical distance score may be calculated using a quantitative technique selected from the group consisting of Earthmover distance, frequency difference gating, stochastic binning, cytometry fingerprinting, and quadratic form. In some cases, the statistical distance score may be a sum calculated based on weighted individual scores generated using two or more of the quantitative techniques described above. As an example, the statistical distance score is calculated using Earthmover distance, which is based on the distance between the cell distribution of cells in a bone marrow sample expressing one or more cell surface markers and a control cell distribution. The control cell distribution may be, for example, the cell distribution of a control sample having approximately 0% hemodilution.

[0121] Step 807 involves correlating the statistical distance score with the blood dilution level of the bone marrow sample. A linear correlation exists between the statistical distance score for cell distribution and the blood dilution level in the bone marrow sample. For example, the statistical distance score may be used to determine the blood dilution percentage of a bone marrow sample. If the blood dilution percentage is below a selected threshold, the bone marrow sample is considered to have a hypodilution state, and this threshold is selected from one of approximately 100%, approximately 75%, approximately 50%, or approximately 25%. If the blood dilution percentage (or statistical distance score) does not pass a certain predetermined criterion, one or more analytical measurements taken from the bone marrow sample may be adjusted in proportion to the blood dilution percentage (or statistical distance score).

[0122] In some cases, one or more cell surface markers include CD71, and an increase in the statistical distance score for the distribution of cells expressing CD71 indicates increased hemodilution in the bone marrow sample. In other cases, one or more cell surface markers include CD33 and CD117, and an increase in the statistical distance score for the distribution of cells expressing CD33 and CD117 indicates increased hemodilution in the bone marrow sample. In yet another case, one or more cell surface markers include CD19, and an increase in the statistical distance score for the distribution of cells expressing CD19 indicates increased hemodilution in the bone marrow sample. In some cases, one or more cell surface markers include CD56 and CD13, and an increase in the statistical distance score for the distribution of cells expressing CD56 and CD13 indicates increased hemodilution in the bone marrow sample. In yet another case, one or more cell surface markers include CD33, and an increase in the statistical distance score for the distribution of cells expressing CD33 indicates increased hemodilution in the bone marrow sample.

[0123] IV.E Kit for performing the analysis of Sections IV.A-D Kits for carrying out methods according to various embodiments may be provided. Such kits may be prepared from readily available materials and reagents. For example, such a kit may include one or more of the following materials: enzymes, reaction tubes, buffers, surfactants, primers, and probes. In certain embodiments, these kits enable practitioners to measure cells expressing CD13 and one or more complementary markers in bone marrow samples, for example, using flow cytometry or IHC. Instructions for carrying out the assay may also be included in the kit. In various embodiments, the kit may include one, two, three, four, five, six or more antibodies for complementary binding to CD13 and one or more complementary markers.

[0124] In various other embodiments, these kits may include multiple activators for assessing the expression of multiple markers, including CD13 and one or more complementary markers. The kits may be housed in containers. The kits may further include instructions for using the kit to assess expression, convert expression data into expression values, and / or analyze expression values ​​to generate a score that predicts hemodilution of bone marrow samples. The activators in the kit for measuring marker expression may include multiple targeted mRNA capture reagents, PCR probes and / or primers for qRT-PCR, and / or multiple antibodies or fragments thereof or multiple sequencing agents for assessing marker expression. For example, the activators in the kit for measuring marker expression may include multiple polynucleotides or arrays of polynucleotides complementary to the mRNA of one or more markers.

[0125] Any composition or component described herein may be included in the kit. In non-limiting examples, the kit may include reagents for isolating mRNA, labeling mRNA, and / or evaluating mRNA populations using array or sequencing methods, nucleic acid amplification, and / or hybridization, as well as reagents for preparing samples from bone marrow samples. The kit may further include reagents for creating or synthesizing targeted mRNA capture agents. In certain embodiments, the kit may include amplification reagents. In other embodiments, the kit may include various supports, e.g., glass, nylon, polymer beads, magnetic beads, etc., and / or reagents for coupling any probe and / or target nucleic acid. The kit may also include one or more buffers, such as reaction buffers, labeling buffers, washing buffers, or hybridization buffers, compounds for preparing targeted mRNA capture agents, and components for isolating mRNA. Other kits may include components for creating nucleic acid arrays containing miNAs, and therefore may include, for example, solid supports.

[0126] Kits for implementing the methods described herein are specifically contemplated. In some embodiments, kits exist for preparing and using targeted mRNA capture agents. The components of the kit may be packaged in an aqueous medium or in lyophilized form. The kit container means generally include at least one vial, test tube, flask, bottle, syringe or other container means in which the components are placed and can be appropriately aliquoted. If there are two or more components in the kit (labeling reagents and labels may be packaged together), the kit also generally includes a second, third or other additional container in which the additional components may be placed separately. However, various combinations of components can be included in the vial. The kit may also include a container for containing nucleic acids and any other tightly closed reagent containers for commercial sale. Such containers may include injection-molded or blow-molded plastic containers in which the desired vials are held.

[0127] If the components of the kit are provided in one and / or more solutions, the solutions are aqueous solutions, and sterile aqueous solutions are particularly preferred. However, the components of the kit may be provided as dry powders. If the reagents and / or components are provided as dry powders, the powders may be reconstituted by the addition of a suitable solvent. It is assumed that the solvent may also be provided in a separate container. In some embodiments, the labeling dye is provided as a dry powder. It is intended that 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 120, 120, 130, 140, 150, 160, 170, 180, 190, 200, 300, 400, 500, 600, 700, 800, 900, 1000 μg or at least or more of these amounts of dry dye are provided in the kit.

[0128] The kit container may include at least one vial, test tube, flask, bottle, syringe, and / or other container means in which the nucleic acid formulation is placed and preferably appropriately dispensed. The kit may also include a second container means for containing a sterile, pharmaceutically acceptable buffer and / or another diluent.

[0129] Table 3 below shows examples of different antibody reagents that can be used for binding to various cell surface markers: JPEG0007874610000003.jpg161170

[0130] V. Exemplary Computer-Implemented Systems In various embodiments, at least a portion of a method for assessing hemodilution in a bone marrow sample and identifying selected expression profiles for use in hemodilution assessment may be implemented by software, hardware, firmware, or a combination thereof.

[0131] In other words, as depicted in Figure 1, the methods disclosed herein can be implemented on a computer system (e.g., a computing device / analytics server) such as computer system 102. In various embodiments, computer system 102 may be communicatively connected to data storage 105 and display system 106 by direct connection or via network connection (e.g., LAN, WAN, Internet, etc.). It should be understood that computer system 102 depicted in Figure 1 may include additional engines or components required by a particular application or system architecture.

[0132] Figure 9 is a block diagram of a computer system according to various embodiments. Computer system 900 may be an example of one implementation of computer system 102 described in Figure 1 above. In one or more examples, computer system 900 may include a bus 902 or other communication mechanism for communicating information and a processor 904 associated with the bus 902 for processing information. In various embodiments, computer system 900 may also include memory, which may be random access memory (RAM) 906 or other dynamic storage device, associated with the bus 902 to determine instructions to be executed by the processor 904. Memory may also be used to store temporary variables or other intermediate information during the execution of instructions executed by the processor 904. In various embodiments, computer system 900 may further include read-only memory (ROM) 908 or other static storage device associated with the bus 902 for storing static information and instructions for the processor 904. A storage device 910, such as a magnetic disk or optical disk, may be provided and associated with the bus 902 for storing information and instructions.

[0133] In various embodiments, the computer system 900 may be connected via a bus 902 to a display 912, such as a cathode ray tube (CRT) or liquid crystal display (LCD), to display information to the computer user. An input device 914, including alphanumeric and other keys, may be connected to the bus 902 to communicate information and command selections to the processor 904. Another type of user input device is a cursor control 916, such as a mouse, joystick, trackball, gesture input device, gaze-based input device, or cursor direction keys, for communicating directional information and command selections to the processor 904 and controlling cursor movement on the display 912. This cursor control 916 typically has two degrees of freedom with two axes, a first axis (e.g., x) and a second axis (e.g., y), allowing the device to specify a position in a plane. However, it should be understood that cursor controls 916 and other such input devices 914 that enable three-dimensional (e.g., x, y, and z) cursor movement are also contemplated herein.

[0134] In accordance with a particular implementation of this teaching, the results may be provided by the computer system 900 in response to a processor 904 executing one or more sequences of one or more instructions stored in RAM 906. Such instructions may be read into RAM 906 from another computer-readable medium or computer-readable storage medium, such as a storage device 910. The execution of a sequence of instructions stored in RAM 906 can cause the processor 904 to carry out the process described herein. Alternatively, a hardwired circuit configuration may be used instead of, or in combination with, software instructions to implement this teaching. Therefore, the implementation of this teaching is not limited to any specific combination of hardware circuit configuration and software.

[0135] As used herein, the terms “computer-readable medium” (e.g., datastore, data storage, storage device, data storage device, etc.) or “computer-readable storage medium” refer to any medium involved in providing instructions to the processor 904 for execution. Such mediums can take many forms, including but not limited to non-volatile media, volatile media, and transmission media. Examples of non-volatile media include, but are not limited to, optical, solid-state, and magnetic disks, such as storage device 910. Examples of volatile media include, but are not limited to, dynamic memory, such as RAM 906. Examples of transmission media include, but are not limited to, coaxial cables, copper wires, and optical fibers, including wires, such as bus 902.

[0136] Common forms of computer-readable media include, for example, floppy disks, flexible disks, hard disks, magnetic tapes, or any other magnetic media, CD-ROMs, any other optical media, punch cards, paper tapes, any other physical media having a pattern of holes, RAM, PROMs, and EPROMs, flash EPROMs, any other memory chips or cartridges, or any other tangible media that a computer can read.

[0137] In addition to computer-readable media, instructions or data may be provided as signals on a transmission medium included in a communication device or system to provide a sequence of one or more instructions to a processor 904 of a computer system 900 for execution. For example, a communication device may include a transceiver having signals indicating instructions and data. The instructions and data are configured to cause one or more processors to implement the functions outlined in this disclosure. Typical examples of data communication transmission connections include, but are not limited to, telephone modem connections, wide area networks (WANs), local area networks (LANs), infrared data connections, NFC connections, optical communication connections, and the like.

[0138] It should be understood that the methodologies, flowcharts, diagrams, and accompanying disclosures described herein may be implemented using the computer system 900 as a standalone device or on a distributed network of shared computing resources, such as a cloud computing network.

[0139] The methodologies described herein can be implemented by various means depending on the application. For example, these methodologies can be implemented in hardware, firmware, software, or any combination thereof. In the case of hardware implementation, the processing unit can be implemented in one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, electronic devices, other electronic units designed to perform the functions described herein, or a combination thereof. In various embodiments, the methods described herein may be implemented as firmware and / or software programs and applications written in conventional programming languages ​​such as C, C++, and Python. When implemented as firmware and / or software, the embodiments described herein may be implemented on a non-temporary computer-readable medium on which a program causing a computer to perform the methods described above is stored. The various engines described herein may be provided on a computer system such as computer system 900, so that the processor 904 performs the analysis and decision-making provided by these engines in accordance with instructions provided by any one or a combination thereof of user input provided via memory components RAM 906, ROM, 908, or storage device 910 and input device 914. [Examples]

[0140] VI. Examples VI.A Flow Cytometry VI.A.1. Simulation / Experimental Methods for Analysis by Flow Cytometry Figure 10 is a flowchart illustrating a method for identifying a desired expression profile according to various embodiments. Method 1000 can be implemented, for example, using the analysis system 100 described with respect to Figure 1, or a similar analysis system.

[0141] Step 1002 includes obtaining bone marrow test samples and blood test samples from each of a plurality of subjects. In these examples, the bone marrow test sample can be obtained by bone marrow aspiration, and the blood test sample, which is whole blood, can be obtained, for example, by blood collection. The number of subjects in the plurality of subjects may be, for example, 3, 4, 5, 11, 25, 50, 110, 500, 1100, or any other number. In these examples, each of the bone marrow test sample and blood test sample is of sufficient volume (i.e., contains a sufficient number of cells) to enable the analysis of Method 1000. In various embodiments, about 110 μL of bone marrow test sample and about 110 μL of blood test sample may be used for analysis.

[0142] Step 1004 includes analyzing bone marrow and blood test samples using flow cytometry to generate data. This data may be referred to as initial cell data. This initial cell data may include characteristics of the bone marrow and blood test samples, including, but not limited to, the count of cells in each of these different test samples and the identification of one or more cell surface markers expressed on each of these cells (e.g., CD13, CD11c, CD16, CD15, HLA-DR, etc.). In various embodiments, step 1004 may also include using automated gating (e.g., flowDensity, AutoGate) to ensure that this initial cell data is generated only for singlets (single cells).

[0143] Step 1006 involves generating simulation samples using initial cell data, each of which is a computationally mixed sample of cells from bone marrow and blood test samples. For example, multiple simulation samples (e.g., 5, 10, 20, 100, or several other number of simulation samples) can be generated for each of several subjects for each desired blood dilution level (e.g., 0%, 25%, 50%, 75%, and 0%). Thus, each of these simulation samples is a computationally mixed random sample of cells from bone marrow and blood test samples. In some examples, 10 simulation samples can be generated for each of several subjects for each blood dilution level. Table 4 below shows an example of how simulation samples can be generated for different blood dilution levels. JPEG0007874610000004.jpg75170

[0144] Step 1008 involves analyzing multiple simulated samples using automated gating based on a panel of cell surface markers (e.g., one or more of CD5, CD10, CD11c, CD13, CD15, CD16, CD19, CD33, CD34, CD38, CD45, CD56, CD57, CD71, CD117, HLA-DR, etc.) to identify a set of expression profiles that correlate with hemodilution levels. For example, a computational workflow may be used to fractionate single-cell data based on multiple expression profiles and identify one or more specific expression profiles that most strongly correlate with hemodilution levels. The computational workflow may use any number of data analysis tools or any combination of data analysis tools selected from the group including but not limited to flowDensity, flowType, RchyOptimyx, FAUST, diffcyt, flowMeans, flowSOM, phonograph, flowCut, CytoML, flowPeaks, densityClust, flowWorkspace, flowCore, and CytoDx.

[0145] In various embodiments, gating is used before automatic gating in step 1008 to exclude outlier events according to signal stability over acquisition time. Automatic gating is used to fractionate cells in each simulation sample into populations based on cell type (e.g., bone marrow cells, monocytes, erythrocytes, blasts, lymphocytes, etc.).

[0146] We analyze the distribution of lateral scattering (SSC) and CD45 expression to filter out singlets and exclude doublets. We apply unsupervised density-based clustering to the singlets to identify clusters corresponding to myeloid cells and lymphocytes based on the location of their centroids. We then run these clusters through a support vector machine algorithm to further refine the gate boundaries for lymphocytes and myeloid cells.

[0147] After filtering the singlet to exclude lymphocytes and myeloid cells, the distribution of lateral scatter areas (SSC-A) and CD45 expression is analyzed to gate monocytes and erythrocytes.

[0148] After filtering the singlets to exclude lymphocytes, myeloid cells, and monocytes, clustering and CD45 were performed based on the density of the remaining singlets. dim Identify blast cells using the expression distribution of CD45. dim The cells are those that have weak expression of CD45.

[0149] Therefore, automated gating is used to identify various populations (e.g., bone marrow cells, monocytes, erythrocytes, blast cells, lymphocytes, etc.). Then, each population is fractionated into at least four subpopulations for each combination of cell surface markers in each panel of cell surface markers (makers).

[0150] As an example, for a specific combination of CD13 and CD15, bone marrow cells may be fractionated into cell subpopulations corresponding to the following expression profiles (or phenotypes): CD13+CD15+, CD13-CD15-, CD13+CD15-, and CD13-CD15+. In other examples, manual gating may be used instead of automated gating in step 1008. Data obtained by manual gating may be performed by an analyst who performs a visual assessment of flow cytometry biplots (e.g., CD45 vs. side scattering, CD13 vs. CD15, etc.).

[0151] Step 1010 involves identifying one or more expression profiles that most strongly correlate with blood dilution levels using one or more algorithms, modeling techniques, or both. For example, linear regression modeling is performed using the frequency of cells matching the expression profile, with blood dilution levels as the independent variable. The coefficient of determination (R) 2Using this method, one or more expression profiles are identified for cell subpopulations that are highly correlated with blood dilution levels. These one or more expression profiles can then be selected for use in the subsequent quantification of blood dilution levels in bone marrow samples. In these examples, the expression profile showing the greatest variation between blood dilution levels is the expression profile with the strongest correlation to blood dilution levels.

[0152] In various embodiments, a process similar to method 1000 described in Figure 10 is carried out using experimentally mixed samples. In these other examples, steps 1004 and 1006 may optionally be replaced with steps for creating multiple experimentally mixed samples. For example, for a particular blood dilution level, a first volume of bone marrow test sample and a second volume of blood test sample may be mixed so that the blood-to-bone marrow ratio matches that particular blood dilution level.

[0153] VI.A.2. Experimental results of analysis using flow cytometry Figures 11–21 are plots showing the relationship between a particular cell subpopulation and hemodilution in various embodiments. These plots are examples of plots generated during the analysis of steps 408 and / or 410 described above with respect to flow cytometry data generated for different cell subpopulations for the same three human subjects (e.g., donors): subject A, subject B, and subject C. Furthermore, at least Figures 11, 14, 16, 18, and 20 may be plots generated based on simulated samples created by computational mixing of bone marrow and blood according to method 1000 described above with respect to Figure 10. For example, the plots in Figures 11, 14, 16, 18, and 20 may be plots generated during the analysis of step 1008 in Figure 10.

[0154] Figure 11 is plot 1102 showing the relationship between cell subpopulations matching the expression profile CD13+CD11c- and blood dilution levels in various embodiments. Plot 1102 is based on simulated samples generated by computational mixing of bone marrow and blood. Plot 1102 includes a y-axis 1104 representing the percentage of cells in the sample matching the expression profile CD13+CD11c-, and an x-axis 1106 representing the blood dilution level. These percentages are tracked by curves 1108, 1110, and 1112 for subjects A, B, and C, respectively. As plot 1102 shows, as the blood dilution level increases, the percentage of cells having the expression profile CD13+CD11c- decreases in a substantially linear manner. This substantially linear correlation allows for the determination or measurement of blood dilution based on a given percentage of this particular cell subpopulation in a blood-bone marrow sample.

[0155] Figure 12 is plot 1202 showing the relationship between cell subpopulations matching the expression profile CD13+CD11c- and blood dilution levels in various embodiments. Plot 1202 is based on samples prepared by experimental mixing of bone marrow and blood. Plot 1202 includes a y-axis 1204 representing the percentage of cells in the sample matching the expression profile CD13+CD11c-, and an x-axis 1206 representing the blood dilution level. These percentages are tracked by curves 1208, 1210, and 1212 for subjects A, B, and C, respectively. As plot 1202 shows, as the blood dilution level increases, the percentage of cells with the expression profile CD13+CD11c- generally decreases in a substantially linear manner. This correlation generally validates the findings of plot 1102 for simulated samples.

[0156] Figure 13 is a series of plots 1302 demonstrating that the expression profiles CD13+CD11c- in various embodiments have a strong correlation with blood dilution levels. The series of plots 1302 includes plots 1304, 1306, 1308, 1310, and 1312, each corresponding to a different blood dilution level. Plot 1304 corresponds to 100% blood dilution, plot 1306 corresponds to 75% blood dilution, plot 1308 corresponds to 50% blood dilution, plot 1310 corresponds to 25% blood dilution, and plot 1312 corresponds to 0% blood dilution. For plots 1304, 1306, 1308, 1310, and 1312, the y-axis represents the measured CD13 expression on a logarithmic scale, and for plots 1304, 1306, 1308, 1310, and 1312, the x-axis represents the measured CD11c expression on a logarithmic scale.

[0157] The upper left quarter of plots 1304, 1306, 1308, 1310, and 1312 represents cells that express CD13 (CD13+) but not CD11c (CD11c-). As the blood dilution level increases, the number of CD13-expressing cells decreases and the number of CD11c-non-expressing cells decreases, in a manner consistent with the findings shown by plot 1102 in Figure 11 and plot 1202 in Figure 12.

[0158] Figure 14 shows plot 1402 illustrating the relationship between cell subpopulations matching the expression profile CD13+CD15+ and blood dilution levels in various embodiments. Plot 1402 is based on simulated samples generated by computational mixing of bone marrow and blood. Plot 1402 includes a y-axis 1404 representing the percentage of cells in the sample matching the expression profile CD13+CD15+, and an x-axis 1406 representing the blood dilution level. These percentages are tracked by curves 1408, 1410, and 1412 for subjects A, B, and C, respectively. As plot 1402 shows, as the blood dilution level increases, the percentage of cells with the expression profile CD13+CD15+ decreases in a substantially linear manner. This substantially linear correlation allows for the determination or measurement of blood dilution based on a given percentage of this particular cell subpopulation in a blood-bone marrow sample.

[0159] Figure 15 is plot 1502 showing the relationship between cell subpopulations matching the expression profile CD13+CD15+ and blood dilution levels in various embodiments. Plot 1502 is based on samples prepared by experimental mixing of bone marrow and blood. Plot 1502 includes a y-axis 1504 representing the percentage of cells in the sample matching the expression profile CD13+CD15+, and an x-axis 1506 representing the blood dilution level. These percentages are tracked by curves 1508, 1510, and 1512 for subjects A, B, and C, respectively. As plot 1502 shows, as the blood dilution level increases, the percentage of cells with the expression profile CD13+CD15+ generally decreases in a substantially linear manner. This correlation generally validates the findings of plot 1402 for the simulated samples.

[0160] Figure 16 is plot 1602 showing the relationship between cell subpopulations matching the expression profile CD13+CD16+ and blood dilution levels in various embodiments. Plot 1602 is based on simulated samples generated by computational mixing of bone marrow and blood. Plot 1602 includes a y-axis 1604 representing the percentage of cells in the sample matching the expression profile CD13+CD16+, and an x-axis 1606 representing the blood dilution level. These percentages are tracked by curves 1608, 1610, and 1612 for subjects A, B, and C, respectively. As plot 1602 shows, as the blood dilution level increases, the percentage of cells with the expression profile CD13+CD16+ decreases in a substantially linear manner. This substantially linear correlation allows for the determination or measurement of blood dilution based on a given percentage of this particular cell subpopulation in a blood-bone marrow sample.

[0161] Figure 17 shows plot 1702 illustrating the relationship between cell subpopulations matching the expression profile CD13+CD16+ and blood dilution levels in various embodiments. Plot 1702 is based on samples prepared by experimental mixing of bone marrow and blood. Plot 1702 includes a y-axis 1704 representing the percentage of cells in the sample matching the expression profile CD13+CD16+, and an x-axis 1706 representing the blood dilution level. These percentages are tracked by curves 1708, 1710, and 1712 for subjects A, B, and C, respectively. Plot 1702 shows that as the blood dilution level increases, the percentage of cells with the expression profile CD13+CD16+ generally decreases in a substantially linear manner. This correlation generally validates the findings of plot 1702 for the simulated samples.

[0162] Figure 18 is plot 1802 showing the relationship between cell subpopulations matching the expression profile CD13+HLA-DR- and blood dilution levels in various embodiments. Plot 1802 is based on simulated samples generated by computational mixing of bone marrow and blood. Plot 1802 includes a y-axis 1804 representing the percentage of cells in the sample matching the expression profile CD13+HLA-DR-, and an x-axis 1806 representing the blood dilution level. These percentages are tracked by curves 1808, 1810, and 1812 for subjects A, B, and C, respectively. As plot 1802 shows, as the blood dilution level increases, the percentage of cells with the expression profile CD13+HLA-DR- decreases in a substantially linear manner. This substantially linear correlation allows for the determination or measurement of blood dilution based on a given percentage of this particular cell subpopulation in a blood-bone marrow sample.

[0163] Figure 19 is plot 1902, showing the relationship between cell subpopulations matching the expression profile CD13+HLA-DR- and blood dilution levels in various embodiments. Plot 1902 is based on samples prepared by experimental mixing of bone marrow and blood. Plot 1902 includes a y-axis 1904 representing the percentage of cells in the sample matching the expression profile CD13+HLA-DR-, and an x-axis 1906 representing the blood dilution level. These percentages are tracked by curves 1908, 1910, and 1912 for subjects A, B, and C, respectively. Plot 1902 shows that as the blood dilution level increases, the percentage of cells with the expression profile CD13+HLA-DR- generally decreases in a substantially linear manner. This correlation generally validates the findings of plot 1802 for the simulated samples.

[0164] Figure 20 is plot 2002, showing the relationship between cell subpopulations matching the expression profile CD13-HLA-DR- and blood dilution levels in various embodiments. Plot 2002 is based on simulated samples generated by computational mixing of bone marrow and blood. Plot 2002 includes a y-axis 2004 representing the percentage of cells in the sample matching the expression profile CD13-HLA-DR-, and an x-axis 2006 representing the blood dilution level. These percentages are tracked by curves 2008, 2010, and 2012 for subjects A, B, and C, respectively. As plot 2002 shows, as the blood dilution level increases, the percentage of cells having the expression profile CD13-HLA-DR- also increases in a substantially linear manner. This substantially linear correlation allows for the determination or measurement of blood dilution based on a given percentage of this particular cell subpopulation in blood and bone marrow samples.

[0165] Figure 21 is plot 2102 showing the relationship between cell subpopulations matching the expression profile CD13-HLA-DR- and blood dilution levels in various embodiments. Plot 2102 is based on samples prepared by experimental mixing of bone marrow and blood. Plot 2102 includes a y-axis 2104 representing the percentage of cells in the sample matching the expression profile CD13-HLA-DR-, and an x-axis 2106 representing the blood dilution level. These percentages are tracked by curves 2108, 2110, and 2112 for subjects A, B, and C, respectively. Plot 2102 shows that as the blood dilution level increases, the percentage of cells having the expression profile CD13-HLA-DR- also increases in a generally substantially linear manner. This correlation generally validates the findings of plot 2102 for the simulated samples.

[0166] Figures 11, 12, and 14–21 show substantially linear correlations between the cell subpopulations identified above and blood dilution levels. However, it should be understood that other cell subpopulations may reveal different kinds of mathematical functions (e.g., logarithmic, exponential, etc.) or other mathematically quantifiable relationships / correlations with blood dilution levels.

[0167] VI.B Statistical distance VI.B.1. Experimental results on analysis using statistical distance Figures 22–26 are plots showing the relationship between a particular cell subpopulation and hemodilution in various embodiments. These plots are examples of plots generated during the analysis of steps 804 and 806 described above with respect to statistical distance scores generated for different cell subpopulations for the same three human subjects (e.g., donors): Subject A, Subject B, and Subject C.

[0168] Figure 22 is a plot 2200 showing the relationship between statistical distance scores and blood dilution levels for subpopulations of CD71-expressing cells in various embodiments. In particular, plot 2200 includes a y-axis 2202 showing Earthmover distance (EMD) scores for subpopulations of CD71-expressing cells, and an x-axis 2204 representing blood dilution levels (shown as percentages). These percentages are tracked by curve 2206 for subjects A, B, and C.

[0169] Figure 23 is a plot 2300 showing the relationship between statistical distance scores and blood dilution levels for subpopulations of CD33-expressing cells in various embodiments. In particular, plot 2300 includes a y-axis 2302 showing EMD scores for subpopulations of CD33-expressing cells, and an x-axis 2304 representing blood dilution levels (shown as percentages). These percentages are tracked by curve 2306 for subjects A, B, and C.

[0170] Figure 24 is a plot 2400 showing the relationship between statistical distance scores and blood dilution levels for cell subpopulations expressing CD33 and CD117 in various embodiments. In particular, plot 2400 includes a y-axis 2402 showing EMD scores for cell subpopulations expressing CD33 and CD117, and an x-axis 2404 representing blood dilution levels (shown as percentages). These percentages are tracked by curve 2406 for subjects A, B, and C.

[0171] Figure 25 is a plot 2500 showing the relationship between statistical distance scores and blood dilution levels for cell subpopulations expressing CD56 and CD13 in various embodiments. In particular, plot 2500 includes a y-axis 2502 showing EMD scores for cell subpopulations expressing CD56 and CD13, and an x-axis 2504 representing blood dilution levels (shown as percentages). These percentages are tracked by curve 2506 for subjects A, B, and C.

[0172] Figure 26 is a plot 2600 showing the relationship between statistical distance scores and blood dilution levels for CD19-expressing cell subpopulations in various embodiments. In particular, plot 2600 includes a y-axis 2602 showing EMD scores for CD19-expressing cell subpopulations, and an x-axis 2604 representing blood dilution levels (shown as percentages). These percentages are tracked by curve 2606 for subjects A, B, and C.

[0173] VI.B.2. Methodology for Analysis Using Statistical Distance The statistical distance scores discussed in Figures 22-26 are EMD scores. The following is a discussion of methods for calculating such EMD scores.

[0174] EMD is calculated by comparing two (or more) cell populations defined by a manual or automated gating algorithm. First, the cell populations are identified. Second, for each of the two cell populations, a signature is calculated using adaptive binning, a histogram-like approximation method for the data. Once the signatures are generated, the EMD calculation can be described as a linear programming problem.

[0175] Identifying cell populations: Identify the target cell population in the preprocessed flow cytometry data. The target cell population can be identified by manual analysis (e.g., FlowJo), automated analysis (e.g., AutoGate (http: / / CytoGenie.org / ) or any other clustering algorithm), or by a physical cell sorting procedure (FACS sort).

[0176] Figure 27 is a series of plots 2700 illustrating exemplary gating strategies implemented in AutoGate to identify a target cell population in various embodiments. In Figure 27, the series of plots 2700 includes plot 2702 for identifying singlet 2704, plot 2706 for identifying live singlet 2708, and plot 2710 for identifying bone marrow 2712 and lymphocytes 2714.

[0177] The preprocessing steps can include correction of flow cytometry data, logicle transformation (Moore WA and Parks DR. Update for the logicle data scale including operational code implementations, Cytometry A. 2012;81:273-277.), and clustering of the transformed data using DBM (Walther G, Zimmerman N, Moore W, Parks D, Meehan S, Belitskaya I et al. Automatic clustering of flow cytometry data with density-based merging, Adv Bioinformatics. 2009;686759-686765). To perform the preprocessing steps, AutoGate can be used, and in other examples, other software libraries can be used. The preprocessing method of flow cytometry data used here does not require user input for parameters such as the number of clusters, the number of grid bins, the density threshold, and manual gating for correction purposes.

[0178] Signature: For efficiency, the distribution is summarized by a signature, which allows for increasing the granularity in high-density areas of the data and decreasing the granularity in low-density areas, i.e., signature bins are of variable size, whereas histogram bins typically result from fixed-size fractions of the distribution.

[0179] Formally, the signature {s j =(m j ,w mj )} is the mean (m j ) of the group j of observations, and the proportion (w mj) is represented by ). The binning algorithm used to bin the data into groups used in the signature is described by Roederer et al. (Roederer M, Moore W, Treister A, Hardy RR, Herzenberg LA. Probability binning comparison: a metric for quantitating multivariate distribution differences. Cytometry. 2001;45:37~46). Therefore, first, the variance of the data is calculated for each parameter (dimension) included in the analysis. Next, the dimension with the largest variance is selected. The events are divided into two bins along the median in that dimension so that half of the events fall into each of the two obtained bins. Next, this process is carried out recursively until a predefined threshold, for example, 2ln(N) observations per bin (where N is the total number of events), is met. In order for each bin to be divided, the algorithm selects the dimension that maximizes the variance and divides the data around the median in that dimension. The result is a series of n-dimensional hyperrectangle bins, each containing an equal number of events.

[0180] EMD calculation: Formally, EMD is a linear programming problem: two distributions represented by the signature P={(p1,w p1 ),···,(p m ,w pm )} and Q={(q1,w q1 ),···,(q n ,w qn )}(here, p i , q i is, frequency w pi , w qi The centroid of the bin having, as well as p for all i, j i and q j D=[d ij It can be described as ]. To ensure that P and Q have the same total mass of the identity element (equal to 1), normalize each of the two distributions. Then, minimize the total cost p i and qj Flow F = [f ij ] to decide: JPEG0007874610000005.jpg14170 However, the following constraints shall apply: JPEG0007874610000006.jpg60170

[0181] Constraint (2) ensures that the mass is transported in only one direction (e.g., from the supply sample to the demand sample). Constraints (3) and (4) limit the amount of mass that can be moved from / to a given signature chavin to its respective weight, and constraint (5) ensures that the amount of mass moved does not exceed the maximum possible amount.

[0182] For signatures with the same total mass, EMD is an accurate metric of the distribution and is equivalent to Mallow's distance (Mallow's CL. A Note on Asymptotic Joint Normality, Ann. Math. Statist. 1972;43:508~515) (demonstrated by Levina E and Bickel P. The earth mover's distance is the Mallow's distance: Some insights from statistics. Proc. ICCV, 2001;2:251~256). Therefore, when applied to probability distributions, EMD has a clear probabilistic interpretation as Mallow's distance. In this specification, the process ensures that the two samples have equal mass but retains EMD notation.

[0183] By solving the above linear programming problem according to constraints (2-5), the optimal flow F between the signature of the supply location and the signature of the demand location is determined. Then, EMD is the optimal flow F = [f ij ] and ground distance D = [d ij Defined as a function of ]: JPEG0007874610000007.jpg25170 As an example, EMD calculation can be performed using the code available at https: / / www.mathworks.com / matlabcentral / fileexchange / 22962-the-earth-mover-s-distance. The performance of EMD for flow data analysis was first investigated by Zimmerman, who reported the robustness of EMD's performance in terms of binning parameters and sample size in his doctoral dissertation (Zimmerman NA computational approach to identification and comparison of cell subsets in flow cytometry data. Doctoral dissertation, Stanford University. 2011. Available at: https: / / stacks.stanford.edu / file / druid:hg137hq6178 / Zimmerman-Dissertation-v2-augmented.pdf). Comparing small populations of cells with low frequencies may require finer binning than comparing larger populations. However, as Zimmerman demonstrates, the overall EMD is robust regardless of the number of bins selected.

[0184] VII. Additional Considerations The headings and subheadings between sections and subsections in this document are included solely for readability and do not suggest that features cannot be combined across sections and subsections. Therefore, sections and subsections do not describe separate embodiments.

[0185] Some embodiments of this disclosure include a system comprising one or more data processors. In some embodiments, the system includes a non-temporary computer-readable storage medium containing instructions, which, when executed on one or more data processors, causes one or more data processors to perform some or all of one or more of the methods disclosed herein and / or some or all of one or more processes. Some embodiments of this disclosure include a computer program product tangibly embodied in a non-temporary machine-readable storage medium, which includes instructions configured to cause one or more data processors to perform some or all of the methods disclosed herein and / or some or all of one or more processes.

[0186] The terms and expressions used are for illustrative purposes only, not limitation, and in using such terms and expressions there is no intention to exclude equivalents or parts of the features shown and described, however it is recognized that various modifications are possible within the scope of the invention as described in the claims. Accordingly, although the invention as described in the claims is specifically disclosed by embodiments and optional features, modifications and variations of the concepts disclosed herein may be used by those skilled in the art, and it should be understood that such modifications and variations are considered to be within the scope of the invention as defined by the appended claims.

[0187] The subsequent description provides only preferred exemplary embodiments and is not intended to limit the scope, applicability, or configuration of this disclosure. Rather, the subsequent description of preferred exemplary embodiments provides a practical description for implementing various embodiments for those skilled in the art. It will be understood that various modifications can be made to the function and arrangement of the elements without departing from the spirit and scope set forth in the appended claims.

[0188] Specific details are given in the following description to provide a complete understanding of the embodiments. However, it will be understood that embodiments may be carried out without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form to avoid unnecessarily obscuring the embodiments with excessive detail. In other cases, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail to avoid obscuring the embodiments.

[0189] In describing various embodiments, this specification may present methods and / or processes as steps in a specific order. However, unless a method or process relies on a specific order of steps described herein, the method or process should not be limited to such a specific order of steps, and those skilled in the art will readily understand that the order may vary and still remain within the spirit and scope of various embodiments.

[0190] All references cited herein, including patent applications, patent publications, and UniProtKB / Swiss-Prot accession numbers, are incorporated herein by reference in whole, as if each individual reference were specifically and individually indicated to be incorporated by reference.

Claims

1. A method for determining hemodilution of a bone marrow sample, The bone marrow sample is analyzed to determine the number of cells in the bone marrow sample that express the CD13 protein and complementary markers that constitute a set of cell surface markers, Correlating the number of cells expressing CD13 protein and complementary markers with the blood dilution level of bone marrow samples. A method including, (i) Does a decrease in the number of cells in a bone marrow sample that include the complementary marker CD11c and are CD13+ and CD11c- indicate an increase in hemodilution in the bone marrow sample? (ii) Does a decrease in the number of cells in a bone marrow sample that contain a complementary marker CD15 and are CD13+ and CD15+ indicate an increase in hemodilution in the bone marrow sample? (iii) An increase in the number of cells in a bone marrow sample whose complementary markers include human leukocyte antigen-DR isotype (HLA-DR) protein, and which are CD13- and HLA-DR-, indicates an increase in hemodilution in the bone marrow sample; or (iv) A decrease in the number of cells in a bone marrow sample that contain the complementary marker HLA-DR protein and are CD13+ and HLA-DR- indicates an increase in hemodilution in the bone marrow sample. method.

2. The method according to claim 1, wherein there is a linear correlation between the number of cells expressing the CD13 protein and complementary markers and the hemodilution in the bone marrow sample.

3. The method according to claim 1, wherein the number of cells is determined using the expression of two, three, four, five or more complementary markers.

4. The method according to any one of claims 1 to 3, wherein the number of cells expressing the CD13 protein and complementary markers is measured using a flow cytometer.

5. The method according to claim 4, wherein the complementary marker comprises one or more cell surface markers selected from the group consisting of CD11c, CD15, CD16, and HLA-DR.

6. The number of cells expressing CD13 protein and complementary markers is The bone marrow sample is stained with a fluorophore conjugate antibody for complementary binding of the CD13 protein and a complementary marker, Applying excitation energy to a bone marrow sample, The number of cells is determined by measuring the fluorescence emission level from the bone marrow sample. The method according to claim 1 or 2, as determined by...

7. The method according to claim 6, wherein the complementary marker comprises one or more cell surface markers selected from the group consisting of CD11c, CD15, CD16, and HLA-DR.

8. The number of cells expressing CD13 protein and complementary markers is Isolating each cell in the bone marrow sample, To extract targeted mRNA molecules associated with the expression of CD13 protein and complementary markers, The process involves sequencing targeted mRNA molecules extracted from each isolated cell to obtain sequencing data, and Using sequence data obtained from targeted mRNA molecules, the expression levels of CD13 protein and complementary markers in each isolated cell are determined, Cells expressing the CD13 protein and complementary markers are grouped from cells that do not express the CD13 protein and complementary markers. The method according to claim 1 or 2, as determined by...

9. The method according to claim 8, wherein the complementary marker comprises one or more cell surface markers selected from the group consisting of CD11c, CD15, CD16, and HLA-DR.

10. The number of cells expressing CD13 protein and complementary markers is Extraction of targeted mRNA molecules associated with the expression of CD13 protein and complementary markers from bone marrow samples, Sequencing targeted mRNA molecules extracted from bone marrow samples, Using sequence data obtained from targeted mRNA molecules, the expression levels of CD13 protein and complementary markers in bone marrow samples are determined to determine the number of cells. The method according to claim 1 or 2, as determined by...

11. The method according to claim 10, wherein the complementary marker comprises one or more cell surface markers selected from the group consisting of CD11c, CD15, CD16, and HLA-DR.

12. Determining the blood dilution percentage of a bone marrow sample. The method according to any one of claims 1 to 11, further comprising:

13. The method according to claim 12, wherein the blood dilution percentage is calculated as the number of cells expressing CD13 protein and complementary markers in the bone marrow sample relative to the total number of cells counted in the bone marrow sample.

14. If the blood dilution percentage is less than approximately 25%, determine the state of hypodilution in the bone marrow sample. The method according to claim 13, further comprising:

15. If the blood dilution percentage is less than approximately 50%, determine the state of hypodilution in the bone marrow sample. The method according to claim 13, further comprising:

16. If the blood dilution percentage is less than approximately 75%, determine the state of hypodilution in the bone marrow sample. The method according to claim 13, further comprising:

17. If the blood dilution percentage is less than approximately 100%, determine the state of hypodilution in the bone marrow sample. The method according to claim 13, further comprising:

18. Adjusting one or more analytical measurements taken from a bone marrow sample in proportion to the blood dilution percentage of the bone marrow sample. The method according to any one of claims 12 to 17, further comprising:

19. Adjust one or more analytical measurements taken from a bone marrow sample in proportion to the expression levels of CD13 protein and complementary markers in the bone marrow sample. The method according to any one of claims 1 to 11, further comprising:

20. To generate reports based on blood dilution levels correlated with the number of cells expressing CD13 protein and complementary markers. The method according to any one of claims 1 to 19, further comprising:

21. The method according to claim 20, wherein the report includes an assessment of the quality of the bone marrow sample.

22. The method according to claim 20 or 21, wherein the report includes recommended actions to be taken by the operator based on the blood dilution level.

23. The report is displayed on a display system, allowing the operator to simultaneously see the assessment of bone marrow sample quality and the recommended actions to be taken by the operator based on the blood dilution level. The method according to any one of claims 20 to 22, further comprising:

24. A kit comprising two or more binders for carrying out the method according to any one of claims 1 to 7 or 12 to 23.

25. The kit according to claim 24, wherein two or more binders are for labeling CD13, and the complementary marker comprises one or more cell surface markers selected from the group consisting of CD11c, CD15, CD16, and HLA-DR.

26. A kit comprising two or more targeted mRNA capture reagents for performing the method according to any one of claims 8 to 23.

27. The kit according to claim 26, wherein two or more targeted mRNA capture reagents are for capturing mRNA molecules associated with CD13 expression, and the complementary marker comprises one or more cell surface markers selected from the group consisting of CD11c, CD15, CD16, and HLA-DR.

28. A method for monitoring the progression of disease or disability in a subject by analyzing bone marrow samples obtained from the subject, To determine the number of cells in a bone marrow sample expressing the CD13 protein and complementary markers, Correlating the number of cells expressing CD13 protein and complementary markers with the blood dilution level of bone marrow samples, To assess whether the blood dilution level meets the predetermined blood dilution criteria, In response to the blood dilution level meeting a predetermined blood dilution standard, the bone marrow sample is assessed or analyzed. Includes, The complementary marker is selected from the group consisting of CD11c, CD15, CD16, and HLA-DR, and at least one of the following is present: A decrease in the number of CD13+ and CD11c- cells in a bone marrow sample indicates an increase in hemodilution in the bone marrow sample. A decrease in the number of CD13+ and CD15+ cells in a bone marrow sample indicates an increase in hemodilution in the bone marrow sample. An increase in the number of cells in a bone marrow sample that are CD13- and HLA-DR- indicates an increase in hemodilution in the bone marrow sample, or A decrease in the number of cells in a bone marrow sample that are CD13+ and HLA-DR- indicates an increase in hemodilution in the bone marrow sample. method.

29. A method for monitoring the progression of disease or disability in a subject by analyzing bone marrow samples obtained from the subject, Extraction of CD13 protein and targeted mRNA molecules associated with complementary markers from bone marrow samples, Sequencing targeted mRNA molecules extracted from bone marrow samples, Using sequence data obtained from targeted mRNA molecules, the expression levels of CD13 protein and complementary markers in bone marrow samples are determined, To assess whether the expression levels of the CD13 protein and complementary markers meet the pre-defined complementary marker expression criteria, In response to the expression levels of the CD13 protein and complementary markers meeting pre-defined complementary marker expression criteria, the bone marrow sample is assessed or analyzed. Includes, The complementary marker is selected from the group consisting of CD11c, CD15, CD16, and HLA-DR, and at least one of the following is present: A decrease in the number of CD13+ and CD11c- cells in a bone marrow sample indicates an increase in hemodilution in the bone marrow sample. A decrease in the number of CD13+ and CD15+ cells in a bone marrow sample indicates an increase in hemodilution in the bone marrow sample. An increase in the number of cells in a bone marrow sample that are CD13- and HLA-DR- indicates an increase in hemodilution in the bone marrow sample, or A decrease in the number of cells in a bone marrow sample that are CD13+ and HLA-DR- indicates an increase in hemodilution in the bone marrow sample. method.

30. A method for monitoring the effectiveness of a treatment regimen in the treatment of a disease or disorder by analyzing bone marrow samples obtained from subjects being treated with a treatment regimen, To determine the number of cells in a bone marrow sample expressing the CD13 protein and complementary markers, Correlating the number of cells with the blood dilution level of the bone marrow sample, To assess whether the blood dilution level meets the predetermined blood dilution criteria, In response to the blood dilution level passing a predetermined blood dilution standard, an assessment or analysis of a bone marrow sample is performed for at least one of monitoring or verifying the therapeutic effectiveness of the treatment regimen. Includes, The complementary marker is selected from the group consisting of CD11c, CD15, CD16, and HLA-DR, and at least one of the following is present: A decrease in the number of CD13+ and CD11c- cells in a bone marrow sample indicates an increase in hemodilution in the bone marrow sample. A decrease in the number of CD13+ and CD15+ cells in a bone marrow sample indicates an increase in hemodilution in the bone marrow sample. An increase in the number of cells in a bone marrow sample that are CD13- and HLA-DR- indicates an increase in hemodilution in the bone marrow sample, or A decrease in the number of cells in a bone marrow sample that are CD13+ and HLA-DR- indicates an increase in hemodilution in the bone marrow sample. method.

31. A method for monitoring the effectiveness of a treatment regimen in the treatment of a disease or disorder by analyzing bone marrow samples obtained from subjects being treated with a treatment regimen, Extraction of CD13 protein and targeted mRNA molecules associated with complementary markers from bone marrow samples, Sequencing targeted mRNA molecules extracted from bone marrow samples, Using sequence data obtained from targeted mRNA molecules, the expression levels of CD13 protein and complementary markers in bone marrow samples are determined, To assess whether the expression levels of the CD13 protein and complementary markers meet the pre-defined complementary marker expression criteria, In response to the CD13 protein and complementary marker expression levels meeting pre-defined complementary marker expression criteria, an assessment or analysis of a bone marrow sample is performed for at least one of monitoring or validating the therapeutic efficacy of the treatment regimen. Includes, The complementary marker is selected from the group consisting of CD11c, CD15, CD16, and HLA-DR, and at least one of the following is present: A decrease in the number of CD13+ and CD11c- cells in a bone marrow sample indicates an increase in hemodilution in the bone marrow sample. A decrease in the number of CD13+ and CD15+ cells in a bone marrow sample indicates an increase in hemodilution in the bone marrow sample. An increase in the number of cells in a bone marrow sample that are CD13- and HLA-DR- indicates an increase in hemodilution in the bone marrow sample, or A decrease in the number of cells in a bone marrow sample that are CD13+ and HLA-DR- indicates an increase in hemodilution in the bone marrow sample. method.

32. A non-temporary computer-readable medium for storing computer instructions for determining blood dilution of a bone marrow sample, wherein the computer instructions are One or more processors receive data obtained from a bone marrow sample, Using one or more processors, the data is analyzed to determine the number of cells in the bone marrow sample that express the CD13 protein and complementary markers, Correlating the number of cells expressing CD13 protein and complementary markers with the blood dilution level of a bone marrow sample using one or more processors. Includes, The complementary marker includes one or more selected from the group consisting of CD11c, CD15, CD16, and HLA-DR, and at least one of the following is present: A decrease in the number of CD13+ and CD11c- cells in a bone marrow sample indicates an increase in hemodilution in the bone marrow sample. A decrease in the number of CD13+ and CD15+ cells in a bone marrow sample indicates an increase in hemodilution in the bone marrow sample. An increase in the number of cells in a bone marrow sample that are CD13- and HLA-DR- indicates an increase in hemodilution in the bone marrow sample, or A decrease in the number of cells in a bone marrow sample that are CD13+ and HLA-DR- indicates an increase in hemodilution in the bone marrow sample. Non-temporary computer-readable media.

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