Method for detection and enumeration of epigenetic immune cells in human blood samples for immunodiagnosis and neonatal screening

文档序号：889806 发布日期：2021-02-23 浏览：22次中文

阅读说明：本技术 用于免疫诊断和新生儿筛查的人类血液样品中表观遗传免疫细胞检测和计数的方法 (Method for detection and enumeration of epigenetic immune cells in human blood samples for immunodiagnosis and neonatal screening ) 是由斯文·欧莱克于 2019-07-03 设计创作，主要内容包括：本发明涉及表观遗传的血液和免疫细胞的检测和计数的改进方法,以及各自的用途和试剂盒。(The present invention relates to improved methods for the detection and enumeration of epigenetic blood and immune cells, as well as respective uses and kits.)

1. A method for an improved methylation assay for identifying blood immune cells, comprising the steps of:

a) providing a human blood sample, in particular from a newborn, comprising genomic DNA of blood immune cells;

b) treating the genomic DNA of the immune cell with bisulfite to convert unmethylated cytosines to uracil;

c) amplifying the treated genomic DNA using a suitable primer pair to produce an amplicon, and

d) identifying the blood immune cells based on analyzing the amplified amplicons,

wherein the amplification and analysis comprises amplification and/or qPCR using primers and probes selected from at least one of the following groups: SEQ ID NOs 1 to 12 for CD4, SEQ ID NOs 13 to 20 for CD8 β, SEQ ID NOs 21 to 28 for LRP5, SEQ ID NOs 29 to 36 for MVD, SEQ ID NOs 37 to 44 for LCN2, SEQ ID NOs 45 to 56 for CD3 γ/δ, and

wherein demethylation of at least one CpG position in the amplicon is indicative of at least one blood immune cell selected from the group consisting of: CD3⁺T cell, CD4⁺T cell, CD8⁺T cells, neutrophils, CD14⁺Monocyte, CD56⁺NK cells and CD19⁺B cells.

2. The method of claim 1, further comprising analysis of the methylation status of an amplicon of CD3 epsilon.

3. The method according to claim 1 or 2, further comprising performing the analysis and first normalization using a demethylation standard gene selected from genes expressed in all cells to be identified, such as housekeeping genes, such as GAPDH and β -actin, preferably using primers and probes selected from SEQ ID NOs 57 to 61 for said GAPDH gene.

4. The method according to any one of claims 1 to 3, further comprising a second normalization using a computer-simulated bisulfite-converted recombinant nucleic acid, preferably using primers and probes selected from SEQ ID NO 62 to 64 for GAP [ GC ] constructs, comprising a sequence that inverts all CpG dinucleotides of the at least one demethylating standard gene (GAP [ GC ] construct) to GpC.

5. The method of any one of claims 1 to 4, further comprising a third normalization using a calibration plasmid comprising one copy of each amplicon sequence in its unconverted genomic (i.e., unmethylated) state.

6. The method of any one of claims 1 to 5, wherein the analysis further comprises quantification of the blood immune cells identified.

7. The method according to any one of claims 1 to 6, wherein the method further comprises an additional FCM of the blood immune cells to be identified.

8. The method according to any one of claims 1 to 7, wherein more than one blood cell specific gene is analyzed, e.g. a set of 1,2, 3,4, 5 or 6 blood cell specific genes, optionally together with analyzing more than one demethylation standard gene.

9. The method of any one of claims 1 to 9, wherein the blood sample is selected from the group consisting of: peripheral blood samples, capillary blood samples or venous blood samples or their subtractions, such as for example peripheral blood mononuclear cells, blood clots and dried blood spots.

10. The method of any one of claims 3 to 10, further comprising the step of inferring the immune status of the human based on at least one quantification of the at least one immune cell type.

11. A diagnostic kit comprising materials for performing the method of any one of claims 1 to 10, optionally together with instructions for use.

12. The kit of claim 11, wherein the material is selected from primers and probes selected from any one of the following: SEQ ID NOs 1 to 12 for the CD4 gene, SEQ ID NOs 13 to 20 for the CD8 β gene, SEQ ID NOs 21 to 28 for the LRP5 gene, SEQ ID NOs 29 to 36 for the MVD gene, SEQ ID NOs 37 to 44 for the LCN2 gene, SEQ ID NOs 45 to 56 for the CD3 γ/δ gene region, SEQ ID NOs 57 to 61 for the GAPDH gene, and SEQ ID NOs 62 to 64 for the GAP [ GC ] construct.

13. Use of a kit according to claim 11 or 12 for performing the method according to any one of claims 1 to 10.

14. Selected from the group consisting of SEQ ID NO: 1 to 64, and an amplicon amplified by a primer pair selected from the group consisting of: SEQ ID No.1 and 2; 3 and 4; 5 and 6; 7 and 8; 10 and 11; 13 and 14; 15 and 16; 18 and 19; 21 and 22; 23 and 24; 26 and 27; 29 and 30; 1 and 32; 34 and 35; 37 and 38; 39 and 40; 42 and 43; 45 and 46; 47 and 48; 49 and 50; 51 and 52; 54 and 55; 57 and 58; 59 and 60; and 62 and 63.

Background

Quantitative abnormalities of lymphoid and myeloid immune cell subsets are indicative of several human diseases and therefore constitute important parameters for diagnosis and patient monitoring. Currently, immune cell quantification is mainly performed by Flow Cytometry (FCM), which provides flexibility regarding the cell type and accuracy analyzed (1). However, although hematology analyzers used in diagnostic laboratories have been highly developed and sample streams have been widely used, FCM still has inherent limitations. FCM-based cell counting requires a fresh, anticoagulated or well-preserved blood sample with intact leukocytes. Even with fresh samples, it is recommended to work quickly, since analysis time can affect the results, where cell regression begins the first few hours after blood draw. Analysis time affects results due to cell degeneration within hours after blood collection. Due to variations in biology, technology and manipulation, standardization remains a challenge (2-5), and there is still a need to establish standardization protocols, especially for samples with small numbers of certain cell populations, such as samples in immunodeficiency (6, 7). The key challenge is that FCM-based cell counting requires intact leukocytes, but fresh or well-preserved blood cannot be used for all medical applications.

However, the most critical challenge is that not all medical applications guarantee the availability of fresh or well-preserved blood samples, and that flow cytometers cannot be applied in these situations.

Treatment decisions for HIV-infected patients depend on CD4⁺T cell counts. Below 500 CD4⁺At a frequency of T cells/μ l blood, antiretroviral therapy is recommended and becomes imperative when the frequency is below 200 cells/μ l. In resource-starved areas, proper cell counting is hampered when blood collection and measurement cannot be performed in close succession. Therefore, treatment is initiated based only on HIV-associated clinical symptoms, which may lead to sub-optimal results (8, 9). Furthermore, FCM is not applicable to severe but treatable congenital defects in neonatal screening, routinely performed on Dry Blood Spots (DBS). Primary Immunodeficiency (PID) constitutes such a group of congenital diseases and is considered or already part of the screening procedure (10). Often, genetic defects result in a lack of quantification of a particular leukocyte subpopulation. Severe Combined Immunodeficiency (SCID) represents such a PIDAnd is clinically characterized by a deficiency in T cells or B cells. Currently, the detection of SCID in newborns is based on quantitative PCR helper T cell receptor (TREC) and immunoglobulin kappa deletion recombinant excision loop (KREC) assays (11). These methods reliably detect the lack of recent thymic T cell and bone marrow B cell migrations, which are the predominant T cell and B cell subset present in the blood of a newborn, i.e., the predominant T and B cell subset present in the blood of a newborn. However, TREC/KREC analysis failed to detect other specific lymphocyte subpopulation defects (such as Natural Killer (NK) cells or neutrophils) in severe PID. Despite this limitation, TREC neonatal screening is effective and shows improved disease outcomes due to earlier diagnosis (12). TREC analysis in neonatal analysis was used only for initial screening. Differential diagnosis and patient monitoring before and after therapeutically effective hematopoietic stem cell transplantation requires technical changes and is performed by flow cytometry.

To overcome current technical and diagnostic limitations and expand the applicability of immune monitoring, the inventors of the present invention established quantitative assessment of immune cells based on DNA (un) methylation (epigenetic qPCR). This technique provides relative and absolute immune cell counts that can be applied to fresh, frozen or paper stains, dry blood. The signal is digital, i.e., indicates one positive or negative value per cell, rather than an arbitrarily defined threshold for "positive" as in FCM. It can be performed in an automated, operator independent manner and reduces susceptibility to reagent variability (e.g., antibodies).

In a first aspect of the invention, the above object is achieved by a method for an improved methylation assay for identifying blood immune cells, said method comprising the steps of:

a) providing a human blood sample, in particular from a newborn, comprising genomic DNA of blood immune cells;

b) treating the genomic DNA of the immune cell with bisulfite to convert unmethylated cytosines to uracil;

c) amplifying the treated genomic DNA using a suitable primer pair to produce an amplicon, and

d) identifying the blood immune cells based on analyzing the amplified amplicons,

Preferred is a method according to the invention, further comprising analysis of the amplicon for CD3 epsilon as disclosed, for example, in US 2012-0107810.

Preferred is a method according to the invention, further comprising an additional FCM of said blood immune cells to be identified.

Epigenetic immune cell counting provides a powerful platform that can diagnose immunodeficiency and optionally and conveniently complement flow cytometry and T-cell receptor excision loop assays, however, without their respective limitations.

The invention also relates to the accurate quantification of methylation data obtained using the above assay. This involves several components and considerations:

1. internal standards, such as plasmids that are converted via computer simulation.

2. For example, GAPDH standard as opposed to methylated variants of a particular gene.

3. Thus, all demethylated copies of forced demethylated GAPDH were compared to specific (but present at the same copy number) demethylated genes, according to quantification with 1.

4. However, the above does not allow true "absolute" quantification, since the standards converted via computer simulation do not correspond to biological samples (which are only converted in reaction vials).

5. The problem at 4 is solved based on the addition and measurement of so-called GNOM (methylated genomic standards), where all original sequences are included in the plasmid equimolar, and then the whole process is performed (bisulfite treatment and purification). Since they are present at 1:1, standards can be identified after quantification using the standard in 1, which show differences between simulated methylation and in situ methylation via computer. Using this factor, the measured methylation values can be corrected, which improves the results significantly.

6. The use of a defined amount of nucleic acid (plasmid) of a standard gene with inverted CG bases, and in addition, any material loss during the process can be taken into account, which further improves the process.

7. A reliable and specific assay kit designed for clinical practice and needs.

Cell type specific DNA methylation markers (13-15) amplified in qPCR potentially allow immune cell detection and quantification even in limited number and quality samples. The rationale for identifying cell type specific epigenetic markers has been described previously (14, 16-18). Alternative methods for DNA methylation-based immune cell quantification include analysis of individual CpG sites on a genome-wide basis relying on microarray analysis (19). Such methods allow the estimation of leukocyte subpopulations based on calculated beta values (methylation intensity). The inventors of the present invention postulate that locus specific individualized apparent qPCR is highly specific and sensitive and therefore well suited for diagnostic methods.

For epigenetic qPCR, genomic DNA was treated with bisulfite. Unmethylated CpG dinucleotides are converted into TpG, whereas methylated CpG remains unchanged. Thus, bisulfite conversion translates epigenetic markers into sequence information, which allows discrimination and quantification of both variants. Since DNA is a stable substrate, epigenetic qPCR is less susceptible to loss of cellular integrity. It can be performed on freshly frozen blood, DBS or other samples without special requirements on the state of preservation. In addition, PCR components are synthetically produced and are easily standardized. However, counting of immune cells via epigenetic qPCR has not been demonstrated due to the lack of well-defined specific biomarkers and the lack of defined and absolute quantification (20).

The inventors of the present invention investigated immune cell type specific epigenetic qPCR for quantifying leukocyte populations in human blood. For total CD3⁺、CD4⁺And CD8⁺Cytotoxic T cells (21,22) analysed for the methylation status of regulatory elements in genes encoding cell type determining proteins. Epigenetic markers of neutrophils, B cells and NK cells were identified from whole genome discovery and resulting maps of candidate genes. Determination of the absolute number of cells (i.e., cells/. mu.l of blood) constitutes a gold standard, e.g., for CD4 in HIV patients⁺Gold standard for T cell counts. The inventors of the present invention tested the determination and absolute enumeration of immune cells based on their cell type specific epigenetic signatures in healthy donors and in a homogeneous population of HIV patients and analyzed their equivalence to FCM. For DBS where blood volume is difficult to determine, copies of the unmethylated immune cell type specific marker gene are correlated with copies of the universal denominator (GAPDH). Furthermore, the diagnostic potential of epigenetic qPCR was demonstrated by the identification of PID cases in a clinically unpredictable neonatal cohort using DBS.

In a preferred embodiment of the method according to the invention, the method is integrated and further comprises performing the analysis and the first normalization using a demethylation standard gene selected from genes expressed in all cells to be identified (such as e.g. housekeeping genes, such as e.g. GAPDH and β -actin), preferably using primers and probes selected from SEQ ID NOs 57 to 61 for said GAPDH gene.

In another preferred embodiment of the method according to the invention, the method is integrated and further comprises a second normalization using a computer-simulated bisulfite-converted recombinant nucleic acid comprising a sequence that inverts all CpG dinucleotides of the at least one demethylating standard gene (GAP [ GC ] construct) to GpC, preferably using primers and probes selected from SEQ ID NOs 62 to 64 for GAP [ GC ] constructs.

In another preferred embodiment of the method according to the invention, the method is integrated and further comprises a third normalization using a calibration plasmid comprising one copy of each amplicon sequence in its unconverted genomic (i.e. unmethylated) state.

In another preferred embodiment of the method according to the invention, said method further comprises quantification of said blood immune cells identified.

In a preferred embodiment thereof, the process according to the invention comprises:

a) providing a defined volume of a human blood sample (in particular a human blood sample from a newborn) comprising (e.g. diploid) genomic DNA of blood cells to be quantified;

b) providing a computer-simulated bisulfite-converted recombinant nucleic acid comprising a demethylation standard gene, a sequence that inverts all CpG dinucleotides of the demethylation standard gene into GpC, and a blood cell-specific gene;

c) providing a recombinant nucleic acid comprising the demethylated genomic sequence of the demethylated standard gene described in b), the sequence that inverts all CpG dinucleotides of the demethylated standard gene into GpC, and the blood cell-specific gene described in b);

d) providing a recombinant nucleic acid comprising a sequence that inverts all CpG dinucleotides of the at least one demethylation standard gene described in b) into GpC;

e) adding a defined amount of the recombinant nucleic acid described in d) ("spiking") to the sample described in a);

f) treating the (e.g. diploid) genomic DNA of the cell to be quantified in a) and the recombined nucleic acids in c) and d) with bisulphite to convert unmethylated cytosine to uracil;

g) amplifying the nucleic acid molecules of a), b), c) and f) using suitable primer pairs to produce amplicons; and

h) identifying blood immune cells based on analyzing the amplicons,

Optionally, as described herein, a step of quantifying the blood immune cells is included.

Preferred is a method according to the invention, which further comprises analysis of the amplicon of CD3 epsilon described above.

Preferred is a method according to the invention, further comprising an additional FCM of said blood immune cells to be identified.

Preferably, the demethylation standard gene is selected from genes expressed in all cells to be detected, such as for example housekeeping genes (such as for example GAPDH and β -actin).

In one aspect of the method according to the invention, more than one blood cell specific gene is analyzed, e.g., as needed or desired to produce a set of 1,2, 3,4, 5, or 6 blood cell specific genes, optionally in conjunction with analyzing more than one demethylation standard gene as described herein.

Preferably, the nucleic acid is a plasmid (e.g., a linearized plasmid, such as a bacterial plasmid (e.g., pUC)), a Yeast Artificial Chromosome (YAC), an artificial chromosome (HAC), a PI-derived artificial chromosome (PAC), a Bacterial Artificial Chromosome (BAC), and/or a PCR product.

In one aspect of the method, amplification is normalized using a first computer-simulated bisulfite converted nucleic acid (plasmid) comprising a demethylation standard gene (e.g., GAPDH), "artificial sequence" (sequence that inverts all CpG dinucleotides to GpC), and a blood cell specific gene ("specific gene", e.g., CD 4). All three elements are present identically (equimolar) on the nucleic acid and are bisulfite converted via computer simulation. Thus, the normalization curve and the corresponding calibration curve can be directly compared to the sample to determine the relative cell count from the ratio of blood cell specific genes to demethylation standard genes. However, nucleic acids do not correspond to "true" sequences, as each C is replaced by a T. Serial dilutions of all genes and determination of each concentration as described above resulted in a calibration curve for the assay.

To increase the accuracy of this method, a second nucleic acid (plasmid) is used which comprises a demethylation standard gene (e.g. GAPDH), "artificial sequence" (sequence which inverts all CpG dinucleotides to GpC) and a blood cell specific gene ("specific gene"). However, these sequences are not bisulfite converted via computer simulation and correspond to genomic sequences (in the case of genomic counterparts, see below) and can therefore only be used to measure amplification (e.g., qPCR) efficiency.

The reasons for the second criterion are two. A) For a defined quantification, the same standard as in the biological sample to be analyzed is required (this is also a regulatory requirement). However, in the first nucleic acid, the double-stranded AT-rich sequence is compared to the single-stranded U-rich sequence. Only "true" bisulfite conversion of double-stranded nucleic acids allows this defined comparison. The quotient of the bisulfite conversion of the blood cell specific gene normalized using the first nucleic acid to the demethylated standard gene then gives a factor for the efficiency. The same applies to the quotient, based on the division of the bisulfite conversion of the sequence, which converts all CpG dinucleotides into GpC by bisulfite conversion of the demethylation standard gene.

Preferably, an "artificial sequence" (a sequence that inverts all CpG dinucleotides to GpC) is a random sequence comprising C and CpG sequences (for bisulfite conversion), which is not present in the human genome. In one embodiment, the artificial sequence is the exact sequence of the amplified GAPDH portion (amplicon), wherein the CpG sequence is inverted to the GpC sequence. As described above, "artificial sequences" are found on all three nucleic acids, i.e.on the first nucleic acid (bisulfite converted via computer simulation), on the second nucleic acid (for bisulfite conversion) and on the third nucleic acid-as the only sequence analyzed- (bisulfite converted via computer simulation).

The third nucleic acid is administered in a defined amount to a defined amount of blood (in particular blood from a newborn) and then analyzed (e.g. purification, bisulfite treatment, secondary purification, desulfonation, specific amplification). The first nucleic acid is then normalized (how many copies are measured and given to the reaction), efficiency is determined using a comparison with the second nucleic acid, and the (residual) copy number is determined using the third nucleic acid. Any loss was compared to the loss of genomic DNA following the same procedure. The overall process allows for the accurate determination and absolute quantification of the DNA and cells in a blood sample (such as e.g. whole blood) by the process.

In one embodiment, the invention relates to an artificial sequence, which is the exact sequence of the amplified GAPDH part (amplicon), wherein the CpG sequence is inverted to the GpC sequence as a tool when performing the method of the invention.

The composition of the cellular immune system possesses valuable diagnostic information for various diseases. The standard technique for quantitative immune cell monitoring is flow cytometry. However, this method is limited to blood samples in which the integrity of the cells is preserved. In clinical practice, this effectively limits the analysis of fresh blood samples as substrates for analysis.

To expand the scope of use of diagnostic immune monitoring, the inventors of the present invention implemented an epigenetic qPCR system to quantify the major leukocyte population. After determination of immune cell type specific methylation markers, Guthrie cards from 25 healthy donors, 97 HIV patients, and 325 from newborns (including 25 cards from patients with Primary Immunodeficiency (PID), including but not limited to XLA, SCID, SCN) were analyzed. Methodological consistency between flow cytometry and epigenetic data for B cells, NK cells, total T cells, helper T cells, neutrophils and cytotoxic T cells was determined and the ability of this new technology to identify quantitative immune cell defects was tested.

The data show that quantification via epigenetic qPCR assays and flow cytometry were performed equally in healthy subjects and HIV patients according to the Bland-Altman test. Epigenetic quantification applies to the relative and absolute frequencies of leukocyte subpopulations in fresh and frozen blood samples. In contrast to flow cytometry, immunocytological analysis of Guthrie cards accurately identified case PIDs in newborns. Epigenetic quantification of immune cell populations is highly comparable to standard flow cytometry offering a wider range of possible applications, including dry spot analysis that may pave the way for blood counts in remote areas or patients from newborns.

Thus, preferred is a method according to the invention, wherein the blood sample is selected from the group consisting of peripheral, capillary or venous blood samples or sub-fractions thereof (such as e.g. peripheral blood mononuclear cells, blood clots and dried blood spots). Also preferred is a method according to the invention comprising a step of diagnosing Primary Immunodeficiency (PID) in a human, in particular a neonate, based on said quantification, wherein said sample is preferably a dried sample such as Guthrie card (see further also below) or DBS (dried blood spots).

Preferred is a method according to the invention, further comprising the step of inferring the immune status of the human based on at least one quantification of said at least one immune cell type.

Preferred is a method according to the invention, wherein the recombinant nucleic acid molecule is selected from the group consisting of a plasmid, a Yeast Artificial Chromosome (YAC), an artificial chromosome (HAC), a PI-derived artificial chromosome (PAC), a Bacterial Artificial Chromosome (BAC) and a PCR product.

Preferred is a method according to the invention, wherein the demethylation standard gene is selected from genes expressed in all cells to be detected, such as for example housekeeping genes, such as for example GAPDH and β -actin.

Preferred is a method according to the invention, wherein said blood cell specific gene is selected from the group consisting of: genes expressed in all blood cells to be tested, CD4, CD8 β and/or α, low density lipoprotein receptor-related protein 5(LRP5), mevalonate pyrophosphate decarboxylase (MVD), lipocalin-2 (LCN2) and CD3 γ/δ region (gene) are known or found.

Another aspect of the invention relates to a diagnostic kit comprising materials for performing the method according to the invention, optionally together with instructions for use. Preferred materials are nucleic acid molecules and/or bisulphite reagents. Preferred materials are selected from primers and probes selected from any one of the following: SEQ ID NOs 1 to 12 for the CD4 gene, SEQ ID NOs 13 to 20 for the CD8 β gene, SEQ ID NOs 21 to 28 for the LRP5 gene, SEQ ID NOs 29 to 36 for the MVD gene, SEQ ID NOs 37 to 44 for the LCN2 gene, SEQ ID NOs 45 to 56 for the CD3 γ/δ gene region, SEQ ID NOs 57 to 61 for the GAPDH gene, and SEQ ID NOs 62 to 64 for the GAP [ GC ] construct.

A further aspect of the invention relates to the use of a kit according to the invention for performing a method according to the invention.

Yet another aspect of the invention relates to a polypeptide selected from the group consisting of SEQ ID NOs: 1 to 64, and an amplicon amplified by a primer pair selected from the group consisting of: SEQ ID No.1 and 2; 3 and 4; 5 and 6; 7 and 8; 10 and 11; 13 and 14; 15 and 16; 18 and 19; 21 and 22; 23 and 24; 26 and 27; 29 and 30; 1 and 32; 34 and 35; 37 and 38; 39 and 40; 42 and 43; 45 and 46; 47 and 48; 49 and 50; 51 and 52; 54 and 55; 57 and 58; 59 and 60; and 62 and 63.

The invention also includes a method of treating an immune-related disorder in a human (particularly a neonate, a patient in need thereof) comprising performing a method as described herein and providing treatment for the immune-related disorder based on the results of the method. An additional embodiment includes immune cell monitoring, and immune related diseases include, for example, PID, other immunodeficiency or cancer.

Current immune cell monitoring requires fresh or well-preserved blood, which hinders diagnosis in the medical field where such substrates are not available. Here, the inventors of the present invention describe immune cell type specific epigenetic qPCR that allows determination of immune cell counts from conserved, pappus dried blood or fresh samples that were not observed. The overall feasibility of epigenetic qPCR has been shown previously using the "Treg-specific demethylation region (TSDR)" in regulatory T cells (13). After identifying specific epigenetic markers for many diagnostically relevant immune cell populations, the inventors of the present invention demonstrated that the corresponding epigenetic qPCR performs equivalently to the gold standard techniques of immune cell analysis (FCM, TREC/KREC analysis). To this end, the quantification of immune cells in fresh frozen blood and/or DBS from healthy controls, a homogeneous cohort of Primary Immunodeficiency (PID) or acquired (HIV) immunodeficiencies, and a homogeneous cohort of newborns with or without congenital immunodeficiencies were analyzed.

The ideal DNA methylation marker for cell type identification is distinguishable between the target cell (close to 0% methylation) and all control cells (close to 100% methylation). In addition to the analysis of T cell-associated genes CD3G/D, CD4 and CD8B, the loci in genes MVD, LRP5 and LCN2 were found to be unmethylated only in NK cells, B cells and neutrophils, respectively. MVDs are a component of the mevalonate pathway (33) and are expressed in the testis, duodenum and colon. LRP5 is involved in osteogenesis (34). LCN2 is an extracellular transport protein and the major protein of human tear fluid (35). The causes and functions of specific lack of methylation in these regions remain to be analyzed, but do not affect their use for cell quantification in peripheral blood. All markers were verified by bisulfite sequencing and discriminatory CpG dinucleotides were selected for qPCR development and characterization of artificially generated methylated and unmethylated DNA. Quantitative amplification of target DNA is achieved without detection of background from non-target templates. qPCR assay performance is robust with low variation as shown by small intra-and inter-assay CVs in fresh, frozen or dried blood.

To simultaneously quantify different cell types in a biological sample, the inventors of the present invention designed a calibration plasmid containing the unmethylated genomic sequence of GAPDH as a reference quantification and a cell type specific marker. Although GAPDH was previously described as an unstable gene expression normalizer (36) and contained fragment repeats (37), the GAPDH locus selected here was stable diploid and always unmethylated. Thus, by adjusting the quantification of the biological sample with computer-simulated bisulfite-converted standards and calibrators, it is possible to correct for the problem of low specific technical efficiency of the assay and allow for a defined quantification (20) of the individual loci relative to unmethylated GAPDH (i.e., all nucleated cells). Thus, epigenetic qPCR shows a proportional relationship with the cell type determined by FCM. The remaining observed bias between methods (38) may be due to biological and technical differences between nucleic acid-based and antibody-based methods. The uniform error distribution and accuracy is comparable to data previously compared in different antibody-based methods (39). Taken together, these data suggest that epigenetic qPCR from liquid and dried blood substrates performs relative quantification of immune cells comparable to FCM.

Relative cell quantification is accepted by the WHO in the HIV treatment guidelines for clinical applications, but in medical practice treatment decisions depend on cell counts per volume (40, 41). For epigenetic qPCR, this poses a problem because DNA recovery is not quantitative and the relationship between DNA quantity and blood volume is not fixed. To this end, the experimental setup of the inventors of the present invention included the spiking of artificial GAP [ GC ] at defined concentrations into the blood, providing approximate inferences of the original DNA content in a defined blood volume in a subsequent qPCR analysis. Although differential efficiencies of genomic and plasmid DNA have been described (42), such differences are further reduced after bisulfite treatment and fragmentation of the resulting genomic DNA. When applied to healthy donors and HIV cohorts, the data showed high correlations and deviations similar to those described for the relative quantitative method comparisons, as well as consistent limits. Thus, the inventors concluded that immune cell counts per microliter can be performed by epigenetic qPCR equivalent to FCM.

At present, neonatal screening is always performed from DBS. Since FCM is not suitable for this substrate, TREC/KREC assays were used for PID screening. Therefore, introduction of epigenetic qPCR in such screening requires an equivalent test to TREC/KREC. The method comparison is not feasible due to the different parameters tested (i.e., DNA excision circle versus genomic DNA). In contrast, the inventors of the present invention estimated the specificity and sensitivity of TREC/KREC from (43). When using 99% confidence regions, epigenetic qPCR reliably identifies newborns with different types of PID with similar sensitivity and specificity. It cannot identify only one neonatal PID patient with maternal cell transplantation (i.e., a patient in which the depletion of T and B cells is masked by maternal cells). Unlike the analysis of the excision loop, epigenetic analysis is not limited to major lymphocyte subpopulations. This problem can be solved by extending the epigenetic qPCR combination to markers of memory T cells or B cells that are not present in the non-transplanted neonates. Such markers may allow detection of transplants when detected in newborns, thereby indicating the absence of a healthy innate immune system.

Other quantitative deficiencies of other immune cell populations appear in neutrophils and highly specialized tregs. The data of the present inventors indicate that identification of such patients based on epigenetic qPCR on neutrophils and tregs is possible early after birth, allowing early diagnosis of SCN (which potentially constitutes a life-threatening PID) (43, 44). The importance of detecting and treating these severe immune disorders has been exemplified previously (46).

Due to the scarcity of patients, thorough research into rare genetic diseases poses a significant challenge. Here, this limitation had the greatest impact on the analysis of only 6 SCN patients, but the group of SCID patients with different genetic backgrounds was comparable to the previously published study (47). The limited data set provided in the present invention only demonstrates technical feasibility, but does not allow conversion to neonatal screening. Despite the severe limitations of the present invention, the present inventors' data indicate that epigenetic qPCR can provide a choice in medical screening programs.

In summary, the present invention shows that epigenetic qPCR provides an accurate and precise means for immune cell detection and monitoring, and emphasizes that epigenetic qPCR can help or even replace current immunodiagnostics, especially for blood or DBS that is stored inadvertently.

The invention will now be further described and explained in the following examples and figures, to which, however, the invention is not restricted. For the purposes of the present invention, all references as cited herein are incorporated by reference in their entirety.

FIG. 1 shows bisulfite sequencing-derived DNA methylation of cell-specific marker genes in purified immune cells. Immune cell types are arranged in columns and Amplicons (AMPs) and related gene names are arranged in rows. Different loci are separated by red lines and amplicons within the same locus are separated by black lines. Each individual row represents a single CpG site. The methylation rates were color coded from light gray (0%) to dark gray (100%).

Figure 2 shows a comparison of immune cell quantification by FCM and epigenetic qPCR. Immune cells from 25 blood samples from independent donors were measured using flow cytometry (y-axis) and epigenetic qPCR (x-axis). In A), the relative immune cell count is shown as a percentage of total leukocytes. In B), the absolute immune cell count is shown as the number of cells per. mu.l of whole blood. The regression line is shown in red and the black line in the bisector, as calculated from all data points.

FIG. 3 shows a methodological comparison of T cell subsets in the HIV cohort. Determination of CD3 in% of total nucleated cells by (A) FCM and epigenetic qPCR in liquid whole blood, (B) from DBS by FCM and epigenetic qPCR as in A), and (C) comparing from liquid blood and DBS by epigenetic qPCR⁺、CD4⁺And CD8⁺Relative counts of T cells. On the left hand side, the data are represented as scatter plots. The regression line is shown in red and the black line in the bisector, as calculated from all data points. On the right hand side, the Bland-Altman plots show the mean cell counts for each analysis (x-axis) plotted on their relative difference (y-axis). The red line reflects the contracted limit. The central gray line represents the systematic deviation.Each 95% CI is shown in dashed lines. The upper diagram: total CD3⁺A T cell; middle diagram: CD4⁺A T cell; right lower: CD8⁺T cells.

Fig. 4 shows epigenetic qPCR for neonatal DBS. Copies from cell type specific qPCR (y-axis) were plotted against GAPDH copies (x-axis). (A) Unmethylated CD3G/D, B) MVD and C) LRP 5. DBS from healthy neonates (n-250, gray circle) evaluated the reference range for each assay as defined by the 99% confidence region (red ellipse) and the 99.9% confidence region (blue). 24 DBSs from the PID diagnosed neonate are shown as colored circles, each with reference to the disease characteristics shown in Table 2.

Figure 5 shows epigenetic qPCR on DBS from neonates with SCN. Epigenetic qPCR was performed on DBS from healthy controls (grey + boxes) and SCN-confirmed neonates to quantify CD15⁺Neutrophils. Healthy cohort groups are shown in the block diagram, while the results for patients with disease are shown in light grey.

FIG. 6 shows a schematic of different quantitative methods of epigenetic cell counting. In a), locus specificity relative percentage quantification is shown. qPCR allows counting of copy number as based on calculating serial dilutions of computer mock-converted plasmids by linear insertion (f-1) of the amplification result (f). The relative percent methylation at a genomic locus is calculated by dividing the number of insert copies of the original unmethylated copy at that locus by the number of all copies at that locus (i.e., methylated and unmethylated copy numbers). Transitions in biological samples disturb the integrity of the genomic DNA, while plasmids represent amplification products rather than substrates. The resulting difference in amplification efficiency is given by the unknown "Conversion Factor (CF)". Amplification of two highly homologous sequences was considered negligible when compared to few methylation state dependent SNPs. In (B), the ubiquitously unmethylated GAPDH locus (representing the total number of genomic DNA copies) was used as a denominator to determine the ratio of any cell type-specific unmethylated locus. Here, CF resulted in a substantial shift between different qPCR assays. In C), calibration plasmids containing equimolar genomic target sequences were used to compensate for the conversion efficiency at different genomic loci incorporating the Efficiency Factor (EF). To count the absolute number of cells in a defined volume of blood, plasmids containing synthetic, non-natural DNA sequences (GAP-GC) were supplemented with known copy numbers. The initial amount of inserted GAP-GC allows monitoring of DNA preparation, conversion and qPCR, providing a good estimate of process efficacy.

Fig. 7 shows a correlation analysis of absolute quantification of T cell subpopulations in HIV cohort. Analysis by flow cytometry and epigenetic qPCR from 97 HIV⁺Absolute immune cell counts (expressed as cells/blood) are measured in a blood sample of a patient. The left scatter plot shows three T cell populations measured by epigenetic qPCR analysis (x-axis) and flow cytometry (y-axis). The black and red lines represent the bisector and regression lines, respectively. Pearson correlation coefficient r is 0.955 (p)<0.0001). The right panel shows a Bland-Altman plot, where the mean cell counts (cells/μ Ι) averaged between each epigenetic measurement and cell count measurement on the x-axis are plotted on their (relative) difference (y-axis). The red line reflects the limit of consistency and the central gray line illustrates the deviation of the system. Above and below each of these lines, the respective 95% confidence intervals are shown as dashed gray lines. The upper diagram: total CD3⁺A T cell; middle diagram: CD8⁺A T cell; right lower: CD4⁺T cells.

Fig. 8 shows epigenetic immune cell quantification on DBS from newborns. Epigenetic qPCR analysis of dried blood spots on Guthrie cards to quantify unmethylated CD4 (A; specific for CD4)⁺T cells) and CD8B (B, specific for CD8B)⁺T cell) gene copy. The calculated values from the immune cell specific assay (y-axis) were spread over the GAPDH copies measured in parallel (x-axis). Reference samples from healthy newborns (n-250, gray point) were measured and used to estimate the normal range for each assay defined by red (99% confidence zone) and sky blue (99.9% confidence zone) ellipses, respectively. Red, blue, green and black circles show 24 samples from neonates, each with a diagnosed PID (classification as bottom right corner)Shown in legend box) each circle is associated with an identifier that references a disease characteristic according to table 2.

TABLE 1 cell type specific epigenetic qPCR System

TABLE 1.RD_ls: relative unmethylated (locus specific) (%); RD_U: relative unmethylated (general) (%); EF: an efficiency factor; DD (DD) with high heat dissipating capacity_U: defined unmethylated (general) (%)

Table 4: data from the method comparison analysis. Pearson's coefficient, bias and accuracy from comparison of epigenetic qPCR and flow cytometry on blood samples (fluid; spotted and dried on Guthrie card, DBS) of 97 HIV + patients.

Table 5: stability testing of DBS. T cell subsets were measured from blood by epigenetic qPCR analysis, spotted, dried on Guthrie cards, and stored at different times and different temperatures.

Table 6: epigenetic qPCR of DBS spotted with diluted blood. Three major T cell subsets at different concentrations were measured by epigenetic qPCR from a series of diluted EDTA blood samples.

TABLE 7A/B: oligonucleotides for bisulfite sequencing and qPCR and their respective concentrations used in the reaction.

TABLE 7A

TABLE 7B

Examples

Study design-the objective of the study was to determine whether epigenetic qPCR could complement current methods for diagnosing immune cell counts. To test this, the inventors of the present invention identified and evaluated cell type specific unmethylated DNA loci for related immune cells, including CD15⁺Neutrophils, CD19⁺B、CD56⁺NK、CD3⁺、CD4⁺And CD8⁺T cells and tregs. Epigenetic qPCR was developed and normalized using the determined normalization parameters. The key step of this normalization is to provide comparable measurements for all cell-specific qPCR by adjusting qPCR efficiencies between different genomic loci and different bisulfite conversion effects of different regions, and normalizing DNA purification efficiencies to absolute quantification of cells per blood volume. Both relative and absolute quantification were applied to evaluate whole blood from 25 healthy donors, 97 HIV patients, and dry spots from 250 dry blood spots from healthy newborns and 24 newborn cards from newborn patients with primary immunodeficiency. The results of epigenetic qPCR proved comparable to standard FCM and were also tested in applications where the current diagnosis in immune cell counting is insufficient, in particular primary and acquired immunodeficiency. Patient material was provided from hospitals in germany and california, and was not known prior to performing data analysis.

Dried blood spots-three 3.2mm DBS punches of genetically confirmed IPEX, SCID, SCN and XLA patients were obtained from the capillary blood of 250 randomly selected anonymous newborns and one patient diagnosed with IPEX. Sequencing and genetic confirmation of included PID patients was performed according to the practitioner kit of Clinical Sequencing Exploration Research (CSER) consortium. Written parental consent was obtained for all participants. The study was approved by the Medical Association Committee of the institute of review or the institute of ethics committee of the Sacc University of Fleviberg (the Medical Association Chamber of Saxony ethics committee or institutional review Board at University of Freiburg, Germany).

Peripheral whole blood-97 HIV treated with ethical consent from 25 healthy subjects and at the University of leipizin (leiprazig University)⁺In the patient, peripheral blood anticoagulated with EDTA was collected. The samples were subjected to epigenetic qPCR and standard FCM (48). The experimenter had no knowledge of the information.

DNA preparation and bisulfite conversion-for purified cells, genomic DNA was isolated and bisulfite treated using DNeasy tissue and epitec rapid bisulfite conversion kit (Qiagen, hilden, germany) according to the manufacturer's instructions. For EDTA blood, 20. mu.l of substrate was supplemented with 16. mu.l lysis buffer, 3. mu.l proteinase K (30mg/mL) and GAP [ GC ] plasmid (final concentration of 20,000 copies/. mu.l) and lysed at 56 ℃ for 10 min. For conversion, an epitec rapid bisulfite conversion kit was used. 3X 3.2mm DBS punches were added to 68.75. mu.l lysis buffer, 10.75. mu.l proteinase K (30mg/mL), 20,000 copies/. mu.l GAP [ GC ] plasmid (final concentration) and lysed for 60 min at 56 ℃. Mu.l ammonium bisulfite (68% -72%, pH 4.8-5.3, Chemos AG, Munich, Germany) and 60. mu.l tetrahydrofurfuryl alcohol (Sigma-Aldrich) were added and the conversion was carried out at 80 ℃ for 45 min. For purification, the My Silane Genomic DNA kit (My Silane Genomic DNA kit) was used (Invitrogen, Calsbaud, Calif.) according to the manufacturer's instructions. The conversion of bisulfite has been previously analyzed and values above 98% are provided in the manufacturer's manual (49). Conversion efficiency was routinely checked by bisulfite sequencing, showing conversion rates above 98%. As a process control, the genome calibrator included a conversion control in each individual qPCR. BioPerl was used for in silico bisulfite conversion 50) of sequences.

Epigenetic qPCR-Using RThe oche LightCycler 480 Probes Master was thermocycled in 5. mu.l (DBS) or 10. mu.l (EDTA blood) as follows: 1X 95 ℃ for 10 or 35min, then 50X 95 ℃ for 15sec and 61 ℃ for 1 min. To calculate the number of cells from the autosomal gene, the ratio of the 2:1 allele to the cells was assumed. For RD_ls[％]The TpG copy is divided by the TpG + CpG-copy. For RD_U[％]The quotient of TpG copies and GAPDH copies (of the respective immune cell types) was calculated. For DD_U[％]By EF correction of RD_UTo compensate for performance differences between different qPCR. For determining specific EF, the inventors of the present invention used a plasmid-based calibrator containing all genomic target regions for qPCR, including GAPDH (universal denominator) and artificial GAP GC]And (4) a zone. The calibrator performed bisulfite conversion followed by qPCR. EF was calculated by dividing the measured TpG copies by the parallel measured GAPDH copies. EF was derived from approximately 25 experiments. 95% CI is 0.90-1.19(CD3G/D), 0.47-0.63(CD4), 0.75-1.00(CD8B), 0.58-0.77(LRP5), 0.89-1.18(MVD), and 0.38-0.48(LCN 2). For absolute quantification, the conversion of all CpG dinucleotides to GpC (GAP [ GC ] was designed]) The artificial GAPDH sequence of (a) and its corresponding epigenetic qPCR, without cross-reactivity to endogenous GAPDH. GAP [ GC ]]Has an EF of 0.87, wherein 95% CI is from 0.75 to 1.00.

Combination TREC/KREC neonatal screening assay-TREC/KREC screening was applied as previously described (51). Briefly, one 3.2-mm punched DNA from raw DBS was extracted in a 96-well format and quantitative triple real-time qPCR for TREC, KREC, and β -Actin (ACTB) was performed using a ViiA7 real-time PCR system (Applied Biosystems, foster city, CA, usa). TREC and KREC copy numbers were determined per 3.2-mm punch. ACTB was used to verify the appropriate amount of DNA per DBS, but not to normalize TREC/KREC copies.

Plasmid-sequences corresponding to methylated or unmethylated bisulfite-converted genomic regions were designed and inserted via computer simulation into plasmid pUC57(Genscript inc., hong kong, china) and used for assay establishment and as qPCR quantification standard. The standard plasmid contains all assay target sequences equimolar. Plasmids were quantified spectrophotometrically, linearized by ScaI, and serially diluted in 10 ng/. mu.L of lambda-phage DNA (New England Biolabs) to obtain 31250, 6250, 1250, 250, 50 or 30 copies in the final reaction. The calibration plasmid contains all assay target sequences in a genome-unconverted, unmethylated form, equimolar to each other. The plasmid carrying unconverted GAPDH (GAP [ GC ]) with inverted CpG dinucleotides was artificially spiked.

Oligonucleotides-oligonucleotides are described in table 7 (Metabion AG, munich, germany).

Flow cytometry-for leukocyte purification, peripheral blood from healthy adult donors was fractionated by FCM into CD15⁺Cell, CD14⁺Cell, CD56⁺NK cells, CD19⁺B cell, CD4⁺T cells and CD8⁺T cells, wherein, as previously described (13), the cells are pure>97% survival rate>99 percent. For the analysis of cell quantification, absolute CD45 was determined by a MACSQuant cell counter (Milteny Biotec, Belrgygrasradbach, Germany)⁺And (4) counting white blood cells. Calculate CD15 as previously described (13,48)⁺Neutrophils, CD19⁺B cell, CD56⁺NK cells, CD3⁺T cell, CD4⁺T cells and CD8⁺T cells and FOXP3⁺Frequency and absolute count of tregs.

Statistical analysis-the CP (cross-over point) of triplicate measurements was calculated by second derivative maximum using LC480 software (Roche, mannheim, germany) to generate copy number (plasmid units) by insertional amplification (f) from a calibration curve generated with serial dilutions of plasmid-based standards. The sample size for the method comparisons was selected to be 100 to provide 95% CI, with limits on +/-0.34X base standard deviation agreement. The estimation of the reference range requires a healthy population of at least 120 individuals for non-parametric estimation of 95% CI. The number of healthy cases increases until the available samples are depleted to accommodate the estimates of multidimensional and extreme quantiles. Multivariate normality was checked using the Henze-Zinkler test. A method comparison between flow cytometry and qPCR-based measurement techniques is as follows: bivariate data from both methods are illustrated in scatter plots. a) Linear regression is performed on slopes other than 1 and b) on intercepts other than 0. The Bland-Altman plots were examined and analyzed for bias and exact statistics (29). Acceptable accuracy is considered as the average percentage of deviation. The limit of quantitation of the qPCR assay defined by the inter-assay CV (0.2) was used as an accurate standard, and the acceptable limit of consistency was 0.4. The Wilcoxon-Rank-Sum test was used for median differences. The estimated deviation, accuracy statistics and the respective 95% CI are reported. For correlation, pearson product-moment correlation is used. All p values are two-sided. Statistical software R3.3.0 is used.

Cell type specific bisulfite conversion-methylation dependent conversion of CpG dinucleotides was analyzed by bisulfite sequencing (24) with the aim of identifying markers for immune cell populations from human peripheral blood. Candidate loci are selected from the literature or found using Illumina-based 450k array assays. The data of the inventors of the present invention show that for CD56⁺NK cells, CD19⁺B cells and neutrophils (target cell type) did not show significant methylation at each CpG position, whereas in control cell types the same CpG was methylated (table 3). Based on these findings, Amplicons (AMPs) were designed for more dense CpG methylation analysis in the identified regions. As CD4⁺Possible candidate markers for T cells, the inventors of the present invention designed three AMPs for bisulfite sequence analysis, covering the regulatory elements (21,22) in the 5' region of the first intron (AMP 1255, 2000, and 2001) in the CD4 gene. In bisulfite conversion and expansion only in target cells (i.e., CD4)⁺T lymphocytes), unmethylated CpG sites were detected as TpG residues. In control cell types (including CD 56)⁺NK cells, CD8⁺T lymphocytes, CD14⁺Monocyte, CD19⁺B lymphocytes and CD15⁺Granulocytes), the same CpG is inert to bisulfite conversion (fig. 1). Next, the inventors of the present invention investigated the CD8B gene as CD8 by designing an amplicon that targets the regulatory element within its third intron (AMP2007)⁺Potential epigenetic markers of T cells (21, 22). Here, only at CD8⁺Bisulphite mediated CpG conversion was observed in T cells, whereas thoseCpG is inert to conversion in control cells. The corresponding epigenetic markers of B cells, NK cells and neutrophils were identified in the genes encoding low density lipoprotein receptor-related protein 5(LRP5, AMP2249), mevalonate decarboxylase (MVD, AMP2674) and lipocalin 2(LCN2, AMP1730), respectively. Each AMP is uniquely unmethylated in the target cell type and completely methylated in the corresponding control leukocyte population (fig. 1). Intergenic CD3G and CD3D regions (AMP 1405, 1406 and 1408) (constituting CD3⁺Markers for T cells), as well as the methylation profile of GAPDH (AMP1570) have been previously disclosed (15).

Locus specificity versus qPCR measurement-in order to target the different methylated CpG positions described above, a discriminating qPCR assay system was designed. These were characterized on synthetic template DNA cloned into a plasmid. The template corresponds to bisulfite modified genomic DNA, i.e. all cytosines (C) are replaced with thymidine (T). For TpG templates (mimicking unmethylated CpG), equimolar stoichiometries of universal plasmids carrying targets for all assays were designed. Universal CpG plasmids (mimicking methylated CpG) were generated accordingly. For all qPCR, exclusive amplification of the desired DNA sequence was demonstrated without cross-reactivity with mutually exclusive templates (table 1). Assay specificity was tested on immune cell populations purified as described in the materials and methods section. For the target cells, high copy numbers were observed in their respective TpG-specific systems, and low copy numbers were measured in the corresponding CpG systems. In contrast, for control cells, low and high copies were found in the TpG and CpG systems, respectively. The original copy number of the target gene is determined by correlating the qPCR signal from the respective amplifications (f') with the amplifications of serially diluted standard plasmids (f), each having a defined concentration of unmethylated versions by computer analog conversion. Relative determination of locus-specific unmethylated DNA (RD)_ls) Between 89.9% and 100% in the target cell types and between 0 and 3% in the controls (Table 1). For CD4⁺Exceptions were observed with T cells, showing 8.9% RD at the CD8B locus_lsAnd vice versa (i.e., at CD 8)⁺T cell 9.6% CD4 RD_ls) This may be due to mutual and residual cell contamination.

The universal and defined quantitative-amplification efficiency and estimated copy number were different for each locus specific qPCR system (25). Thus, the constant unmethylated regulatory region (26) of the GAPDH gene was used as a universal denominator to determine each cell type locus relative to all nucleated cells. Application of the System to purified CD3⁺T cell, CD4⁺T cells and CD8⁺T cells, neutrophils, CD14⁺Monocyte, CD56⁺NK cells and CD19⁺B cells. With unmethylated cell type-specific locus and Universal unmethylated GAPDH as denominator (RD_U) When at a particular epigenetic locus (RD)_ls) When using the methylated and unmethylated amplification data, the quantification was different (Table 1).

Since the double-stranded, GC-rich plasmid converted via computer simulation does not fully represent true bisulfite converted single-stranded GC-depleted DNA (27,28), a "calibration plasmid" is employed, in which one copy containing all detection targets is in their unconverted genomic (i.e., unmethylated) state. The calibrators were bisulfite converted parallel to the sample. When the copy number from this calibrator was obtained by standard plasmid insertion, the systematic amplification difference between assays was detected and converted to an Efficiency Factor (EF), adjusting for the deviation between the cell type specific assay and GAPDH. Cell type-specific EF was measured in approximately 25 experiments, ranging between 0.53 for CD4 (95% Confidence Interval (CI) ═ 0.42,0.61) and 1.17 for CD3D/G (95% CI ═ 0.95,1.31) (see "epigenetic qPCR" in materials and methods section). The calculated EF provides the contribution of unmethylated DNA (DD) for each assay_U) The universally determined assay of (1). Using this approach, the inventors of the present invention applied epigenetic qPCR for universal and defined quantification of immune cells from biological samples. The concept of immune cell quantification used in this work is shown in figure 6.

Methods of FCM and epigenetic qPCR-to allow absolute cellular quantification (i.e., cells/. mu.l) comparable to FCM, the inventors of the present invention introduced a "spiked-in plasmid" containing an artificial GAPDH-derived sequence generated by converting all CpG dinucleotides to GpC (GAP [ GC ]), and the corresponding epigenetic qPCR. For absolute immune cell counts, the plasmid was added to the blood sample at a defined concentration. Bisulfite converted artificial GAP [ GC ] sequences, simulated via computer, were included in the quantitative standards and unconverted sequences were included into the calibration plasmids.

To assess the overall performance of epigenetic cell counts, B cells, NK cells, CD3 were analyzed in blood samples from 25 adult healthy donors compared to FCM⁺T cell, CD4⁺T cell, CD8⁺T cells, FOXP3⁺Markers for tregs and neutrophils. Scatter plots of data from both methods as relative cell counts (FIG. 2A) or absolute cell counts (FIG. 2B), each method yielding greater than 0.95 (p)<0.0001) is used.

To test individual epigenetic markers in a clinically relevant setting, the inventors of the present invention used a marker derived from 97 HIV viruses⁺Blood of subjects and quantification of CD3 by standard FCM and epigenetic qPCR⁺T cell, CD4⁺T cell, CD8⁺T cell counts. For the latter, EDTA blood or DBS is used as substrate. A method comparison was made for all three methods. To compare FCM in liquid blood with epigenetics qPCR, correlation analysis resulted in from 0.96 to 0.98 (p) for relative quantification<0.001) of the sample (fig. 3A). The white blood cell count/microliter of blood determined by the epigenetic qPCR is highly correlated by FCM (pearson r ═ 0.8; p<0.001) (fig. 7). Comparative analysis from DBS and FCM (FIG. 3B) yielded Pearson r (p) from 0.7 to 0.95<0.001). Epigenetic measurements from liquid blood and DBS yield Pearson r (p) of 0.8 to 0.95<0.001) (fig. 3C).

The systematic bias and accuracy of all the methods and markers listed in Table 6 were determined by the Bland-Altman analysis (29) shown in FIGS. 3A-C (right panel). For the cell types tested, the biological reads of FCMs and epigenetic counts from either substrate were well correlated. In the fluid bloodMinor deviations (4.3-, -6.6, 10.3 for CD3, CD4, CD8, respectively) and high precision (all) were detected between FCM and epigenetic qPCR of the solutions<20%) while in DBS and CD4⁺Significant changes were observed between the two other approaches to T cells: (>20%). To investigate the effect of substrate instability in DBS, different storage times and conditions and sample dilutions simulating the unobserved collected DBS were monitored. The data show no signs of degradation under different storage conditions, with variation (CV) of less than 15% for all temperature and time point variations (table 5). CV was less than 30% at 1:9 blood dilution (Table 6). As analyzed previously, genomic DNA is a stable analyte and can be extracted from DBS stored for one year (30-32).

Epigenetic qPCR in neonatal screening samples-epigenetic qPCR was applied in a case/control study consisting of the original neonatal screening card (i.e. DBS) from 24 PID patients and 250 randomly selected newborns, measuring total T cells, B cells and NK cells (figure 4). PID cases included SCID patients with different gene defects and X-linked agammaglobulinemia (XLA) associated with BTK mutations (table 2). The reference range was established using the combined distribution of all leukocytes (GAPDH specific qPCR) and specific immune cell types. The copy number was logarithmically transformed and used to estimate a binary normal distribution whose confidence regions (99% and 99.9% curves) define the reference range for the neonate. When each of the three groups was adjusted to 99% or 99.9% confidence, the Bonferroni correction guaranteed a family error rate below 3% or 0.3%, resulting in a final confidence of 99.7% or 97%, respectively.

For CD3⁺T cells and GAPDH measurements, 13 of the 16 samples from SCID patients were outside the 99.9% confidence region, SCID15 was found to be outside the 99% but within the 99.9% region, and SCID9 and 18 appeared to be suspect. However, SCIDs 15 and 18 were outside the 99.9% confidence regions for NK cell and GAPDH measurements. In addition, SCID18 was found outside the 99.9% confidence region and SCID15 was found outside the 99% region for B cells and GAPDH measurements (fig. 4). Thus, 15 of the 16 SCID patients were unambiguously identified by epigenetic testing based on their neogenetic cardIs abnormal. However, SCID9 exhibited maternal lymphocyte transplantation, showed no quantitative impairment of cell counts, and was classified as normal, as confirmed by FCM and chromosomal analysis. CD4⁺T cell, CD8⁺Epigenetic qPCR of T cells and GAPDH confirmed these findings without CD3 in SCID samples⁺T cell analysis adds diagnostic information. DBS was also analyzed for delayed-type SCID (DO-SCID) associated with sub-allelic JAK3 or ADA mutations. JAK3 deficient delayed patients (DO-SCID14) showed decreased CD3, NK, B and CD4, CD8 values outside the 99.9% confidence region (figure 4). ADA-associated DO-SCID4 is outside the 99.9% confidence region in NK and B cells and outside the 99% region in CD3, while DO-SCID3 is outside the 99.9% confidence region in B cells and in the 99% confidence region in NK but in CD3⁺Normal in T cells. In general, all three DO-SCID samples were identified based on epigenetic analysis. DBS from four of five patients with XLA showed B cell counts outside the 99.9% confidence region. In B cells and NK cells, the sub-allele XLA24 is outside the 99% confidence region. NK cell and T cell counts were at the boundary of the reference range for other XLA samples (XLA 23 and XLA20 in NK, XLA23 in T cells). The XLA phenotype is completely consistent with B cell deficiency. Comparison with TREC/KREC values shows that epigenetics quantitatively detected all but one patient, whereas TREC/KREC failed to detect 2 of five cases with a delayed genetic background or a suballelic genetic background. However, maternal implantation masking was detected via epigenetic counting, whereas TREC analysis was unaffected (table 2). The screening classification was based on three tests of significant cell counts, using 99% confidence regions to achieve a sensitivity of 0.958 and specificity of 0.984. For the 99.9% confidence region, the sensitivity dropped to 0.9167, while the specificity reached 1.

IPEX and Severe Congenital Neutropenia (SCN) are other forms of severe PID, and no neonatal screening is currently available. Given their severe early onset and morbidity, patients would benefit from neonatal diagnosis. In young IPEX patients, peripheral tregs are uniquely increased compared to healthy age-matched donors and disease controls (23).

Neonatal patients with SCN were tested by significant depletion of neutrophils (p ═ 4.4 × 10e-6) using epigenetic qPCR on neutrophils (fig. 5A). The median percentage of neutrophils was 55% in the control card (n-26) and 17% in neutropenic patients (n-6).

By Treg and CD3⁺Epigenetic qPCR of T cells DBS from neonates and 2-year-old IPEX patients were tested separately (fig. 5B). Treg/CD3 in IPEX patients compared to unaffected healthy donors⁺The proportion of T cells increases significantly.

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sequence listing

<110> Aipienis GmbH (Epiontis GmbH)

<120> method for the detection and enumeration of epigenetic immune cells in human blood samples for immunodiagnosis and neonatal screening

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<151> 2018-07-05

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<170> PatentIn version 3.5

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