Methods for quantifying hematopoietic stem and progenitor cells

Abstract

The invention relates to a method for quantifying hematopoietic stem and progenitor cells (HSPCs), wherein at least one sample comprising cells, each containing at least one nucleic acid molecule, is provided, and the methylation degree of at least one CpG dinucleotide within at least one specific region of the nucleic acid molecule is determined. The determined methylation degree of the CpG dinucleotide then indicates the proportion of hematopoietic stem and progenitor cells in the sample. According to the invention, the specific region is selected from the group consisting of the genes serine / threonine kinase 17a (STK17A), myosin ID (MYO1D), nuclear body protein SP140 (SP140), CD48 molecule (CD48), and nuclear factor of activated T cells 1 (NFATC1). The predictions determined with the signature according to the invention correlate with the flow cytometric analysis of CD34. + -Numbers in mobilized peripheral blood.

Description

[0001] The invention relates to a method for quantifying hematopoietic stem and progenitor cells (HSPCs), which comprises providing at least one sample comprising cells each comprising at least one nucleic acid molecule, and determining the methylation level of at least one CpG dinucleotide within at least one specific region of the nucleic acid molecule, wherein the determined methylation level of the CpG dinucleotide indicates the proportion of hematopoietic stem and progenitor cells in the sample. The invention further relates to the use of at least one artificial nucleic acid molecule in a kit for quantifying hematopoietic stem and progenitor cells according to the method of the invention, wherein the nucleic acid molecule comprises at least one nucleotide sequence.

[0002] Monitoring hematopoietic stem and progenitor cells is crucial to determine the efficiency of HSPC mobilization for stem cell apheresis in routine clinical practice. HSPCs are typically determined by flow cytometry using the cell surface marker CD34 in daily clinical practice (1). For more accurate quantification of early hematopoietic progenitor cells, such as primitive hematopoietic stem cells (HSCs), lymphoid-primed multipotent progenitors (LMPPs), or common myeloid progenitors (CMPs), additional surface markers and the absence of lineage-specific markers can be used (2). However, immunophenotypic analysis requires fresh blood samples, and the measurement of multiple surface markers is labor-intensive and costly.

[0003] However, the composition of cells in tissues, and especially in blood, can also be estimated based on epigenetic parameters (3). DNA methylation (DNAm) is a reversible alteration of cytosine, particularly at CG dinucleotides (CpG sites). Epigenetic signatures based on hundreds of CpGs have already been used for the deconvolution of leukocyte subsets (4). The first predictors were generated and applied to "IIIumina Bead Chip" microarray datasets—initially on the 450k platform (4) and more recently on the human "EPIC 850k Bead Chip" (5). Furthermore, multi-CpG signatures have been adapted to better distinguish additional and non-hematopoietic cell types (6). Even targeted analysis of individual cell type-specific CpG sites can be used to estimate granulocytes, CD4 T cells, CD8 T cells, B cells, NK cells, and monocytes (7, 8).However, no epigenetic biomarkers for HSPCs have been identified to date.

[0004] The object of the invention is to identify and provide CpG sites that represent reliable biomarkers for HSPCs and thus enable simplified quantification of hematopoietic stem and progenitor cells.

[0005] The object is achieved according to the invention by a method for quantifying hematopoietic stem and progenitor cells (HSPCs) of the type mentioned above, in which the specific region is selected from the group consisting of the genes serine / threonine kinase 17a (STK17A), myosin ID (MYO1D), nuclear body protein SP140 (SP140), CD48 molecule (CD48), and nuclear factor of activated T cells 1 (NFATC1). To identify these specific regions, the DNAm profiles of sorted subsets from peripheral blood were examined to identify CpG sites that could represent reliable biomarkers for HSPCs.It was demonstrated that HSPCs can also be identified through targeted DNA methylation (DNAm) analyses. The determined methylation level (DNAm concentration [%]) of at least one CpG dinucleotide located within at least one of the specific regions according to the invention indicates the proportion of hematopoietic stem and progenitor cells in a sample. These CpG dinucleotides can reliably distinguish HSPCs from other cell types, so that epigenetic measurements can reflect the proportion of HSPCs in the blood. The solution according to the invention can therefore facilitate the monitoring of the mobilization of hematopoietic stem cells or the measurement of HSPCs during transplantation. Using the epigenetic biomarkers according to the invention, even the number of leukemic blasts can be determined.In contrast to flow cytometric measurements, DNAm analysis can also be used on frozen blood or dried blood spots, for example, allowing retrospective analysis or self-assessment with a finger prick.

[0006] For example, the determined methylation level of the CpG dinucleotide correlates with the number of CD34 + -cells and / or with the number of blasts in leukemia, ie the DNAm concentrations at the five individual CG dinucleotides in the genes STK17A, MYO1D, SP140, CD48 and NFATC1 correlate with the number of CD34 +Cells in mobilized peripheral blood and with the blast count in leukemia. The high correlation of the data underlying the invention with the CD34 or blast count demonstrates that the epigenetic biomarkers according to the invention can reliably and efficiently support the assessment of stem cell mobilization, HSPC collection, or blast count in leukemia in the future.

[0007] Preferably, the CpG dinucleotide(s) for carrying out the method according to the invention is selected from CpG dinucleotides located in the gene body of the SKT17A gene, the MYO1D gene, the CD48 gene or the NFATC1 gene, or in the promoter region of the SP140 gene.

[0008] In particular, the method according to the invention can be characterized in that the CpG dinucleotide is selected from the group consisting of the CpG dinucleotides cg17707057 (STK17A), cg00164282 (MYO1D), cg17607231 (SP140), cg13311440 (CD48), cg11977716 (NFATC1), and at least one CpG dinucleotide located within a region of 10,000 nucleotides upstream and / or downstream of each of the mentioned CpG dinucleotides. The sample number "cg..." within the meaning of the invention refers to the position of the respective CpG dinucleotide on the "450k Illumina Bead Chip" (Illumina, San Diego, California, USA).

[0009] Positions of the CpG dinucleotides according to the invention in the human genome (“chr” = chromosome, followed by the number of the chromosome and the position of the CpG on the corresponding chromosome;CpG-Dinukleotide in eckigen Klammern und fett gedruckt): cg17707057: chr7:43630711 (GRCh37): CCCCCTCCCATTGCACCAGTCATTTAGAGATGAAGGCATGCATGCTTCCTCA AATCATTG[CG]ACCTGCTGTCATAATGTCAGTTTCAGTGGTATGTGGTATTAG AATGATGGAAGAGTTTTA (SEQ ID NO: 29) cg00164282: chr17:30830732 (GRCh37): CAAAGTGATAGTAAATGCATTAAGTGCTTCATGAGAAACTCTAGAGTCACAGA GACAGGC[CG]TCCTCCATCTGGATGTCACTAGGACATAATGCATGCTCTCTG GCTCACACTGAGGACAAC (SEQ ID NO: 30) cg17607231: chr2:231090329 (GRCh37): GCTTGAGTTGAATGACTTTTCCACCCTCTTTCCTTGGCAGGAACAAGTGACCC TGTCTTC[CG]TGGCCTCGTGGGGCTGGCTCTGCTCCTCCTCCCTTGACTGAG AGACGTCATGGAGATTGG (SEQ ID NO: 31) cg13311440: chr1:160681404 (GRCh37): TGGAATGGAAATACCTGCTTTCCAGACACCAGATCTGGCTTCTAGGGTTGAA CTACATCC[CG]TCTCCCCTTTCCCAACTGACCATGCCTTCAGTGCACAATCAC AGATACACAGTTGGTGGA (SEQ ID NO: 32) cg11977716: chr18:77284742 (GRCh37): CAGACCCAGGCAGAGAGAGCAAAGGAGGCTGTGGTGGCCCCCGACGGGAA CCTGGGTGGC[CG]GGGGACACACCGAGGAACTTTCCGCCCCCCGACGGGC TCTCCCACCGAGGCTCAGGTGCT (SEQ ID NO: 33);

[0010] Preferably, at least one CpG dinucleotide is located within a region of 5,000, 1,000, 500, 400, 300, 200 or 100 nucleotides upstream and / or downstream of each of the CpG dinucleotides cg17707057, cg00164282, cg17607231, cg13311440 and cg11977716.

[0011] The sample can, for example, be a blood sample comprising mobilized peripheral blood, bone marrow aspirate, and / or umbilical cord blood from a human individual. This can advantageously also be frozen blood or dried blood spots. The sample can, for example, also be a bone marrow sample and / or bone marrow aspirate from a human individual.

[0012] In a particularly advantageous embodiment of the method according to the invention, the sample is provided in a volume of less than 100 µl, less than 90 µl, less than 80 µl, less than 70 µl, less than 60 µl, less than 50 µl, at most 50 µl, 20-80 µl, 30-70 µl, 40-60 µl, 5-50 µl, 10-50 µl, 20-50 µl, 30-50 µl, 40-50 µl, or preferably 50 µl. The method according to the invention thus requires very small sample volumes, so that large sample quantities do not need to be available for successful and accurate quantification of the stem and progenitor cells.

[0013] In an advantageous embodiment of the invention, the sample can even be provided in a frozen, fixed, and embedded, or dried state. For example, the method according to the invention can also be performed with paraffin-fixed samples (e.g., bone marrow sections).

[0014] Determining the degree of methylation or measuring DNA methylation (DNAm) can be performed cost-effectively and in high-throughput methods for specific regions (e.g., DNA segments) using pyrosequencing or MassArray. Alternatively, more advanced testing techniques such as sequencing and microarray-based methods are also possible, for example, digital droplet PCR, Epityper, amplicon sequencing, nanopore sequencing, SMART sequencing, and similar methods.

[0015] The object is further achieved according to the invention by the use of at least one artificial nucleic acid molecule in a kit for quantifying hematopoietic stem and progenitor cells (HSPCs) according to the method according to the invention, in which the nucleic acid molecule comprises at least one nucleotide sequence selected from the group consisting of: a) A nucleotide sequence selected from the group consisting of SEQ ID NO: 10, SEQ ID NO: 11, SEQ ID NO: 12, SEQ ID NO: 16, SEQ ID NO: 17, SEQ ID NO: 18, SEQ ID NO: 19, SEQ ID NO: 20, SEQ ID NO: 21, SEQ ID NO: 22, SEQ ID NO: 23, SEQ ID NO: 24, SEQ ID NO: 25, SEQ ID NO: 26, SEQ ID NO: 27 and SEQ ID NO: 28; (b) a nucleotide sequence which differs from the nucleotide sequence according to (a) by the exchange of not more than 10% of the nucleotides, preferably not more than 5% of the nucleotides, except for the CpG dinucleotide(s); and c) a nucleotide sequence corresponding to the complementary strand of the nucleotide sequence according to a) or b).

[0016] The invention includes, for example, a kit with a compilation of suitable reference DNAs, primer sets, as well as optional instructions and evaluation software. The invention consequently comprises a kit, in particular a kit for quantifying hematopoietic stem and progenitor cells (HSPCs), preferably for carrying out the method according to the invention, which comprises at least one oligonucleotide primer (for example oligonucleotide primer pair, e.g. a “primer set”, see Table 2 below) for amplifying and / or sequencing at least at least one CpG dinucleotide of at least one nucleotide sequence which comprises at least one CpG dinucleotide from the group consisting of the CpG dinucleotides cg17707057 (STK17A), cg00164282 (MYO1D), cg17607231 (SP140), cg13311440 (CD48), cg11977716 (NFATC1) and at least one CpG dinucleotide which is located within a region of 10.000 nucleotides upstream and / or downstream of each of the CpG dinucleotides mentioned. The kit according to the invention can, for example, comprise at least one primer set according to the invention and optionally at least one buffer solution and / or at least one reagent for carrying out at least one method selected from the group consisting of DNA amplification, bisulfite treatment of DNA, DNA sequencing, preferably pyrosequencing of DNA, MassArray analysis, deep sequencing of bisulfite-converted DNA, flow cytometry-based bead assays, SNP genotyping, digital droplet PCR, Epityper, amplicon sequencing, nanopore sequencing, and SMART sequencing.

[0017] The invention is explained in more detail below with reference to the illustrations, figures, tables and examples. Short description of the figures Fig.Figure 1 shows diagrams for identifying epigenetic biomarkers for hematopoietic stem and progenitor cells: A) Candidate CpG sites to distinguish HSPCs (GSE72867) from other leukocytes (GSE35069) were selected based on the difference in mean DNAm values ​​(β-value) and the variation in β-values ​​within these two groups. B) The DNAm values ​​of three candidate CpGs are shown in independent DNAm profiles (n=301) from 42 studies (Table 1). C) The DNAm values ​​of three relevant CpGs in the genes MYO1D, STK17A, and SP140 were determined using pyrosequencing in dilutions of CD34 +HSPCs from mobilized peripheral blood (mPB, n=2) and cord blood (CB, n=3) were analyzed and measured using flow cytometry. D) Multivariable models for the three CpGs were trained using the dilution data for mPB or CB. These models were then applied to estimate the proportion of HSPCs in different types of frozen samples (mPB model for peripheral blood (PB), mobilized peripheral blood (mPB), CD34 + Bone marrow (BM) cells and apheresis samples; CB model for umbilical cord blood (CB). E) Estimates of HSPC counts based on the multivariable mPB model were compared with flow cytometric CD34 measurements in mPB (n=9). F) The CB-HSPC predictor was applied to individual colonies in colony-forming units (CFU). G) Correlation of DNAm at the CpG in MYO1D (cg00164282) with manual blast counts in leukemic samples (n=39). Correlations were assessed using the Pearson correlation coefficient r. Fig. Figure 2 shows diagrams of DNA methylation in subsets of hematopoietic progenitor cells: A) Candidate CpGs were selected for hematopoietic stem cells (HSCs), lymphoid-myeloid progenitor cells (LMPPs), and myeloid progenitor cells (CMPs). The selection was based on 1) the difference in the mean β value in the DNAm profiles of these progenitor subsets (GSE63409) compared to all other subsets and leukocytes (GSE35069) and 2) the variance of β values ​​within these groups. For each subset, three hypomethylated (blue) and three hypermethylated CpGs (red) were mapped. B) Gene expression profiles were compared in HSPCs with leukocytes (GSE24759), with 12 of the 18 selected CpGs being significantly differentially expressed (adjusted P value < 0.05). C) Heatmap showing the DNAm (β values) for 18 CpGs (GSE63409, GSE35069). D) Estimation of HSPC subgroups based on a non-negative least squares (NNLS) model for 6 CpGs in different types of cryopreserved blood samples.E) Pearson correlation of DNAm values ​​in pyrosequencing with CD34 count in mPB (HSPCs, flow cytometry, n=9) and leukemic blasts (manual count, n=39). Fig. Figure 3 shows diagrams with DNA methylation data in genes with candidate CpGs: The candidate CpGs for hematopoietic stem and progenitor cells (HSPCs, GSE72867) were identified in the genes MYO1D, STK17A, SP140. Fig. Figure 3 shows the DNAm values ​​(β-values) of the different probe sets on the 450k Illumina BeadChip for these genes in relation to different parts of the genes (TSS1500 = 1500bp upstream of the transcription start site, TSS200, 5'UTR = 3'untranslated region, 1 st Exon, body, and 3'UTR, according to Illumina annotation). For comparison, DNAm values ​​are predicted for mature myeloid and lymphoid cells (GSE35069). Fig. Figure 4 shows diagrams correlating DNA methylation of candidate CpGs with manual counts: A) Correlation of DNAm at the CpGs in STK17A (cg17707057) and in SP140 (cg17607231) with manual blast counts in leukemic samples (n=39). B) HSPC count estimates based on the multivariable mPB model were compared with manual blast counts in leukemic samples (n=39). Pearson correlation coefficients (r) are shown. Fig. Figure 5 shows a schematic representation of the hematopoietic subgroups: The schematic representation shows the surface markers used to sort the HSPC subgroups into CMPs, LMPPs and HSCs (2). Fig. Figure 6 shows a heatmap of DNA methylation levels at candidate CpGs in different cell types: DNA methylation levels (β-values) are shown for the 18 candidate CpG sites for the subsets of HSPCs in different cell types. The heatmap shows the ssNoob-normalized data from 301 cell preparations derived from 42 different studies (Table 1). Fig. Figure 7 shows a graph with predictions of hematopoietic progenitor cells in colony forming units: CD34 +Cord blood cells were seeded in colony formation assays, and after 14 days of culture, single colonies were picked for DNA isolation. DNA was converted with bisulfite and analyzed by pyrosequencing at six CpGs (one hyper- and one hypomethylated CpG for HSCs, LMPPs, and CMPs). DNAm values ​​were used for a non-negative least squares (NNLS) model to predict the composition of HSPC subsets. All colonies were predicted to contain high numbers of progenitor cells—particularly for CFU-GEMM and BFU-E, which may be enriched for more primitive progenitor cells. On the other hand, CFU-GEMM and BFU-E were predicted to contain lower proportions of HSCs, which may be counterintuitive, as CFU-GEMM, in particular, have high replication and self-renewal potential (9).It is conceivable that the high proliferation rates in these colonies contribute to the low HSC fraction predictions in these large colonies, but this requires further investigation in the future. Lymphoid progenitor cells were barely predicted in all colony types, consistent with the fact that CFU conditions favor myeloid lineage. Fig. Figure 8 shows diagrams and a heatmap for the identification of CpGs with cell type-specific DNAm in hematopoietic stem and progenitor cells using the “CimpleG tool” (10): (A) Flowchart showing how DNA methylation datasets from different studies (N) and Illumina BeadChip datasets (n) were compiled. (B) Based on this dataset, candidate CpGs were ranked by CimpleG for each cell type (based on mean difference and total variance). The top 3 CpGs with the highest CimpleG score for HSPC specificity are highlighted. (C) The heatmap shows the DNAm (beta values) for the 3 candidate CpGs for hematopoietic stem and progenitor cells (HSPCs). nRBC: nucleated red blood cells. MethodsSelection of cell type-specific CpGs

[0018] For the selection of candidate CpGs for HSPCs (cg17707057, cg00164282 and cg17607231), the inventors used “IIIumina 450k BeadChip” datasets of sorted CD34 +Cells from five donors, each with three technical replicates (GSE72867) (11). Their DNAm profiles were compared with other sorted leukocyte subsets (granulocytes, monocytes, CD4 T cells, CD8 T cells, NK cells, B cells) from six donors (GSE35069) (12). The latter dataset was also used for a previous selection of cell type-specific CpGs (8, 13). By analogy, the inventors also selected CpGs for LMPPs, CMPs, and HSCs by analyzing DNAm profiles of these cell subsets isolated from five normal bone marrow samples (GSE63409) (14). The IDAT raw files were retrieved from Gene Expression Omnibus (GEO), and the data were processed using the program minfi in R (version 4.1.3) (15).Features were restricted to CpGs shared between the 450K and EPIC platforms, and probes of poor quality (P-value > 0.05), probes related to sex chromosomes, and probes not shared across all samples were excluded. Furthermore, DNAm (β values) were normalized using the Single-Sample Noob (ssNoob) application (16). Noob (normal-exponential out-of-band) is a background correction method with dye-bias normalization for Illumina Infinium methylation arrays. This normalization procedure has been further developed for stepwise preprocessing of individual methylation arrays for cross-study comparison (extended minfi package) (16).Candidate CpGs were identified based on two parameters, as previously described (7,8): 1) a large difference in mean DNAm values ​​(β values) between HSPCs and all other cell types, and 2) low variation in β values ​​within each of these two groups, according to the inventors' new "CimpleG tool" (10). For graphical representation, CpGs in HSPCs were classified as hypermethylated (red) or hypomethylated (blue), using cutoffs for the difference in mean β values ​​>0.5 or <-0.5, respectively. Heatmaps were generated using "Heatmapper" (17). The inventors randomly selected the most relevant CpGs according to both parameters, also taking into account the corresponding gene functions. The DNAm of the candidate CpGs was further investigated using 301 independent DNAm profiles from 42 different studies (Table 1).The CpG candidates cg17607231 and cg13311440 were also found in another analysis performed by the inventors using the new CimpleG tool on IIIumina 450k Beadchip and IIIumina EpicArray datasets of sorted CD34. + cells and other sorted leukocyte subsets from 40 different studies (Table 4). This analysis additionally identified cg11977716 as a candidate CpG for HSPCs. Blood samples

[0019] Peripheral blood samples from healthy donors (PB, n=8), PB from patients with hematological diseases (n=39; especially acute myeloid leukemia), umbilical cord blood (CB, n=5), mobilized peripheral blood (mPB, n=9), and stem cell apheresis products (n=6) were collected after informed and written consent according to the guidelines specifically approved by the local ethics committee of RWTH Aachen University (EK206 / 09, EK099 / 14). DNA isolation and bisulfite conversion

[0020] DNA was isolated from blood samples using the QlAamp DNA Mini Kit (Qiagen). Cells were lysed with proteinase K for 10 minutes at 56°C. Silica gel spin columns were used to bind and purify DNA from the lysed samples. DNA was eluted in 150 µl of elution buffer, the concentration was measured with Nanodrop (Thermo Scientific), and stored in a freezer at -20°C until further analysis. For samples with lower cell counts, such as single colony-forming blood units or artificial cell dilutions, DNA was isolated using NucleoSpin Tissue XS (Macherey Nagel). Cells were lysed with proteinase K in sodium dodecyl sulfate (SDS) lysis buffer for 15 minutes at 70°C. Solid-support silica gel spin columns were used to bind and purify DNA. The DNA was eluted in 20 µl of elution buffer (5 mM Tris / HCl, pH 8.5). The genomic DNA was then bisulfite-treated using the EZ DNA Methylation Kit (Zymo research). Pyrosequencing

[0021] Bisulfite-converted DNA (10–20 ng) was amplified using the PyroMark PCR Kit (Qiagen) with primers designed using the PyroMark Assay Design 2.0 Software (Qiagen) and purchased from Metabion (Table 2). PCR amplicons were sequenced on a PyroMark Q96 ID (Qiagen).

[0022] Specific regions of the bisulfite-treated DNA were amplified in a thermocycler (Eppendorf) using the following program: DNA polymerase activation for 15 minutes at 95°C; 45 cycles of DNA denaturation (30 seconds at 94°C), annealing (30 seconds at 56°C), and DNA extension (30 seconds at 72°C); and finally, extension for 10 minutes at 72°C. Subsequently, 20 µl of PCR products were bound to 5 µl of Streptavidin Sepharose High Performance Beads (GE Healthcare) and annealed to 1 µl of sequencing primer (5 µM) for 2 minutes at 80°C. The PCR amplicons were sequenced with a “PyroMark Q96 ID” (Qiagen) and analyzed with “PyroMark Q96 CpG 1.0.9” (Qiagen). Digital droplet PCR (ddPCR)

[0023] ddPCR is an absolute quantification method that provides an absolute number of DNA copies per reaction. Primers were designed for ddPCR. Unlike pyrosequencing, the reverse primer is not conjugated with biotin, and two sequences conjugated with fluorescent markers were used instead of a sequencing primer. The sequences described below were tested in a mixed DNA sample from 10 donors. Primers for ddPCR:

[0024] Forward: TAGATTTAGGTAGAGAGAGTAAAGGAG (SEQ ID NO: 25) Reverse: CATAAAACTTCCCCTTACTACCCA (SEQ ID NO: 26) Unmethylated CpG marker (fluorescent marker: FAM): TTAGTTGGGAAAGGGGAGATGGGA (SEQ ID NO: 27) Methylated CpG marker (fluorescent marker: HEX): TAGTTTGGGAAAGGGGGAGACGGG (SEQ ID NO: 28)

[0025] First, the DNA was packed into droplets using a droplet generator (BioRad). Then, within these droplets, these regions of bisulfite-treated DNA were amplified in a thermocycler (BioRad) using the following program: DNA polymerase activation for 10 minutes at 95°C; 45 cycles of DNA denaturation (30 seconds at 94°C) and DNA extension (30 seconds at 56°C); and finally, for 10 minutes at 98°C to deactivate the enzymes. The droplets were quantified using a QX200 Droplet Digital Reader (BioRad) and analyzed using QuantaSoft Analysis Pro software (Bio-Rad). Sorting of CD34 + -Cells and cell dilutions

[0026] Mononuclear cells were isolated from umbilical cord blood or mobilized peripheral blood (10-20 ml) using Ficoll density gradient centrifugation. Subsequently, CD34 +Cells were separated by magnetically activated cell sorting (MACS) using the CD34 MicroBead Kit (Miltenyi). The purified cells were manually quantified using the Neubauer chamber and divided into different CD34 + -cell parts with the CD34 - Flow-through was mixed (0-100%; mixtures of cells, not DNA). To determine the CD34 content of these dilutions, the cells were stained with APC anti-CD34 and V500 anti-CD45 antibodies. Fluorescence intensities were measured using the Becton Dickinson (BD) FACS Canto II. Data were acquired using BD FACSDiva™ software and analyzed using FlowJo™ v10. Analysis of colony-forming units

[0027] Colony-forming units (CFUs) arise from primitive clonogenic HSPCs. To assess whether this is also reflected in the epigenetic biomarkers, the inventors generated CFUs from umbilical cord blood from four donors. Approximately 1,000 CD34 +Cells were cultured in human "StemMACS™" HSC-CFU media with EPO (Miltenyi) supplemented with cytokines for myeloid differentiation (GM-CSF, IL-3, erythropoietin). After 14 days of incubation at 37°C, colonies were classified into the following colony types based on morphological parameters according to the manufacturer's instructions: CFU-E = CFU erythrocytes; BFU-E = "burst forming unit" erythrocytes; CFU-M = CFU macrophages; CFU-G = CFU granulocytes; CFU-GM = CFU granulocyte-macrophage; CFU-GEMM = CFU granulocyte-erythrocyte-macrophage-megakaryocyte. Single colonies were picked with a pipette tip for DNA isolation using a Leica DM1000 LED microscope (Leica Microsystems) at 40x magnification. Models for the epigenetic assessment of HSPCs

[0028] To derive an epigenetic predictor of HSPC content, the inventors trained a multivariable model based on the artificial dilution results of five donors (47 measurements in total; Fig. 1C). Since there was a DNAm difference between CB and mPB CD34 cell dilutions, the inventors trained two alternative models for these samples using Excel: HSPCsCB=0.6232*DNAmcg17707057+1.236*DNAmcg00164282+0.95*DNAmcg17607231−34.38 HSPCsCB=0.444*DNAmcg17707057+0.3123*DNAmcg00164282+0.6601*DNAmcg17607231−13.070

[0029] For deconvolution of HSPC subsets, we used a nonnegative least squares (NNLS) model (8). This model is based on the matrix of mean DNAm values ​​of the reference datasets (Table 3). For deconvolution of HSPC subsets in CFU data, we focused on the following six CpGs because the DNA amount for individual colonies was very low: BMF cg09749364; FTO cg01986630; HLF cg08865625; STK17A cg17707057; TESC cg06768361; and MYO1D cg00164282. Analysis of gene expression

[0030] To assess whether DNAm at candidate CpGs is also reflected at the level of gene expression, the inventors used microarray data from purified hematopoietic cell subsets from umbilical cord blood (GSE24759) (18) and compared expression levels between HSPCs (n=34) and mature leukocytes (n=118). Data were analyzed with GEO2R and normalized with quantile normalization using the R package limma (version 3.54.0) (19). Significant genes (adjusted P-value <0.05) were selected, and the P-value was plotted against the log2 fold change. Summary and evaluation of the results

[0031] To select individual CpGs that distinguish between HSPCs and other cell types, the inventors profiled CD34 +Cells from mobilized peripheral blood (mPB) and purified leukocyte subsets were used. Cell-type-specific CpGs were identified as previously described (7, 8), and most of them were hypermethylated in HSPCs. The inventors selected three candidate CpGs located in the genes serine / threonine kinase 17a (STK17A, cg17707057), myosin ID (MYO1D, cg00164282), and SP140 nuclear body protein (SP140; cg17607231) ( Fig. 1A). These CpGs were located either in the gene body (SKT17A, MYO1D) or in the promoter region of the corresponding genes (SP140; Fig.3). To further validate that the CpGs can distinguish HSPCs from other cell types, the inventors compiled a dataset of 301 DNAm profiles (Table 1). Indeed, all three CpGs were consistently methylated in various subsets of HSPCs, while they were hypomethylated in mature leukocytes of all lineages. Notably, the HSPC-associated CpGs were also methylated in other non-hematopoietic cell types such as fibroblasts, endothelial cells, and epithelial cells ( Fig. 1B). Thus, the three candidate CpGs are not specifically methylated in HSPCs, but rather specifically hypomethylated during hematopoietic differentiation.

[0032] To test whether these three CpG sites can be used for the targeted deconvolution of HSPC fractions, the inventors created artificial mixtures of CD34 + - and CD34 -Cells were prepared from umbilical cord blood (CB, n=2) and mobilized peripheral blood (mPB; n=3). Pyrosequencing experiments were performed and tested on the DNA isolated from these artificial mixtures. Overall, DNA concentrations at the three CpGs in CD34 were + -cells from mPB higher than in CB, which could be attributed to epigenetic differences between fetal and adult hematopoiesis ( Fig. 1C). Despite this difference, the DNAm values ​​in the dilutions of all donor samples showed a high correlation with the flow cytometrically determined CD34 + - numbers, which provides further evidence that the three candidate CpGs are indicative of HSPC fractions.

[0033] To estimate HSPC fractions, the inventors trained a multivariable three-CpG model based on dilution measurements for mPB or CB. The CB model was initially tested on cryopreserved cord blood samples (n=5), and the mPB model was tested on peripheral blood (n=11), CD34-positive + enriched cells from bone marrow (n=5), mobilized peripheral blood (n=7) and stem cell apheresis products (n=6; Fig. 1D). As expected, HSPCs were not predicted in non-mobilized blood and the highest number of HSPCs was found in CD34 + enriched cells from the bone marrow. Furthermore, the predictions correlated with the flow cytometric analysis of CD34 + -numbers in mPB (n=9; r=0.95), although the HSPC numbers were overestimated and the correlation is determined in particular by a leverage point ( Fig.1E). The inventors also tested their HSPC predictor on colony-forming units (CFUs) derived from clonogenic HSPCs. CFUs consistently showed high predictions for HSPCs ( Fig. 1F). This was expected, as CFUs are used as a surrogate assay for HSPCs. Next, the inventors tested whether the HSPC predictor could also reflect the number of leukemic blasts, which also frequently express the CD34 antigen and are derived from HSPC-related cell types. They found that, in particular, DNAm levels in MYO1D showed a high correlation with blast number (n=39; r=0.97; Fig. 1G). The number of blasts also correlated with DNAm in STK17A (r=0.92) and to a lesser extent in SP140 (r=0.62; Fig. 4A). However, the predictors for HSPCs in blood showed deviations in the estimation of absolute blast numbers ( Fig.4B). In any case, these results demonstrate that such epigenetic analysis also supports the evaluation of blast counts, especially when no driver mutations are available to more accurately determine blast load.

[0034] There are lineage-specific DNAm differences between the small fraction of hematopoietic stem cells, myeloid progenitor cells, and lymphoid progenitor cells (2, 20). Therefore, the inventors investigated whether even subsets of HSPCs could be distinguished based on the DNAm of individual CpGs. For this purpose, the inventors used 450k DNAm profiles of CMPs, LMPPs, and HSCs ( Fig. 5) (2). For each of these subgroups, the inventors selected three hypo- and three hypermethylated candidate CpGs compared to the other two HSPC subgroups and leukocyte subgroups ( Fig.2A). MYO1D and STK17A were again among the 18 best candidate CpGs (see Table 3). It is noteworthy that many of the corresponding genes were previously shown to be more highly expressed in primitive hematopoietic stem cells, including HOXB3, MEIS1, CD48, and hepatic leukemia factor (HLF) (21). Indeed, HLF appears to be a key regulator of the earliest lineage commitment in the transition from multipotency to lineage-restricted progeny (22). When analyzing differential gene expression in HSPCs compared to leukocytes (GSE24759), we found that 12 of the 18 genes were significantly differentially expressed (adjusted P value < 0.05; Fig. 2B). Thus, the selected candidate CpGs appear to be associated with functionally relevant genes. The DNAm values ​​of all 18 CpGs were able to distinguish hematopoietic progenitor cells from leukocytes ( Fig.2C). The inventors also observed significant DNAm differences between LMPPs and CMPs. However, these differences could not be clearly distinguished from the DNAm patterns of HSCs. This suggests that hematopoietic differentiation is a continuum without clearly defined intermediate stages. In contrast to the CpGs originally identified for CD34 + associated cells were selected, DNAm values ​​in non-hematopoietic cell types varied greatly ( Fig. 6).

[0035] Specifically, six CpGs (one hypo- and one hypermethylated CpG for each HSPC subtype) were analyzed in more detail: HLF (cg08865625), STK17A (cg17707057), Bcl-2-modifying factor (BMF, cg09749364), FTO alpha-ketoglutarate-dependent dioxygenase (FTO, cg01986630), tescalcin (TESC, involved in myeloid differentiation, cg06768361), and MYO1D (cg00164282). For epigenetic predictions, a non-negative least squares (NNLS) model was trained on the mean DNAm data of the reference datasets for HSCs, LMPPs, CMPs, and leukocytes (data not shown here) (8). Since no DNAm profiles with known composition of the different HSPC subsets were available, the inventors applied their NNLS model to the same PB-, CB-, BM-derived sorted CD34 + cells, mPB and stem cell apheresis samples that they used to estimate total HSPCs ( Fig.2D). The sum of the estimated HSZ, LMPP, and CMP fractions was very similar to the total HSPC fraction predicted by the inventors with their multivariable HSPC model ( Fig. 1D). When they tested this approach on individual CFUs, higher proportions of progenitor cells were predicted, particularly for CFU-GEMM and BFU-E ( Fig. 7) All six selected CpGs correlated with the number of CD34 + -cells in mPB (n=9). Furthermore, all three hypermethylated CpGs showed a very high correlation with blast counts in leukemia samples (n=39; STK17A r=0.92; FTO r=0.64; MYOD1 r=0.96), whereas this was not observed for the hypomethylated CpGs ( Fig.2E). It is known that aberrant DNAm exists in leukemia, which varies greatly between different samples (23). However, the correlation of hypermethylated candidate CpGs with blast count suggests that DNAm at these CpGs is influenced to a lesser extent by the disease entity or patient-specific variation.

[0036] To identify further suitable candidate CpGs for cell type-specific DNAm in HSPCs, a dataset of 1,303 DNAm profiles from 40 different studies was compiled ( Fig. 8A; Table 4). Using the CimpleG pipeline (10), the inventors selected the three top-ranked CpGs ( Fig. 8B). Similar to the original identification method, all top-ranked CpGs exhibited HSPC-specific hypermethylation. The consistency of the cell-type-specific DNAm differences was also confirmed by heatmap analysis ( Fig.8C). There was a striking overlap of two of the top three CpGs with the previous selection: SP140 (cg17607231) and CD48 (cg13311440). Thus, the selected candidate CpGs appeared to be suitable for clearly distinguishing HSPCs across many different DNAm datasets. Table 1: DNA methylation studies for the reference dataset GEO ID Sample type Number of samples GSE35069 Granulocytes 6 GSE35069 CD4 T cells 6 GSE35069 CD8 T cells 6 GSE35069 Monocytes 6 GSE35069 B cells 6 GSE35069 NK cells 6 GSE40699 Epithelial cells 5 GSE49618 Hematopoietic stem and progenitor cells 3 GSE52025 Fibroblasts 2 GSE58477 Hematopoietic stem and progenitor cells 10 GSE59065 CD4 T cells 4 GSE63409 Hematopoietic stem cells 6 GSE63409 Multipotent progenitor cells 6 GSE63409 Lymphoid multi-progenitor cells 6 GSE63409 Common myeloid progenitor cells 6 GSE63409 Granulocyte-macrophage precursor cells 6 GSE63409 Macrophage erythroid precursor cells 6 GSE65078 Fibroblasts 4 GSE66562 NK cells 14 GSE72867 Hematopoietic stem and progenitor cells 15 GSE74877 Mesenchymal stromal cells 2 GSE74877 Fibroblasts 2 GSE74877 Epithelial cells 1 GSE77135 Fibroblasts 13 GSE79695 Mesenchymal stromal cells 12 GSE85566 Epithelial cells 6 GSE85647 Monocytes 4 GSE87797 Mesenchymal stem cells 6 GSE92843 Epithelial cells 1 GSE103541 Granulocytes 14 GSE106099 Endothelial cells 12 GSE107226 Fibroblasts 4 GSE1 09042 Epithelial cells 6 GSE129266 Mesenchymal stromal cells 3 GSE130030 CD8 T cells 14 GSE130030 CD4 T cells 11 GSE131989 Monocytes 10 GSE131989 B cells 14 GSE140078 Endothelial cells 2 GSE140295 Endothelial cells 8 GSE146376 Smooth muscle cells 25 GSE196046 Mesenchymal stromal cells 2 Table 2: Pyrosequencing and PCR primers Primer Sequence 5'-3' BMF Forward Backward Sequencing AGTTTGATAGTAGGATTTGGGATAA (SEQ ID NO:1) Biotin-ACAATAAACCATAAATAATCCCTCTTAAA (SEQ ID NO: 2) GGTTTTAGTTAGTAGGAGAG (SEQ ID NO: 3) FTO Forward Reverse Sequencing Biotin-TAGGAGGGTTGTGAGTTAGAAGTT (SEQ ID NO: 4) ACAAACCCCTTTAATATAATATTCAT (SEQ ID NO: 5) AAATTAACACATAAAACCACA (SEQ ID NO: 6) HLF Forward Backward Sequencing Biotin-ATGGGGGAGTTGGTTAAAATGTATA (SEQ ID NO: 7) ACTCCTAACAAAAACTCTCCCACTTTAA (SEQ ID NO: 8) ACATCCATATCACTCATC (SEQ ID NO: 9) STK17A Forward Reverse Sequencing TGAGTGGTTTGAAGGGTTTTTT (SEQ ID NO: 10) Biotin-AATTTACTAACCCCTACTCTATCC (SEQ ID NO: 11) AAGGTATGTATGTTTTTTTAAATTA (SEQ ID NO: 12) TESC Forward Backward Sequencing GGGAGAGGTTAGGGAAATATAGG (SEQ ID NO: 13) Biotin-TAACCCTACCCTCCCCAATA (SEQ ID NO: 14) GGAAATATAGGGTTAGTTTG (SEQ ID NO: 15) SP140 Forward Backward Sequencing AAGGGAGGAGGAGTAGAGTT (SEQ ID NO: 16) Biotin-ATTTACTTCCTCCTCATAATACCACCTTAC (SEQ ID NO: 17) GAGGAGTAGAGTTAGTTTT (SEQ ID NO: 18) MYO1D forward reverse sequencing GGTTTGATATTAGTAGGGGGATT (SEQ ID NO: 19) Biotin-AACCTTACACCAAACAAAAACAAATACTT (SEQ ID NO: 20) AGTGATATTTAGATGGAGGA (SEQ ID NO: 21) CD48 Forward TTGTTGTTTTTGTTATTTTTGGTGATTAG (SEQ ID NO: 22) Backward Biotin-CCAACCATACCATAACCATACTATT (SEQ ID NO: 23) Sequencing GGTTAGTTGGGAAAGG (SEQ ID NO: 24) Table 4: DNA methylation studies for HSPC-specific CpG candidates using CimpleG GEO ID Sample type Number of samples GSE184500 CD8 T cells 9 GSE166067 B cells 26 GSE180130 CD4 T cells 8 GSE180130 Monocytes 8 GSE157611 Monocytes 16 GSE147667 CD4 T cells 4 GSE147667 CD8 T cells 2 GSE154971 Lympho 8 GSE146093 CD4 T cells 16 GSE156792 T cells 48 GSE121192 CD4 T cells 10 GSE121192 Monocytes 6 GSE131177 Monocytes 3 GSE118696 Monocytes 4 GSE131627 Monocytes 15 GSE131989 Monocytes 31 GSE131989 CD4 T cells 62 GSE131989 B cells 30 GSE130030 CD8 T cells 14 GSE43976 Monocytes 36 GSE130029 CD4 T cells 11 GSE124565 Granulocytes 12 GSE103541 B cells 28 GSE103541 CD4 T cells 28 GSE103541 CD8 T cells 28 GSE103541 Granulocytes 28 GSE103541 Monocytes 28 GSE118570 T cells 9 GSE110554 Granulocytes 6 GSE110554 NK cells 6 GSE110554 B cells 6 GSE110554 CD4 T cells 7 GSE110554 CD8 T cells 6 GSE110554 Monocytes 6 GSE112696 CD4 T cells 6 GSE75405 CD4 T cells 10 GSE75405 CD8 T cells 9 GSE75405 T cells 5 GSE67170 T cells 10 GSE85647 Monocytes 6 GSE100561 Monocytes 6 GSE104287 NK cells 8 GSE88824 Granulocytes 8 GSE88824 CD4 T cells 8 GSE88824 CD8 T cells 8 GSE88824 NK cells 8 GSE88824 B cells 8 GSE88824 Monocytes 8 GSE87095 B cells 73 GSE98876 T cells 71 GSE82084 nucleated red blood cells 9 GSE82084 T cells 10 GSE82084 Granulocytes 8 GSE82084 Monocytes 9 GSE76170 Monocytes 30 GSE72364 CD4 T cells 6 GSE71955 CD4 T cells 31 GSE71955 CD8 T cells 31 GSE68456 T cells 5 GSE68456 Monocytes 12 GSE68456 nucleated red blood cells 12 GSE68456 B cells 7 GSE68456 CD4 T cells 7 GSE68456 CD8 T cells 6 GSE68456 Granulocytes 7 GSE68456 NK cells 6 GSE72338 Monocytes 10 GSE72338 Granulocytes 10 GSE59065 CD4 T cells 99 GSE59065 CD8 T cells 100 GSE66562 NK cells 15 GSE66562 CD8 T cells 8 GSE63409 Hematopoietic stem and progenitor cells 30 GSE49618 Hematopoietic stem and progenitor cells 3 GSE72867 Hematopoietic stem and progenitor cells 15 literature 1. Armitage S, Hargreaves R, Samson D, Brennan M, Kanfer E, Navarrete C. CD34 counts to predict the adequate collection of peripheral blood progenitor cells. Bone marrow transplant. 1997;20(7):587-91. 2. Jung N, Dai B, Gentles AJ, Majeti R, Feinberg AP. An LSC epigenetic signature is largely mutation independent and implicates the HOXA cluster in AML pathogenesis. Nat Commun. 2015;6:8489. 3. Schmidt M, Maié T, Dahl E, Costa IG, Wagner W. Deconvolution of cellular subsets in human tissue based on targeted DNA methylation analysis at individual CpG sites. BMC Biology. 2020;18:178. 4. Houseman EA, Molitor J, Marsit CJ. Reference-free cell mixture adjustments in analysis of DNA methylation data. Bioinformatics. 2014;30(10):1431-9. 5. Salas LA, Koestler DC, Butler RA, Hansen HM, Wiencke JK, Kelsey KT, et al. An optimized library for reference-based deconvolution of whole-blood biospecimens assayed using the Illumina HumanMethylationEPIC BeadArray. Genome Biol. 2018;19(1):64. 6. Teschendorff AE, Breeze CE, Zheng SC, Beck S. A comparison of reference-based algorithms for correcting cell-type heterogeneity in Epigenome-Wide Association Studies. BMC Bioinformatics. 2017;18(1):105. 7. Sontag S, Bocova L, Hubens WHG, Nüchtern S, Schnitker M, Look T, et al. Toward Clinical Application of Leukocyte Counts Based on Targeted DNA Methylation Analysis. Clinical Chemistry. 2022;68(5):646-56. 8. Frobel J, Bozic T, Lenz M, Uciechowski P, Han Y, Herwartz R, et al. Leukocyte Counts Based on DNA Methylation at Individual Cytosines. Clin Chem. 2018;64(3):566-75. 9. Broxmeyer HE, Srour EF, Hangoc G, Cooper S, Anderson SA, Bodine DM. High-efficiency recovery of functional hematopoietic progenitor and stem cells from human cord blood cryopreserved for 15 years. Proc Natl Acad Sci U S A. 2003;100(2):645-50. 10. Maie T, Schmidt M, Erz M, Wagner W, Costa IG. CimpleG: Finding simple CpG methylation signatures. BioRxiv. 2022;507513. 11. Aranyi T, Stockholm D, Yao R, Poinsignon C, Wiart T, Corre G, et al. Systemic epigenetic response to recombinant lentiviral vectors independent of proviral integration. Epigenetics Chromatin. 2016;9:29. 12. Reinius LE, Acevedo N, Joerink M, Pershagen G, Dahlen SE, Greco D, et al. Differential DNA methylation in purified human blood cells: implications for cell lineage and studies on disease susceptibility. PLoS ONE. 2012;7(7):e41361. 13. Neuberger EWI, Sontag S, Brahmer A, Philippi KFA, Radsak MP, Wagner W, et al. Physical activity specifically evokes release of cell-free DNA from granulocytes thereby affecting liquid biopsy. BioRxiv. 2021;Preprint. 14. Jung N, Dai B, Gentles AJ, Majeti R, Feinberg AP. An LSC epigenetic signature is largely mutation independent and implicates the HOXA cluster in AML pathogenesis. Nature Communications. 2015;6(1):8489. 15. Aryee MJ, Jaffe AE, Corrada-Bravo H, Ladd-Acosta C, Feinberg AP, Hansen KD, et al. Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics. 2014;30(10):1363-9. 16. Fortin JP, Triche TJ, Jr., Hansen KD. Preprocessing, normalization and integration of the Illumina HumanMethylationEPIC array with minfi. Bioinformatics. 2017;33(4):558-60. 17. Babicki S, Arndt D, Marcu A, Liang Y, Grant JR, Maciejewski A, et al. Heatmapper: web-enabled heat mapping for all. Nucleic Acids Res. 2016;44(W1):W147-53. 18. Novershtern N, Subramanian A, Lawton LN, Mak RH, Haining WN, McConkey ME, et al. Densely interconnected transcriptional circuits control cell states in human hematopoiesis. Cell. 2011;144(2):296-309. 19. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43:e47. 20. Farlik M, Halbritter F, Müller F, Choudry FA, Ebert P, Klughammer J, et al. DNA Methylation Dynamics of Human Hematopoietic Stem Cell Differentiation. Cell Stem Cell. 2016;19(6):808-22. 21. Gazit R, Garrison BS, Rao TN, Shay T, Costello J, Ericson J, et al. Transcriptome analysis identifies regulators of hematopoietic stem and progenitor cells. Stem Cell Reports. 2013;1(3):266-80. 22. Wahlestedt M, Ladopoulos V, Hidalgo I, Sanchez Castillo M, Hannah R, Sawen P, et al. Critical Modulation of Hematopoietic Lineage Fate by Hepatic Leukemia Factor. Cell Rep. 2017;21(8):2251-63. 23. Bozic T, Kuo CC, Hapala J, Franzen J, Eipel M, Platzbecker U, et al. Investigation of measurable residual disease in acute myeloid leukemia by DNA methylation patterns. Leukemia. 2022;36(1):80-9. This is followed by a sequence listing as an electronic document. This can be accessed both in DEPATISnet and in the DPMA register. QUOTES CONTAINED IN THE DESCRIPTION

[0000] This list of documents submitted by the applicant was generated automatically and is included solely for the convenience of the reader. This list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions. Cited non-patent literature

[0000] Armitage S, Hargreaves R, Samson D, Brennan M, Kanfer E, Navarrete C. CD34 counts to predict the adequate collection of peripheral blood progenitor cells. Bone marrow transplant. 1997;20(7):587-91

[0036] Jung N, Dai B, Gentles AJ, Majeti R, Feinberg AP. An LSC epigenetic signature is largely mutation independent and implicates the HOXA cluster in AML pathogenesis. Nat Commun. 2015;6:8489

[0036] Schmidt M, Maié T, Dahl E, Costa IG, Wagner W. Deconvolution of cellular subsets in human tissue based on targeted DNA methylation analysis at individual CpG sites. BMC Biology. 2020;18:178

[0036] Houseman EA, Molitor J, Marsit CJ. Reference-free cell mixture adjustments in analysis of DNA methylation data. Bioinformatics. 2014;30(10):1431-9

[0036] Salas LA, Koestler DC, Butler RA, Hansen HM, Wiencke JK, Kelsey KT, et al. An optimized library for reference-based deconvolution of whole-blood biospecimens assayed using the Illumina HumanMethylationEPIC BeadArray. Genome Biol. 2018;19(1):64

[0036] Teschendorff AE, Breeze CE, Zheng SC, Beck S. A comparison of reference-based algorithms for correcting cell-type heterogeneity in Epigenome-Wide Association Studies. BMC Bioinformatics. 2017;18(1):105

[0036] Sontag S, Bocova L, Hubens WHG, Nüchtern S, Schnitker M, Look T, et al. Toward Clinical Application of Leukocyte Counts Based on Targeted DNA Methylation Analysis. Clinical Chemistry. 2022;68(5):646-56

[0036] Frobel J, Bozic T, Lenz M, Uciechowski P, Han Y, Herwartz R, et al. Leukocyte Counts Based on DNA Methylation at Individual Cytosines. Clin Chem. 2018;64(3):566-75

[0036] Broxmeyer HE, Srour EF, Hangoc G, Cooper S, Anderson SA, Bodine DM. High-efficiency recovery of functional hematopoietic progenitor and stem cells from human cord blood cryopreserved for 15 years. Proc Natl Acad Sci U S A. 2003;100(2):645-50

[0036] Maie T, Schmidt M, Erz M, Wagner W, Costa IG. CimpleG: Finding simple CpG methylation signatures. BioRxiv. 2022;507513

[0036] Aranyi T, Stockholm D, Yao R, Poinsignon C, Wiart T, Corre G, et al. Systemic epigenetic response to recombinant lentiviral vectors independent of proviral integration. Epigenetics Chromatin. 2016;9:29

[0036] Reinius LE, Acevedo N, Joerink M, Pershagen G, Dahlen SE, Greco D, et al. Differential DNA methylation in purified human blood cells: implications for cell lineage and studies on disease susceptibility. PLoS ONE. 2012;7(7):e41361

[0036] Neuberger EWI, Sontag S, Brahmer A, Philippi KFA, Radsak MP, Wagner W, et al. Physical activity specifically evokes release of cell-free DNA from granulocytes thereby affecting liquid biopsy. BioRxiv. 2021;Preprint

[0036] Jung N, Dai B, Gentles AJ, Majeti R, Feinberg AP. An LSC epigenetic signature is largely mutation independent and implicates the HOXA cluster in AML pathogenesis. Nature Communications. 2015;6(1):8489

[0036] Aryee MJ, Jaffe AE, Corrada-Bravo H, Ladd-Acosta C, Feinberg AP, Hansen KD, et al. Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics. 2014;30(10):1363-9

[0036] Fortin JP, Triche TJ, Jr., Hansen KD. Preprocessing, normalization and integration of the Illumina HumanMethylationEPIC array with minfi. Bioinformatics. 2017;33(4):558-60

[0036] Babicki S, Arndt D, Marcu A, Liang Y, Grant JR, Maciejewski A, et al. Heatmapper: web-enabled heat mapping for all. Nucleic Acids Res. 2016;44(W1):W147-53

[0036] Novershtern N, Subramanian A, Lawton LN, Mak RH, Haining WN, McConkey ME, et al. Densely interconnected transcriptional circuits control cell states in human hematopoiesis. Cell. 2011;144(2):296-309

[0036] Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43:e47.

[0036] Farlik M, Halbritter F, Müller F, Choudry FA, Ebert P, Klughammer J, et al. DNA Methylation Dynamics of Human Hematopoietic Stem Cell Differentiation. Cell Stem Cell. 2016;19(6):808-22

[0036] Gazit R, Garrison BS, Rao TN, Shay T, Costello J, Ericson J, et al. Transcriptome analysis identifies regulators of hematopoietic stem and progenitor cells. Stem Cell Reports. 2013;1(3):266-80

[0036] Wahlestedt M, Ladopoulos V, Hidalgo I, Sanchez Castillo M, Hannah R, Sawen P, et al. Critical Modulation of Hematopoietic Lineage Fate by Hepatic Leukemia Factor. Cell Rep. 2017;21(8):2251-63

[0036] Bozic T, Kuo CC, Hapala J, Franzen J, Eipel M, Platzbecker U, et al. Investigation of measurable residual disease in acute myeloid leukemia by DNA methylation patterns. Leukemia. 2022;36(1):80-9

[0036]

Claims

[1] A method for quantifying hematopoietic stem and progenitor cells (HSPCs), the method comprising: Providing at least one sample comprising cells each comprising at least one nucleic acid molecule, and Determining the degree of methylation of at least one CpG dinucleotide within at least one specific region of the nucleic acid molecule, where the determined methylation level of the CpG dinucleotide indicates the proportion of hematopoietic stem and progenitor cells in the sample, characterized by that the specific region is selected from the group consisting of the genes serine / threonine kinase 17a (STK17A), myosin ID (MYO1D), nuclear body protein SP140 (SP140), CD48 molecule (CD48) and nuclear factor of activated T cells 1 (NFATC1). [2] Method according to claim 1, characterized by that the determined methylation level of the CpG dinucleotide correlates with the number of CD34 +-cells and / or with the number of blasts in leukemia. [3] Method according to claim 1 or 2, characterized by that the CpG dinucleotide is located in the gene body of the SKT17A gene, the MYO1D gene, the CD48 gene or the NFATC1 gene, or in the promoter region of the SP140 gene. [4] Method according to one of the preceding claims, characterized by that the CpG dinucleotide is selected from the group consisting of the CpG dinucleotides cg17707057 (STK17A), cg00164282 (MYO1D), cg17607231 (SP140), cg13311440 (CD48), cg11977716 (NFATC1) and at least one CpG dinucleotide located within a region of 10,000 nucleotides upstream and / or downstream of each of said CpG dinucleotides. [5] Method according to claim 4, characterized bythat at least one CpG dinucleotide is located within a region of 5,000, 1,000, 500, 400, 300, 200 or 100 nucleotides upstream and / or downstream of each of the CpG dinucleotides cg17707057, cg00164282, cg17607231, cg13311440 and cg11977716. [6] Method according to one of the preceding claims, characterized by that the sample is a blood sample comprising mobilised peripheral blood, bone marrow aspirate and / or umbilical cord blood of a human individual. [7] Method according to one of the preceding claims, characterized by that the sample is a bone marrow sample and / or bone marrow aspirate from a human individual. [8] Method according to claim 6 or 7, characterized bythat the sample is provided in a volume of less than 100 µl, less than 90 µl, less than 80 µl, less than 70 µl, less than 60 µl, less than 50 µl, at most 50 µl, 20 - 80 µl, 30 - 70 µl, 40 - 60 µl, 5 - 50 µl, 10 - 50 µl, 20 - 50 µl, 30 - 50 µl, 40 - 50 µl or preferably 50 µl. [9] Method according to one of the preceding claims, characterized by that the sample is provided in a frozen, fixed and embedded or dried state. [10] Use of at least one artificial nucleic acid molecule in a kit for quantifying hematopoietic stem and progenitor cells (HSPCs) according to the method of claim 1, wherein the nucleic acid molecule comprises at least one nucleotide sequence selected from the group consisting of: a) A nucleotide sequence selected from the group consisting of SEQ ID NO: 10, SEQ ID NO: 11, SEQ ID NO: 12, SEQ ID NO: 16, SEQ ID NO: 17, SEQ ID NO: 18, SEQ ID NO: 19, SEQ ID NO: 20, SEQ ID NO: 21, SEQ ID NO: 22, SEQ ID NO: 23, SEQ ID NO: 24, SEQ ID NO: 25, SEQ ID NO: 26, SEQ ID NO: 27 and SEQ ID NO: 28; (b) a nucleotide sequence which differs from the nucleotide sequence according to (a) by the exchange of not more than 10% of the nucleotides, preferably not more than 5% of the nucleotides, except for the CpG dinucleotide(s); and c) a nucleotide sequence corresponding to the complementary strand of the nucleotide sequence according to a) or b).

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