Improved method for quantifying the cellular composition of a biological sample

Abstract

The invention relates to a method for determining at least a part of the cellular composition of at least one biological sample comprising leukocytes, wherein the proportion of at least one leukocyte type is determined based on the identified methylation levels in order to predict the relative distribution of that leukocyte type in the sample. The invention further relates to the use of at least one artificial nucleic acid molecule for determining at least a part of the cellular composition of a biological sample comprising leukocytes according to the above-mentioned method. Within the scope of the invention, the following CpG sites according to the invention with cell-type-specific methylation could be identified: cg22381196 (DHODH), cg12483340 (TMEM87A), cg06270401 (DYRK4), cg23054181 (FAM169BP), cg26076724 (RP11-146I2).2), cg12249234 (KSR1), cg04468741 (MICAL2), cg05074138 (CTLA4), cg05705140 (LINC01237), cg15564619 (SKI), cg11531557 (HMBOX1), cg02212339 (TRPV1), cg22488278 (ZFYVE28), cg02240030 (MAD1L1) and cg24408769 (JARID2).

Description

[0001] The invention relates to a method for determining at least part of the cellular composition of at least one biological sample comprising leukocytes, wherein the proportion of at least one leukocyte type is determined based on the identified methylation levels in order to predict the relative distribution of this leukocyte type in the sample. The invention further relates to the use of at least one artificial nucleic acid molecule for determining at least part of the cellular composition of a biological sample comprising leukocytes according to the above-mentioned method.

[0002] The analysis of the cellular composition of biological samples, especially blood, is a fundamental diagnostic procedure that allows conclusions to be drawn about many diseases. To date, differential blood counts, for example, have been generated primarily using flow cytometry, immunophenotypic analyses, and cell morphological characteristics. The examination of white blood cells is an important component of hematological diagnostics. Nevertheless, many challenges remain even today. First, the analysis is difficult to standardize, which leads to the same blood samples producing completely different results in different laboratories. Second, blood cannot be stored for too long (a maximum of a few hours). For venous blood collection, patients usually have to see a specialist. The coronavirus crisis has clearly shown that this is not always possible and that a self-collection option might be useful.

[0003] Depending on the cell types to be analyzed, several methods are currently available. A stained blood smear can be examined under a microscope. There are also automated cell counters that display information on the number of granulocytes, lymphocytes, and monocytes. If the determination of the cellular immune status, which also distinguishes between the various lymphocyte subtypes, is required, this can be achieved using antibody staining followed by flow cytometry. Flow cytometry measurements are relatively labor-intensive and cost-intensive and can only be applied to fresh blood samples. Fresh blood is required, which must be less than 24 hours old. In addition, the required blood volume is usually larger than for a small capillary blood sample from a fingertip.

[0004] Epigenetic methods such as cell-type-specific DNA methylation (DNAm) can also be used to determine the number of leukocyte subsets in the blood. However, most known methods rely on larger methylation profiles (>100 CpG dinucleotides) rather than individual CpGs. However, the selection of a few or individual CpGs is particularly advantageous for clinical applications. In contrast to conventional methods based on cell morphology or the expression of surface markers, cellular deconvolution based on DNAm values ​​is also suitable for frozen or dried blood.

[0005] For example, EP 3 561 074 A1 discloses a method for epigenetic blood cell counting, in which a quantitative image of the cell composition in a blood sample is identified using a normalization standard. The normalization standard is a nucleic acid molecule comprising at least one marker region specific for each of the blood cells to be detected and at least one control region that is cell-nonspecific, wherein these regions are present in the same number of copies on this molecule and / or a natural blood cell sample of known composition.

[0006] EP 3 382 033 A1 discloses an alternative epigenetic method for determining the cellular composition of peripheral blood, in which DNA methylation (DNAm) is measured at specific genomic regions by pyrosequencing and used for the relative quantification of granulocytes, lymphocytes, monocytes, CD4 +-T cells, CD8 + -T cells, B cells and NK cells.

[0007] The object of the invention is to improve the accuracy of determining the cellular composition of biological samples by expanding the selection of cell type-specific CpGs dinucleotides.

[0008] The object is achieved according to the invention by a method for determining at least part of the cellular composition of at least one biological sample comprising leukocytes, which comprises the following steps: - Identifying DNA methylation levels of at least one region of the genomic DNA in the sample, wherein the specific region comprises at least one CpG dinucleotide, and wherein the specific region comprises at least one nucleotide sequence selected from the group consisting of cg22381196 (DHODH), cg12483340 (TMEM87A), cg06270401 (DYRK4), cg23054181 (FAM169BP), cg26076724 (RP11-14612.2), cg12249234 (KSR1), cg04468741 (MICAL2), cg05074138 (CTLA4), cg05705140 (LINC01237), cg15564619 (SKI), cg11531557 (HMBOX1), cg02212339 (TRPV1), cg22488278 (ZFYVE28), cg02240030 (MAD1L1) and cg24408769 (JARID2); and - Determining the proportion of at least one leukocyte type based on the identified methylation levels in order to predict the relative distribution of that leukocyte type in the sample, wherein - the degree of methylation of at least one CpG dinucleotide selected from the group consisting of CpG dinucleotides cg22381196 (DHODH), cg12483340 (TMEM87A), cg06270401 (DYRK4) and at least one CpG dinucleotide located within a region of 1,000 nucleotides upstream and / or downstream of each of said CpG dinucleotides indicates the proportion of granulocytes, - the degree of methylation of the CpG dinucleotide cg04468741 (MICAL2) or of at least one CpG dinucleotide located within a region of 1,000 nucleotides upstream and / or downstream of said CpG dinucleotide indicates the proportion of monocytes, - the degree of methylation of at least one CpG dinucleotide selected from the group consisting of the CpG dinucleotides cg23054181 (FAM169BP), cg26076724 (RP11-14612.2), cg12249234 (KSR1) and at least one CpG dinucleotide located within a region of 1,000 nucleotides upstream and / or downstream of each of said CpG dinucleotides indicates the proportion of lymphocytes, - the degree of methylation of the CpG dinucleotide cg02212339 (TRPV1) or of at least one CpG dinucleotide located within a region of 1,000 nucleotides upstream and / or downstream of said CpG dinucleotide indicates the proportion of B cells, - the degree of methylation of at least one CpG dinucleotide selected from the group consisting of the dinucleotides cg05074138 (CTLA4), cg15564619 (SKI), cg05705140 (LINC01237) and at least one CpG dinucleotide located within a region of 1,000 nucleotides upstream and / or downstream of each of said CpG dinucleotides, the proportion of CD4 + - T cells, - and the degree of methylation of the CpG dinucleotide cg11531557 (HMBOX1) or of at least one CpG dinucleotide located within a region of 1,000 nucleotides upstream and / or downstream of said CpG dinucleotide, the proportion of CD8 + - T cells, and - the degree of methylation of at least one CpG dinucleotide selected from the group consisting of the dinucleotides cg22488278 (ZFYVE28), cg02240030 (MAD1L1), cg24408769 (JARID2) and at least one CpG dinucleotide located within a region of 1,000 nucleotides upstream and / or downstream of each of said CpG dinucleotides indicates the proportion of NK cells.

[0009] The sample number “cg...” in the sense of the invention refers to the position of the respective CpG dinucleotide on the “450k Illumina Bead Chip” (Illumina, San Diego, California, USA).

[0010] Based on DNAm profiles from over 1,300 sorted cell samples from 40 different studies, the following CpG sites with cell type-specific methylation were identified according to the invention: cg22381196 (DHODH), cg12483340 (TMEM87A), cg06270401 (DYRK4), cg23054181 (FAM169BP), cg26076724 (RP11-1461I2.2), cg12249234 (KSR1), cg04468741 (MICAL2), cg05074138 (CTLA4), cg05705140 (LINC01237), cg15564619 (SKI), cg11531557 (HMBOX1), cg02212339 (TRPV1), cg22488278 (ZFYVE28), cg02240030 (MAD1L1), and cg24408769 (JARID2). Using these CpG sites, flow cytometric and epigenetic estimates of relative leukocyte counts in venous blood correlated for granulocytes (r=0.95), lymphocytes (r=0.96), monocytes (r=0.82), CD4 T cells (r=0.84), CD8 T cells (r=0.94), B cells (r=0.96), and NK cells (r=0.72). Similar correlations were obtained in dried blood samples.Spike-in with a reference plasmid enabled accurate epigenetic estimation of absolute leukocyte counts from dried blood samples, which correlated with conventional venous (r = 0.85) and capillary (r = 0.80) blood measurements.

[0011] The novel genomic regions according to the invention are even more suitable for epigenetic blood counts than the CpG sites already known from EP 3 382 033 A1. The method was validated in a large cohort of healthy donors and patients with hematopoietic diseases. The measurements were performed using digital droplet PCR (ddPCR). Overall, the precision and sensitivity of the measurement method could be further improved using the new genomic regions and the ddPCR measurement. The advantages of the invention over conventional blood counts lie in particular in the fact that the method according to the invention is based on DNA, which is stable over a long period of time, so that the samples to be tested can be stored for an extended period of time (for decades in a frozen state). It has also been demonstrated that the method according to the invention can also be performed with dried blood.Patients could thus take a blood sample at home by pricking their finger. Furthermore, the method according to the invention is quantitative, easy to standardize, and correlates with conventional measurement methods. The invention thus opens up the possibility of analyzing patients' blood counts regardless of how long the samples have been stored. Even retrospective analyses years later (e.g., for scientific studies) are possible. Furthermore, DNA can also be isolated and analyzed from dried blood samples. In order to optimize the analysis method as much as possible, all possible public datasets with methylation data were searched on "Gene Expression Omnibus." A total of datasets from 40 studies, comprising 1,303 samples, were identified. Using a proprietary, newly developed data analysis pipeline called "CimpleG," the inventors created methylation data for each cell type (granulocytes, lymphocytes, monocytes, CD4.+ -T cells, CD8 + For each cell type (T cells, B cells, and NK cells), the three most important CpGs were identified, i.e., those with the largest differences in DNA methylation between one cell type compared to all other cell types, and for which the standard deviation is the smallest. ddPCR assays were developed for all of these 21 CpGs and tested in patient samples with known cell counts.

[0012] The following CpGs proved to be suitable in the ddPCR validation and have not yet been described in connection with a blood count determination (see Table 1): Table 1: List of CpG sites according to the invention Blutzelltyp CpG-Stelle Gen, Position im Genom Korrelation Granulozyten cg22381196 DHODH („Dihydroorotate dehydrogenase"), chr16:72041376 0.83 Granulozyten cg12483340 TMEM87A („Transmembrane protein 87A"), chr15:42503039 0.83 Granulozyten cg06270401 DYRK4 („Dual specificity tyrosine phosphorylation regulated kinase 4"), chr12:4699085 0.81 Lymphozyten cg23054181 FAM169BP („Family with sequence similarity 169 Member B"), chr15:99048945 0.83 Lymphozyten cg26076724 RP11-146I2.2 („Long non-coding RNA"), chr6:15090163 0.83 Lymphozyten cg12249234 KSR1 („Kinase suppressor of ras 1"), chr17:25867613 0.83 Monozyten cg04468741 MICAL2 („Microtubule associated monooxygenase, calponin and LIM domain containing 2"), chr11:12181467 0.5 CD4 + -T cells cg05074138 CTLA4 („Cytotoxic T-lymphocyte associated protein 4"), chr2:204732687 0.92 CD4 + -T cells cg05705140 LINC01237 („Long Intergenic Non-Protein Coding RNA 1237"), chr2:242945114 0.86 CD4 + -T cells cg15564619 SKI („SKI proto-oncogene"), chr1:2163437 0.71 CD8 + -T cells cg11531557 HMBOX1 („Homeobox containing 1"), chr8:28919234 0.69 B-Zellen cg02212339 TRPV1 (“Transient receptor potential cation channel subfamily V member 1”), chr17:3493666 0.90 NK cells cg22488278 ZFYVE28 (“Zinc finger FYVE-type containing 28”), chr4:2321957 0.68 NK cells cg02240030 MAD1L1 (“Mitotic Arrest Deficient 1 Like 1”), chr7:2089880 - NK cells cg24408769 JARID2 (“Jumonji and AT-Rich Interaction Domain Containing 2”), chr6:15506085 - It is noteworthy that to date, some cell types cannot be accurately predicted, with the selection of the genomic region playing a very important role. Since the CpG sites according to the invention are based on a wide variety of public data and are measured using ddPCR, a relatively new and highly precise method, instead of quantitative PCR (qPCR) or pyrosequencing, the various cell types can be predicted much better than before and with even smaller volumes (<30 µl of blood). The epigenetic leukocyte determination according to the invention also represents an alternative measurement method for cases where conventional methods do not provide a clear result (e.g., due to coagulation).

[0013] Preferably, at least one CpG dinucleotide is located within a region of 500, 400, 300, 200 or 100 nucleotides upstream and / or downstream of each of said CpG dinucleotides cg22381196, cg12483340, cg06270401, cg23054181, cg26076724, cg12249234, cg04468741, cg05074138, cg05705140, cg15564619, cg11531557, cg02212339, cg22488278, cg02240030 and cg24408769.

[0014] In an advantageous embodiment of the invention, it is further provided that the sample is provided with a volume of at most or less than 50 µl, at most or less than 40 µl, at most or less than 30 µl, at most or less than 20 µl, at most or less than 10 µl, 10-40 µl, 10-30 µl, 10-20 µl, 20-40 µl, 30-40 µl, 20-30 µl, 10-20 µl, 25-35 µl, or preferably 30 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 a successful and accurate determination of the cellular composition.

[0015] The biological sample can, for example, be a blood sample comprising mobilized peripheral blood from a human individual. This can advantageously also be frozen blood or dried blood spots. However, the biological sample can also comprise bone marrow, cerebrospinal fluid, and / or other tissue containing leukocytes.

[0016] The determination of the degree of methylation or measurement of DNA methylation (DNAm) can preferably be carried out using digital PCR (dPCR), for example digital droplet PCR (ddPCR).

[0017] The object is further achieved according to the invention by the use of at least one artificial nucleic acid molecule for determining at least part of the cellular composition of a biological sample comprising leukocytes 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: 1, SEQ ID NO: 2, SEQ ID NO: 3 and SEQ ID NO: 4 for determining the proportion of granulocytes; b) a nucleotide sequence selected from the group consisting of SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7 and SEQ ID NO: 8 for determining the proportion of granulocytes; c) a nucleotide sequence selected from the group consisting of SEQ ID NO: 9, SEQ ID NO: 10, SEQ ID NO: 11 and SEQ ID NO: 12 for determining the proportion of granulocytes; d) a nucleotide sequence selected from the group consisting of SEQ ID NO: 13, SEQ ID NO: 14, SEQ ID NO: 15 and SEQ ID NO: 16 for determining the proportion of monocytes; e) a nucleotide sequence selected from the group consisting of SEQ ID NO: 17, SEQ ID NO: 18, SEQ ID NO: 19 and SEQ ID NO: 20 for determining the proportion of lymphocytes; f) a nucleotide sequence selected from the group consisting of SEQ ID NO: 21, SEQ ID NO: 22, SEQ ID NO: 23 and SEQ ID NO: 24 for determining the proportion of lymphocytes; g) a nucleotide sequence selected from the group consisting of SEQ ID NO: 25, SEQ ID NO: 26, SEQ ID NO: 27 and SEQ ID NO: 28 for determining the proportion of lymphocytes; h) a nucleotide sequence selected from the group consisting of SEQ ID NO: 29, SEQ ID NO: 30, SEQ ID NO: 31 and SEQ ID NO: 32 for determining the proportion of B cells; i) a nucleotide sequence selected from the group consisting of SEQ ID NO: 33, SEQ ID NO: 34, SEQ ID NO: 35 and SEQ ID NO: 36 for determining the proportion of CD4 + - T cells; j) a nucleotide sequence selected from the group consisting of SEQ ID NO: 37, SEQ ID NO: 38, SEQ ID NO: 39 and SEQ ID NO: 40 for determining the proportion of CD4 + - T cells; k) a nucleotide sequence selected from the group consisting of SEQ ID NO: 41, SEQ ID NO: 42, SEQ ID NO: 43 and SEQ ID NO: 44 for determining the proportion of CD4 + - T cells; l) a nucleotide sequence selected from the group consisting of SEQ ID NO: 45, SEQ ID NO: 46, SEQ ID NO: 47 and SEQ ID NO: 48 for determining the proportion of CD8 + - T cells; m) a nucleotide sequence selected from the group consisting of SEQ ID NO: 49, SEQ ID NO: 50, SEQ ID NO: 51 and SEQ ID NO: 52 for determining the proportion of NK cells; n) a nucleotide sequence selected from the group consisting of SEQ ID NO: 53, SEQ ID NO: 54, SEQ ID NO: 55 and SEQ ID NO: 56 for determining the proportion of NK cells; o) a nucleotide sequence selected from the group consisting of SEQ ID NO: 57, SEQ ID NO: 58, SEQ ID NO: 59 and SEQ ID NO: 60 for determining the proportion of NK cells; p) a nucleotide sequence which differs from one of the nucleotide sequences according to a) to o) by the exchange of not more than 10% of the nucleotides, preferably not more than 5% of the nucleotides; and q) a nucleotide sequence corresponding to the complementary strand of one of the nucleotide sequences according to a) to p).

[0018] 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 also comprises a kit, in particular a kit for determining at least part of the cellular composition of blood, preferably for carrying out the method according to the invention, which comprises at least one oligonucleotide primer (for example oligonucleotide primer set", see e.g. Table 2) for amplifying and / or sequencing at least one CpG dinucleotide of at least one nucleotide sequence comprising at least one CpG dinucleotide from the group consisting of the CpG dinucleotides cg22381196, cg12483340, cg06270401, cg23054181, cg26076724, cg12249234, cg04468741, cg05074138, cg05705140, cg15564619, cg11531557, cg02212339, cg22488278, cg02240030, cg24408769 and at least one CpG dinucleotide located within a region of 1.000 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, semiquantitative PCR, digital PCR, digital droplet PCR, Epityper, amplicon sequencing, nanopore sequencing, and SMART sequencing. Table 2: List of primers and probes for ddPCR CpG site Primer sequence SEQ ID NO : cg22381196 Before GGTTTTATTGTGTTAGTTAGGAT 1 Back ACACCTAAATCTACAAAAAACTAT 2 Sm- TGTTTTATGAAGAATTTTGAAATGAAA 3 Sm+ TGTTTTACGAAGAATTTTGAAATGA 4 cg06270401 Before TATTGAGGAAGATTTGTGTGATATAATA 5 Back AAAATAAAACCTACCTCATAAAACTTTT 6 Sm- TTGTGTTAGGGTGGTAAGATATTGGAG 7 Sm+ TTGCGTTAGGGTGGTAAGATATTGG 8 cg12483340 Before TGTAGAGAGGTGTTTTTAAGTGT 9 Back ACCAACAAACCTATTTCCTT 10 Sm- AGTGTTGTGATTGAGTATTGTGTTAGAG 11 Sm+ AGTGTTGTGATTGAGTATCGTGTTAGA 12 cg04468741 Before DAYDAYDAYDAYATTGGGGAAG 13 Back TATAAATCATACCCAAATAAACACA 14 Sm- TTTTGTGTTTTGTTTGGTGTGATAAG 15 Sm+ TTTGTGTTTTGTTTGGTGCGATA 16 cg23054181 Before TTTGGGTTTTAAAGAAGTTTTTTGAG 17 Back AAACCTTAATATAACCTACCTTCC 18 Sm- AGGGTTTGGTTATAGTTTTATTATGTTGT 19 Sm+ AGGGTTTGGTTATAGTTTTATTATGTCGT 20 cg26076724 Before TAGAAGGATAGGGTGGTTAGTTTAAG 21 Rück AAACTCCAAAACAAAAAACAAAA 22 Sm- AAGAAGGAGGAATTGTAAGATTTAGG 23 Sm+ AAGAAGGAGGAATTACGTAAGATTTAG 24 cg12249234 Before GGTATGGGATAGAGTGAGA 25 Rück AAACCTAACAACTTCCCCAAA 26 Sm- TTTGGTTTATTTGTATTTTTTTGGGA 27 Sm+ TTCGGTTTATTCGTATTTTTTTGGGGA 28 cg02212339 Before TGTAGTAGTTGTTGTTTGTTTAGA 29 Rück CCAAAAAAATCACCAAAACCATA 30 Sm- TGTAGGTTAGTATTGTATATTGTT 31 Sm+ TAGTTTAGTATTGTATATCGTTA 32 cg15564619 Before AGTAGGGTTTGGGTATTGTG 33 Rück CCCCTTAAATCTCCTCAAAAC 34 Sm- TATGTGTTGTTTTTGTGTATAGAGAT 35 Sm+ TGTGTCGTTTTTGTGTATAGAGA 36 cg05074138 Before GGATTTTGAAAGGTTTTGTTTTATTT 37 Rück ACCAAAATCCTAATAACCAAATTCA 38 Sm- TGGATTTTAGTGGTATAAGGTTTAGTTG 39 Sm+ TGGATTTTAGCGGTATAAGGTTTAGT 40 cg05705140 Before GTGAGATTGGATTTATAGAGTTTTTT 41 Rück CTTACTAACTATCTCCCATACTATCTAC 42 Sm- AGAATGTGAAATAGTGAGTGATTAAAG 43 Sm+ AGAATGCGAAATAGTGAGTGATTA 44 cg11531557 Before GTTTTTTTTGGAAGAGTTTATAGTTTTT 45 Rück TCCAAAAATCTATATCTTACTTAAACAC 46 Sm- TTTTGTTTTTTGGTTATGGTTGTTG 47 Sm+ TTTTGTTTTTTACGGTTATGGTTGTT 48 cg22488278 Before TTTTTTTAGTTTTTTTTTTTTTTTTTT 49 Rück CCACAAAACCTAACATCCTAACTTT 50 Sm- TTTTATTTTGTGGTTTAAGTTTAGAGGTT 51 Sm+ ATTTTGCGGTTTAAGTTTAGAGGTT 52 cg02240030 Before GATTATATGTTTATGGGGTATTTTATTT 53 Back TAATTTCTAATCTAACCTCATACCTAAC 54 Sm- TTATTGTTTTTTTTATTTGTTGTATTTTTA 55 Sm+ TTATCGTTTTTTTTATTTGTTGTATTTTTA 56 cg24408769 Before GDDDAYDDDAYAAAGTTAAGTGT 57 Back CCAACCACGATATCAACTAAAC 58 Sm- TAATGTGTGTTTTGGAAGTAGTTG 59 Sm+ TAACGCGTGTTTTGGAAGTAG 60 "Vor" = forward primer "Back" = reverse primer "Sm-" = probe for unmethylated strand, 5' 6-FAM - [sequence] - 3' BHQ-1 "Sm+" = probe for methylated strand, 5' HEX - [sequence] - 3' BHQ-1

[0019] The invention is explained in more detail below using the figures and tables as examples. Short description of the characters Fig.1: Identification of CpGs with cell type-specific DNAm in leukocyte subsets. (A) Flowchart showing how Illumina BeadChip measurements from purified leukocyte subset samples (n) were compiled across different studies (N). Data from 40 studies were eligible for reanalysis by CimpleG, which included methylation data from 1303 sorted cell samples from 726 patients. (B) Based on this dataset, candidate CpGs for each cell type were ranked by CimpleG based on mean difference and total variance. The top 3 CpGs with the highest CimpleG score for each cell type are highlighted. (C) The heatmap shows the DNAm (beta values) for the 27 cell-type-specific CpG sites. HSPC: hematopoietic stem and progenitor cells; nRBC: nucleated red blood cells. Fig. 2: Epigenetic estimates of granulocytes, lymphocytes and monocytes. (A) Simple linear regression models for monocytes, lymphocytes, and granulocytes were trained for 50 healthy donors based on DNAm measurements (ddPCR) compared to conventional cell counts (measured with Cell Dyn Emerald). An inverse correlation is shown for monocytes and granulocytes (hypomethylated CpGs) and a positive correlation for lymphocytes (hypermethylated CpGs). (B,C) These models were tested on 110 independent blood samples from healthy donors (B; also measured with Cell Dyn Emerald) and 150 patients (C; measured with Sysmex XS800i). Due to the systemic offset observed in epigenetic estimates from samples measured with the Sysmex XS800i, individual linear regression models for this instrument were retrained on 50 randomly selected samples.(D,E) ddPCR measurements were compared on 36 proficiency testing samples measured by approximately 400 laboratories using various conventional cell counters from different manufacturers. The epigenetic predictions trained on the Sysmex XS800i were compared with the average cell counts from the proficiency test (D). In addition, alternative epigenetic prediction models trained on either Cell Dyn Emerald (green) or Sysmex XS800i (red) for each individual sample were compared (E). The Pearson correlation r and the mean absolute error (MAE) are shown for each cell type. Fig. 3: Epigenetic blood values ​​correlate with flow cytometric measurements. (A) DNAm values ​​determined by ddPCR were correlated with the conventional blood count in blood samples from 50 randomly selected patients tested for granulocytes, monocytes, pan-lymphocytes (Sysmex XS800i) and CD4 + -T cells, CD8 +T cells, B cells, and NK cells (flow cytometry). A positive correlation is observed for lymphocytes (hypermethylated CpG) and a negative correlation for the other cell types (hypomethylated CpGs). (B) Linear regression models for each cell type, trained on these data, were then applied to an independent validation set (n = 100) to estimate cell counts. The Pearson correlation r and mean absolute error (MAE) are given for each leukocyte subset. Fig. 4: Relative leukocyte counts for dried blood samples. (AC) Correlation of conventional cell counts (Sysmex XS800i and flow cytometry) with relative blood counts estimated by ddPCR for cryopreserved venous blood from 75 patients, either thawed and used directly (A) or dried overnight on either Mitra 30µl microsamplers (Neoterxy) (B) or Whatman protein saver cards (C). (D) Fresh capillary blood was collected from 31 donors and immediately counted using a cell counter (Cell Dyn Emerald) or dried on Whatman protein paper saver cards for 3–5 days prior to ddPCR analysis. The Pearson correlation r and mean absolute error (MAE) are given for each cell type. Fig. 5: Total leukocyte counts by ddPCR with a reference plasmid. (A) Workflow diagram for absolute leukocyte quantification. Venous blood, dried blood Whatman Protein Saver card punches, or dried blood Mitro microsampler tips were mixed with a reference plasmid prior to DNA isolation and ddPCR. (B) Cell counts were estimated based on the ratio of genomic to plasmid DNA. (C) Combination of relative leukocyte count estimates with absolute cell count. The Pearson correlation between epigenetic estimates and conventional measurements is indicated. Fig. 6: Principal component analysis based on the 27 cell type-specific CpGs. For each of the nine cell types, three differentially methylated CpGs were identified. Principal component analysis of the DNAm data for these 27 CpGs showed that the cell types clustered together, even though the data were drawn from many different studies. Note that in PC1 / PC2, the nRBCs clustered very closely with the HSPCs, which is expected, as nRBCs also tend to resemble immature blood cells. HSPC: hematopoietic stem and progenitor cells; RBC: red blood cells. Fig. 7: Evaluation of cell type-specific CpGs. The behavior of granulocyte-, lymphocyte-, and monocyte-specific CpG sites was evaluated in 92 healthy donors. The behavior of CpG sites of lymphocyte subsets was evaluated in 21 patients. The Pearson correlation r is presented for the DNAm values ​​compared to the cell counts determined by automated cell counters or flow cytometry. Fig.8: Separation of methylated and unmethylated signals in the ddPCR analysis. Example results for ddPCR measurements. The individual droplets were assessed as either methylated CpG (blue), unmethylated CpG (green), or both (orange). Based on this, the DNAm content is determined using the Fischer distribution. For absolute quantification, the droplets contain genomic DNA (blue), plasmid DNA (green), or both (orange). Fig. 9: DNA methylation of genes with the top candidate CpGs. Candidate CpG sites were identified in DHODH, FAM169BP, MICAL2, TRPV1, CTLA4, CD8B, and MVD. This figure shows the DNAm values ​​(β values) measured with a 450k Illumina Beadchip for the sorted leukocytes from the publicly available dataset GSE35069. The genes are represented schematically according to Illumina annotations and show 1500 bp and 200 bp upstream of the transcription start site (TSS1500 and TSS200), the 5' and 3' untranslated regions (UTR), the first exon, and the gene body. Each dot represents a CpG site, with the red line indicating the selected CpG. Fig. 10: Cell type-specific DNAm is hardly reflected at the level of gene expression. The single-cell transcriptome data available in the Human Protein Atlas (proteinatlas.org) show that most genes that have a cell-type-specific CpG site are expressed in different cell types. Only for CD8B (CpG for CD8+ -T cells) and CTLA4 (CpG for CD4 + Specific upregulation was observed in the corresponding cell types (T cells). DHODH (CpG granulocytes) was particularly expressed in hepatocytes. Fig. 11: Accuracy of epigenetic estimates depending on age and gender. (A) The comparison between young (age ≤ 30 years, n = 24) and elderly patients (age ≥ 60 years, n = 58) showed that age had no significant influence on the accuracy of epigenetic cell type prediction. (B) No significant differences in prediction accuracy were found between female (n = 74) and male donors (n = 76). The data are presented as a whisker-box plot of the deviation between epigenetic estimates and conventional cell counts. The boxes indicate the first and third quartiles, the horizontal lines within the boxes indicate the median, and the whiskers indicate the 1.5-fold interquartile range. Fig. 12: Summary of epigenetic estimates for individual cell types. To further assess the consistency of the epigenetic cell type predictions, we estimated whether the proportions of each cell type deviated from the expected 100%. (A, B) For this purpose, the predictions for granulocytes, monocytes, and pan-lymphocytes (3 CpG score) for healthy donors (Cell Dyn Emerald model, A) and patients (Sysmex XN9000; B) were summed. The slightly higher deviation in patient samples could be due to hematological diseases. (C) Alternatively, the predictions for granulocytes, monocytes, CD4+ T cells, CD8+ T cells, B cells, and NK cells (6 CpG score) were summed and compared with the patient samples. Fig. 13: Absolute quantification based on genomic DNA copies. Digital droplet PCR is an absolute quantification method that provides an absolute number of DNA copies per reaction. Within the scope of the invention, a unique genomic region (R5), which has also been used alternatively for the reference plasmid-based approach, was analyzed. While there is a clear correlation between the genomic copies of R5 detected by ddPCR and the conventional leukocyte count in venous blood, there are several outliers in the analysis of dried blood. The Pearson correlation r is provided. Description of exemplary and preferred embodiments of the invention

[0020] DNAm profiles from 40 different studies were compiled to identify CG dinucleotides (CpGs) with cell-type-specific DNAm using CimpleG. DNAm concentrations at these CpGs were then measured using digital droplet PCR (ddPCR) in venous blood from 160 healthy donors and 150 patients with various hematological diseases. Deconvolution was further validated using venous blood (n=75) and capillary blood (n=31) dried either overnight (16 hours) or for up to 5 days on Whatman paper or on Mitra microsamplers.

[0021] Differential blood counts are widely used to aid in the diagnosis of a variety of diseases or to monitor treatment. They are typically performed using either automated cell counters or flow cytometry (1, 2). Fresh blood must be drawn for this purpose, as cell counts are affected within 24 hours by loss of cellular integrity, clotting, impaired electrical impedance, or poor antibody binding (3-5). In contrast, dried blood spot-based leukocyte counting would facilitate self-collection by finger prick without the need for skilled personnel, facilitate long-term storage and shipping of blood samples, and carry a significantly lower risk of bleeding compared to venipuncture (6).

[0022] The deconvolution of leukocyte subsets is also possible using epigenetic means. DNA methylation (DNAm) patterns are consistently modulated in a cell-type-specific manner during differentiation (7). Epigenetic signatures that integrate DNAm values ​​at many CG dinucleotides (CpGs) can therefore be used to determine the composition of cell types in tissue (8, 9) or of leukocytes in blood (10-12). Epigenetic signatures for the deconvolution of hematopoietic subsets were initially developed for Illumina BeadChip data (10-12). However, since genome-wide DNAm profiling is hardly suitable for routine diagnostics, it is important to define targeted DNAm analysis of individual CpGs for clinical application (13). Many different methods can be used for this purpose, e.g., E.g. quantitative PCR (qPCR) (14), pyrosequencing (15), barcoded amplicon sequencing (16) or methylation-specific digital droplet PCR (ddPCR) (17).In particular, ddPCR can enable more reliable DNAm measurements, as there is no PCR bias between methylated and unmethylated sequences (18). In addition to selecting a sensitive method, the selection of cell-type-specific candidate CpGs is crucial for the development of reliable targeted biomarkers. In a previous study, the inventors selected such genomic sites based on a dataset of sorted leukocyte subsets (15, 19). Although these signatures were validated in patient material, they did not show a very strong correlation with conventional blood counts for all leukocyte subsets (17).

[0023] Therefore, in the present study, the available Illumina BeadChip DNAm profiles of sorted leukocytes were compiled in the NCBI's Gene Expression Omnibus (GEO). Based on these profiles, novel cell-type-specific CpGs were identified using a computer-assisted system called "CimpleG" (20). Epigenetic predictions based on ddPCR measurements at these CpGs significantly improved the accuracy of leukocyte counting in blood samples from healthy donors and patients with hematological diseases. Furthermore, the applicability for dried blood samples was demonstrated. Selection of differently methylated sites

[0024] The GEO database was searched for publicly available DNAm profiles (450k or EPIC Illumina BeadChip arrays) of sorted human hematopoietic cells (Supplementary Table S1). These profiles were subsequently analyzed using CimpleG (20). Blood samples

[0025] All blood samples (cryopreserved venous blood from 160 healthy donors and 150 patients with hematological diseases, as well as fresh capillary blood from 31 healthy donors) were collected at the RWTH Aachen University Hospital after informed and written consent in accordance with the local ethics committee (EK041 / 15, EK206 / 09). For a subset of samples, dried blood spots were collected using two different microsamplers: Mitra® microsamplers (Neoteryx) and Whatman® protein saver cards (Cytiva). Additionally, 36 cryopreserved blood samples were purchased from the Reference Institute for Bioanalytics (RfB, Bonn, Germany). Digital droplet PCR

[0026] Primers were designed using the Bisulfite Primer Finder tool (Zymo Research), and fluorescent probes were manually designed with melting temperatures several degrees higher than those of the primers (Tables 2 and 3). A QX200 droplet generator (Bio-Rad) was used to generate droplets for 20 µl of reaction mixture containing 2x Supermix for probes (no dUTPs, Bio-Rad), 1 µM primer, 0.25 µM TaqMan probes (both Metabion), and 10–30 ng of bisulfite-converted DNA. The DNA was then amplified on a C1000 Touch Thermal Cycler (Bio-Rad) using the following program: 1 x 95°C for 10 min, 40 x 94°C for 30 s and 54°C for 30 s, 1 x 98°C for 10 min. The droplets were quantified using a QX200 Droplet Digital Reader and analyzed using QuantaSoft analysis Pro software (both Bio-Rad). Epigenetic leukocyte counts

[0027] To estimate relative leukocyte counts, 50 healthy donor or 50 patient samples were randomly selected to obtain individual linear regressions based on DNAm values ​​compared to conventional manual differential cell counts for the respective cell type. The remaining samples were used as independent validation sets. Absolute leukocyte counts were estimated for a subset of samples relative to the reference plasmid (17). Based on the detected plasmid copies and genomic copies, the absolute cell count was calculated as described below. CpG selection

[0028] To identify leukocyte subset-specific CpG sites, the Gene Expression Omnibus (GEO) was searched for publicly available datasets. The following search term was used: "lymphocytes OR NK cells OR CD4 + OR CD8 +OR B cells OR monocytes OR granulocytes OR nucleated red blood cells OR CD34 +OR progenitor cells OR stem cells)." The search was restricted to "Homo sapiens" as the organism. The datasets were filtered according to the study types "Methylation Profiling by Array" or "Methylation Profiling by Genome Tiling Array," using either the Illumina HumanMethylation450 BeadChip (GPL16304 OR GPL13534) or the Infinium MethylationEPIC (GPL21145) platform. To enable better reanalysis, only studies that published their raw data in IDAT format were considered. This search was last performed on October 14, 2021, and yielded 150 datasets. Datasets containing samples from diseased patients, especially those with hematopoietic malignancies, were subsequently excluded from further analysis because they could influence the epigenetic profile (21, 22).In addition, a dataset containing a mixture of 450K and EPIC BeadChip data was excluded during the initial quality assessment because it was considered an outlier. This resulted in 40 eligible datasets containing 1303 DNAm profiles.

[0029] These 1303 DNAm profiles were analyzed using the CimpleG pipeline (20). First, the data were processed using the R package minfi v1.30.0 (minfi, RRID:SCR_012830) and normalized using ssNoob, a within-sample normalization method. Subsequently, probes with poor mapping quality or those mapping to sex chromosomes were excluded. Finally, only CpGs present in both the 450k and EPIC platforms were retained. These remaining CpGs were assessed for cell-type specificity based on 10-fold stratified cross-validation. Within each fold or loop, each CpG is scored using a t-like statistic, and its performance on the classification task is measured using the area under the precision-recall (AUPR) curve.A final score for each best-performing probe was determined by combining the probe's average t-like statistic with the average training AUPR multiplied by the proportion of folds in which a CpG was selected as the top candidate. This ranking analysis for CpG selection was performed twice. The first analysis was used to identify CpGs with methylation specific to monocytes, granulocytes, and pan-lymphocytes. The second analysis was used to identify CpG sites for more specific blood cell subsets, including B cells and CD4. + T cells, CD8 + T cells, natural killer cells (NK), nucleated red blood cells (nRBC) and CD34 + hematopoietic stem and progenitor cells (HSPC). Blood sample collection

[0030] The inventors analyzed cryopreserved venous blood samples (160 healthy donors and 150 patients with hematological diseases) stored at -80°C for up to five years prior to DNA isolation. These samples were collected at the RWTH Aachen University Hospital after informed and written consent in accordance with the local ethics committee of RWTH Aachen University (EK041 / 15, EK206 / 09). For dried blood spot analysis, cryopreserved venous blood from 75 patients was thawed and subsequently dried to 30 µl using Mitra® microsamplers (Neoteryx, Torrance, USA) and Whatman® Protein Saver cards (Cytiva, Freiburg im Breisgau, Germany). In addition, fresh capillary blood was collected from 31 patients using a Microvette® (Sartstedt, Nümbrecht, Germany), which was immediately dried on Whatman® Protein Saver cards and analyzed up to 5 days later.At the time of blood collection, conventional leukocyte counts were immediately performed using either a Cell Dyn Emerald (Abbott, Wiesbaden, Germany) or a Sysmex XS800i (Sysmex, Norderstedt, Germany) in combination with flow cytometry, as indicated. DNA isolation and bisulfite treatment

[0031] DNA was isolated from cryopreserved venous blood either directly from 150 µl using the QIAamp DNA Mini Kit (Qiagen, Hilden, Germany) in 200 µl of elution buffer, or alternatively, 30 µl was dried for at least 16 hours (overnight) on either Mitra® microsampling devices or Whatman® Protein Saver cards. DNA from these dried samples was isolated using the QIAamp DNA micro kit (Qiagen, Hilden, Germany). For this purpose, the 30 µl Mitra® tip was fragmented, and three areas (each 3 mm in diameter) were removed from the Whatman® Protein Saver cards using punch knives. For capillary blood, the volume applied to the Whatman paper varied, but DNA was always isolated from three areas (each 3 mm in diameter) using the QIAamp DNA micro kit (Qiagen, Hilden, Germany). The final elution volume for DNA from dried blood was 30 µl.For methylation analysis, the isolated DNA (50-500 ng) was bisulfite-treated with the EZ DNA Methylation Kit (Zymo research) according to the manufacturer's instructions and eluted in a total volume of 20 µl. Additional information for digital droplet PCR

[0032] For the relative leukocyte count, three primer pairs were initially developed for each CpG and tested on a temperature gradient from 50°C to 60°C using a mixture of DNA from 10 patients. The best primer pairs performed equally well at 54°C, so they can be combined in one assay. Absolute quantification of the leukocyte count was performed using a reference plasmid containing a genomic region unique and conserved in the human genome, altered at three positions (Table 5), as previously described (17). The sample (150 µl of blood, 3x3 mm Whatman paper, or the fragmented Mitra tip) is mixed with 15 µl of plasmid (corresponding to 0.00389 ng or 580,908 copies), followed by proteinase K and lysis buffer, and vortexed vigorously for 15 seconds. The isolation protocol is then performed according to the manufacturer's instructions.In ddPCR, the number of copies of genomic DNA and reference DNA is measured, and the absolute cell counts can then be calculated using the following formula:. cells / μl=Cg×5809082×Cr×v×cf where Cg is the measured copy number of genomic DNA (copies / µl), Cr is the measured copy number of the reference plasmid (copies / µl), and v is the initial volume (150µl for venous blood and 30µl for dried blood). The "2" is used to correct for the fact that each cell contains two copies of the genomic DNA region of interest. To account for contamination of the plasmid DNA (bacterial genome and other plasmids), technical variations in pipetting, DNA quantification during reference plasmid preparation, efficiency of DNA isolation, and changes in the initial volume of dried blood, an empirically determined correction factor cf was included (for venous blood cf = 0.07; venous blood dried on Whatman cf = 5; venous blood dried on Mitra cf = 8; and capillary blood dried on Whatman cf = 10). Data analysis and statistics

[0033] Scatterplots and principal component analysis were created in R. The heatmap was generated using heatmapper (23). The Pearson correlation coefficient r and the mean absolute error (MAE) of the DNAm were analyzed and plotted in Windows Office 2016 Excel (Microsoft) and GraphPad Prism version 9 (Graph Pad Software Inc.). Identification of cell type-specific methylation sites

[0034] To select the best candidate CpGs for cell type-specific DNAm, a dataset of 1303 DNAm profiles from 40 different studies was compiled ( Fig. 1A; Supplementary Table S1). Using CimpleG, the three best CpGs for granulocytes, monocytes, lymphocytes, CD4 T cells, CD8 T cells, B cells, NK cells, hematopoietic stem and progenitor cells (HSPCs), and nucleated red blood cells (nRBCs; Fig.1B). The selected CpGs showed cell-type-specific hypomethylation, with the exception of pan-lymphocytes and HSPCs, which were hypermethylated. Heatmap analysis also confirmed the consistency of the cell-type-specific DNAm differences ( Fig. 1C). Furthermore, principal component analysis using these CpG sites revealed distinct clusters for each cell type ( Fig. 6). Thus, the selected candidate CpGs appeared to be suitable for clearly distinguishing the respective cell types across many different DNAm datasets. Targeted DNA methylation analysis using digital droplet PCR

[0035] Subsequently, ddPCR assays were developed for targeted DNAm analysis at the selected CpGs. The CpGs for HSPCs and nRBCs were not considered for this analysis, as these cell types are generally poorly present in venous blood. On the other hand, ddPCR assays were also designed for cell-type-specific CpGs, which had previously been selected based on DNAm profiles from a single study (15, 17). Since the CD8A-associated cg25939861 overlapped in both selections for CD8 T cells, the inventors analyzed 27 CpGs. An initial screening was performed on 92 venous blood samples from healthy donors to select the best CpGs for granulocytes, monocytes, and pan-lymphocytes. For CD4 T cells, CD8 T cells, B cells and NK cells, lymphocytes from 21 patients were further stratified by flow cytometry ( Fig.7). Overall, the correlation of DNAm values ​​with the conventional blood count was very high (r > 0.8 or r < -0.8). The best performing CpG candidates (Table 1) were selected based on these correlations and, if similar performance, on their rank in the CimpleG analysis or on how well the positive ddPCR drops could be distinguished ( Fig. 8).

[0036] The selected CpGs were located either in the 1500 bp region before the transcription start site (DHODH), in the 5'UTR region (FAM1698P and MICAL2), in the first exon (CTLA4) or in the gene body (CD88, TRPV1 and MVD; Fig. 9). Analysis of gene expression in the human protein atlas showed that, with the exception of CTLA4 and CD8B, the corresponding genes do not exhibit characteristic upregulation in the respective cell types ( Fig.10) (24). Therefore, differences in DNAm in these epigenetic biomarkers are not necessarily reflected at the level of gene expression. Epigenetic estimation of granulocytes, monocytes and lymphocytes

[0037] For the epigenetic estimation of the granulocyte, monocyte, and pan-lymphocyte fraction, 50 samples from healthy donors were randomly selected to train the individual linear regression models for each cell type ( Fig. 2A, Table 4). These models could accurately predict the number of granulocytes (r = 0.81; mean absolute error [MAE] = 3.7) and pan-lymphocytes (r = 0.82; MAE = 3.4) and moderately predict the number of monocytes (r = 0.40; MAE = 2.1) in 110 independent donors measured with the same Cell Dyn Emerald cell counter ( Fig.2B). However, when these predictors were tested on 150 patient samples analyzed in another department using a Sysmex XS800i, a systematic underestimation of granulocytes (MAE = 9.5) and a systematic overestimation of lymphocytes (MAE = 8.0) and monocytes (MAE = 3.7) was observed ( Fig. 2C). However, the very high correlations with conventional counts (r = 0.95, r = 0.96, and r = 0.86, respectively) indicate that this discrepancy is due to the reference measurements with different cell counters. In particular, the CpG for monocytes showed a better correlation with the automated cell counts in the patient samples, suggesting that these measurements might be more precise. Therefore, the models were retrained on a randomly selected set of 50 patient samples measured with a Sysmex XS800i (Table 4).

[0038] To further compare these assays, the inventors participated in a collaborative study with more than 400 other institutes using various conventional automated cell counters. The ddPCR measurements clearly correlated with the average total cell count ( Fig. 2D). However, the results of the interlaboratory test showed a large deviation between the different devices ( Fig. 2E). This demonstrates the importance of precise reference measurements for fine-tuning linear regression models for ddPCR. While ddPCR measurements are robust, predictions trained on Cell Dyn measurements (green) overestimated lymphocytes and monocytes, whereas predictions trained on Sysmex XS800i measurements (red) tended to overestimate granulocytes. Epigenetic blood count correlates with flow cytometry in patient samples

[0039] To compare the performance of CpGs specific for CD4 T cells, CD8 T cells, B cells, and NK cells, blood samples from 150 patients with various hematological diseases were analyzed. These blood samples were analyzed for granulocytes, monocytes, and pan-lymphocytes using a Sysmex XS800i, and the lymphocytes were further stratified using flow cytometry. As mentioned above, a subset of 50 patients was randomly selected for individual linear regression models for each of these cell types ( Fig. 3A; Table 4). Using these models for the independent set of 100 patient samples, the proportion of granulocytes (r = 0.95, MAE = 3.2), pan-lymphocytes (r = 0.97; MAE = 2.5), monocytes (r = 0.82; MAE = 1.9), CD4 T cells (r = 0.84; MAE = 2.5), CD8 T cells (r = 0.94; MAE = 1.7), B cells (r = 0.96; MAE = 1.2), and NK cells (r = 0.72; MAE = 1.4) ( Fig.3B). The accuracy of the predictions was not significantly affected by age or gender ( Fig. 11).

[0040] To further evaluate the consistency of the estimates independent of the conventional cell counters, it was determined whether the predictions for the individual cell types add up to 100% ( Fig. 12). The estimated proportions of granulocytes, monocytes, and pan-lymphocytes in patients added up to 99.3 ± 5.2%, which corresponded to the sum in healthy donors (100.1 ± 2.6%), and for both groups, the sum did not differ significantly from the expected 100% (p = 0.12 and p = 0.78, respectively). The sum also did not differ significantly from 100% (99.2 ± 6.8%; p = 0.18) when the pan-lymphocytes were further stratified into B cells, CD4 T cells, CD8 T cells, and NK cells. Thus, the epigenetic estimates are robust and can provide consistent results even for patient samples. Evaluation of leukocyte fractions from dried blood

[0041] Next, the feasibility of epigenetic blood counting on dried blood samples was investigated. For this purpose, 30 µl of venous blood from 75 patients was pipetted and dried overnight on two alternative sampling devices: Whatman Protein Saver cards and Mitra 30 µl microsamplers. Notably, the predictions of individual cell fractions showed a similar precision to that observed for 150 µl of venous blood, particularly when the blood was dried on Whatman Protein Saver cards, which slightly outperformed the Mitra microsamplers ( Fig. 4A-C).

[0042] Next, the applicability was tested for capillary blood collected from 31 healthy donors by fingerstick. A fresh sample was measured using a Cell Dyn Emerald device, and in parallel, blood spots were dried on Whatman Protein Saver cards for up to five days prior to epigenetic blood counting. Good correlations were observed, particularly for granulocytes (r = 0.79, MAE = 5.1) and pan-lymphocytes (r = 0.80, MAE = 4.6). Fig. 4D). A lower correlation was observed for monocytes (r = 0.16, MAE = 2.0), which may be due to the inconsistency of the reference counts on the Cell Dyn Emerald device, as also mentioned above. Overall, the results demonstrate that the method of the invention is suitable for dried capillary blood collected by fingerstick. Absolute quantification of leukocyte count

[0043] Since each leukocyte possesses two DNA copies, the absolute cell count is expected to correlate with the DNA concentration. This can be estimated from the number of copies of a specific genomic region detected by ddPCR in non-biosulfite-converted DNA. Indeed, for DNA isolated from 150 µl of venous blood, the number of positive drops in ddPCR for the genomic region "R5" correlated with the leukocyte count (r = 0.95; Fig.13). However, several outliers were observed in dried blood, particularly in blood dried on Mitra microsamplers (r = 0.58). A direct comparison of positive ddPCR events in 30 µl of fresh blood compared to 30 µl of the same sample dried overnight revealed that significantly fewer copies were detected on both Whatman Protein Saver cards (67.7 ± 21.4%) and Mitra microsamplers (33.5 ± 27.6%), thus suggesting lower and more variable DNA isolation efficiency for dried blood spots.

[0044] Therefore, prior to DNA isolation, the samples were spiked with a reference plasmid in a known amount as previously described ( Fig.5A)(17). The cell number can then be estimated based on the ratio of genomic to plasmid DNA copies. This approach offers a higher correlation with conventional leukocyte counts of venous blood dried on either Whatman Protein Saver cards (r = 0.86) or Mitra microsamplers (r = 0.84) ( Fig. 5B). This also applied to capillary blood dried on Whatman paper (r = 0.80). By combining estimates of leukocyte counts with relative counts based on cell-type-specific DNA, the absolute counts for each of the leukocyte subsets could be calculated. For each cell type, a clear correlation was observed between the conventional absolute cell counts and the epigenetic estimates ( Fig. 5C). Summary

[0045] Using the novel method of the invention, epigenetic leukocyte counts can be further improved through a comprehensive selection of cell-type-specific candidate CpGs and optimized ddPCR assays. The results surpassed the precision of previous models based on various CpGs and pyrosequencing measurements (17). Furthermore, it was demonstrated that dried blood spots obtained by fingersticking can be used for epigenetic leukocyte counts according to the invention.

[0046] For the selection of candidate CpGs, a very large dataset of DNAm profiles was compiled (1303 cell-type-specific DNAm profiles from 726 donors, derived from 40 different studies). In contrast, the previous selection of cell-type-specific CpGs was based on DNAm profiles from only six donors (19). In another study, Baron et al. initially focused on functionally relevant genes to select CpG candidates, particularly for CD4 T cells (CD4 gene) and CD8 T cells (CD8B gene) (14). Notably, despite the very different selection approaches, a candidate CpG for CD8 T cells (cg04329870) was also found in this CD8B region. They also analyzed sorted cells from two donors to identify cell-type-specific CpGs for B cells, NK cells, and granulocytes (14). Remarkably, a CpG candidate for NK cells (cg05355684) was also selected in their work.More recently, the inventors have identified cell type-specific CpGs for HSPCs based on DNAm profiles of CD34. + Cells from five donors were identified (25) compared to DNAm profiles of other sorted leukocyte subsets from six donors (19). On this basis, the most important candidate CpGs for HSPCs were identified (26). Although the HPSCs were not validated in the current study, since CD34 + cells in normal peripheral blood, there was a striking overlap of two of the three top CpGs in the different selections: SP140 (cg17607231) and CD48 (cg13311440) were also identified in this independent selection and correlated with CD34 +Cells in mobilized peripheral blood and blast counts in leukemia (26). The finding that different studies select the same genomic region or even exactly the same CpGs suggests that genomic regions with well-suited candidate CpGs for cell-type-specific DNAm may be limited.

[0047] For a more systematic selection of top candidate CpGs, the new CimpleG pipeline was used (20). DNA from non-leukocyte cell types was not considered, as the present application focuses specifically on the analysis of blood. While it is theoretically possible that skin cells, endothelial cells, or even cell-free DNA influence the predictions (8, 9, 27), the vast majority of DNA in blood clearly originates from leukocytes. Notably, some of the selected CpGs did not show a clear correlation with cell number in the ddPCR measurements (PPM1F and TOP1MT for monocytes, LCN8 for B cells, and PLXND1 for NK cells). Thus, either the Illumina BeadChip array probe sets or the corresponding ddPCR assays did not provide specific or accurate DNAm measurements. Such discrepancies between arrays and targeted assays have been previously reported (28, 29) and underscore the need to validate such biomarkers.

[0048] Several methods exist for targeted DNAm measurements (18). Previous work has primarily relied on pyrosequencing (17, 19), whereas Baron et al. performed their assays based on methylation-specific qPCR (14). It has been shown that DNAm concentrations can vary considerably between pyrosequencing and qPCR measurements (30). The improved precision of epigenetic blood counts in the current study is likely due in part to the use of ddPCR. In contrast to qPCR, ddPCR has fewer PCR biases for methylated or unmethylated sequences, as individual droplets are only classified as positive or negative (31). Indeed, for epigenetic estimations of CD3 T lymphocytes, ddPCR has been shown to offer higher precision and greater accuracy, especially in samples with low copy numbers of target genes (32, 33).

[0049] A major advantage of epigenetic blood counts is that blood samples can be collected with a simple finger prick and sent as dried blood spots. This facilitates self-testing without trained medical personnel—e.g., for elderly patients who have difficulty seeing a doctor. Furthermore, the method is suitable for newborn screening for severe combined immunodeficiency (14, 34). Other clinically relevant examples include CD4 T-cell counting during HIV infection (14) or monitoring neutropenia after chemotherapy. Two alternative dried blood sampling devices were tested here. Filter paper, such as Whatman Protein Saver cards, are commonly used and less expensive, while microsamplers, such as Mitra microsamplers, can collect a more defined blood volume.Surprisingly, in the experiments conducted, epigenetic cell counts were more precise using Whatman Protein Saver cards, which might be due to less efficient DNA isolation from the Mitra tips.

[0050] Although the estimates from dried blood are promising, there are still limitations and challenges that need to be addressed before the method can be used in clinical applications. 1) One limitation is that red blood cells and platelets cannot be included because they do not contain genomic DNA. 2) A particular challenge for epigenetic blood counts is the detection of rare cell types (<5%) or closely related cell types, as their differential DNA methylation has only a minor impact on the mean methylation values ​​in bulk DNA. 3) The method is relatively time-consuming. With the current protocol, the turnaround time is approximately 2 days because the bisulfite conversion occurs overnight (12-16 hours). However, with alternative bisulfite conversion methods that are directly applicable to blood, the entire workflow can be reduced to approximately 8 hours.4) For absolute quantification, especially in dried blood, the efficiency of DNA extraction must be improved. Here, the variability in DNA isolation efficiency was addressed using a reference plasmid, but its stability at a defined concentration still needs to be further validated in independent cohorts. 5) Although no significant influence on epigenetic blood levels was observed in healthy cohorts and patients, epigenetic composition can be influenced by specific diseases—particularly leukemia. (16) Furthermore, variations may occur between samples from children and adults. Therefore, the method must be specifically validated for specific clinical applications. 6) Finally, regulatory hurdles must be overcome. The biomarkers must be validated and accredited according to local regulatory requirements—in Europe, according to the new directive for in vitro diagnostic medical devices (35).In general, it should be feasible to meet these demanding requirements, as similar procedures are clinically approved, the instruments for DNA isolation and ddPCR are already CE-certified, and the required analysis software, which in this case is based on relatively simple linear regression models, could also be certified accordingly.

[0051] Despite these limitations and challenges, this study demonstrated that epigenetic leukocyte counting by ddPCR is an accurate alternative to cryopreserved blood or dried fingerstick blood. Table 3: Other primers and probes used for ddPCR (see also Table 2). Gen, cg ... Primer / Probe Sequence, SEQ ID NO:[...] PPM1F Forward primer GTTTAGTTTAGATTTTGTTTAGGAAAGG

[61] cg25898577 Reverse primer ATAACTAAAACCTCTATATAACTACCAA

[62] 6-FAM- Probe not methylated TTTTTGGATTTTTTGTTGATTTAGTTAT -BHQ1

[63] Probe methylated HEX - TTTTTGGATTTTTCGTTGATTTAGTTAT -BHQ1

[64] TOP1MT Forward primer GGTTTGATTGATTTTATGTGTGTG

[65] cg17117981 Reverse primer AAACAAAAATTAATCAAACACC

[66] Probe not methylated 6-FAM-TGTGAGGTTTTTTTTGATTGTTTTTTTTT - BHQ1

[67] Probe methylated HEX - TGTGAGGTTCGTTTTTGATTGTTTG - BHQ1

[68] LRP5 Forward primer GTTTTTTAGGTTGTAGGTGTTTATTG

[69] cg00464927 Reverse primer AATAAACAACTCCAAATAATTTTCAATC

[70] Probe not methylated 6-FAM-TATGAATTAGTTGTGTTGTTTGGTTGTTG - BHQ1

[71] Probe methylated HEX - TATGAATTAGTTGTGTCGTTTGGTTGTT - BHQ1

[72] LCN8 Forward primer GGGGTGGTATAGGAAAGTGT

[73] cg14482811 Reverse primer CACCCCACATAACACTTATTTT

[74] Probe not methylated 6-FAM- AGTGTTGGAGGTTTTGGTTTGTTG - BHQ1

[75] Probe methylated HEX - TGTTGGAGGTTTTGGTTCGTTGT - BHQ1

[76] CD8A Forward primer TGATGATGGTTAGATTTGGGG

[77] cg25939861 Reverse primer AAAAAACTACCACCCCACTAA

[78] Probe not methylated 6-FAM- AGTTTTATGGAAATTAGTTTGGGGGTT -BHQ1

[79] Probe methylated HEX - AGTTTTACGGAAATTAGTTTGGGGGGT - BHQ1

[80] CD88 Forward primer TGGGGGTTTATATTATTGGGTTAA

[81] cg04329870 Reverse primer AACTCCTTCAAACCCTAAACC

[82] Probe not methylated 6-FAM- TTTAGTTATGGAGGTTTTTGAGTGT - BHQ1

[83] Probe methylated HEX - TAGTTATGGAGGTTTTCGAGTGT -BHQ1

[84] CD248 cg06419846 Forward primer GAGTTTGGGTATAGGGGTTT

[85] Reverse primer AACTAACTACAACCCTAACA

[86] Probe not methylated 6-FAM- TTTGTTTGTTGTTAGTCGGAAGTTT-BHQ1

[87] Probe methylated HEX- TTCGTTTGTTGTTAGTCGGAAGT -BHQ1

[88] PLXND1 Forward primer GGTTTTGGGTGAATATTTTGAAG

[89] cg21333217 Reverse primer ACTAACCTTACCCAACTACAA

[90] Probe not methylated 6-FAM-TGTTTTTTAGTTATTGTGTAGGTTTTTAT - BHQ1

[91] Probe methylated HEX - TTTTTAGTTATCGCGTAGGTTTT -BHQ1

[92] MVD Forward primer GGGTAGGTTTTTGATTATTAGAAGGT

[93] cg05355684 Reverse primer AAACTATTAATTTCTTCCCTCCC

[94] Probe not methylated 6-FAM-TATGTTTTTAGATATTTTTAAATGGGAGG - BHQ1

[95] Probe methylated HEX - TACGTTTTAGATATTTTTAAATGGGAG -BHQ1

[96] R5 reference Forward primer 5'-GTTTCCACACACAGAGGAAGTAG-3'

[97] Reverse primer 5'-ACGGGATGAGGAGATTTTAAGCC-3'

[98] Probe genomic DNA 5' 6-FAM-TGGCTGGCAGAGACCCAGGA - 3' BHQ-1

[99] Probe plasmid DNA 5' HEX - TGGCTGCGAGAGACCGAGGA - 3' BHQ-1

[100] Table 4: Simple linear regression models for epigenetic estimates of relative leukocyte count Cell type Cell Dyn Emerald - Measurements from healthy donors Sysmex XS800i measurements in patients with hematological diseases Granulocytes 82.0817 - 0.9072*DNAm 98.7825 - 1.2030*DNAm Lymphocytes 10.6012 + 1.0541*DNAm 1.0172 + 1.1007*DNAm Monocytes 54.6789 - 0.4986*DNAm 76.5412 - 0.7925*DNAm CD4 T cells KA 58.2602 - 0.6056*DNAm CD8 T cells KA 87.0866 - 0.9230*DNAm B cells KA 45.4241 - 0.4825*DNAm NK cells KA 53.3261 - 0.5447*DNAm DNAm: DNA methylation, expressed in percent (0 - 100) Tabelle 5: Sequenz Im Referenzplasmid für die absolute Leukozytenquantifizierung mittels ddPCR Cloned sequence Literatur: 1. Pitoiset F, Cassard L, Ei Soufi K, Boselli L, Grivel J, Roux A, et al. Deep phenotyping of immune cell populations by optimized and standardized flow cytometry analyses. Cytometry A 2018;93:793-802. 2. Bruegel M, Nagel D, Funk M, Fuhrmann P, Zander J, Teupser D. Comparison of five automated hematology analyzers in a university hospital setting: Abbott Cell-Dyn Sapphire, Beckman Coulter DxH 800, Siemens Advia 2120i, Sysmex XE-5000, and Sysmex XN-2000. Clin Chem Lab Med 2015;53:1057-71. 3. Navas A, Giraldo-Parra L, Prieto MD, Cabrera J, Gomez MA. Phenotypic and functional stability of leukocytes from human peripheral blood samples: considerations for the design of immunological studies. BMC Immunol 2019;20:5. 4. McGann LE, Yang HY, Walterson M. Manifestations of cell damage after freezing and thawing. Cryobiology 1988;25:178-85. 5. Herzenberg LA, Tung J, Moore WA, Herzenberg LA, Parks DR. Interpreting flow cytometry data: a guide for the perplexed. Nat Immunol 2006;7:681-5. 6. Jimenez Vera E, Chew YV, Nicholson L, Burns H, Anderson P, Chen HT, et al. Standardisation of flow cytometry for whole blood immunophenotyping of islet transplant and transplant clinical trial recipients. PLoS One 2019;14:e0217163. 7. Mattei AL, Bailly N, Meissner A. DNA methylation: a historical perspective. Trends Genet 2022;38:676-707. 8. Moss J, Magenheim J, Neiman D, Zemmour H, Loyfer N, Korach A, et al. Comprehensive human cell-type methylation atlas reveals origins of circulating cell-free DNA in health and disease. Nat Commun 2018;9:5068. 9. Schmidt M, Maie 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 Biol 2020;18:178. 10. Houseman EA, Accomando WP, Koestler DC, Christensen BC, Marsit CJ, Nelson HH, et al. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics 2012;13:86. 11. Accomando WP, Wiencke JK, Houseman EA, Nelson HH, Kelsey KT. Quantitative reconstruction of leukocyte subsets using DNA methylation. Genome Biol 2014;15:R50. 12. Salas LA, Koestler DC, Butler RA, Hansen HM, Wiencke JK, Kelsey KT, Christensen BC. An optimized library for reference-based deconvolution of whole-blood biospecimens assayed using the Illumina HumanMethylationEPIC BeadArray. Genome Biol 2018;19:64. 13. Wagner W. How to Translate DNA Methylation Biomarkers Into Clinical Practice. Front Cell Dev Biol 2022;10:854797. 14. Baron U, Werner J, Schildknecht K, Schulze JJ, Mulu A, Liebert UG, et al. Epigenetic immune cell counting in human blood samples for immunodiagnostics. Sci Transl Med 2018;10. 15. 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;64:566-75. 16. 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;36:80-9. 17. Sontag S, Bocova L, Hubens WHG, Nuchtern S, Schnitker M, Look T, et al. Toward Clinical Application of Leukocyte Counts Based on Targeted DNA Methylation Analysis. Clin Chem 2022;68:646-56. 18. Han Y, Franzen J, Stiehl T, Gobs M, Kuo CC, Nikolic M, et al. New targeted approaches for epigenetic age predictions. BMC Biol 2020;18:71. 19. 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:e41361. 20. Maie T, Schmidt M, Erz M, Wagner W, Costa IG. CimpleG: Finding simple CpG methylation signatures. bioRxiv 2022:2022.09.12.507513. 21. Robertson KD. DNA methylation and human disease. Nat Rev Genet 2005;6:597-610. 22. Ehrlich M. DNA hypermethylation in disease: mechanisms and clinical relevance. Epigenetics 2019;14:1141-63. 23. Babicki S, Arndt D, Marcu A, Liang Y, Grant JR, Maciejewski A, Wishart DS. Heatmapper: web-enabled heat mapping for all. Nucleic Acids Res 2016;44:W147-53. 24. Karlsson M, Zhang C, Mear L, Zhong W, Digre A, Katona B, et al. A singlecell type transcriptomics map of human tissues. Sci Adv 2021;7. 25. 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. 26. Bocova L, Hubens W, Engel C, Koschmieder S, Jost E, Wagner W. Quantification of hematopoietic stem and progenitor cells by targeted DNA methylation analysis. Clin Epigenetics 2023;15:105. 27. Neuberger EWI, Sontag S, Brahmer A, Philippi KFA, Radsak MP, Wagner W, Simon P. Physical activity specifically evokes release of cell-free DNA from granulocytes thereby affecting liquid biopsy. Clin Epigenetics 2022; 14:29. 28. Cheung K, Burgers MJ, Young DA, Cockell S, Reynard LN. Correlation of Infinium HumanMethylation450K and MethylationEPIC BeadChip arrays in cartilage. Epigenetics 2020;15:594-603. 29. Roessler J, Ammerpohl O, Gutwein J, Hasemeier B, Anwar SL, Kreipe H, Lehmann U. Quantitative cross-validation and content analysis of the 450k DNA methylation array from Illumina, Inc. BMC Res Notes 2012;5:210. 30. De Chiara L, Leiro-Fernandez V, Rodriguez-Girondo M, Valverde D, Botana-Rial MI, Fernandez-Villar A. Comparison of Bisulfite Pyrosequencing and Methylation-Specific qPCR for Methylation Assessment. Int J Mol Sci 2020;21. 31. Warnecke PM, Stirzaker C, Melki JR, Millar DS, Paul CL, Clark SJ. Detection and measurement of PCR bias in quantitative methylation analysis of bisulphite-treated DNA. Nucleic Acids Res 1997;25:4422-6. 32. Wiencke JK, Bracci PM, Hsuang G, Zheng S, Hansen H, Wrensch MR, et al. A comparison of DNA methylation specific droplet digital PCR (ddPCR) and real time qPCR with flow cytometry in characterizing human T cells in peripheral blood. Epigenetics 2014;9:1360-5. 33. Malic L, Daoud J, Geissler M, Boutin A, Lukic L, Janta M, et al. Epigenetic subtyping of white blood cells using a thermoplastic elastomer-based microfluidic emulsification device for multiplexed, methylation-specific digital droplet PCR. Analyst 2019;144:6541-53. 34. Blom M, Pico-Knijnenburg I, Imholz S, Vissers L, Schulze J, Werner J, et al. Second Tier Testing to Reduce the Number of Non-actionable Secondary Findings and False-Positive Referrals in Newborn Screening for Severe Combined Immunodeficiency. J Clin Immunol 2021;41:1762-73. 35. Lubbers BR, Schilhabel A, Cobbaert CM, Gonzalez D, Dombrink I, Bruggemann M, et al. The New EU Regulation on In Vitro Diagnostic Medical Devices: Implications and Preparatory Actions for Diagnostic Laboratories. Hemasphere 2021;5:e568. ZITATE ENTHALTEN IN DER BESCHREIBUNG

[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 patent literature

[0000] EP 3 561 074 A1

[0005] EP 3 382 033 A1 [0006, 0011] Cited non-patent literature

[0000] Pitoiset F, Cassard L, Ei Soufi K, Boselli L, Grivel J, Roux A, et al. Deep phenotyping of immune cell populations by optimized and standardized flow cytometry analyses. Cytometry A 2018;93:793-802

[0051] Bruegel M, Nagel D, Funk M, Fuhrmann P, Zander J, Teupser D. Comparison of five automated hematology analyzers in a university hospital setting: Abbott Cell-Dyn Sapphire, Beckman Coulter DxH 800, Siemens Advia 2120i, Sysmex XE-5000, and Sysmex XN-2000. Clin Chem Lab Med 2015;53:1057-71

[0051] Navas A, Giraldo-Parra L, Prieto MD, Cabrera J, Gomez MA. Phenotypic and functional stability of leukocytes from human peripheral blood samples: considerations for the design of immunological studies. BMC Immunol 2019;20:5

[0051] McGann LE, Yang HY, Walterson M. Manifestations of cell damage after freezing and thawing. Cryobiology 1988;25:178-85

[0051] Herzenberg LA, Tung J, Moore WA, Herzenberg LA, Parks DR. Interpreting flow cytometry data: a guide for the perplexed. Nat Immunol 2006;7:681-5

[0051] Jimenez Vera E, Chew YV, Nicholson L, Burns H, Anderson P, Chen HT, et al. Standardisation of flow cytometry for whole blood immunophenotyping of islet transplant and transplant clinical trial recipients. PLoS One 2019;14:e0217163

[0051] Mattei AL, Bailly N, Meissner A. DNA methylation: a historical perspective. Trends Genet 2022;38:676-707

[0051] Moss J, Magenheim J, Neiman D, Zemmour H, Loyfer N, Korach A, et al. Comprehensive human cell-type methylation atlas reveals origins of circulating cell-free DNA in health and disease. Nat Commun 2018;9:5068

[0051] Schmidt M, Maie 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 Biol 2020;18:178

[0051] Houseman EA, Accomando WP, Koestler DC, Christensen BC, Marsit CJ, Nelson HH, et al. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics 2012;13:86

[0051] Accomando WP, Wiencke JK, Houseman EA, Nelson HH, Kelsey KT. Quantitative reconstruction of leukocyte subsets using DNA methylation. Genome Biol 2014;15:R50

[0051] Salas LA, Koestler DC, Butler RA, Hansen HM, Wiencke JK, Kelsey KT, Christensen BC. An optimized library for reference-based deconvolution of whole-blood biospecimens assayed using the Illumina HumanMethylationEPIC BeadArray. Genome Biol 2018;19:64

[0051] Wagner W. How to Translate DNA Methylation Biomarkers Into Clinical Practice. Front Cell Dev Biol 2022;10:854797

[0051] Baron U, Werner J, Schildknecht K, Schulze JJ, Mulu A, Liebert UG, et al. Epigenetic immune cell counting in human blood samples for immunodiagnostics. Sci Transl Med 2018;10

[0051] 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;64:566-75.

[0051] 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;36:80-9

[0051] Sontag S, Bocova L, Hubens WHG, Nuchtern S, Schnitker M, Look T, et al. Toward Clinical Application of Leukocyte Counts Based on Targeted DNA Methylation Analysis. Clin Chem 2022;68:646-56

[0051] Han Y, Franzen J, Stiehl T, Gobs M, Kuo CC, Nikolic M, et al. New targeted approaches for epigenetic age predictions. BMC Biol 2020;18:71

[0051] 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:e41361

[0051] Maie T, Schmidt M, Erz M, Wagner W, Costa IG. CimpleG: Finding simple CpG methylation signatures. bioRxiv 2022:2022.09.12.507513

[0051] Robertson KD. DNA methylation and human disease. Nat Rev Genet 2005;6:597-610

[0051] Ehrlich M. DNA hypermethylation in disease: mechanisms and clinical relevance. Epigenetics 2019;14:1141-63

[0051] Babicki S, Arndt D, Marcu A, Liang Y, Grant JR, Maciejewski A, Wishart DS. Heatmapper: web-enabled heat mapping for all. Nucleic Acids Res 2016;44:W147-53

[0051] Karlsson M, Zhang C, Mear L, Zhong W, Digre A, Katona B, et al. A singlecell type transcriptomics map of human tissues. Sci Adv 2021;7

[0051] 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

[0051] Bocova L, Hubens W, Engel C, Koschmieder S, Jost E, Wagner W. Quantification of hematopoietic stem and progenitor cells by targeted DNA methylation analysis. Clin Epigenetics 2023;15:105

[0051] Cheung K, Burgers MJ, Young DA, Cockell S, Reynard LN. Correlation of Infinium HumanMethylation450K and MethylationEPIC BeadChip arrays in cartilage. Epigenetics 2020;15:594-603

[0051] Roessler J, Ammerpohl O, Gutwein J, Hasemeier B, Anwar SL, Kreipe H, Lehmann U. Quantitative cross-validation and content analysis of the 450k DNA methylation array from Illumina, Inc. BMC Res Notes 2012;5:210

[0051] De Chiara L, Leiro-Fernandez V, Rodriguez-Girondo M, Valverde D, Botana-Rial MI, Fernandez-Villar A. Comparison of Bisulfite Pyrosequencing and Methylation-Specific qPCR for Methylation Assessment. Int J Mol Sci 2020;21

[0051] Warnecke PM, Stirzaker C, Melki JR, Millar DS, Paul CL, Clark SJ. Detection and measurement of PCR bias in quantitative methylation analysis of bisulphite-treated DNA. Nucleic Acids Res 1997;25:4422-6

[0051] Wiencke JK, Bracci PM, Hsuang G, Zheng S, Hansen H, Wrensch MR, et al. A comparison of DNA methylation specific droplet digital PCR (ddPCR) and real time qPCR with flow cytometry in characterizing human T cells in peripheral blood. Epigenetics 2014;9:1360-5

[0051] Malic L, Daoud J, Geissler M, Boutin A, Lukic L, Janta M, et al. Epigenetic subtyping of white blood cells using a thermoplastic elastomer-based microfluidic emulsification device for multiplexed, methylation-specific digital droplet PCR. Analyst 2019;144:6541-53

[0051] Blom M, Pico-Knijnenburg I, Imholz S, Vissers L, Schulze J, Werner J, et al. Second Tier Testing to Reduce the Number of Non-actionable Secondary Findings and False-Positive Referrals in Newborn Screening for Severe Combined Immunodeficiency. J Clin Immunol 2021;41:1762-73

[0051] Lubbers BR, Schilhabel A, Cobbaert CM, Gonzalez D, Dombrink I, Bruggemann M, et al. The New EU Regulation on In Vitro Diagnostic Medical Devices: Implications and Preparatory Actions for Diagnostic Laboratories. Hemasphere 2021;5:e568

[0051]

Claims

[1] A method for determining at least part of the cellular composition of at least one biological sample comprising leukocytes, the method comprising the following steps: - Identifying DNA methylation levels of at least one region of the genomic DNA in the sample, wherein the specific region comprises at least one CpG dinucleotide, and wherein the specific region comprises at least one nucleotide sequence selected from the group consisting of cg22381196 (DHODH), cg12483340 (TMEM87A), cg06270401 (DYRK4), cg23054181 (FAM169BP), cg26076724 (RP11-14612.2), cg12249234 (KSR1), cg04468741 (MICAL2), cg05074138 (CTLA4), cg05705140 (LINC01237), cg15564619 (SKI), cg11531557 (HMBOX1), cg02212339 (TRPV1), cg22488278 (ZFYVE28), cg02240030 (MAD1L1) and cg24408769 (JARID2); and - Determining the proportion of at least one leukocyte type based on the identified methylation levels in order to predict the relative distribution of that leukocyte type in the sample, wherein - the degree of methylation of at least one CpG dinucleotide selected from the group consisting of CpG dinucleotides cg22381196 (DHODH), cg12483340 (TMEM87A), cg06270401 (DYRK4) and at least one CpG dinucleotide located within a region of 1,000 nucleotides upstream and / or downstream of each of said CpG dinucleotides indicates the proportion of granulocytes, - the degree of methylation of the CpG dinucleotide cg04468741 (MICAL2) or of at least one CpG dinucleotide located within a region of 1,000 nucleotides upstream and / or downstream of said CpG dinucleotide indicates the proportion of monocytes, - the degree of methylation of at least one CpG dinucleotide selected from the group consisting of the CpG dinucleotides cg23054181 (FAM169BP), cg26076724 (RP11-14612.2), cg12249234 (KSR1) and at least one CpG dinucleotide located within a region of 1,000 nucleotides upstream and / or downstream of each of said CpG dinucleotides indicates the proportion of lymphocytes, - the degree of methylation of the CpG dinucleotide cg02212339 (TRPV1) or of at least one CpG dinucleotide located within a region of 1,000 nucleotides upstream and / or downstream of said CpG dinucleotide indicates the proportion of B cells, - the degree of methylation of at least one CpG dinucleotide selected from the group consisting of the dinucleotides cg05074138 (CTLA4), cg15564619 (SKI), cg05705140 (LINC01237) and at least one CpG dinucleotide located within a region of 1,000 nucleotides upstream and / or downstream of each of said CpG dinucleotides, the proportion of CD4 + - T cells, - and the degree of methylation of the CpG dinucleotide cg11531557 (HMBOX1) or of at least one CpG dinucleotide located within a region of 1,000 nucleotides upstream and / or downstream of said CpG dinucleotide, the proportion of CD8 + - T cells, and - the degree of methylation of at least one CpG dinucleotide selected from the group consisting of the dinucleotides cg22488278 (ZFYVE28), cg02240030 (MAD1L1), cg24408769 (JARID2) and at least one CpG dinucleotide located within a region of 1,000 nucleotides upstream and / or downstream of each of said CpG dinucleotides indicates the proportion of NK cells. [2] Method according to claim 1, characterized by that at least one CpG dinucleotide is located within a region of 500, 400, 300, 200 or 100 nucleotides upstream and / or downstream of each of the said CpG dinucleotides cg22381196, cg12483340, cg06270401, cg23054181, cg26076724, cg12249234, cg04468741, cg05074138, cg05705140, cg15564619, cg11531557, cg02212339, cg22488278, cg02240030 and cg24408769. [3] Method according to claim 1 or 2, characterized bythat the sample is provided in a volume of not more than or less than 50 µl, not more than or less than 40 µl, not more than or less than 30 µl, not more than or less than 20 µl, not more than or less than 10 µl, 10 - 40 µl, 10 - 30 µl, 10 - 20 µl, 20 - 40 µl, 30 - 40 µl, 20 - 30 µl, 10 - 20 µl, 25 - 35 µl or preferably 30 µl. [4] Method according to one of the preceding claims, characterized by that the biological sample includes blood, bone marrow, cerebrospinal fluid and / or other tissue containing leukocytes. [5] Use of at least one artificial nucleic acid molecule for determining at least part of the cellular composition of a biological sample comprising leukocytes according to the method according to any one of claims 1 to 4, 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: 1, SEQ ID NO: 2, SEQ ID NO: 3 and SEQ ID NO: 4 for determining the proportion of granulocytes; b) a nucleotide sequence selected from the group consisting of SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7 and SEQ ID NO: 8 for determining the proportion of granulocytes; c) a nucleotide sequence selected from the group consisting of SEQ ID NO: 9, SEQ ID NO: 10, SEQ ID NO: 11 and SEQ ID NO: 12 for determining the proportion of granulocytes; d) a nucleotide sequence selected from the group consisting of SEQ ID NO: 13, SEQ ID NO: 14, SEQ ID NO: 15 and SEQ ID NO: 16 for determining the proportion of monocytes; e) a nucleotide sequence selected from the group consisting of SEQ ID NO: 17, SEQ ID NO: 18, SEQ ID NO: 19 and SEQ ID NO: 20 for determining the proportion of lymphocytes; f) a nucleotide sequence selected from the group consisting of SEQ ID NO: 21, SEQ ID NO: 22, SEQ ID NO: 23 and SEQ ID NO: 24 for determining the proportion of lymphocytes; g) a nucleotide sequence selected from the group consisting of SEQ ID NO: 25, SEQ ID NO: 26, SEQ ID NO: 27 and SEQ ID NO: 28 for determining the proportion of lymphocytes; h) a nucleotide sequence selected from the group consisting of SEQ ID NO: 29, SEQ ID NO: 30, SEQ ID NO: 31 and SEQ ID NO: 32 for determining the proportion of B cells; i) a nucleotide sequence selected from the group consisting of SEQ ID NO: 33, SEQ ID NO: 34, SEQ ID NO: 35 and SEQ ID NO: 36 for determining the proportion of CD4 + - T cells; j) a nucleotide sequence selected from the group consisting of SEQ ID NO: 37, SEQ ID NO: 38, SEQ ID NO: 39 and SEQ ID NO: 40 for determining the proportion of CD4 + - T cells; k) a nucleotide sequence selected from the group consisting of SEQ ID NO: 41, SEQ ID NO: 42, SEQ ID NO: 43 and SEQ ID NO: 44 for determining the proportion of CD4 + - T cells; I) a nucleotide sequence selected from the group consisting of SEQ ID NO: 45, SEQ ID NO: 46, SEQ ID NO: 47 and SEQ ID NO: 48 for determining the proportion of CD8 + - T cells; m)a nucleotide sequence selected from the group consisting of SEQ ID NO: 49, SEQ ID NO: 50, SEQ ID NO: 51 and SEQ ID NO: 52 for determining the proportion of NK cells; n) a nucleotide sequence selected from the group consisting of SEQ ID NO: 53, SEQ ID NO: 54, SEQ ID NO: 55 and SEQ ID NO: 56 for determining the proportion of NK cells; o) a nucleotide sequence selected from the group consisting of SEQ ID NO: 57, SEQ ID NO: 58, SEQ ID NO: 59 and SEQ ID NO: 60 for determining the proportion of NK cells; p) a nucleotide sequence which differs from one of the nucleotide sequences according to a) to o) by the exchange of not more than 10% of the nucleotides, preferably not more than 5% of the nucleotides; and q) a nucleotide sequence corresponding to the complementary strand of one of the nucleotide sequences according to a) to p).

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