Detection of hematological disorders using cell-free DNA in blood

Abstract

To provide a novel technology of screening subjects for hematological disorders, determining a cause of hematological disorders, monitoring subjects suffering from hematological disorders, and / or determining appropriate treatment for subjects suffering from hematological disorders.SOLUTION: There is provided a technology for detecting a hematological disorder using cell-free DNA in a blood sample, for example, using plasma or serum. For example, an assay can target one or more differentially-methylated regions specific to a particular hematological cell lineage (e.g., erythroblasts). A methylation level can be quantified from the assay to determine an amount of methylated or unmethylated DNA fragments in a cell-free mixture of the blood sample. The methylation level can be compared to one or more cutoff values, e.g., that correspond to a normal range for the particular hematological cell lineage as part of determining a level of a hematological disorder.SELECTED DRAWING: None

Description

[Background technology]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority from and is a non-provisional application of U.S. Provisional Patent Application No. 62 / 343,050, entitled "Detecting Hematological Disorders Using Cell-Free DNA In Blood," filed May 30, 2016, the entire contents of which are incorporated herein by reference for all purposes.

[0002] In order to determine whether a hematological disorder (e.g., anemia) exists in a person, conventional techniques involve histological examination of bone marrow biopsy. However, bone marrow biopsy is an invasive procedure that can lead to pain and anxiety for patients undergoing such a procedure. Therefore, it is desirable to identify new techniques for detecting and characterizing hematological disorders in people.

[0003] Anemia can be caused by multiple clinical conditions, each with its own unique treatment. Therefore, it would be clinically useful to confirm the cause of an anemia case and then further test or treat accordingly. One cause of anemia is a deficiency of nutrients necessary for erythropoiesis (the process of producing red blood cells), such as, but not limited to, iron, B12, and folate. Another cause of anemia is blood loss, which can be acute or chronic. Blood loss can be caused by, for example, heavy menstrual bleeding or bleeding from the digestive tract. Anemia is also frequently observed in many chronic disorders, also known as anemia of chronic disease, which can be observed in cancer and inflammatory bowel disease.

[0004] Therefore, it is desirable to provide new techniques for screening subjects for hematological disorders, determining the cause of a hematological disorder, monitoring subjects suffering from a hematological disorder, and / or determining appropriate treatment for subjects suffering from a hematological disorder. Summary of the Invention

[0005] Some embodiments provide systems, methods, and devices for detecting hematological disorders using cell-free DNA in a blood sample, for example, using plasma or serum. For example, an assay can target one or more differentially methylated regions specific to a particular blood cell lineage (e.g., erythroblasts). Methylation levels can be quantified from an assay for measuring the amount of methylated or unmethylated DNA fragments in a cell-free mixture of a blood sample. The methylation level can be compared to one or more cutoff values ​​corresponding to the normal range for a particular blood cell lineage, for example, as part of determining the level of a hematological disorder. Some embodiments can use one or more methylation levels to measure the amount of DNA from a particular blood cell lineage (e.g., erythroblast DNA) in a blood sample in a similar manner.

[0006] Such analysis can provide detection of hematological disorders without the invasive procedure of bone marrow biopsy. For example, our results demonstrate that bone marrow cells contribute a significant proportion to circulating cell-free DNA. Analysis of the methylation signature of hematopoietic cells in circulating cell-free DNA can reflect the status of bone marrow cells. Such embodiments may be particularly useful for monitoring bone marrow response to treatment, for example, response to oral iron therapy in patients suffering from iron deficiency anemia. Embodiments can also be used to assign patients to different procedures, such as bone marrow biopsy or less invasive tests.

[0007] Other embodiments relate to systems and computer-readable media associated with the methods described herein.

[0008] A better understanding of the nature and advantages of embodiments of the present invention may be gained with regard to the following detailed description and accompanying drawings. [Brief explanation of the drawings]

[0009] [Figure 1]1 shows the methylation density of CpG sites in the promoter of the ferrochelatase (FECH) gene according to an embodiment of the present invention. [Figure 2A] 1 shows the analysis of universally methylated and unmethylated DNA using a digital PCR assay designed to detect methylated and unmethylated DNA according to an embodiment of the present invention. [Figure 2B] 1 shows the analysis of universally methylated and unmethylated DNA using a digital PCR assay designed to detect methylated and unmethylated DNA according to an embodiment of the present invention. [Figure 3A] 1 is a plot showing the correlation between E% and the number of nucleated RBCs (erythroblasts) in blood cells according to an embodiment of the present invention. [Figure 3B] 3 is a flow chart illustrating a method 300 for determining cell abundance of a particular cell lineage in a biological sample by analyzing cell-free DNA according to an embodiment of the present invention. [Figure 4] 1 shows % unmethylated in buffy coat and plasma of healthy non-pregnant subjects and pregnant women at different stages of pregnancy according to an embodiment of the present invention. [Figure 5] 1 is a plot showing the lack of correlation between % unmethylated in buffy coat and plasma. [Figure 6A] FIG. 1 shows the percentage of erythroid DNA (E%(FECH)) in healthy subjects according to an embodiment of the present invention. E% can be defined as the same as % unmethylated. [Figure 6B] FIG. 1 shows the percentage of erythroid DNA (E%(FECH)) in healthy subjects according to an embodiment of the present invention. E% can be defined as the same as % unmethylated. [Figure 7] Figure 1 shows the lack of correlation between E%(FECH) results in plasma DNA and age in healthy subjects. [Figure 8] 1 is a plot of % unmethylated versus hemoglobin concentration in aplastic anemia patients, beta thalassemia major patients, and healthy control subjects according to an embodiment of the present invention. [Figure 9] 1 is a plot of % plasma unmethylated in patients with iron (Fe) deficiency anemia and acute blood loss according to an embodiment of the present invention. [Figure 10] 1 shows the relationship between plasma erythroid DNA percentage (E%(FECH)) and hemoglobin levels among aplastic anemia patients, chronic renal failure (CRF) patients, β-thalassemia major patients, iron deficiency anemia patients, and healthy subjects according to an embodiment of the present invention. [Figure 11A] 1 shows the association between reticulocyte count / index and hemoglobin levels among patients with aplastic anemia, chronic renal failure (CRF), beta-thalassemia major, and iron deficiency anemia according to an embodiment of the present invention. [Figure 11B] 1 shows the association between reticulocyte count / index and hemoglobin levels among patients with aplastic anemia, chronic renal failure (CRF), beta-thalassemia major, and iron deficiency anemia according to an embodiment of the present invention. [Figure 12] 1 is a plot of plasma % unmethylated in myelodysplastic syndrome and polycythemia vera patients according to an embodiment of the invention. [Figure 13A] 1 shows the percentage of erythroid DNA in plasma (E%(FECH)) between aplastic anemia (AA) and myelodysplastic syndrome (MDS) patients according to an embodiment of the present invention. [Figure 13B] 1 shows the percentage of erythroid DNA in plasma (E%(FECH)) between treatment responders and treatment non-responders in aplastic anemia according to an embodiment of the present invention. [Figure 14] 1 is a plot of % unmethylated in plasma versus hemoglobin concentration in a normal subject and two leukemia patients according to an embodiment of the present invention. [Figure 15A] 1 shows the methylation density of CpG sites within the erythroblast-specific DMR on chromosome 12 according to an embodiment of the present invention. [Figure 15B] 1 shows the methylation density of CpG sites within the erythroblast-specific DMR on chromosome 12 according to an embodiment of the present invention. [Figure 16]Histone modifications (H3K4me1 and H3K27Ac) for two other erythroblast-specific DMRs (Ery-1 and Ery-2) from the ENCODE database are shown. [Figure 17A] Figure 17 shows the correlation between the percentage of erythroid DNA sequences (E%) in buffy coat DNA of patients with β-thalassemia major, measured by digital PCR assays targeting the Ery-1 (Figure 17A) and Ery-2 (Figure 17B) markers, and the percentage of erythroblasts in total peripheral white blood cells, measured using an automated hematology analyzer. [Figure 17B] Figure 17 shows the correlation between the percentage of erythroid DNA sequences (E%) in buffy coat DNA of patients with β-thalassemia major, measured by digital PCR assays targeting the Ery-1 (Figure 17A) and Ery-2 (Figure 17B) markers, and the percentage of erythroblasts in total peripheral white blood cells, measured using an automated hematology analyzer. [Figure 18A] 1 shows the correlation between E%(FECH) results and E%(Ery-1) and E%(Ery-2) in buffy coat DNA from patients with β-thalassemia major. [Figure 18B] 1 shows the correlation between E%(FECH) results and E%(Ery-1) and E%(Ery-2) in buffy coat DNA from patients with β-thalassemia major. [Figure 19] Figure 1 shows the percentage of erythroid DNA in healthy subjects and patients with aplastic anemia and β-thalassemia major using digital PCR analysis targeting three erythroblast-specific DMRs according to an embodiment of the present invention. [Figure 20A] 1 shows serial measurements of the percentage of erythroid DNA in plasma DNA (E%(FECH)) and the percentage of reticulocyte count in iron deficiency anemia receiving intravenous iron therapy in the pre-treatment state and 2 days after treatment according to an embodiment of the present invention. [Figure 20B] 1 shows serial measurements of the percentage of erythroid DNA in plasma DNA (E%(FECH)) and the percentage of reticulocyte count in iron deficiency anemia receiving intravenous iron therapy in the pre-treatment state and 2 days after treatment according to an embodiment of the present invention. [Figures 21A-21B] 1 shows the serial changes in plasma E% of erythroblast DMR in patients with menorrhagia-induced iron deficiency anemia receiving oral iron treatment according to an embodiment of the present invention. 1 shows the changes in hemoglobin after treatment. [Figure 22] Figure 1 shows serial changes in plasma % unmethylated in erythroblast DMR in patients with chronic kidney disease (CKD) receiving recombinant erythropoietin (EPO) or erythropoiesis-stimulating agents (ESA) treatment. [Figures 23A-23B] 1 shows the serial changes in plasma % unmethylated in erythroblast DMR of aplastic anemia patients receiving antithymocyte globulin (ATG) treatment or cyclosporine as immunosuppressive therapy according to an embodiment of the present invention. 2 shows the serial changes in hemoglobin of aplastic anemia patients receiving treatment. [Figure 24A] 1 shows a plot of % unmethylated in plasma against hemoglobin concentration in four patients with aplastic anemia. [Figure 24B] 1 shows a plot of % unmethylated in plasma against hemoglobin concentration in four patients with aplastic anemia. [Figure 25] 1 shows a box and whisker plot illustrating the absolute concentration of erythroid DNA in the FECH gene-associated DMR (copy number / ml plasma) in healthy subjects and anemic patients according to an embodiment of the present invention. [Figure 26] 1 is a flow chart illustrating a method for analyzing a mammalian blood sample according to an embodiment of the present invention. [Figure 27] 27 illustrates a system 2700 according to an embodiment of the present invention. [Figure 28] 1 shows a block diagram of an exemplary computer system usable with systems and methods according to embodiments of the present invention.

[0010] term A "methylome" provides a measure of the amount of DNA methylation at multiple sites or loci in a genome. The methylome may correspond to the entire genome, a substantial portion of the genome, or a relatively small portion(s) of the genome.

[0011] "Cell lineage" refers to the developmental history of a tissue or organ from a fertilized embryo. Different types of tissue (e.g., different types of blood cells) are expected to have different cell lineages. Red blood cells (RBCs) are derived from proerythroblasts through a series of intermediate cells. Proerythroblasts, megakaryoblasts, and myeloblasts are derived from a common myeloid progenitor. Lymphocytes are derived from a common lymphoid progenitor. Nucleated RBCs are erythroblasts, immature enucleated RBCs are reticulocytes, and mature enucleated RBCs are red blood cells, the red blood cells in the bloodstream that carry hemoglobin.

[0012] A "cell-free mixture" corresponds to a sample containing cell-free DNA fragments derived from various cells. For example, a cell-free mixture can contain cell-free DNA fragments derived from various cell lineages. Plasma and serum are examples of cell-free mixtures obtained from a blood sample, for example, via centrifugation. Other cell-free mixtures can be derived from other biological samples. A "biological sample" refers to any sample collected from a subject (e.g., a pregnant woman, a person suffering from or suspected of suffering from cancer, an organ transplant recipient, or a person suspected of having a disease process involving an organ, such as the heart in myocardial infarction, the brain in stroke, or the hematopoietic system in anemia) and containing one or more nucleic acid molecules of interest. A biological sample can be blood, plasma, serum, urine, vaginal fluid, fluid from edema (e.g., testes), or body fluids such as vaginal lavage fluid, pleural effusion, ascites, cerebrospinal fluid, saliva, sweat, tears, sputum, bronchoalveolar lavage fluid, etc. A stool sample can also be used. In various embodiments, the majority of the DNA in a cell-free DNA enriched biological sample (e.g., a plasma sample obtained via a centrifugation protocol) can be cell-free (as opposed to cellular), e.g., greater than 50%, 60%, 70%, 80%, 90%, 95%, or 99%. The centrifugation protocol can include 3,000 g x 10 minutes to obtain a fluid portion, followed by recentrifugation at 30,000 g for an additional 10 minutes to remove remaining cells.

[0013] "Plasma methylome" refers to the methylome measured from the plasma or serum of an animal (e.g., a human). Plasma methylome is an example of a cell-free methylome because plasma and serum contain cell-free DNA. Plasma methylome is also an example of a mixed methylome because it is a mixture of DNA from different organs, tissues, or cells in the body. In one embodiment, such cells are hematopoietic cells, including, but not limited to, cells of the erythroid (i.e., red cell) lineage, myeloid lineage (e.g., neutrophils and their precursors), and megakaryocytic lineage. During pregnancy, the plasma methylome may contain methylomic information from the fetus and the mother. In cancer patients, the plasma methylome may contain methylomic information from tumor cells and other cells in the patient's body. "Cellular methylome" refers to the methylome measured from a patient's cells (e.g., blood cells). The methylome of blood cells is referred to as the blood cell methylome (or blood methylome). Techniques for measuring the methylome are described in PCT Patent Application No. WO2014 / 043763, entitled "Non-Invasive Determination Of Methylome Of Fetus Or Tumor From Plasma," the disclosure of which is incorporated by reference in its entirety for all purposes.

[0014] A "site" corresponds to a single site, which may be a single base position or a group of correlated base positions, e.g., a CpG site. A "locus" may correspond to a region containing multiple sites. A locus may contain only one site, which would make the region equivalent to the site in its context.

[0015] A "methylation index" for each genomic site (e.g., a CpG site) can refer to the proportion of DNA fragments (e.g., as determined from sequence reads or sequence probes) that exhibit methylation at that site across the total number of reads covering that site. A "read" can correspond to information obtained from a DNA fragment (e.g., the methylation state of the site). A read can be obtained using reagents (e.g., primers or probes) that preferentially hybridize to DNA fragments of a particular methylation state. Typically, such reagents are applied after a process that differentially modifies a DNA molecule depending on its methylation state, such as bisulfite conversion or treatment with a methylation-sensitive restriction enzyme. A read can be a sequence read. A "sequence read" refers to a chain of nucleotides sequenced from any part or all of a nucleic acid molecule. For example, a sequence read can be a short chain of nucleotides (e.g., about 20-150) sequenced from a nucleic acid fragment, a short chain of nucleotides at one or both ends of a nucleic acid fragment, or the sequencing of an entire nucleic acid fragment present in a biological sample. Sequence reads can be obtained in a variety of ways, for example, using sequencing techniques (e.g., in hybridization arrays or capture probes, or amplification techniques such as polymerase chain reaction (PCR) or linear or isothermal amplification) or using probes.

[0016] The "methylation density" of a region can refer to the number of reads of a site in the region that show methylation divided by the total number of reads that cover the site in this region. This site can have a specific feature, for example, it can be a CpG site. Therefore, the "CpG methylation density" of a region refers to the number of reads that show CpG methylation divided by the total number of reads that cover the CpG site in this region (for example, a specific CpG site, a CpG site in a CpG island, or a larger region). For example, the methylation density of each 100 kb bin in the human genome can be determined from the total number of uncovered cytosines (corresponding to methylated cytosines) of CpG sites after bisulfite treatment as a ratio of the total CpG sites covered by the sequences mapped to the 100 kb region. This analysis can also be performed for other bin sizes, such as 500 bp, 5 kb, 10 kb, 50 kb, or 1 Mb. The region can be the whole genome or the whole chromosome or a part of a chromosome (for example, a chromosome arm). The methylation index of a CpG site is the same as the methylation density of this region if the region contains only that CpG site. The "percentage of methylated cytosines" can refer to the total number of analyzed cytosine residues in this region, i.e., the number of cytosine sites "C" that are shown to be methylated (e.g., uncovered after bisulfite conversion), including cytosines outside the CpG context. The methylation index, methylation density, and percentage of methylated cytosines are examples of "methylation levels." Aside from bisulfite conversion, other processes known to those skilled in the art can be used to examine the methylation status of DNA molecules, including, but not limited to, methylation-state-sensitive enzymes (e.g., methylation-sensitive restriction enzymes), methylation-binding proteins, and single-molecule sequencing using methylation-state-sensitive platforms (e.g., single-molecule sequencing by nanopore sequencing (Schreiber et al. Proc Natl Acad Sci 2013;110:18910-18915) and Pacific Biosciences single-molecule real-time analysis (Flusberg et al. Nat Methods 2010;7:461-465)).

[0017] "Methylation profile" (also called methylation status) includes information related to DNA methylation of a region. Information related to DNA methylation can include, but is not limited to, the methylation index of CpG sites, the methylation density of CpG sites in a region, the distribution of CpG sites across a contiguous region, the pattern or level of methylation of each individual CpG site within a region containing two or more CpG sites, and non-CpG methylation. The methylation profile of a substantial portion of a genome can be considered equivalent to a methylome. "DNA methylation" in mammalian genomes typically refers to the addition of a methyl group to the 5' carbon of cytosine residues in CpG dinucleotides (i.e., 5-methylcytosine). DNA methylation can occur at cytosine in other contexts, such as CHG and CHH (where H is adenine, cytosine, or thymine). Cytosine methylation can also be in the form of 5-hydroxymethylcytosine. N 6 Non-cytosine methylations such as -methyladenine have also been reported.

[0018] A "tissue" corresponds to a group of cells that group together as a functional unit. Two or more types of cells can be found within a single tissue. Different types of tissues can consist of different types of cells (e.g., hepatocytes, alveoli, or blood cells), tissues from different organisms (mother vs. fetus), or healthy vs. tumor cells. A "reference tissue" corresponds to the tissue used to determine tissue-specific methylation levels. Multiple samples of the same tissue type from different individuals can be used to determine the tissue-specific methylation level of that tissue type. The same tissue from the same individual at different times may exhibit differences due to physiology (e.g., pregnancy) or pathology (e.g., cancer, anemia, infection, or mutation). The same tissue type from different individuals may exhibit differences due to physiology (e.g., age, sex) or pathology (e.g., cancer, anemia, infection, or mutation).

[0019] The term "level of disorder," also referred to as "classification of disorder," can refer to a classification of whether a disorder is present or absent, the type of disorder, the stage of the disorder, and / or other measures of the severity of the disorder. A level can be a number or other letter. A level can be zero. The level of a disorder can be used in a variety of ways. For example, screening can determine whether a disorder is present in a person not previously known to have cancer. Evaluation can examine a person diagnosed with a disorder to monitor the progression of the disorder over time, study the effectiveness of a therapy, or determine a prognosis. In one embodiment, a prognosis can be expressed as the likelihood that a patient will die from the disorder or that the disorder will progress for a specified duration or after a specified time. Detection can mean "screening" or determining whether a person with suggestive characteristics of the disorder (e.g., symptoms or other positive test) has the disorder.

[0020] Anemia refers to a condition in which the number of red blood cells or their oxygen-carrying capacity is insufficient to meet physiological demands, which may vary depending on age, sex, altitude, smoking, and pregnancy status. According to World Health Organization (WHO) recommendations, anemia can be diagnosed when the hemoglobin concentration is less than 130 g / L in men and less than 110 g / L in women. The term "degree of anemia" can be reflected by the subject's hemoglobin concentration. Lower hemoglobin levels indicate more severe anemia. According to WHO recommendations, severe anemia refers to a hemoglobin concentration less than 80 g / L in men and less than 70 g / L in women, moderate anemia refers to a hemoglobin concentration between 80 and 109 g / L in men and between 70 and 99 g / L in women, and mild anemia refers to a hemoglobin concentration between 110 and 129 g / L in men and 100 and 109 g / L in women.

[0021] A "separation value" is a difference or ratio between two values, such as two fractional contributions or two methylation levels. A separation value can be a simple difference or ratio. A separation value can include other factors, such as multiplicative factors. As another example, a difference or ratio of a function of values ​​can be used, such as the difference or ratio of the natural logarithms (ln) of two values. A separation value can include a difference and a ratio.

[0022] The term "classification" as used herein refers to any number(s) or other feature(s) associated with a particular characteristic of a sample. For example, a "+" sign (or the word "positive") can mean that the sample is classified as having a deletion or amplification. Classification can be binary (e.g., positive or negative) or can have more levels of classification (e.g., a 1-10 or 0-1 scale). The terms "cutoff" and "threshold" refer to a predetermined number used in a procedure. A threshold can be a value above or below what a particular classification requires. Any of these terms can be used in any of these contexts. DETAILED DESCRIPTION OF THE INVENTION

[0023] In some embodiments, the contribution of cell-free DNA from erythroblasts (also called circulating DNA) is quantified using one or more methylation signatures (e.g., one signature per marker) specific to erythroblasts, compared to cell-free DNA from other tissues. A marker (e.g., a differentially methylated region (DMR)) can include a site or a group of sites that contribute to the same signature.

[0024] The contribution of cell-free DNA from erythroblasts can be used to determine the level of hematological disorders such as anemia. For example, embodiments can be used to assess anemia in fetuses, newborns, or children. In the context of anemia, embodiments can be used to examine individuals suspected of or diagnosed with anemia to (i) elucidate the cause of anemia, (ii) monitor the progression of clinical conditions over time, (iii) study the effectiveness of therapy, or (iv) determine prognosis. Thus, embodiments identify erythrocyte DNA as a previously unrecognized major component of the circulating DNA pool and as a noninvasive biomarker for the differential diagnosis and monitoring of anemia and other hematological disorders.

[0025] 1. Introduction Plasma DNA is an increasingly popular analyte for molecular diagnostics. There is ongoing research into the clinical applications of plasma DNA, particularly in noninvasive prenatal testing (1-7) and oncology (8-12). Despite the wide variety of clinical applications, the tissue origins of circulating DNA are not fully understood.

[0026] Circulating DNA has been shown to be primarily released from hematopoietic cells using sex-mismatched bone marrow transplantation as a model system (13, 14). Kun et al. recently demonstrated that a significant proportion of plasma DNA carries the methylation signature of neutrophils and lymphocytes (15). However, there is currently no information on whether DNA of erythroid origin (erythroblasts) can also be detectable in plasma.

[0027] Red blood cells (RBCs) are the most numerous hematopoietic cells in blood. Their concentration is approximately 5 × 10 per liter of blood. 12 Considering that each RBC has a lifespan of approximately 120 days, the body can produce 2 x 10 RBCs per day. 11 RBCs, or 9.7 x 10 per hour 9 Mature human RBCs lack a nucleus.

[0028] It is during the enucleation stage that erythroblasts lose their nuclei and mature into reticulocytes in the bone marrow (16). The enucleation process is a complex, multistep process involving the tightly regulated actions of cell signaling and cytoskeletal functions. The nuclear material of erythroblasts is phagocytosed and degraded by bone marrow macrophages in erythroblast islands, e.g., in the bone marrow (17). We hypothesize that some of the degraded DNA material of bone marrow-derived erythroid lineages is released into the circulation.

[0029] Embodiments identify methylation signatures of DNA from cells of erythroid origin and can use such signatures to determine whether erythrocyte DNA is detectable in human plasma. High-resolution reference methylomes of different tissues and hematopoietic cell types have been publicly available through collaborative projects, including the BLUEPRINT Project (18, 19) and the Roadmap Epigenomics Project (20). The present inventors and others have previously demonstrated that it is possible to trace the origin of plasma DNA through analysis of tissue-associated methylation signatures (15, 21, 22). Further details of such analyses to determine the contribution of a specific tissue to a cell-free mixture (e.g., plasma) can be found in PCT Patent Application No. WO2016 / 008451, entitled "Methylation Pattern Analysis of Tissues in a DNA Mixture," the disclosure of which is incorporated by reference in its entirety for all purposes.

[0030] To test our hypothesis and demonstrate the presence of erythroid DNA in plasma, we identified specific differentially methylated regions (DMRs) in erythroblasts through analysis of the methylation profiles of erythroblasts and other tissue types. Based on this discovery, we developed a digital polymerase chain reaction (PCR) assay targeting the erythroblast-specific DMRs, enabling quantitative analysis of erythroid DNA in biological samples. Specifically, using high-resolution methylation profiles of erythroblasts and other tissue types, we found that three genomic loci are hypomethylated in erythroblasts but hypermethylated in other cell types. We developed a digital PCR assay to measure erythroid DNA using the differentially methylated regions for each locus.

[0031] The inventors applied these digital PCR assays to study plasma samples from healthy subjects and patients with different types of anemia. The inventors also investigated the potential clinical utility of the assay in assessing anemia. Although the examples use PCR assays, other assays, such as sequencing, may also be used.

[0032] In subjects suffering from anemia of different etiologies, the inventors have shown that quantitative analysis of circulating erythroid DNA (e.g., using methylation markers) reflects erythropoietic activity in bone marrow. For patients with reduced erythropoietin activity, the percentage of circulating erythroid DNA is reduced, as exemplified by aplastic anemia. For patients with increased but ineffective erythropoiesis, the percentage is elevated, as exemplified by β-thalassemia major. In addition, the plasma level of erythroid DNA has been found to correlate with treatment response in aplastic anemia and iron deficiency anemia. Plasma DNA analysis using digital PCR assays targeting two other differentially methylated regions has also shown similar findings.

[0033] 2. Differentially methylated regions (DMRs) in erythroblasts We hypothesize that the erythroblast enucleation process or other processes involved in RBC maturation will significantly contribute to the pool of circulating cell-free DNA. To determine the contribution of circulating DNA from erythroblasts, we identified differentially methylated regions (DMRs) in the DNA of erythroblasts by comparing their DNA methylation profiles with those of other tissues and blood cells. We examined the methylation profiles of erythroblasts and other blood cells (neutrophils, B lymphocytes, and T lymphocytes) and tissues (liver, lung, colon, small intestine, pancreas, adrenal gland, esophagus, heart, brain, and placenta) from the BLUEPRINT Project and Roadmap Epigenomics Project and from methylomes generated by our group (18-20,23).

[0034] In a simple example, one or more DMRs can be used directly to determine the contribution of circulating DNA from erythroblasts, for example, by determining the percentage of DNA fragments that are methylated (for hypermethylated DMRs) or unmethylated (for hypomethylated DMRs). This percentage can be used directly or can be modified (e.g., multiplied by a scaling factor). Other embodiments can perform more complex procedures, such as solving a linear equation. As described in PCT Patent Application No. WO2016 / 008451, the methylation levels of N genomic sites can be used to calculate the contribution from M tissues, where M is less than or equal to N. The methylation level of each site can be calculated for each tissue. The linear equation Ax=b can be solved, where b is a vector of methylation densities measured at N sites, x is a vector of contributions from M tissues, and A is a matrix with M rows and N columns, where each row provides the methylation density in N tissues for a particular site in that row. A least-squares optimization can be performed if M is smaller than N. An N×M dimensional matrix A can be formed from the tissue-specific methylation levels of reference tissues, as obtained from previous sources.

[0035] 1. Identifying DMR To identify differentially methylated regions (DMRs), tissues of a particular type / lineage (e.g., erythroblasts) can be isolated and then analyzed, for example, using methylation-aware sequencing, as described herein. The methylation density of sites across tissue types (e.g., only two types of erythroblasts) can be analyzed to determine whether there are sufficient differences to identify sites for use in DMRs.

[0036] In some embodiments, methylation markers for erythroblasts can be identified using one or more of the following criteria: (1) A CpG site is hypomethylated in erythroblasts if the methylation density of the CpG site is less than 20% in erythroblasts and more than 80% in other blood cells and tissues, or vice versa. (2) To be a DMR, a region may need to contain multiple hypomethylated CpG sites (e.g., 3, 4, 5, or more). Therefore, multiple CpG sites within a DMR can be selected and analyzed by the assay to improve the signal-to-noise ratio and specificity of the DMR. (3) DMRs can be selected to be representative of DNA molecules in cell-free mixtures. In plasma, there are primarily short DNA fragments, most of which are shorter than 200 bp (1, 24, 25). For embodiments determining the presence of erythroid DNA molecules in plasma, DMRs can be defined within a size representative of plasma DNA molecules (i.e., 166 bp) (1). Such differences in criteria can be used in conjunction with these three criteria; for example, different thresholds other than 20% and 80% can be used to identify CpG sites as hypomethylated. As discussed later, some results use selected CpG sites within three erythroblast-specific DMRs that are hypomethylated in erythroblasts.

[0037] Using the previously defined criteria, we identified three erythroblast-specific DMRs across the genome. One DMR was located within the intronic region of the ferrochelatase (FECH) gene on chromosome 18. In this region, the difference in methylation density between erythroblasts and other cell types was the largest among the three identified DMRs. The FECH gene encodes ferrochelatase, an enzyme responsible for the final step in heme biosynthesis (26). As shown in Figure 1, the four selected CpG sites within the erythroblast-specific DMR were all hypomethylated in erythroblasts but hypermethylated in other blood cells and tissues.

[0038] FIG. 1 shows the methylation density of CpG sites within the promoter of the ferrochelatase (FECH) gene according to an embodiment of the present invention. The FECH gene is located on chromosome 18, and the genomic coordinates of the CpG sites are shown on the X-axis. As shown, the methylation density of the CpG sites is within the intronic region of the FECH gene. Four CpG sites located within the region 110 bounded by two vertical dotted lines were all hypomethylated in erythroblasts but hypermethylated in other tissues or cell types. For illustrative purposes, individual results for lung, heart, small intestine, colon, thymus, stomach, adrenal gland, esophagus, bladder, brain, ovary, and pancreas are not shown. These average values ​​are represented by "other tissues."

[0039] Because the CpG sites located within this region are hypomethylated, sequences that are unmethylated for all four CpG sites within the two dotted lines in Figure 1 will be enriched for DNA derived from erythroblasts. Therefore, the amount of hypomethylated sequences in a DNA sample will reflect the amount of DNA derived from erythroblasts.

[0040] An assay has been developed to detect methylated or unmethylated DNA at specified CpG sites.The more CpGs there are in plasma DNA molecules, the more specific the assay will be.Most plasma DNA molecules are less than 200bp, with an average of 166bp.Therefore, all CpG sites can be within 166bp of each other, but can also be within 150, 140, 130, 120, 110, or 100bp of each other.In other embodiments, only pairs of CpG sites can be within such distances.

[0041] In other embodiments, a CpG site can be defined as hypomethylated in erythroblasts if the methylation density of the CpG site is less than 10% (or other threshold) in erythroblasts and greater than 90% (or other threshold) in all other tissues and blood cells. A CpG site can be defined as hypermethylated in erythroblasts if the methylation density of the CpG site is greater than 90% (or other threshold) in erythroblasts and less than 10% (or other threshold) in all other tissues and blood cells. In some embodiments, a DMR can have at least two CpG sites within 100 bp, all of which show differential methylation for erythroblasts.

[0042] In one embodiment of identifying DMR, to be diagnostically useful, all CpG sites within 100bp (or any other length) may be required to show hypomethylation or hypermethylation in erythroblasts compared with all other tissues and blood cells.For example, multiple CpG sites can be spread over 100bp or less on the reference genome corresponding to mammal.As another example, each CpG site can be within 100bp of another CpG site.Therefore, CpG sites can be spread over more than 100bp.

[0043] In some embodiments, the one or more differentially methylated regions can be identified in the following manner: The methylation index (e.g., density) of a plurality of sites can be obtained for each of a plurality of cell lineages, including a specific blood cell lineage and other cell lineages, as shown, for example, in FIG. 1 . At each site of the plurality of sites, the methylation indices of the multiple cell lineages can be compared with each other. Based on this comparison, one or more sites of the plurality of sites can be identified as each having a methylation index in the specific blood cell lineage below / above a first methylation threshold and a methylation index in each of the other cell lineages above / below a second methylation threshold. In this manner, hypomethylated and / or hypermethylated sites can be identified. Examples of the first methylation threshold are 10%, 15%, or 20% for hypomethylated sites, and examples of the second methylation threshold can be 80%, 85%, or 90%. The differentially methylated region containing the one or more sites can then be identified, for example, using the criteria described above.

[0044] 2. Detection of methylated and unmethylated DNA sequences To detect methylated and unmethylated DNA sequences in erythroblast-specific DMRs, two digital PCR assays can be developed: one targeting the unmethylated sequence and the other targeting the methylated sequence. In other embodiments, other methods can be used to detect and / or quantify methylated and unmethylated sequences in DMRs, such as methylation-aware sequencing (e.g., bisulfite sequencing, or sequencing following a biochemical or enzymatic process that will differentially modify DNA based on its methylation status), real-time methylation-specific PCR, methylation-sensitive restriction enzyme analysis, and microarray analysis. Thus, in addition to PCR assays, other types of assays can be used.

[0045] In one example, erythroblast DMR can be detected after bisulfite treatment. The methylation status of CpG sites can be determined based on the detection results (e.g., PCR signals). For the FECH gene, the following primers can be used to amplify erythrocyte DMR after bisulfite treatment for sequencing: 5'-TTTAGTTTATAGTTGAAGAGAATTTGATGG-3' and 5'-AAACCCAACCATACAACCTCTTAAT-3'.

[0046] In another example, to increase the specificity of the analysis, two forward primers can be used that cover both the methylated and unmethylated states of a particular CpG. Listed below are such primer sets used in two digital PCR assays that specifically target methylated and unmethylated sequences. [Table 1] [Table 2]

[0047] The underlined nucleotides in the reverse primer and probe were differentially methylated cytosines at CpG sites. The reverse primer and probes for the unmethylated and methylated assays bind specifically to the unmethylated and methylated sequences due to the differences at the underlined nucleotides.

[0048] 3. Validation using universally methylated and universally unmethylated DNA Analysis of universally methylated and universally unmethylated DNA was performed to confirm the accuracy rates of the two assays.

[0049] The specificity of these two digital PCR assays, designed for the detection and quantification of methylated and unmethylated sequences of erythroblast-specific DMRs, was confirmed using universally methylated sequences derived from CpGenome Human Methylated DNA (EMD Millipore) and universally unmethylated sequences derived from EpiTect Unmethylated Human Control DNA (Qiagen). CpGenome Human Methylated DNA was purified from HCT116DKO cells, and all CpG nucleotides were enzymatically methylated using M.SssI methyltransferase. Universally methylated and universally unmethylated DNA sequences were run on the same plate as positive and negative controls. The cutoff value for positive fluorescent signal was determined relative to the controls. The number of methylated and unmethylated DNA sequences in each sample was calculated using Poisson correction after combined counting from duplicate wells (4).

[0050] 2A and 2B show the analysis of universally methylated and universally unmethylated DNA using a digital PCR assay designed to detect methylated and unmethylated DNA according to an embodiment of the present invention. The vertical axis corresponds to the relative fluorescent signal intensity of the unmethylated sequence. The horizontal axis corresponds to the relative fluorescent signal intensity of the methylated sequence. Data were generated using DNA known to be either methylated or unmethylated. These analyses are intended to demonstrate the specificity of the assay for methylated or unmethylated DNA.

[0051] For analysis of universally unmethylated DNA, an amplified signal was detected using the assay for unmethylated DNA (blue dot 210 in plot 205 in Figure 2A corresponds to a positive FAM signal), whereas the blue dot 210 was not detected using the assay for methylated DNA (plot 255 in Figure 2B). For analysis of universally methylated DNA, an amplified signal was detected using the assay for methylated DNA (green dot 220 in plot 250 in Figure 2B), whereas the green dot 220 was not detected using the assay for unmethylated DNA (plot 200 in Figure 2A). Black dots in each panel represent droplets that did not contain any amplified signal. The thick vertical and horizontal lines within each of the four panels represent the threshold fluorescent signal for a positive result. These results confirmed the specificity of these two assays for methylated and unmethylated DNA in the erythroblast-specific DMR.

[0052] To further evaluate the analytical sensitivity of the FECH gene-associated DMR-based assay, samples with unmethylated sequences were serially diluted at designated fractional concentrations (i.e., the percentage of unmethylated sequences among all (unmethylated and methylated) sequences of the FECH gene-associated DMR). There were a total of 1,000 molecules per reaction. Unmethylated sequences could be detected at as low as 0.1% of the total amount of methylated and unmethylated sequences (see Table 3). [Table 3]

[0053] Additionally, to assess potential variations (e.g., from pipetting), we measured the percentage of unmethylated sequences in an artificially mixed sample of methylated and unmethylated sequences in 20 separate reactions at a specified fractional concentration (% unmethylated sequences = 30%). We used a total of 500 methylated and unmethylated molecules for each reaction. This figure is comparable to that observed in the total number of methylated and unmethylated molecules in our digital PCR analysis of plasma DNA samples. We observed a mean of 30.4% and a standard deviation of 1.7% for the 20 replicate measurements of the percentage of unmethylated sequences. The intra-assay coefficient of variation was calculated to be 5.7%.

[0054] 3. Specificity and Sensitivity of the Assay for Different Samples To confirm the tissue specificity of the digital PCR assay targeting the FECH gene-related DMR for erythroid DNA, we tested the digital PCR assay in various samples with different amounts of erythroid cells, as measured using techniques other than these digital PCR assays. The amount of unmethylated DNA sequences detected by the digital PCR assay should reflect the amount of erythroid DNA. Similarly, the amount of methylated sequences should reflect DNA derived from other tissues or cell types. Therefore, we defined the percentage of erythroid DNA (E%) in a biological sample as the percentage of unmethylated sequences among all sequences (unmethylated and methylated) detected in the erythroblast-specific DMR. Therefore, blood samples were analyzed using assays specific for methylated and unmethylated sequences in the DMR region to determine the correlation between the percentage of unmethylated sequences, unmethylated % (also referred to as E%), and the presence of DNA derived from erythroblasts. Unmethylated % (E%) is an example of a methylation level.

[0055] The percentage of erythrocyte DNA (E%) was calculated as follows:

number

[0056] Because the difference in methylation density between erythroblasts and other cell types is greatest for the DMR within the FECH gene, we first proceeded to analyze E% based on this marker site to verify our hypothesis. We then analyzed E% based on two other erythroblast-specific DMRs in a subset of samples to verify the E% results from the FECH gene-associated DMR. The E% results based on the DMR within the FECH gene will be denoted as E%(FECH). Other percentages or ratios, such as the percentage of methylated sequences or the ratio of methylated sequences to unmethylated sequences alone, can also be used, and either value can be in the numerator and denominator of this ratio.

[0057] Specifically, the number of methylated and unmethylated DNA sequences in each sample at four CpG sites on the FECH gene from Figure 1 was measured using digital PCR. The percentage of unmethylated DNA in the sample (% unmethylated / E%) was then calculated. In one embodiment, all four CpG sites must be unmethylated for a DNA fragment to be considered unmethylated.

[0058] Two scenarios are used to test the ability of the assay signal to quantify erythroblasts: one scenario is cord blood versus adult blood, since the number of erythroblasts differs between the two samples, and the other scenario is that subjects with beta-thalassemia major have a significant number of erythroblasts in their blood.

[0059] 1. Erythroblast-enriched samples versus buffy coats from healthy subjects The number of erythroblasts in adult blood is very low. Umbilical cord blood has a very high number of erythroblasts. Therefore, the E% for the four CpG sites should be much higher in umbilical cord blood than in healthy patients. Therefore, to confirm the tissue specificity of the digital PCR assay targeting the FECH gene-related DMR in erythroid DNA, we tested the digital PCR assay in samples containing DNA extracted from 12 different normal tissue types and in erythroblast-enriched samples. We included four samples from different individuals for each tissue type. Erythroblast-enriched samples were prepared from umbilical cord blood for analysis.

[0060] Specifically, to confirm the association between DMR methylation density and E%, venous blood samples were collected from 21 healthy subjects and 30 pregnant women (10 in the first trimester, 10 in the second trimester, and 10 in the third trimester). Blood samples were centrifuged at 3,000 g for 10 minutes to separate plasma and blood cells. Buffy coats were collected after centrifugation. Plasma samples were collected and recentrifuged at 30,000 g to remove residual blood cells.

[0061] For 12 different normal tissue types, we included four samples from different individuals for each tissue type. As shown in Table 4, the median E%(FECH) values ​​from all tissue DNA were low (median range: 0.00% to 2.63%). [Table 4]

[0062] The experimental procedures for flow cytometry and cell sorting, followed by DNA extraction and enrichment from umbilical cord blood, are described below. After delivery, 1–3 mL of umbilical cord blood was collected from each of eight pregnant women. Mononuclear cells were isolated from the cord blood samples after density gradient centrifugation using a Ficoll-Paque PLUS kit (GE Healthcare). After collection, 1 × 10 mononuclear cells were collected. 8The cells were incubated with 1 mL of a 1:10 dilution of fluorescein isothiocyanate (FITC)-conjugated anti-CD235a (glycophorin A) and phycoerythrin (PE)-conjugated anti-CD71 antibody (Miltenyi Biotec) in phosphate-buffered saline for 30 minutes at 4°C in the dark. CD235a+CD71+ cells were then sorted and analyzed using a BD FACSAria Fusion Cell Sorter (BD Biosciences). Because CD235a and CD71 are specifically present on erythroblasts, CD235a+CD71+ cells would be enriched for erythroblasts (Bianchi et al. Prenatal Diagnosis 1993;13:293-300).

[0063] Because only a small number of cells were obtained from each case, cells from eight cases were pooled for downstream analysis. These two antibodies are specific for erythroblasts and attach to their surface. These two antibodies are conjugated to FITC and phycoerythrin, respectively. These two substances bind to magnetic beads, which can be sorted using a cell sorter. Therefore, Ab-labeled erythroblasts can be captured. Erythroblasts were enriched from eight umbilical cord blood samples using flow cytometry and cell sorting with anti-CD71 (transferrin receptor) and anti-CD235a (glycophorin A) antibodies (see Supplementary Materials and Methods) and then pooled. DNA was extracted from the pooled samples.

[0064] The E%(FECH) of DNA from pooled cord blood samples was 67% at the four CpG sites tested in the assay for CD235a+CD71+ cells (mostly erythroblasts). Regarding the E%(FECH) in buffy coat DNA from 20 healthy subjects with undetectable erythroblast counts in peripheral blood, the median E% of buffy coat DNA was 2.2% (interquartile range: 1.2-3.1%). The observation of a low proportion of erythroblast-specific unmethylated sequences in the buffy coats of healthy subjects is consistent with the fact that mature RBCs do not have nuclei. Because CD235a and CD71 are cell surface markers specific to erythroblasts (Bianchi et al. Prenatal Diagnosis 1993;13:293-300), the high E%(FECH) in CD235a- and CD71-enriched cells indicates that an assay for unmethylated DNA in the erythroblast-specific DMR would be able to detect DNA derived from erythroblasts. Thus, this high E% for erythroblast-enriched samples, along with the low E% results for DNA from other tissue types and buffy coat DNA from healthy subjects, indicates that the digital PCR assay for unmethylated FECH sequences was specific for DNA derived from erythroblasts.

[0065] 2. About patients with beta-thalassemia major In patients with beta-thalassemia major, the bone marrow is trying to make many red blood cells (RBCs). However, there is a defect in hemoglobin production. As a result, many RBCs do not contain enough hemoglobin and contain many excess alpha globin chains. These defective RBCs will be removed from the bone marrow and will never become mature RBCs. There are two types of globin chains: alpha and beta. One hemoglobin molecule requires two alpha chains and two beta chains. If beta chains are not produced, the excess alpha chains will clump together and functional hemoglobin cannot be formed.

[0066] In patients with beta-thalassemia major, high but ineffective red blood cell production may result in decreased production of mature RBCs (Schrier et al. Current Opinion in Hematology 2002;9:123-6). This is accompanied by compensatory extramedullary hematopoiesis and the presence of nucleated red blood cells in the circulation. As described below, patients with beta-thalassemia major have more nucleated red blood cells than healthy patients. The number of nucleated RBCs in peripheral blood can be counted on blood smears and expressed as the number of nucleated RBCs per 100 white blood cells (WBCs).

[0067] Patients with thalassemia major generally have higher numbers of erythroblasts in their peripheral blood than healthy individuals due to ineffective red blood cell production (27), and these patients also provide a good mechanism for testing the specificity and sensitivity of the assay. Therefore, we tested the sensitivity of our digital PCR assay in buffy coat DNA from 15 patients with β-thalassemia major. All patients had detectable numbers of erythroblasts in their peripheral blood, as measured by an automated hematology analyzer (UniCel DxH 800 Coulter Cellular Analysis System, Beckman Coulter) and confirmed by manual counting.

[0068] 3A is a plot showing the correlation between E%(FECH) and the number of nucleated RBCs (erythroblasts) in blood cells, according to an embodiment of the present invention. E% is measured by a digital PCR assay targeting the FECH gene-associated DMR. As indicated by the vertical and horizontal axes, the plot shows the correlation between the percentage of erythroid DNA sequences in buffy coat DNA (E%(FECH)) and the percentage of erythroblasts in total peripheral white blood cells, as measured using an automated hematology analyzer.

[0069] As shown in Figure 3A, the E% (FECH) in buffy coat DNA correlated well with the percentage of erythroblasts in peripheral white blood cells measured by the hematology analyzer (r = 0.94, P < 0.0001, Pearson correlation). The good linear relationship between E% and erythroblast count in the buffy coats of thalassemia patients indicates that the digital PCR assay provided a good quantitative measurement of the erythroid DNA content in the sample, since erythroblasts are unmethylated for the DMR and other blood cells are methylated. Therefore, the higher the proportion of erythroblasts in a blood sample, the higher the E% would be. The purpose of this experiment was to demonstrate that these assays can be used to reflect the amount of erythroblast-derived DNA in a sample. These results further support that E% for the FECH gene reflects the proportion of erythroblast-derived DNA.

[0070] This correlation may exist for other patients as well. However, erythroblast counts may be higher for patients with beta-thalassemia major, and samples from these patients provide a good test for identifying such correlations. As can be seen from Figure 3A, patients had a wide range of E% and erythroblast counts, thereby providing a good mechanism for testing correlations.

[0071] 3. Methods for measuring the amount of cellular DNA of specific cell lineages In some embodiments, when the amount of unmethylated or methylated DNA fragments in a cell-free mixture (e.g., a plasma or serum sample) is counted at one or more DMRs specific to a particular cell lineage, this amount can be used to measure the number of cells (or other amount of DNA) of a particular cell lineage. As shown in Figure 3A, the percentage of DNA fragments that are unmethylated at the FECH DMR correlates with the number of erythroblasts in a blood sample. Absolute concentrations can also be used. For hypermethylated DMRs, the amount (e.g., percentage or absolute concentration) of methylated DNA fragments can be used. As described herein, various cell lineages can be used.

[0072] A calibration function can be used to determine the number of cell counts. In the example of Figure 3A, a line fit to the data points can provide the calibration function. For example, the calibration function can be defined by its function parameters (e.g., the slope and y-intercept for the line, or more parameters for other functions), or by a set of data points from which a curve fit can be obtained. The data points (e.g., referred to as calibration data points) can have known values ​​for the DNA content of the cell lineage (e.g., cell number), as can be determined by another technique in the same way as the erythroblast count.

[0073] Thus, the method can determine the amount of DNA derived from a specific cell lineage in a blood sample. Multiple methylated or unmethylated sequences in one or more DMRs can be determined from the assay, as described herein. The methylation level can be determined and compared to a calibration value of a calibration function. For example, the methylation level can be compared to a line (or other calibration function) to determine the intersection of the function with the methylation level and, therefore, the corresponding DNA amount (e.g., the value on the horizontal axis in Figure 3A). In other embodiments, the methylation level can be compared to individual calibration data points, e.g., having a methylation level close to the measured methylation level of the sample.

[0074] FIG. 3B is a flow chart illustrating a method 300 for determining the cell abundance of a specific cell lineage in a biological sample by analyzing cell-free DNA according to an embodiment of the present invention. Method 300 can use measurements such as those shown in FIG. 3A. Portions of method 300 can be performed manually, while other portions can be performed by a computer system. In one embodiment, a system can perform all of the steps. For example, the system can include robotic elements (e.g., for obtaining the sample and performing the assay), a detection system for detecting a signal from the assay, and a computer system for analyzing the signal. Instructions for controlling such a system can be stored on one or more computer-readable media, such as configuration logic in a field-programmable gate array (FPGA), flash memory, and / or a hard drive. FIG. 27 illustrates such a system.

[0075] At block 310, a cell-free mixture of a biological sample is obtained. The biological sample may be a blood sample, but may also be other samples containing cell-free DNA, as described herein. Examples of cell-free mixtures include plasma or serum. The cell-free mixture may contain cell-free DNA derived from multiple cell lineages.

[0076] At block 320, the DNA fragments in the cell-free mixture are contacted with an assay corresponding to one or more differentially methylated regions, each of which is specific to a particular cell lineage (e.g., a particular blood cell lineage, such as erythroblasts) by being hypomethylated or hypermethylated relative to other cell lineages.

[0077] In various embodiments, the assay can include PCR or sequencing. Contacting the DNA fragments can include flow cells, droplets, beads, or other mechanisms for providing interaction between the assay and the DNA fragments. Examples of such assays include whole-genome bisulfite sequencing, targeted bisulfite sequencing (by hybridization capture or amplicon sequencing), other methylation-specific sequencing (e.g., single-molecule real-time (SMRT) DNA sequencing from Pacific Biosciences), real-time methylation-specific PCR, and digital PCR. Further examples of assays that can be used in method 300 are described herein, e.g., in Section XII. While the example in Figure 3A is for erythroblasts, other blood lineages, including other blood cell lineages, can be used.

[0078] In block 330, a first number of methylated or unmethylated DNA fragments is detected in the cell-free mixture in one or more differentially methylated regions based on the signal obtained from the assay. The assay can provide various signals, such as optical or electrical signals. The signal can provide a specific signal per DNA fragment, or an aggregate signal indicating the total number of DNA fragments with a methylation signature (e.g., as in real-time PCR).

[0079] In one embodiment, sequencing can be used to obtain sequence readings for DNA fragments, and the DNA fragments can be aligned with a reference genome. If the DNA fragment aligns with one of the DMRs, a counter can be incremented. Assuming that the signal is derived from a specific methylation of an unmethylated assay, the DNA fragment can be assumed to have that methylation signature. In another embodiment, readings from PCR (e.g., optical signals from positive wells) can be used to increment such counters.

[0080] In block 340, a first methylation level is determined using the first number. The first methylation level can be normalized or can be an absolute concentration, for example, per volume of the biological sample. Examples of absolute concentrations are provided in Figure 25.

[0081] For normalized values, the first number and total number of DNA fragments in the cell-free mixture of one or more differentially methylated regions can be used to determine the methylation level.As explained above, the methylation level can be the percentage of unmethylated DNA fragments.In other embodiments, this percentage can be the methylated DNA fragments, which will have the opposite relationship to the previous example for erythroblasts.In various implementations, the methylation level can be determined by using the percentage across all DMR sites, by averaging the individual percentages of each site, or by the weighted average of each site.

[0082] At block 350, one or more calibration data points are obtained. Each calibration data point can specify (1) a cell mass of a particular blood cell lineage and (2) a calibration methylation level. The one or more calibration data points are determined from a plurality of calibration samples.

[0083] The amount of cells can be specified as a specific amount (e.g., number or concentration) or a range of amounts. Calibration data points can be determined from calibration samples with known amounts of cells, which can be measured via various techniques described herein. Some calibration samples may have the same amount of cells, although at least some calibration samples will have different amounts of cells.

[0084] In various embodiments, the one or more calibration points may be defined as a discrete point, a set of discrete points, a function, a discrete point and a function, or some other combination of a discrete or continuous set of values. By way of example, a calibration data point may be determined from a calibration methylation level for a sample having a particular amount of cells of a particular lineage.

[0085] In one embodiment, measurements of the same methylation level from multiple samples of the same amount of cells can be combined to determine a calibration data point for a particular amount of cells. For example, an average methylation level can be obtained from the methylation data of samples of the same amount of cells to determine a particular calibration data point (or provide a range corresponding to the calibration data point). In another embodiment, multiple data points with the same calibration methylation level can be used to determine the average amount of cells.

[0086] In one run, methylation levels are measured for many calibration samples. A calibration value for the methylation level is determined for each calibration sample, where the methylation level can be plotted against the known amount of cells in the sample (e.g., as in Figure 3A). A function is then fitted to the data points of the plot, where the function fit defines the calibration data points to be used in determining the amount of cells for a new sample.

[0087] In block 360, the first methylation level is compared to a calibrated methylation level of at least one calibration data point. This comparison can be performed in various ways. For example, the comparison can be whether the first methylation level is higher or lower than the calibrated methylation level. This comparison can involve comparing to a calibration curve (composed of calibration data points), thereby identifying a point on the curve having the first methylation level. For example, the calculated value X of the first methylation level can be used as an input to a function F(X), where F is the calibration function (curve). The output of F(X) is the cell mass. A variable error range can be provided for each X value, thereby providing a range of values ​​as the output of F(X).

[0088] At block 370, the cell mass of a particular cell lineage in the biological sample is estimated based on comparing. In one embodiment, it can be determined whether the first methylation level is above or below a threshold calibration methylation level, thereby determining whether the cell mass of the instantaneous sample is above or below the cell mass corresponding to the threshold calibration methylation level. For example, if the first methylation level X1 calculated for the biologic is above or below the calibration methylation level X2, the cell mass of the instantaneous sample can be estimated based on comparing the first methylation level X1 with the calibration methylation level X3. C If the cell mass N1 of the biological sample is greater than X C The cell mass corresponding to N C This relationship between high and low may depend on how the parameters are defined. In such an embodiment, only one calibration data point may be required.

[0089] In another embodiment, this comparison is accomplished by inputting the first methylation level into a calibration function, which can effectively compare the first methylation level to the calibration methylation level by identifying a point on the curve that corresponds to the first methylation level, and then providing the estimated cell mass as an output value of the calibration function.

[0090] 4. Origin of cell-free DNA from plasma erythroblasts Using the established relationship between percent unmethylated and DNA derived from erythroblasts, the percent unmethylated in plasma can be used to quantify erythroblast-derived DNA in plasma. Using the assay described above, the percent unmethylated in plasma was determined. Differences in the percent unmethylated in buffy coat and plasma were observed. This analysis indicates that cell-free erythroblast DNA in plasma is derived from red blood cell production in the bone marrow and not from erythroblasts in the bloodstream.

[0091] After confirming that the % unmethylated determined by the two digital PCR assays accurately reflected the amount of erythroblast-derived DNA in the samples, we proceeded to compare the proportion of erythroblast-derived DNA in the buffy coat and plasma of healthy control subjects and pregnant women.

[0092] 4 shows % unmethylation in buffy coat and plasma of healthy non-pregnant subjects and pregnant women at different stages of pregnancy according to an embodiment of the present invention. Plasma samples had significantly higher % unmethylation compared to buffy coat for each group of subjects (P<0.01 by Wilcoxon signed rank test for each paired comparison between plasma and buffy coat).

[0093] The results in Figure 4 show that the amount of erythroblast-derived DNA is low in blood cells, as expected due to the low number of nucleated RBCs. A surprising result is the high amount of erythroblast-derived DNA in plasma. If the erythroblast-derived DNA in plasma were derived from blood cells, one would expect these two amounts to be similar. Therefore, this data indicates that the origin of the erythroblast-derived DNA in plasma is derived from red blood cell production in the bone marrow.

[0094] Figure 5 is a plot showing the lack of correlation between % unmethylated in buffy coat and plasma. No significant correlation was observed between % unmethylated for buffy coat DNA and plasma DNA (R 2 = 0.002, P = 0.99, Pearson correlation). The lack of correlation is seen across all subjects, including non-pregnant, first-trimester, second-trimester, and third-trimester pregnant women. Similar to the results in Figure 4, this is surprising, as one would expect the two to be correlated if the origin of the erythroblast-derived DNA was from blood cells in the bloodstream.

[0095] The observation that plasma DNA has a much higher percentage of unmethylated DNA than buffy coat DNA, as well as the lack of correlation between the percentages of unmethylated DNA in plasma and buffy coat, suggests that circulating cell-free DNA bearing the erythroblast methylation signature likely originates from bone marrow during the process of erythropoiesis, rather than from circulating blood cells. Thus, cell-free plasma DNA bearing the erythroblast methylation signature is generated in bone marrow, as opposed to being generated from nucleated RBCs in the bloodstream, since the number of nucleated RBCs in the bloodstream is very low in healthy subjects and pregnant women. Furthermore, the contribution from white blood cells (WBCs) to the erythroblast methylation signature is so low that this contribution does not provide a measurable dependency on cell-free plasma DNA bearing the erythroblast methylation signature.

[0096] 5. Methylation levels as a measure of erythropoietic activity Based on previous observations, we determined that the unmethylated percentage of erythroblast DMR would reflect erythropoietic activity in the bone marrow. A higher unmethylated percentage would indicate increased erythropoietic activity. In other words, analysis of erythroblast DNA in plasma / serum would serve as a liquid biopsy of the bone marrow. This analysis would be particularly useful for investigating anemia, for example, to determine whether anemia is due to decreased erythropoiesis (e.g., aplastic anemia), defective erythropoiesis (e.g., failure to produce mature RBCs in thalassemia), or elevated RBC consumption (e.g., blood loss and hemolytic anemia). To this end, we recruited 35 healthy subjects and 75 anemic patients with different etiologies. Peripheral blood sample collection and processing, plasma and buffy coat DNA extraction, and DNA bisulfite conversion were performed. Further details of the method are described in Section XII.

[0097] 1. Measurement of cell-free erythroid DNA in plasma of healthy subjects After confirming the specificity of our assays, we used these assays to analyze the plasma of healthy subjects. We analyzed E%(FECH) in the plasma of 35 healthy subjects, including 20 subjects from the same group who also provided buffy coat samples. The median E%(FECH) of plasma DNA was 30.1% (interquartile range: 23.8-34.8%). This suggested that erythroid DNA constitutes a significant proportion of the circulating DNA pool in the plasma of healthy individuals. To determine the origin of plasma erythroid DNA, we compared the corresponding E%(FECH) results in the plasma and buffy coat of 20 healthy subjects.

[0098] Figures 6A and 6B show the percentage of erythroid DNA (E%(FECH)) in healthy subjects. Figure 6A shows E% in buffy coat DNA and plasma DNA from healthy subjects, where E% values ​​are higher in plasma (cell-free fraction) than in buffy coat (cellular fraction). The median E% in plasma DNA (median: 26.7%, interquartile range: 23.7-30.4%) was significantly higher than the median E% in paired buffy coat DNA (median: 2.2%, interquartile range: 1.2-3.1%) (P<0.0001 by Wilcoxon signed-rank test).

[0099] Figure 6B shows the lack of correlation between E% in buffy coat DNA and the corresponding plasma DNA of healthy subjects. There was a lack of correlation between the paired E% (FECH) results in plasma DNA and buffy coat DNA (r = 0.002, P = 0.99, Pearson correlation). The findings in both Figures 6A and 6B indicate that circulating erythroid DNA was unlikely to be primarily derived from circulating erythroblasts in peripheral blood.

[0100] Figure 7 shows the lack of correlation between plasma DNA E%(FECH) results and age in healthy subjects. The plot shows that E%(FECH) results are not correlated with subject age (R=0.21, P=0.23, Pearson correlation).

[0101] 2. Distinguishing between patients with beta-thalassemia major and patients with aplastic anemia After determining that erythroid DNA in plasma was not primarily released from circulating intact erythroblasts, we proposed that these DNA molecules were more likely released from the bone marrow during erythropoiesis. We reasoned that quantitative analysis of erythroid DNA in plasma could provide information about erythropoietic activity in the bone marrow.

[0102] To confirm the ability to measure the activity of erythropoiesis in bone marrow using plasma, patients suffering from beta-thalassemia major and aplastic anemia were recruited from the Department of Medicine, Prince of Wales Hospital (Hong Kong). Venous blood samples were collected before transfusion. The percentage of unmethylated plasma DNA was determined for each patient by digital PCR. These results were correlated with hemoglobin levels. Hemoglobin levels can be measured by techniques known to those skilled in the art, for example, by photometric techniques performed on an automated hemocytometer. Hemoglobin levels can be measured, for example, from the RBC fraction obtained after centrifugation.

[0103] These two groups of patients (beta-thalassemia major and aplastic anemia) represent two different spectra of erythropoietic activity. In beta-thalassemia major patients, erythropoiesis is highly active. However, due to the lack of production of functional beta-globin chains, the production of mature RBCs is reduced. In patients with aplastic anemia, erythropoiesis is reduced, resulting in reduced production of RBCs.

[0104] Figure 8 shows a plot of % unmethylation versus hemoglobin concentration in patients with aplastic anemia, patients with beta-thalassemia major, and healthy control subjects according to an embodiment of the present invention. Although hemoglobin concentrations were decreased in beta-thalassemia patients, % unmethylation was significantly elevated compared to healthy control subjects (P<0.01 by Mann-Whitney rank sum test). In fact, 10 of 11 beta-thalassemia patients (89%) had higher % unmethylation values ​​than all healthy control subjects. This observation is consistent with increased but deficient red blood cell production in these patients.

[0105] In contrast, for six patients with aplastic anemia who received regular transfusions, their percent unmethylated values ​​were lower than those of all healthy control subjects, an observation consistent with the low red blood cell production in these patients.

[0106] Three patients with aplastic anemia who were in clinical remission had normal hemoglobin levels and did not require regular blood transfusions. Their percent unmethylated values ​​were not significantly different from those of healthy controls (P = 0.53 by Mann-Whitney rank-sum test). Therefore, quantitative analysis of erythroblast-specific DNA in plasma may be useful for monitoring patients with bone marrow failure, for example, to determine whether aplastic anemia is in remission. Furthermore, quantitative analysis of erythroblast-specific DNA can be used to guide treatment. For example, patients with aplastic anemia who are not in remission can be treated with regular blood transfusions.

[0107] Therefore, the unmethylated % is higher in thalassemia patients and lower in aplastic anemia patients. For thalassemia, patients are anemic and the bone marrow is active because it wants to produce more RBCs for the circulatory system. Therefore, the erythropoiesis rate is higher than in healthy subjects without anemia. For patients with aplastic anemia, the anemia is due to low production of RBCs. Overall, these results indicate that analysis of erythroblast-specific methylation profiles may be useful to reflect erythropoietic activity in the bone marrow.

[0108] Patients can be diagnosed through a combination of hemoglobin measurements and unmethylated %. For example, patients with hemoglobin below 11.8 and E% above 50 can be classified as having β-thalassemia, while patients with hemoglobin below 11.8 and E% below 25 can be classified as suffering from aplastic anemia.

[0109] 3. Iron deficiency anemia and treatment Anemia can result from nutrient deficiencies (eg, iron, B12, folic acid, etc.), blood loss (eg, due to heavy menstrual bleeding or gastrointestinal bleeding), or chronic disorders (eg, cancer, inflammatory bowel disease).

[0110] Figure 9 is a plot of % plasma unmethylation in patients with iron (Fe) deficiency anemia and acute blood loss. Three patients with iron deficiency anemia and one patient with acute gastrointestinal hemorrhage were studied. In two iron-deficient patients, the anemia was due to menorrhagia. For one patient, a blood sample was taken before starting iron supplementation. For the other patient, a blood sample was taken one week after starting iron supplementation therapy. The third patient with iron deficiency anemia had inflammatory bowel disease, and a blood sample was taken before starting iron supplementation.

[0111] Plasma unmethylated % was determined for each patient and compared with the values ​​of healthy control subjects. Elevated plasma unmethylated % was observed in patients with acute gastrointestinal bleeding. In two iron-deficient patients whose samples were collected before starting iron supplementation, their plasma unmethylated % values ​​were not elevated compared to healthy subjects, despite their low hemoglobin levels. In an Fe-deficient patient whose sample was collected one week after starting iron supplementation, elevated plasma unmethylated % was observed.

[0112] These results indicate that plasma unmethylation % reflects erythropoietic activity in response to treatment. For example, iron supplementation therapy shows increased erythropoietic activity. Furthermore, these results indicate that the response in unmethylation % may be more rapid than an increase in hemoglobin level. The use of unmethylation % can be an early indicator of whether such treatment is effective and therefore whether it should be continued or discontinued. Therefore, unmethylation % can provide guidance for predicting the response to anemia treatment, such as iron therapy, before changes in hemoglobin level are observed.

[0113] In some embodiments, plasma unmethylated % can be used to reflect the response to anemia treatment.For example, in patients with iron deficiency anemia, the response to oral iron supplementation may vary among different subjects due to variations in iron absorption through the gastrointestinal tract.In such a scenario, the lack of increase in plasma unmethylated % after starting oral iron supplementation can be used to indicate the need for intravenous iron therapy.

[0114] 4. Differentiating between various anemic disorders We recruited anemic patients suffering from aplastic anemia (AA), chronic renal failure (CRF), iron deficiency anemia due to chronic blood loss, and β-thalassemia major, representing different disease entities to represent both ends of the spectrum of erythropoietic activity in the bone marrow.

[0115] Figure 10 shows the relationship between the percentage of erythroid DNA in plasma (E%(FECH)) and hemoglobin levels in patients with aplastic anemia, chronic renal failure (CRF), beta-thalassemia major, iron deficiency anemia, and healthy subjects. The E%(FECH) of plasma DNA in anemic patients and 35 healthy controls is plotted against hemoglobin levels. The dotted horizontal line represents the median E% for healthy subjects. The vertical line corresponds to the cutoff value (11.5, as shown) for measured hemoglobin levels between subjects with and without anemia.

[0116] We analyzed plasma DNA E% in 13 AA patients who met diagnostic criteria and failed to respond to immunosuppressive therapy (28). The median plasma DNA E% in the AA group was 12.4% (interquartile range: 7.5–13.7%), significantly lower than the median E% in healthy controls (Mann-Whitney rank-sum test, P<0.0001, Figure 10). Similarly, the median E% in 18 CRF patients requiring dialysis was 16.8% (interquartile range: 12.2–21.0%), also significantly lower than the median E% in healthy controls (Mann-Whitney rank-sum test, P<0.0001, Figure 10). These findings are consistent with the pathophysiology of low erythropoietin activity in AA patients (28, 29) and CRF patients (30).

[0117] In patients with β-thalassemia major, the bone marrow attempts to compensate for hypoxic stress with increased but ineffective erythropoiesis (31). Among the 17 recruited patients with β-thalassemia major, the median E% of plasma DNA was 65.3% (interquartile range: 60.1–78.9%), which was significantly higher than the median E% of healthy controls (P<0.0001 by Mann-Whitney rank sum test, Figure 10).

[0118] For subjects with iron deficiency anemia, we recruited 11 patients with menorrhagia or peptic ulcer disease (transferrin saturation less than 16% or serum ferritin levels less than 30 ng / ml). Their median plasma DNA E% was 37.8% (interquartile range: 31.8-43.0%), significantly higher than the median E% of healthy controls (P = 0.002 by Mann-Whitney rank sum test, Figure 10). This finding may be explained by a compensatory increase in bone marrow erythropoiesis activity in response to chronic blood loss (32).

[0119] Therefore, patients can be diagnosed through a combination of hemoglobin measurement and E%. For example, patients with hemoglobin below 11.5 (or other values) and E% above 50 can be classified as suffering from anemia of increased erythropoietic activity, such as β-thalassemia. On the other hand, patients with hemoglobin levels below 11.5 and E% below 50 and above 28 can be classified as suffering from anemia of moderate erythropoietic activity, such as iron deficiency anemia. Also, patients with hemoglobin levels below 11.5 and E% below 28 can be classified as suffering from anemia of reduced erythropoietic activity, such as aplastic anemia or chronic renal failure.

[0120] In some embodiments, the hemoglobin level of a blood sample can be measured to determine the classification of a blood disorder. The hemoglobin level can be compared with a hemoglobin threshold (e.g., 11.5). Thus, the classification of a hematological disorder can be based on, in addition to the methylation level, comparing the hemoglobin level with the hemoglobin threshold.

[0121] A summary of the subjects' E%(FECH), red blood cell, and reticulocyte parameters is shown in Tables 5 and 6 and Figures 11A and 11B, respectively. [Table 5]

[0122] Medians and interquartile ranges (in parentheses) are shown below in Table 6. The following abbreviations are used: hematocrit Hct, mean corpuscular volume MCV, mean cellular hemoglobin MCH, mean cellular hemoglobin concentration MCHC, and red cell distribution width RDW. [Table 6]

[0123] Figures 11A and 11B show the relationship between reticulocyte count / index and hemoglobin levels in patients with aplastic anemia, chronic renal failure (CRF), beta-thalassemia major, and iron deficiency anemia. The reticulocyte index is calculated as reticulocyte count × hematocrit / normal hematocrit. Clearly, the amount of reticulocytes (immature RBCs) in the blood does not reliably differentiate between different disorders. These results indicate that the reticulocyte count and reticulocyte index cannot distinguish between anemias of different etiologies, for example, thalassemia from aplastic anemia.

[0124] 5. Myelodysplastic syndrome and polycythemia vera Figure 12 is a plot of plasma unmethylation percentage in patients with myelodysplastic syndrome and polycythemia vera. In patients with myelodysplastic syndrome, a high plasma unmethylation percentage was observed along with a low hemoglobin level. A high plasma unmethylation percentage was also observed in patients with polycythemia vera. These results indicate that detection and quantification of erythroblast DNA methylation signatures in plasma is useful for detecting and monitoring abnormal proliferation or dysplasia of the bone marrow, including myeloblasts.

[0125] Therefore, as can be seen, these two blood disorders also show a higher amount of cell-free DNA in erythroblasts, thereby enabling the detection of blood disorders. In some embodiments, accurate diagnosis can be based on histological examination of bone marrow biopsy. Therefore, bone marrow biopsy can be performed in response to detecting a high unmethylated %. Similarly, bone marrow biopsy can be performed in response to detecting a low unmethylated % in the presence of anemia but in the absence of nutritional deficiency, such as iron deficiency, vitamin B12 deficiency, or folic acid deficiency. Such a basis for bone marrow biopsy can reduce the number of such biopsies while still allowing the health of bone marrow to be monitored. Therefore, unmethylated % will be more useful for monitoring treatment response.

[0126] 6. Other distinctions about anemia Distinctions between other disorders are also possible.

[0127] 1. Aplastic anemia (AA) and myelodysplastic syndromes (MDS) Both aplastic anemia and MDS are bone marrow failure conditions. Despite their similar clinical features of pancytopenia, these two disease entities have different pathophysiological mechanisms. In AA, there is bone marrow cytopenia without dysplastic features. In MDS, there is usually myeloid hypercellularity and dysplasia involving one or more lineages (33), although cytoreductive MDS is also recognized.

[0128] Figure 13A shows the percentage of erythroid DNA (E%(FECH)) in plasma from patients with aplastic anemia (AA) and myelodysplastic syndrome (MDS) according to an embodiment of the present invention. The median E% of plasma DNA from eight MDS patients was 50.3% (range: 37.4-60.8%). Two cases had MDS with single-lineage dysplasia, four had multilineage dysplasia, and two had MDS with excess blasts (34). All of these previous bone marrow biopsies showed erythroid hypercellularity. The median E% of the MDS patients was significantly higher than that of the 13 recruited AA patients (P<0.0001 by Mann-Whitney rank sum test, Figure 13A). The higher median E% results in the MDS patients are consistent with bone marrow biopsy findings and the pathophysiology of ineffective erythropoiesis in MDS.

[0129] Therefore, MDS can be distinguished from aplastic anemia using E% or other methylation levels. For example, a cutoff value of 30 can be used to classify samples as corresponding to aplastic anemia or MDS.

[0130] 2. AA treatment responders and non-responders Figure 13B shows the percentage of erythroid DNA (E%(FECH)) in plasma between treatment responders and non-responders in aplastic anemia according to an embodiment of the present invention. We analyzed eight additional aplastic anemia patients who responded to immunosuppressive therapy and thereby increased their hemoglobin levels. The median E% of plasma DNA in the treatment responders was 22.5% (interquartile range: 17.2-27.1%), which was higher than the median E% in the non-responders (median: 12.3%, interquartile range: 7.5-13.7%) (Mann-Whitney rank sum test, P = 0.0003, Figure 13B). There was a small but significant difference between the E% results of the treatment responders and healthy controls (Mann-Whitney rank sum test, P = 0.01).

[0131] These results reflect the recovery of erythropoiesis activity in the bone marrow. Because recovery of E% can occur earlier than hemoglobin levels, E% can be used to determine early whether a patient is responding to immunosuppressive therapy. If a patient is not responding, other treatments (e.g., more aggressive treatments) can be pursued, such as stem cell transplantation or the use of bone marrow stimulating agents (e.g., sargramostim, filgrastim, and pegfilgrastim).

[0132] 7.Leukemia Other hematological disorders besides leukemia can also be detected using erythroblast-specific DMR, such as in FECH.

[0133] Figure 14 is a plot of plasma % unmethylation versus hemoglobin concentration in a normal subject and two leukemia patients according to an embodiment of the present invention. % unmethylation is determined using FECH DMR. The % unmethylation values ​​in plasma from patients with leukemia or myeloproliferative disorders are higher than the median % unmethylation in plasma from normal subjects. This observation is consistent with the observation that leukemia patients have high but defective red blood cell production. Therefore, a cutoff value of approximately 45 for % unmethylation can be used to distinguish healthy subjects from subjects with leukemia, thereby determining the level of hematological disorder. Hemoglobin levels can also be used; for example, patients with hemoglobin below 8 can be identified as having leukemia, as opposed to beta-thalassemia, which generally has hemoglobin levels between 8 and approximately 11.8, as shown in Figure 10.

[0134] 6. Results for other methylation markers We analyzed E% based on two other DMRs in the plasma of a subset of samples to verify the previous E% results from the FECH gene-associated DMR. Similar differences in the percentage of erythroid DNA in plasma between healthy subjects and patients with aplastic anemia and β-thalassemia major were observed using the other two erythroblast-specific DMRs as the DMR in the FECH gene.

[0135] 1. Two other erythroblast-specific DMRs Two other DMRs located on chromosome 12 are also hypomethylated. The genomic regions associated with these two DMRs have not previously been identified as any annotated genes.

[0136] Figures 15A and 15B show methylation densities of CpG sites within an erythroblast-specific DMR on chromosome 12 according to embodiments of the present invention. Figure 15A shows region 1510 on chromosome 12 at genomic coordinates 48227688-48227701, which contains three sites. Figure 15B shows region 1560 on chromosome 12 at genomic coordinates 48228144-48228154, which also contains three sites. The genomic coordinates correspond to the human reference genome hg19. All selected CpG sites located within the shaded region were hypomethylated in erythroblasts but hypermethylated in other tissues or cell types. The other tissues represent lung, colon, small intestine, pancreas, adrenal gland, esophagus, heart, and brain.

[0137] These two other erythroblast-specific DMRs are labeled Ery-1 and Ery-2. E% based on the other two DMRs (chr12:48227688-48227701 and chr12:48228144-48228154) will be denoted by E%(Ery-1) and E%(Ery-2), respectively.

[0138] Figure 16 shows histone modifications (H3K4me1 and H3K27Ac) for two other erythroblast-specific DMRs (Ery-1 and Ery-2) from the ENCODE database. We reviewed publicly available data on histone modifications for these two DMRs in erythroid cell types from the ENCODE database and CHIP-seq datasets. The Ery-1 and Ery-2 DMRs are marked by two enhancer-associated histone modifications (H3K4me1 and H3K27Ac), suggesting regulatory functions, particularly enhancer functions. The closest downstream gene is the HDAC7 gene, located approximately 15 kb away.

[0139] 2. Erythroblast-enriched sample We analyzed the percentage of erythroid DNA based on the other two DMRs in the erythroblast-enriched samples from the eight previously described umbilical cord blood samples. The E%(Ery-1) and E%(Ery-2) of DNA extracted from the pooled samples were 66.5% and 68.5%. These E% values ​​were similar to the E% based on the FECH gene-related DMR, i.e., 67%. Given the similar findings from all three DMRs, the lower-than-expected E% values ​​(i.e., lower than expected when enrichment is performed) could be due to the incomplete selectivity of the enrichment protocol.

[0140] 3. Correlation between E% and erythroblasts in buffy coats of patients with β-thalassemia major The percentage of erythroid DNA based on the two DMRs was analyzed in buffy coat DNA from the same group of β-thalassemia major patients. The E% for the two DMRs in buffy coat DNA correlated well with the percentage of erythroblasts in a manner similar to Figure 3A.

[0141] Figures 17A and 17B show the correlation between the percentage of erythroid DNA sequences (E%) in buffy coat DNA of patients with β-thalassemia major, measured by digital PCR assays targeting the Ery-1 marker (Figure 17A) and the Ery-2 marker (Figure 17B), and the percentage of erythroblasts in total peripheral white blood cells, measured by an automated hematology analyzer. E% (Ery-1) and E% (Ery-2) in buffy coat DNA correlated well with the percentage of erythroblasts in peripheral white blood cells, measured by the hematology analyzer (r = 0.938 and r = 0.928, both P < 0.0001, Pearson correlation).

[0142] Figures 18A and 18B show the correlation of E%(FECH) results with E%(Ery-1) and E%(Ery-2) in buffy coat DNA from β-thalassemia major patients. The E% results obtained from these two DMRs also correlated well with the paired E% results derived from the FECH gene marker site in the buffy coat DNA of 15 β-thalassemia major patients.

[0143] 4. E% in plasma of healthy subjects and anemic patients We analyzed E%(ERY-1) and E%(ERY-2) in plasma DNA from healthy subjects and patients with aplastic anemia and β-thalassemia major. We also analyzed E% results from three erythroblast-specific DMRs, seven aplastic anemia cases, and nine β-thalassemia major cases in the same group of healthy subjects.

[0144] Figure 19 shows the percentage of erythroid DNA in healthy subjects and patients with aplastic anemia and β-thalassemia major using digital PCR analysis targeting three erythroblast-specific DMRs according to an embodiment of the present invention. The median E%(Ery-1) in plasma DNA of 13 healthy subjects was 16.7% (interquartile range: 10.9-23.5%), and the median E%(Ery-2) in the same group of healthy subjects was 25.0% (interquartile range: 22.2-27.3%). Based on the Ery-1 marker, the E%(Ery-1) of patients with aplastic anemia and β-thalassemia major was 13.78% and 61.69%, respectively. Based on the Ery-2 marker, the E%(Ery-2) of patients with aplastic anemia and β-thalassemia major was 14.13% and 64.95%, respectively. Similar differences in the percentage of erythroid DNA in plasma between healthy subjects and patients with aplastic anemia and β-thalassemia major were observed using two erythroblast-specific DMRs in the FECH gene.

[0145] 7.Treatment results As explained above, E% can be used to monitor the efficacy of treatment for anemia.

[0146] 1. Measurement of E%(FECH) in plasma DNA in patients with iron deficiency anemia before and after iron therapy We monitored serial changes in hemoglobin levels, reticulocyte counts, and plasma DNA E% in four patients with iron deficiency anemia receiving intravenous iron therapy due to intolerance to the gastrointestinal side effects of oral iron. Instead of patients receiving oral iron therapy, we chose to observe changes in this group of patients to avoid the potential confounding factor of different treatment responses due to variable gastrointestinal absorption. We measured these parameters before treatment and 2 days after treatment.

[0147] 20A and 20B show serial measurements of the percentage of erythroid DNA in plasma DNA (E%(FECH)) and the percentage of reticulocyte count in an iron-deficiency anemia patient receiving intravenous iron therapy in the pre-treatment state and two days after treatment according to an embodiment of the present invention. FIG. 20A shows the serial change in E% of plasma DNA. FIG. 20B shows the serial change in the percentage of reticulocyte count.

[0148] Except for subject 1, plasma DNA E% and reticulocyte counts increased, whereas hemoglobin levels initially remained static only after the initiation of treatment. Regarding final changes in hemoglobin levels, subjects 3 and 4 ultimately experienced dramatic changes in levels of 84.7% and 75.3%, respectively. Subject 2 was a defaulter of follow-up and did not provide additional samples for post-treatment hemoglobin measurement. Subject 1, who had the smallest change in plasma DNA E%, also experienced the smallest increase in hemoglobin level (12.2%). Therefore, changes in plasma DNA E% can demonstrate the dynamic response of bone marrow erythropoietic activity to iron therapy and can be used as an early predictor of patient response to treatment.

[0149] The lack of an increase in reticulocyte count in subject 1 suggests that RBC production did not adequately respond to iron therapy. The lack of response to iron therapy can also be reflected by the lack of an increase in E% corresponding to bone marrow activity. However, subject 1's hemoglobin level before the start of iron therapy was higher than that of the other three subjects and closer to the reference range for healthy subjects. The lack of an increase in E%(FECH) in subject 1 may reflect the lack of a compensatory increase in erythropoietic activity in the bone marrow due to the smaller deficit in hemoglobin level from normal. Subject 1's reticulocytes were initially similar to those of the other subjects, and therefore would not indicate a sufficient level of bone marrow activity. Therefore, for anemia with moderate erythropoietic activity, an E% at the upper limit of normal for healthy patients may indicate a positive, or at least an indeterminate, response to treatment, and therefore, treatment may not be stopped in such cases.

[0150] To return hemoglobin levels to normal, increased RBC production is required. Therefore, in iron deficiency anemia, the normal range of E% can be considered inadequate. The increase in E% in subjects 2-4 indicates an appropriate response after iron therapy, since an E% in the higher normal range (see Figure 10) or just above it is expected for subjects with iron deficiency anemia. Therefore, the E% threshold for determining whether treatment is effective can depend on the starting value of E%. The E% threshold can specify a specific change from the initial value, and the amount of change can depend on the initial value.

[0151] The effect of oral iron therapy was also investigated. For example, patients suffering from chronic blood loss due to menorrhagia may suffer from iron deficiency anemia. Iron supplementation may be used to correct the iron deficiency state.

[0152] Figure 21A shows the serial changes in plasma E% (FECH) in erythroblast DMR of patients with iron deficiency anemia due to menorrhagia receiving oral iron therapy according to an embodiment of the present invention. The E% in the plasma of iron deficiency anemia patients receiving iron therapy was analyzed before and 7 days after iron treatment. In Figure 21A, there was an increase in E% after receiving iron treatment. These results suggest that plasma E% could reflect erythropoietic activity in response to treatment.

[0153] Figure 21B shows the change in hemoglobin after oral iron treatment. Although hemoglobin levels have not yet risen dramatically, there was an increase in E%(FECH) at the same time points after treatment. This is similar to Figure 20A, which shows that E% can be used as an early indicator of whether treatment is effective.

[0154] 2. Treatment for Chronic Kidney Disease (CKD) In CKD patients, the primary cause of anemia is decreased erythropoietin production due to kidney damage. Erythropoietin is a hormone produced by the kidney in response to low tissue oxygen levels. Erythropoietin stimulates the bone marrow to produce red blood cells. Exogenous erythropoietin may be used to treat anemia in CKD.

[0155] Figure 22 shows the serial changes in plasma % unmethylated in erythroblast DMR in chronic kidney disease (CKD) patients treated with recombinant erythropoietin (EPO) or erythropoiesis-stimulating agents (ESAs). The % unmethylated in the plasma of seven CKD patients receiving EPO treatment was analyzed before and 7 to 14 days after EPO treatment. Lines of different shapes (colors) correspond to different patients. All patients showed an increase in % unmethylated after receiving EPO treatment. The % unmethylated values ​​indicate various levels of efficacy in different patients. These results indicate that plasma % unmethylated reflects erythropoietic activity in response to treatment.

[0156] 3.ATG treatment for aplastic anemia Immunosuppressive therapy for patients with aplastic anemia may lead to blood salvage in 60-70% of patients (Young et al. Blood. 2006;108(8):2509-2519). The percent unmethylated plasma values ​​of four patients with aplastic anemia receiving immunosuppressive therapy were analyzed before the start of immunosuppressive therapy and after 2 and 4 months. None of the patients responded to treatment, and hemoglobin levels did not return to normal over the period. All four patients required regular blood transfusions.

[0157] Figure 23A shows the serial changes in plasma unmethylated % in erythroblast DMR in patients with aplastic anemia receiving antithymocyte globulin (ATG) treatment or cyclosporine as immunosuppressive therapy according to an embodiment of the present invention. Three patients showed no change in plasma unmethylated %. One patient showed a significant increase in unmethylated %. This occurred simultaneously with the onset of symptoms of paroxysmal nocturnal hemoglobinuria (PNH) clonal, i.e., the passage of dark urine containing hemoglobin. Such symptoms, even if the unmethylated % increases, can be used to determine whether the patient is not responding to treatment. PNH is known to occur in patients with aplastic anemia and has the pathophysiological mechanism of hemolytic anemia. The increase in unmethylated % reflects increased erythropoietic activity as a result of hemolysis from PNH.

[0158] Figure 23B shows the serial changes in hemoglobin in aplastic anemia patients undergoing treatment. Hemoglobin levels do not increase significantly. These results indicate that the % plasma unmethylated reflects changes in erythropoietic activity during the course of treatment, as none of the patients responded to treatment, as exemplified by the lack of change in hemoglobin shown in Figure 23B.

[0159] Figures 24A and 24B show plots of % unmethylated plasma versus hemoglobin concentration in four patients with aplastic anemia. Each line corresponds to one patient and tracks the change in % unmethylated and hemoglobin levels before and four months after treatment. Figure 23A shows that % unmethylated did not change significantly, except in the PNH patient. Figure 23B shows that hemoglobin levels changed, but not significantly.

[0160] 8. Use of absolute concentrations of erythroid DNA To measure the amount of erythroid DNA in plasma / serum, some embodiments use the parameter E% (also called % unmethylated) of hypomethylated markers, although lineage-specific hypermethylated markers, if present, can also be used. E% corresponds to the amount of erythroid DNA normalized to the majority of DNA in the sample (mostly hypermethylated).

[0161] An alternative parameter would be to measure the absolute concentration of erythroid DNA per unit volume of plasma. For the calculation of E%, embodiments can measure the absolute concentration of unmethylated DNA and the absolute concentration of methylated DNA. In a digital PCR assay, each point can represent one DNA molecule (e.g., as shown in Figures 2A and 2B). The counts of methylated and unmethylated DNA can be calculated directly. In the previous section, normalized values ​​(e.g., E%) were calculated, but embodiments can also use the absolute concentration of unmethylated molecules for hypomethylated markers or the absolute concentration of methylated molecules for hypermethylated markers.

[0162] Figure 25 shows a box and whisker plot showing the absolute concentration of erythroid DNA in the FECH gene-associated DMR (copy number / ml plasma) in healthy subjects and anemic patients according to an embodiment of the present invention. The box and inner line represent the interquartile range and median, respectively. The upper and lower whiskers represent the maximum and minimum values.

[0163] As shown in Figure 25, distinct clusters between different patient groups can be observed using the absolute concentration of erythroid DNA, but normalized values ​​allow for better separation between groups. Theoretically, the E% parameter of plasma can also be affected by the concentration of circulating DNA of non-erythroid origin, such as bone marrow- or lymphoid-derived DNA. For example, in anemic conditions where other hematopoietic lineages are also affected (e.g., aplastic anemia or myelodysplastic syndrome), changes in the release of erythroid DNA may be masked in some of these cases.

[0164] 9. Other Blood Lineages This plasma DNA-based approach for hematological assessment can be generalized to markers of other blood cell lineages, such as myeloid, lymphoid, and megakaryocytic lineages. Previous studies on the use of blood lineage-specific DNA methylation markers have focused on whole blood or blood cells (Houseman EA, et al. Current Environmental Health Reports 2015;2:145-154). Our previously presented data clearly demonstrate that plasma DNA contains information not present in blood cells. Therefore, analysis of plasma DNA using developmental markers derived from multiple blood cell lineages can provide valuable diagnostic information about an individual's blood system. It is therefore a non-invasive replacement for bone marrow biopsy. Assays can be designed that specifically detect the methylation signatures of specific cell lineages in plasma or serum, thereby monitoring the activity of different cell lineages in the bone marrow.

[0165] Such an approach would be useful in the evaluation of many clinical scenarios, including but not limited to the following disorders: Exemplary related pedigrees are provided for the disorders. 1. Hematological malignancies, e.g., leukemia and lymphoma (lymphoid lineage) 2. Bone marrow disorders, e.g., aplastic anemia, myelofibrosis (myeloid and lymphoid lineages) 3. Monitoring the immune system and its function, e.g., encapsulation of immune responses during immune deficiencies and disease and treatment (lymphocyte lineage) 4. Drug effects on bone marrow, e.g. azathioprine (myeloid lineage) 5. Autoimmune diseases with hematological manifestations, such as immune thrombocytopenia (ITP), a condition characterized by low platelet counts but a normal bone marrow. Plasma DNA analysis using blood lineage markers, such as megakaryocytic markers, would provide valuable diagnostic information for such conditions. (Megakaryocytic markers) 6. Infections with hematological complications, such as infection with parvovirus B19, which can be complicated by a decrease in erythropoiesis or even a more severe aplastic crisis. (erythroid lineage)

[0166] 10. Method Figure 26 is a flow chart illustrating a method 2600 for analyzing a mammalian blood sample according to an embodiment of the present invention. Portions of method 2600 may be performed manually, while other portions may be performed by a computer system. In one embodiment, a system may perform all of the steps. For example, the system may include robotic elements (e.g., for obtaining the sample and performing the assay), a detection system for detecting a signal from the assay, and a computer system for analyzing the signal. Instructions for controlling such a system may be stored on one or more computer-readable media, such as configuration logic in a field-programmable gate array (FPGA), flash memory, and / or a hard drive. Figure 27 illustrates such a system.

[0167] At block 2610, a cell-free mixture of the blood sample is obtained. Examples of cell-free mixtures include plasma or serum. The cell-free mixture can include cell-free DNA from multiple cell lineages.

[0168] In some embodiments, a blood sample is separated to obtain a cell-free mixture. Plasma and serum are distinct. Both correspond to the fluid portion of blood. To obtain plasma, an anticoagulant is added to the blood sample to prevent the blood sample from clotting. To obtain serum, the blood sample is allowed to clot. Therefore, clotting factors will be consumed during the clotting process. Regarding circulating DNA, some DNA is released from blood cells into the fluid portion during clotting. Therefore, serum has a higher DNA concentration than plasma. DNA from clotting cells may dilute DNA specific to plasma. Therefore, plasma may be advantageous.

[0169] In block 2620, the DNA fragments in the cell-free mixture are contacted with an assay corresponding to one or more differentially methylated regions. Each of the one or more differentially methylated regions (DMRs) is specific to a particular blood cell lineage by being hypomethylated or hypermethylated compared to other cell lineages. An example of a DMR for the erythroid cell lineage is provided herein.

[0170] In various embodiments, the assay can include PCR or sequencing, and thus can be a PCR assay or a sequencing assay. Contacting the DNA fragments can include flow cells, droplets, beads, or other mechanisms for providing interaction between the assay and the DNA fragments. Examples of such assays include whole-genome bisulfite sequencing, targeted bisulfite sequencing (by hybridization capture or amplicon sequencing), other methylation-specific sequencing (e.g., single-molecule real-time (SMRT) DNA sequencing by Pacific Biosciences), real-time methylation-specific PCR, and digital PCR. Further examples of assays that can be used for method 300 are described herein, e.g., in Section XII. While the example uses erythroblasts, other cell lineages, including other blood cell lineages, may also be used.

[0171] In block 2630, a first number of methylated or unmethylated DNA fragments is detected in the cell-free mixture of one or more differentially methylated regions based on a signal obtained from the assay. The assay can provide various signals, such as optical or electrical signals. The signal can provide a specific signal per DNA fragment, or an aggregation signal indicating the total number of DNA fragments with a methylation signature (e.g., as in real-time PCR).

[0172] In one embodiment, sequencing can be used to obtain sequence readings for DNA fragments, and the DNA fragments can be aligned with a reference genome. If the DNA fragment aligns with one of the DMRs, a counter can be incremented. Assuming that the signal comes from a specific methylated one of the unmethylated assays, the DNA fragment can be assumed to have that methylation signature. In another embodiment, a readout from PCR (e.g., a light signal from a positive well) can be used to increment such a counter.

[0173] At block 2640, a methylation level is determined using the first number. The first methylation level may be normalized or may be an absolute concentration, for example, per volume of the biological sample. Examples of absolute concentrations are provided in FIG. 25. An example of a normalized methylation level includes E% (also called % unmethylated).

[0174] For normalized values, the methylation level can be determined using the first number and total number of DNA fragments in the cell-free mixture of one or more differentially methylated regions.As mentioned above, the methylation level can be the percentage of unmethylated DNA fragments.In other embodiments, the percentage can be the percentage of methylated DNA fragments, which will have the opposite relationship to the previous example for erythroblasts.In various implementations, the methylation level can be determined by using the percentage across all DMR sites, by averaging the individual percentages of each site, or by the weighted average of each site.

[0175] At block 2650, the methylation level is compared to one or more cutoff values ​​as part of determining a classification of the hematological disorder in the mammal. The one or more cutoff values ​​can be selected from a priori data, for example, as shown in Figures 8-10 and 12-14. The cutoff values ​​can be selected to provide optimal sensitivity and specificity for providing an accurate classification of the hematological disorder, for example, based on supervised learning from a dataset of samples known to be normal and affected by the disorder.

[0176] As an example for determining a cutoff value, multiple samples can be obtained. Each sample is known to have a specific classification of hematological disorder, for example, via other techniques, as would be known to one of skill in the art. The multiple samples have at least two classifications of hematological disorder, for example, a disordered and a non-disordered sample. Different types of disorders can also be included, for example, as shown in Figure 10. The methylation levels of one or more differentially methylated regions can be determined for each of the multiple samples, such as the data points in Figures 8-10 and 12-14.

[0177] A first set of samples can be identified as having a first classification of hematological disorder, e.g., a first classification of healthy. The first set can be clustered together, for example, as shown in Figures 8-10 and 12-14. A second set of samples can be identified as having a second classification of hematological disorder. The second set can be patients suffering from the disorder or a different type of disorder from the first classification. The two classifications can correspond to varying degrees of having the same disorder. If the first set of samples has a statistically higher methylation level than the second set of samples, a cutoff value can be determined that distinguishes between the first and second set of samples within a specified specificity and sensitivity. Thus, a balance between specificity and sensitivity can be used to select an appropriate cutoff value.

[0178] 11. Summary RBCs are the most abundant cell type in peripheral blood, but they do not possess nuclei. In this disclosure, we determined that cells of the erythroid lineage contribute a significant proportion of the plasma DNA pool. Prior to this study, it was known that hematopoietic cells contribute significantly to the circulating DNA pool (13, 14), but many researchers assumed that such hematopoietic DNA originated exclusively from the leukocyte lineage (15). More recent results using DNA methylation markers have shown that plasma DNA possesses the DNA methylation signatures of neutrophils and lymphocytes (15).

[0179] Using high-resolution reference methylomes of many tissues, including erythroblasts (18, 20), we distinguished erythroblast-derived DNA molecules from DNA from other tissue types in plasma DNA pools. Our digital PCR assay based on the erythroblast-specific methylation signature enabled us to perform quantitative analysis of such DNA molecules in plasma. This approach allowed us to demonstrate the presence of significant amounts of erythroid DNA in plasma DNA pools from healthy subjects.

[0180] Our results are consistent with erythroid lineage cells in bone marrow contributing DNA to plasma. The conclusion of this hypothesis is that quantitative analysis of erythroid DNA in plasma reflects bone marrow erythropoietic activity and may therefore be useful for the differential diagnosis of anemia. We established reference values ​​for erythroid DNA in plasma from healthy subjects. We further demonstrated that anemic patients will have elevated or decreased proportions of circulating erythroid DNA, depending on the exact nature of their pathology and treatment. In particular, through analysis of the percentage of erythroid DNA in plasma, we were able to distinguish between two bone marrow failure syndromes, namely aplastic anemia (AA) and myelodysplastic syndrome (MDS), in our recruited patients.

[0181] Reticulocyte counts can be used to provide information about bone marrow response in anemic patients. Of the 11 beta-thalassemia patients we studied, four had reticulocyte counts in peripheral blood with a detection limit of less than 1%. For the other seven patients, reticulocyte counts ranged from 1% to 10%. For all nine patients with aplastic anemia, their reticulocyte counts were less than 1%, regardless of whether they received regular transfusions. Therefore, reticulocyte counts may not clearly define normal and reduced erythropoietic activity in the bone marrow due to the high inaccuracy of automated methods for low reticulocyte concentrations (35, 36).

[0182] We demonstrated that analysis of reticulocyte count or reticulocyte index could not distinguish the cause of anemia from reduced erythropoietic activity in our patient cohort (Figures 11A and 11B). Our results indicate that plasma unmethylation % (e.g., as shown in Figure 10) is more accurate than reticulocyte count in reflecting erythropoietic activity in the bone marrow. As shown in Figures 11A and 11B, there was no correlation between plasma unmethylation % and reticulocyte count or reticulocyte index in all patients with both parameters measured (P = 0.3, linear regression).

[0183] Similarly, the presence of an abnormally large number of erythroblasts in peripheral blood indicates abnormal erythropoietic stress (37). However, the absence of erythroblasts in peripheral blood does not indicate normal or reduced erythropoietic activity. Conversely, quantitative analysis of plasma erythroblast-derived DNA provides information about bone marrow erythropoietic activity that is not provided by conventional hematological parameters from peripheral blood.

[0184] Regarding beta-thalassemia and aplastic anemia, these two conditions are typically diagnosed by analyzing iron and hemoglobin patterns in the blood. However, both beta-thalassemia and aplastic anemia present with low hemoglobin levels, and therefore, such techniques do not distinguish between beta-thalassemia and aplastic anemia. The use of % unmethylated can provide more specificity by enabling differentiation between these two disorders, as shown in Figures 8 and 10.

[0185] The % unmethylated can also be used to monitor treatment. For example, analysis of the % unmethylated in patients with iron deficiency anemia can be used to monitor the bone marrow response to oral iron therapy, as shown in Figure 9. In some patients, oral iron supplementation may not be effectively absorbed through the gastrointestinal tract. As a result, erythropoiesis will not increase after treatment initiation. The lack of an increase in the % unmethylated can be used as an indicator of a poor response to oral iron therapy, allowing other treatments, such as parenteral iron therapy (e.g., iron dextran, ferric gluconate, and iron sucrose), to be initiated. Alternatively, the lack of an increase in the % unmethylated can be used to discontinue (stop) treatment, thereby saving the cost of ineffective treatment. In another implementation, the lack of an increase in the % unmethylated can be used to identify when to increase the treatment dose, e.g., increase the iron dosage. If there is an increase in the % unmethylated, treatment can be continued. If the increase in % unmethylated is sufficiently high (e.g., based on a threshold), it can be assumed that erythropoietic activity has reached a level sufficient to ultimately return hemoglobin levels to healthy levels, and treatment can be stopped, thereby avoiding costly or potentially harmful overtreatment.

[0186] Thus, the mammal can be treated for the hematological disorder in response to determining that the classification of the hematological disorder indicates that the mammal is afflicted with a hematological disorder. After treatment, the assay can be repeated to determine an updated methylation level, and a decision can be made whether to continue treatment based on the updated methylation level. In one embodiment, determining whether to continue treatment can include stopping treatment, increasing the therapeutic dose, or administering a different treatment if the updated methylation level does not change relative to the methylation level to within a specified threshold. In another embodiment, determining whether to continue treatment can include continuing treatment when the updated methylation level changes relative to the methylation level to within a specified threshold.

[0187] As another example of monitoring treatment, analysis of the % unmethylated can be used to determine whether ineffective red blood cell production in thalassemia patients has been sufficiently suppressed by treatment, for example, by blood transfusion. Extramedullary red blood cell production is the cause of bone deformities in thalassemia patients. Extramedullary red blood cell production can be suppressed by blood transfusion and restoration of hemoglobin levels. The % unmethylated can indicate a patient's response to these treatments, and failure to suppress the % unmethylated can be used to indicate that treatment should be intensified.

[0188] % Unmethylated can also distinguish patients with iron deficiency alone from those with iron deficiency along with other causes of anemia, e.g., anemia of chronic disease. Patients with iron deficiency alone would be expected to have an increased % Unmethylated after iron treatment, whereas patients with multiple causes of anemia would not respond with an increased % Unmethylated.

[0189] Thus, we demonstrated that the percentage of circulating erythroid DNA increases in response to iron therapy in patients with iron deficiency anemia, thus reflecting increased bone marrow erythropoietic activity. The dynamic changes in the erythroid DNA ratio indicate that quantifying plasma erythroid DNA allows for noninvasive monitoring of relevant cellular processes. The rapid kinetics of plasma DNA (e.g., a half-life on the order of tens of minutes (38, 39)) suggests that such monitoring could provide near-real-time results. Similarly, quantifying the percentage of circulating cell-free DNA of other cell lineages also allows for noninvasive monitoring of relevant cellular processes in the bone marrow for other cell lineages.

[0190] This study serves as evidence to demonstrate the presence of nuclear material from circulating hematopoietic progenitor and precursor cells. Additionally, the presence of circulating DNA released from precursor cells of other hematopoietic lineages may also be used.

[0191] In summary, we have demonstrated that erythroid DNA contributes a significant proportion of the plasma DNA pool. This discovery fills an important gap in our understanding of the fundamental biological characteristics of circulating nucleic acids. Clinically, measuring erythroid DNA in plasma opens new approaches for investigating and monitoring different types of anemia and marks the beginning of a new family of hematological tests based on cell-free DNA.

[0192] 12. Materials and Methods This section describes techniques that have been and can be used to implement the embodiments.

[0193] 1. Sample Collection and Preparation

[0194] In some embodiments, formalin-fixed, paraffin-embedded (FFPE) tissue samples of 12 types of normal tissue (liver, lung, esophagus, stomach, small intestine, colon, pancreas, adrenal gland, bladder, heart, brain, and placenta), each with four cases, were extracted from anonymized surgical specimens. The tissues were confirmed to be normal by histological examination. DNA was extracted from FFPE tissue using a QIAamp DNA Mini Kit (Qiagen), a modified version of the manufacturer's protocol for fixed tissues. Deparaffinization solution (Qiagen) was used instead of xylene to remove paraffin. For reversal of formaldehyde modification of nucleic acids, an additional incubation step of 1 hour at 90°C was performed after lysis with buffer ATL and proteinase K.

[0195] To prepare erythroblast-enriched samples for analysis, 1–3 mL of cord blood was collected into EDTA-containing tubes from each of eight pregnant women immediately after delivery. Mononuclear cells were isolated from the cord blood samples using density gradient centrifugation with Ficoll-Paque PLUS solution (GE Healthcare). 1 × 10 8 Isolated mononuclear cells were incubated with 1 mL of a mixture of two antibodies, fluorescein isothiocyanate (FITC)-conjugated anti-CD235a (Miltenyi Biotec) and phycoerythrin (PE)-conjugated anti-CD71 (Miltenyi Biotec), at a 1:10 dilution in phosphate-buffered saline (PBS) for 30 minutes in the dark at 4°C. CD235a+ and CD71+ cells were then sorted by BD FACSAria Fusion flow cytometry (BD Biosciences) for enrichment of erythroblasts (1). Eight CD235a+CD71+ cells were pooled for downstream analysis. DNA was extracted from the pooled CD235a+CD71+ cells using the QIAamp DNA Blood Mini Kit (Qiagen) according to the manufacturer's instructions.

[0196] Peripheral blood samples were collected in EDTA-containing tubes and immediately stored at 4°C. Ten milliliters of peripheral venous blood was collected from each patient. Plasma separation was performed within 6 hours of collection. Plasma DNA was extracted from 4 mL of plasma. Plasma and buffy coat DNA were obtained as previously described (2). Briefly, blood samples were first centrifuged at 1,600 g for 10 minutes at 4°C, and the plasma fraction was recentrifuged at 16,000 g for 10 minutes at 4°C. To remove any residual plasma, the blood cell fraction was collected after recentrifugation at 2,500 g for 10 minutes. DNA from plasma and buffy coat was extracted using the QIAamp DSP DNA Mini Kit (Qiagen) and the QIAamp DNA Blood Mini Kit (Qiagen), respectively.

[0197] 2. Bisulfite Conversion of DNA Plasma DNA and genomic DNA extracted from blood cells and FFPE tissues were subjected to two rounds of bisulfite treatment using the Epitect Plus Bisulfite Kit (Qiagen) according to the manufacturer's instructions ( 3 ).

[0198] In one embodiment, DNA extracted from a biological sample is first treated with bisulfite. The bisulfite treatment converts unmethylated cytosines to uracil, while leaving methylated cytosines unchanged. Therefore, after bisulfite conversion, methylated and unmethylated sequences can be distinguished based on sequence differences at CpG dinucleotides. For the analysis of plasma samples presented in selected examples of this application, DNA was extracted from 2-4 mL of plasma. For the analysis of DNA extracted from blood cells, 1 μg of DNA was used for downstream analysis in the examples. In other embodiments, other volumes of plasma and DNA amounts may be used.

[0199] In the examples in this application, bisulfite treatment was performed twice for each sample using the EpiTect Bisulfite Kit according to the manufacturer's instructions. Bisulfite-converted plasma DNA was eluted with 50 μL of water. Bisulfite-converted cellular DNA was eluted with 20 μL of water and then diluted 100-fold for downstream analysis.

[0200] 3. Methylation Assay Various methylation assays can be used to quantify the amount of DNA from specific cell lineages.

[0201] 1. PCR Assay Two digital PCR assays were developed for each of the three erythroblast-specific DMRs, one targeting the bisulfite-converted unmethylated sequence and the other targeting the methylated sequence. Primer and probe designs for the assays are listed in Appendix Table 7. [Table 7]

[0202] As an example, a PCR reaction can contain 50 μL with 3 μL of bisulfite-converted template DNA, a final concentration of each primer of 0.3 μM, 0.5 μM MgCl2, and 25 μL of 2×KAPA HiFi HotStart Uracil ReadyMix. The following PCR temperature profile can be used: 95°C for 5 minutes, and 35 cycles of 98°C for 20 seconds, 57°C for 15 seconds, and 72°C for 15 seconds, followed by a final extension step at 72°C for 30 seconds. In other embodiments, non-prioritized genome-wide sequencing can be performed in combination with alignment, although such a procedure may not be cost-effective.

[0203] In some embodiments, a 20 μL reaction mixture was prepared after bisulfite treatment for digital PCR analysis of the samples. In one embodiment, the reaction mixture contained 8 μL of template DNA, 450 nM each of two forward primers, 900 nM of reverse primer, and 250 nM of probe. In another embodiment, each reaction mixture was prepared with a total volume of 20 μL containing 8 μL of template DNA, 900 nM of forward primer, 900 nM of reverse primer, and 250 nM of probe. The reaction mixture was then used for droplet generation using a BioRad QX200 ddPCR droplet generator. Typically, 20,000 droplets would be generated for each sample. In some runs, droplets were transferred to a clean 96-well plate and then thermocycled using identical conditions for both the methylation-specific and non-methylation-specific assays: 95°C for 10 minutes (1 cycle), 40 cycles of 94°C for 15 seconds and 60°C for 1 minute, 98°C for 10 minutes (1 cycle), followed by a hold step at 12°C. After PCR, droplets for each sample were analyzed using a BioRad QX200 droplet reader, and results were interpreted using QuantaSoft (version 1.7) software.

[0204] 2. Examples of other methylation assays Other examples of methylation-aware sequencing include (N 6 The methylation status of DNA molecules (including 5-methyladenine, 5-methylcytosine, and 5-hydroxymethylcytosine) can be directly elucidated without bisulfite conversion using single-molecule sequencing platforms (AB Flusberg et al. 2010 Nat Methods;7:461-465; J Shim et al. 2013 Sci Rep;3:1389) or through immunoprecipitation of methylated cytosine (e.g., by using antibodies against methylcytosine, or using methylated DNA-binding proteins or peptides (LG Acevedo et al. 2011 Epigenomics;3:93-101) followed by sequencing, or through the use of methylation-sensitive restriction enzymes followed by sequencing).

[0205] In some embodiments, the methylation level of genome sites in DNA mixture can be determined by whole genome bisulfite sequencing.In other embodiments, the methylation level of genome sites can be determined by methylation microarray analysis, such as Illumina HumanMethylation450 system, or by methylation immunoprecipitation (for example, by using anti-methylcytosine antibody), or by treatment with methylation-binding protein followed by microarray analysis or DNA sequencing, or by methylation recognition sequencing, for example, by single molecule sequencing (for example, by nanopore sequencing (Schreiber et al. Proc Natl Acad Sci 2013;110:18910-18915) or by Pacific Biosciences single molecule real-time analysis (Flusberg et al. Nat Methods 2010;7:461-465)).Tissue-specific methylation level can be measured by the same method. As another example, targeted bisulfite sequencing, methylation-specific PCR, and non-bisulfite methylation-aware sequencing (e.g., single-molecule sequencing platforms (Powers et al. Efficient and accurate whole genome assembly and methylome profiling of E. coli. BMC Genomics. 2013;14:675) can be used to analyze the methylation levels of plasma DNA for plasma DNA methylation deconvolution analysis. Therefore, the results of methylation-aware sequencing can be obtained in a variety of ways.

[0206] 4.Statistical analysis Pearson's correlation was used to study the correlation between the percentage of erythroid DNA (E%(FECH)) and the percentage of erythroblasts in peripheral leukocytes measured by hematology analyzers in patients with β-thalassemia major. Pearson's correlation was also used to study the correlation between paired E%(FECH) results in plasma DNA and in buffy coat DNA of healthy controls. The Wilcoxon signed-rank test was used to compare the differences between E% in plasma DNA and paired buffy coat DNA in healthy subjects. The Mann-Whitney rank-sum test was used to compare the differences in E% in plasma DNA of healthy subjects and anemic patients from different disease groups.

[0207] We also developed our bioinformatics pipeline to mine erythroblast-specific DMRs based on our criteria described herein, which may be implemented on various platforms, such as the Perl platform.

[0208] 13. Example System

[0209] FIG. 27 illustrates a system 2700 according to one embodiment of the present invention. The illustrated system includes a sample 2705, such as cell-free DNA molecules, in a sample holder 2710, which can contact an assay 2708 to provide a signal of a physical characteristic 2715. An example of a sample holder can be a flow cell containing probes and / or primers for an assay, or a tube through which droplets (along with the droplets containing the assay) travel. The physical characteristic 2715, such as a fluorescence intensity value from the sample, is detected by a detector 2720. The detector can take measurements at intervals (e.g., periodic intervals) to obtain data points that constitute a data signal. In one embodiment, an analog-to-digital converter converts the analog signal from the detector to digital form multiple times. The data signal 2725 is transmitted from the detector 2720 to a logic system 2730. The data signal 2725 can be stored in local memory 2735, external memory 2740, or a storage device 2745.

[0210] Logic system 2730 may or may not include a computer system, ASIC, microprocessor, etc. Logic system 2730 may also include or be coupled to a display (e.g., a monitor, LED display, etc.) and user input devices (e.g., a mouse, keyboard, buttons, etc.). Logic system 2730 and other components may be part of a standalone or networked computer system, or may be directly attached to or incorporated into the thermal cycler device. Logic system 2730 may also include optimization software that executes in processor 2750. Logic system 1030 may include a computer-readable medium storing instructions for controlling system 1000 to perform any of the methods described herein.

[0211] Any computer system referred to herein may utilize any suitable number of subsystems. An example of such a subsystem is shown in computer system 10 in FIG. 28. In some embodiments, the computer system includes a single computer device, and the subsystems may be components of that computer device. In other embodiments, the computer system may include multiple computer devices, each with its own internal components, each of which is a subsystem. The computer system may include desktop and laptop computers, tablets, mobile phones, and other portable devices. The subsystems shown in FIG. 28 are interconnected via a system bus 75. Additional subsystems are shown, such as a printer 74, a keyboard 78, storage device(s) 79, a monitor 76 coupled to a display adapter 82, and others. Peripherals and input / output (I / O) devices coupled to the I / O controller 71 can be connected to the computer system by any number of means known in the art, such as input / output (I / O) ports 77 (e.g., USB, FireWire®). For example, the I / O ports 77 or external interface 81 (e.g., Ethernet, Wi-Fi, etc.) can be used to connect the computer system 10 to a wide area network such as the Internet, a mouse input device, or a scanner. The interconnections via the system bus 75 enable the central processing unit 73 to communicate with each subsystem and control the execution of instructions from the system memory 72 or storage device(s) 79 (e.g., fixed disks such as hard drives or optical disks) and the exchange of information between the subsystems. The system memory 72 and / or storage device(s) 79 may embody computer-readable media. Another subsystem is a data collection device 85, such as a camera, microphone, and accelerometer, and the like. Any of the data mentioned herein can be output from one component to another and can be output to a user.

[0212] A computer system may include multiple identical components or subsystems, connected together, for example, by an external interface 81 or by an internal interface. In some embodiments, computer systems, subsystems, or devices may communicate over a network. In such an example, one computer may be considered a client and another computer a server, each of which may be part of the same computer system. The client and server may each include multiple systems, subsystems, or components.

[0213] Aspects of the embodiments can be implemented in the form of control logic using hardware (e.g., application-specific integrated circuits or field programmable gate arrays) and / or using computer software in conjunction with a processing unit that is generally programmable in a modular or integrated fashion. As used herein, a processing unit includes a single-core processing unit, a multi-core processing unit on the same integrated chip, or multiple processing units on a circuit board or networked. Based on the present disclosure and the teachings provided herein, those skilled in the art will know and appreciate other ways and / or methods for implementing embodiments of the present invention using hardware and a combination of hardware and software.

[0214] Any of the software components or functions described in this application may be implemented as software code executed by a processing device using any suitable computer language, such as, for example, Java, C, C++, C#, Objective-C, Swift, or a scripting language, such as, for example, Perl or Python, using conventional or object-oriented techniques. The software code may be stored as a series of instructions or commands on a computer-readable medium for storage and / or transmission. Suitable non-transitory computer-readable media may include random access memory (RAM), read-only memory (ROM), magnetic media (such as a hard drive or floppy disk), or optical media (such as a compact disc (CD) or DVD (digital versatile disc)), flash memory, and the like. The computer-readable medium may also be any combination of such storage or transmission devices.

[0215] Such programs may also be coded and transmitted using carrier wave signals adapted for transmission over wired, optical, and / or wireless networks according to various protocols, including the Internet. As such, computer-readable media may be created using data signals coded with such programs. Computer-readable media coded with program code may be packaged with a compatible device or provided separately from other devices (e.g., via Internet download). Any such computer-readable medium may reside on or within a single computer product (e.g., a hard drive, CD, or an entire computer system), or may reside on or within different computer products within a system or network. A computer system may include a monitor, printer, or other suitable display for providing any of the results described herein to a user.

[0216] Any of the methods described herein can be implemented, in whole or in part, using a computer system including one or more processing devices that can be configured to perform the steps. Accordingly, embodiments may be directed to a computer system configured to perform the steps of any of the methods described herein, potentially with different components performing each step or each group of steps. While presented as numbered steps, steps of the methods herein can be performed simultaneously or in a different order. In addition, portions of these steps may be combined with portions of other steps of other methods. Also, all or portions of steps may be optional. In addition, any of the steps of any of the methods can be performed by a module, unit, circuit, or other means of a system for performing these steps.

[0217] The specific details of particular embodiments may be combined in any suitable manner without departing from the spirit and scope of the embodiments of the invention. However, other embodiments of the invention may be directed to specific embodiments relating to each individual aspect or specific combinations of these individual aspects.

[0218] The foregoing description of exemplary embodiments of the invention has been presented for purposes of illustration and description. It is not intended to be exhaustive or to limit the invention to the precise form described, and many modifications and variations are possible in light of the above teachings.

[0219] References to "a," "an," or "the" are intended to mean "one or more" unless specifically stated to the contrary. The use of "or" is intended to mean "including or," but not "except or," unless specifically stated to the contrary. A reference to a "first" element does not necessarily require that a second element be provided. Moreover, a reference to a "first" or "second" element does not limit the referenced element to a particular location unless explicitly stated.

[0220] All patents, patent applications, publications, and descriptions referred to herein are incorporated by reference in their entirety for all purposes. None is admitted to be prior art.

[0221] 14. References

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Claims

1. 1. A method for analyzing a mammalian blood sample, comprising: detecting a first number of methylated or unmethylated DNA fragments in a cell-free mixture of the blood sample in one or more differentially methylated regions based on a signal obtained from the assay, wherein the cell-free mixture comprises cell-free DNA from a plurality of cell lineages, and each of the one or more differentially methylated regions is specific to a particular blood cell lineage by being hypomethylated or hypermethylated compared to other cell lineages, and the particular blood cell lineage is an erythroid cell; determining a methylation level using the first number; and comparing the methylation level to one or more cutoff values ​​as part of determining the classification of the mammal as having a hematological disorder that is responsive to treatment, wherein the one or more cutoff values ​​correspond to a range in which the hematological disorder is responsive to treatment. and

2. 10. The method of claim 1, wherein the hematological disorder is aplastic anemia.

3. 10. The method of claim 1, wherein the treatment is immunosuppressive therapy.

4. 10. The method of claim 1, wherein the hematological disorder is iron deficiency anemia and the treatment is iron therapy.

5. 10. The method of claim 1, wherein the hematological disorder is chronic kidney disease.

6. measuring the hemoglobin level of the blood sample; comparing the hemoglobin level to a hemoglobin threshold; determining a classification of the mammal having a hematological disorder into therapeutic response based further on a comparison of the hemoglobin level with the hemoglobin threshold; The method of claim 1 further comprising:

7. 2. The method of claim 1, wherein one of the one or more differentially methylated regions is in the FECH gene.

8. 2. The method of claim 1, wherein one of the one or more differentially methylated regions is on chromosome 12 at genomic coordinates 48227688-48227701.

9. 2. The method of claim 1, wherein one of the one or more differentially methylated regions is on chromosome 12 at genomic coordinates 48228144-48228154.

10. determining the total number of DNA fragments in the cell-free mixture in the one or more differentially methylated regions; 2. The method of claim 1, further comprising determining the methylation level using the first number and the total number.

11. 2. The method of claim 1, further comprising determining a volume of the cell-free mixture, wherein the methylation level is determined using the first number and the volume of the cell-free mixture.

12. 2. The method of claim 1, wherein the one or more differentially methylated regions comprise a CpG site.

13. 13. The method of claim 12, wherein a first region of the one or more differentially methylated regions comprises a plurality of CpG sites that are within 100 bp of each other, and wherein the plurality of CpG sites are all hypomethylated or hypermethylated.

14. 14. The method of claim 13, wherein the plurality of CpG sites spans 100 bp or less on the reference genome of the mammal.

15. The method of claim 1, wherein the red blood cells include erythroblasts.

16. 10. The method of claim 1, wherein the one or more differentially methylated regions are hypomethylated.

17. The method of claim 1 , wherein the cell-free mixture is plasma.

18. A computer readable medium storing a plurality of instructions for controlling a system to perform the method of any one of claims 1 to 17.

19. A computer-readable medium according to claim 18; one or more processors for executing instructions stored on the computer-readable medium.

20. A system configured to perform the method according to any one of claims 1 to 17.

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