Validation of a DNA methylation reference panel for the estimation of nucleated cells types in cord blood

Cord blood is widely used as surrogate tissue in epigenome-wide association studies of prenatal conditions. Cell type composition variation across samples can be an important confounder of epigenome-wide association studies in blood that constitute a mixture of cells. We evaluated a newly developed cord blood reference panel to impute cell type composition from DNA methylation levels, including nucleated red blood cells (nRBCs). We estimated cell type composition from 154 unique cord blood samples with available DNA methylation data as well as direct measurements of nucleated cell types. We observed high correlations between the estimated and measured composition for nRBCs (r = 0.92, R² = 0.85), lymphocytes (r = 0.77, R² = 0.58), and granulocytes (r = 0.72, R² = 0.52), and a moderate correlation for monocytes (r = 0.51, R² = 0.25) as well as relatively low root mean square errors from the residuals ranging from 1.4 to 5.4%. These results validate the use of the cord blood reference panel and highlight its utility and limitations for epidemiological studies.     

DNA methylation of cord blood cell types: Applications for mixed cell birth studies

Epigenome-wide association studies of disease widely use DNA methylation measured in blood as a surrogate tissue. Cell proportions can vary between people and confound associations of exposure or outcome. An adequate reference panel for estimating cell proportions from adult whole blood for DNA methylation studies is available, but an analogous cord blood cell reference panel is not yet available. Cord blood has unique cell types and the epigenetic signatures of standard cell types may not be consistent throughout the life course. Using magnetic bead sorting, we isolated cord blood cell types (nucleated red blood cells, granulocytes, monocytes, natural killer cells, B cells, CD4⁺T cells, and CD8⁺T cells) from 17 live births at Johns Hopkins Hospital. We confirmed enrichment of the cell types using fluorescence assisted cell sorting and ran DNA from the separated cell types on the Illumina Infinium HumanMethylation450 BeadChip array. After filtering, the final analysis was on 104 samples at 429,794 probes. We compared cell type specific signatures in cord to each other and methylation at 49.2% of CpG sites on the array differed by cell type (F-test P < 10^?8). Differences between nucleated red blood cells and the remainder of the cell types were most pronounced (36.9% of CpG sites at P < 10^?8) and 99.5% of these sites were hypomethylated relative to the other cell types. We also compared the mean-centered sorted cord profiles to the available adult reference panel and observed high correlation between the overlapping cell types for granulocytes and monocytes (both r=0.74), and poor correlation for CD8⁺T cells and NK cells (both r=0.08). We further provide an algorithm for estimating cell proportions in cord blood using the newly developed cord reference panel, which estimates biologically plausible cell proportions in whole cord blood samples.     

Quantitative reconstruction of leukocyte subsets using DNA methylation

Background

Cell lineage-specific DNA methylation patterns distinguish normal human leukocyte subsets and can be used to detect and quantify these subsets in peripheral blood. We have developed an approach that uses DNA methylation to simultaneously quantify multiple leukocyte subsets, enabling investigation of immune modulations in virtually any blood sample including archived samples previously precluded from such analysis. Here we assess the performance characteristics and validity of this approach.

Results

Using Illumina Infinium HumanMethylation27 and VeraCode GoldenGate Methylation Assay microarrays, we measure DNA methylation in leukocyte subsets purified from human whole blood and identify cell lineage-specific DNA methylation signatures that distinguish human T cells, B cells, NK cells, monocytes, eosinophils, basophils and neutrophils. We employ a bioinformatics-based approach to quantify these cell types in complex mixtures, including whole blood, using DNA methylation at as few as 20 CpG loci. A reconstruction experiment confirms that the approach could accurately measure the composition of mixtures of human blood leukocyte subsets. Applying the DNA methylation-based approach to quantify the cellular components of human whole blood, we verify its accuracy by direct comparison to gold standard immune quantification methods that utilize physical, optical and proteomic characteristics of the cells. We also demonstrate that the approach is not affected by storage of blood samples, even under conditions prohibiting the use of gold standard methods.

Conclusions

Cell mixture distributions within peripheral blood can be assessed accurately and reliably using DNA methylation. Thus, precise immune cell differential estimates can be reconstructed using only DNA rather than whole cells.     

An epigenome-wide association meta-analysis of prenatal maternal stress in neonates: A model approach for replication

Prenatal maternal stress exposure has been associated with neonatal differential DNA methylation. However, the available evidence in humans is largely based on candidate gene methylation studies, where only a few CpG sites were evaluated. The aim of this study was to examine the association between prenatal exposure to maternal stress and offspring genome-wide cord blood methylation using different methods. First, we conducted a meta-analysis and follow-up pathway analyses. Second, we used novel region discovery methods [i.e., differentially methylated regions (DMRs) analyses]. To this end, we used data from two independent population-based studies, the Generation R Study (n = 912) and the Avon Longitudinal Study of Parents and Children (ALSPAC, n = 828), to (i) measure genome-wide DNA methylation in cord blood and (ii) extract a prenatal maternal stress composite. The meta-analysis (n_total = 1,740) revealed no epigenome-wide (meta P <1.00e-07) associations of prenatal maternal stress exposure with neonatal differential DNA methylation. Follow-up analyses of the top hits derived from our epigenome-wide meta-analysis (meta P <1.00e-04) indicated an over-representation of the methyltransferase activity pathway. We identified no Bonferroni-corrected (P <1.00e-06) DMRs associated with prenatal maternal stress exposure. Combining data from two independent population-based samples in an epigenome-wide meta-analysis, the current study indicates that there are no large effects of prenatal maternal stress exposure on neonatal DNA methylation. Such replication efforts are essential in the search for robust associations, whether derived from candidate gene methylation or epigenome-wide studies.     

Epigenome-wide profiling of DNA methylation in paired samples of adipose tissue and blood

Many epigenetic association studies have attempted to identify DNA methylation markers in blood that are able to mirror those in target tissues. Although some have suggested potential utility of surrogate epigenetic markers in blood, few studies have collected data to directly compare DNA methylation across tissues from the same individuals. Here, epigenomic data were collected from adipose tissue and blood in 143 subjects using Illumina HumanMethylation450 BeadChip array. The top axis of epigenome-wide variation differentiates adipose tissue from blood, which is confirmed internally using cross-validation and externally with independent data from the two tissues. We identified 1,285 discordant genes and 1,961 concordant genes between blood and adipose tissue. RNA expression data of the two classes of genes show consistent patterns with those observed in DNA methylation. The discordant genes are enriched in biological functions related to immune response, leukocyte activation or differentiation, and blood coagulation. We distinguish the CpG-specific correlation from the within-subject correlation and emphasize that the magnitude of within-subject correlation does not guarantee the utility of surrogate epigenetic markers. The study reinforces the critical role of DNA methylation in regulating gene expression and cellular phenotypes across tissues, and highlights the caveats of using methylation markers in blood to mirror the corresponding profile in the target tissue.     

DNA methylation arrays as surrogate measures of cell mixture distribution

ABSTRACT: BACKGROUND: There has been a long-standing need in biomedical research for a method that quantifies the normally mixed composition of leukocytes beyond what is possible by simple histological or flow cytometric assessments. The latter is restricted by the labile nature of protein epitopes, requirements for cell processing, and timely cell analysis. In a diverse array of diseases and following numerous immune-toxic exposures, leukocyte composition will critically inform the underlying immuno-biology to most chronic medical conditions. Emerging research demonstrates that DNA methylation is responsible for cellular differentiation, and when measured in whole peripheral blood, serves to distinguish cancer cases from controls. RESULTS: Here we present a method, similar to regression calibration, for inferring changes in the distribution of white blood cells between different subpopulations (e.g. cases and controls) using DNA methylation signatures, in combination with a previously obtained external validation set consisting of signatures from purified leukocyte samples. We validate the fundamental idea in a cell mixture reconstruction experiment, then demonstrate our method on DNA methylation data sets from several studies, including data from a Head and Neck Squamous Cell Carcinoma (HNSCC) study and an ovarian cancer study. Our method produces results consistent with prior biological findings, thereby validating the approach. CONCLUSIONS: Our method, in combination with an appropriate external validation set, promises new opportunities for large-scale immunological studies of both disease states and noxious exposures.     

Accuracy of routine flow-cytometric bitmap selection for three leukocyte populations

A double-blind study was performed with peripheral blood of 41 human subjects to check the accuracy of determination of lymphocyte, monocyte, and granulocyte windows with which every flow cytometric analysis of leukocyte markers starts. White blood cell suspensions were prepared according to the whole blood method and analyzed on an EPICS-C flow cytometer using the two-parameter 90 degrees light scatter vs. forward angle light scatter (granularity vs. cell size) data distribution. Windows (bitmaps) for lymphocytes, monocytes, and granulocytes were drawn and numbers of cells determined in each. The proportions of lymphocytes, monocytes, and granulocytes were calculated in relation to total cell number, counted and in relation to the sum of cells in three bitmaps, and then compared with proportions determined by microscopic whole blood cell (WBC) differential and a WBC differential determined in an automated hematology analyzer. Average proportions of lymphocytes obtained by the flow cytometer were significantly lower than those obtained by either microscopic or automated differential, suggesting that some of the relevant cells were not included in the bitmaps. Granulocyte proportion related to total cell number was lower and that related to bitmap cell number higher than that obtained by microscopic and automatic differentials, suggesting that nongranulocytic cells were included in the granulocyte bitmaps. Proportions of lymphocytes and granulocytes obtained by the flow cytometer correlated well with those obtained by both microscopic and automatic differential. In contrast, the proportions of monocytes showed a poor correlation, which is probably due to their low number and delicate position in the distribution, and which makes them difficult to delineate.     

Epigenetics,heritability and longitudinal analysis

Background

Longitudinal data and repeated measurements in epigenome-wide association studies (EWAS) provide a rich resource for understanding epigenetics. We summarize 7 analytical approaches to the GAW20 data sets that addressed challenges and potential applications of phenotypic and epigenetic data. All contributions used the GAW20 real data set and employed either linear mixed effect (LME) models or marginal models through generalized estimating equations (GEE). These contributions were subdivided into 3 categories: (a) quality control (QC) methods for DNA methylation data; (b) heritability estimates pretreatment and posttreatment with fenofibrate; and (c) impact of drug response pretreatment and posttreatment with fenofibrate on DNA methylation and blood lipids.

Results

Two contributions addressed QC and identified large statistical differences with pretreatment and posttreatment DNA methylation, possibly a result of batch effects. Two contributions compared epigenome-wide heritability estimates pretreatment and posttreatment, with one employing a Bayesian LME and the other using a variance-component LME. Density curves comparing these studies indicated these heritability estimates were similar. Another contribution used a variance-component LME to depict the proportion of heritability resulting from a genetic and shared environment. By including environmental exposures as random effects, the authors found heritability estimates became more stable but not significantly different. Two contributions investigated treatment response. One estimated drug-associated methylation effects on triglyceride levels as the response, and identified 11 significant cytosine-phosphate-guanine (CpG) sites with or without adjusting for high-density lipoprotein. The second contribution performed weighted gene coexpression network analysis and identified 6 significant modules of at least 30 CpG sites, including 3 modules with topological differences pretreatment and posttreatment.

Conclusions

Four conclusions from this GAW20 working group are: (a) QC measures are an important consideration for EWAS studies that are investigating multiple time points or repeated measurements; (b) application of heritability estimates between time points for individual CpG sites is a useful QC measure for DNA methylation studies; (c) drug intervention demonstrated strong epigenome-wide DNA methylation patterns across the 2 time points; and (d) new statistical methods are required to account for the environmental contributions of DNA methylation across time. These contributions demonstrate numerous opportunities exist for the analysis of longitudinal data in future epigenetic studies.

     

外周全血中与老化相关的DNA甲基化标记的来源

机体老化与癌症、神经退行性疾病等许多复杂疾病相关。目前,研究者已在外周全血中识别了大量的与老化相关的DNA甲基化标记,这些标记可能反映外周血白细胞在机体老化过程中发生的变化,也可能反映外周血中与年龄相关的细胞构成比例的变化。文章利用3组正常个体外周全血DNA甲基化谱,采用Spearman秩相关分析识别了与老化相关的CpG甲基化位点(age-related DNA methylation CpG sites, arCpGs)并评价了其可重复性;利用去卷积算法估计了各外周血样本中髓性和淋巴性细胞的比例并分析了其与年龄的相关性;比较了在外周全血、CD4+T细胞和CD14+单核细胞中识别的arCpGs的一致性。结果显示,在独立外周全血数据中识别的arCpGs具有显著的可重复性(超几何检验,P=1.65×10^-11)。外周血髓性和淋巴性细胞的比例分别与年龄显著正、负相关(Spearman秩相关检验,P<0.05,r≤0.22),它们间DNA甲基化水平差异较大的CpG位点倾向于在外周全血中被识别为arCpGs。在CD4+T细胞中识别的arCpGs与在外周全血中识别的arCpGs显著交叠(超几何检验,P=6.14×10^-12),且99.1%的交叠位点在CD4+T细胞及外周全血中的DNA甲基化水平与年龄的正、负相关性一致。尽管在CD14+单核细胞中识别的arCpGs与在外周全血中识别的arCpGs并不显著交叠,但是在交叠的51个arCpGs中,有90.1%的位点在CD14+单核细胞、外周全血以及CD4+T细胞中的DNA甲基化水平与年龄的正、负相关性一致,提示它们可能主要反映细胞间共同的改变。在外周全血中识别的arCpGs主要反映某些白细胞共同或特异的DNA甲基化改变,但是也有一部分反映外周血细胞比例构成的变化。     

Familial resemblances in blood leukocyte DNA methylation levels

Epigenetic factors such as DNA methylation are DNA alterations affecting gene expression that can convey environmental information through generations. Only a few studies have demonstrated epigenetic inheritance in humans. Our objective is to quantify genetic and common environmental determinants of familial resemblances in DNA methylation levels, using a family based sample. DNA methylation was measured in 48 French Canadians from 16 families as part of the GENERATION Study. We used the Illumina HumanMethylation450 BeadChip array to measure DNA methylation levels in blood leukocytes on 485,577 CpG sites. Heritability was assessed using the variance components method implemented in the QTDT software, which partitions the variance into polygenic (G), common environmental (C), and non-shared environmental (E) effects. We computed maximal heritability, genetic heritability, and common environmental effect for all probes (12.7%, 8.2%, and 4.5%, respectively) and for statistically significant probes (81.8%, 26.9%, and 54.9%, respectively). Higher maximal heritability was observed in the Major Histocompatibility Complex region on chromosome 6. In conclusion, familial resemblances in DNA methylation levels are mainly attributable to genetic factors when considering the average across the genome, but common environmental effect plays an important role when considering statistically significant probes. Further epigenome-wide studies on larger samples combined with genome-wide genotyping studies are needed to better understand the underlying mechanisms of DNA methylation heritability.     

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

Quantitating the copy number of demethylated CpG promoter sites of the CD3Z gene can be used to estimate the numbers and proportions of T cells in human blood and tissue. Quantitative methylation specific PCR (qPCR) is useful for studying T cells but requires extensive calibration and is imprecise at low copy numbers. Here we compared the performance of a new digital PCR platform (droplet digital PCR or ddPCR) to qPCR using bisulfite converted DNA from 157 blood specimens obtained from ambulatory care controls and patients with primary glioma. We compared both ddPCR and qPCR with conventional flow cytometry (FACS) evaluation of CD3 positive T cells. Repeated measures on the same blood sample revealed ddPCR to be less variable than qPCR. Both qPCR and ddPCR correlated significantly with FACS evaluation of peripheral blood CD3 counts and CD3/total leukocyte values. However, statistical measures of agreement showed that linear concordance was stronger for ddPCR than for qPCR and the absolute values were closer to FACS for ddPCR. Both qPCR and ddPCR could distinguish clinically significant differences in T cell proportions and performed similarly to FACS. Given the higher precision, greater accuracy, and technical simplicity of ddPCR, this approach appears to be a superior DNA methylation based method than conventional qPCR for the assessment of T cells.     

Leukocyte DNA methylation signature differentiates pancreatic cancer patients from healthy controls

Pancreatic adenocarcinoma (PaC) is one of most difficult tumors to treat. Much of this is attributed to the late diagnosis. To identify biomarkers for early detection, we examined DNA methylation differences in leukocyte DNA between PaC cases and controls in a two-phase study. In phase I, we measured methylation levels at 1,505 CpG sites in treatment-naïve leukocyte DNA from 132 never-smoker PaC patients and 60 never-smoker healthy controls. We found significant differences in 110 CpG sites (false discovery rate <0.05). In phase II, we tested and validated 88 of 96 phase I selected CpG sites in 240 PaC cases and 240 matched controls (p≤0.05). Using penalized logistic regression, we built a prediction model consisting of five CpG sites (IL10_P348, LCN2_P86, ZAP70_P220, AIM2_P624, TAL1_P817) that discriminated PaC patients from controls (C-statistic = 0.85 in phase I; 0.76 in phase II). Interestingly, one CpG site (LCN2_P86) alone could discriminate resectable patients from controls (C-statistic  = 0.78 in phase I; 0.74 in phase II). We also performed methylation quantitative trait loci (methQTL) analysis and identified three CpG sites (AGXT_P180_F, ALOX12_E85_R, JAK3_P1075_R) where the methylation levels were significantly associated with single nucleotide polymorphisms (SNPs) (false discovery rate <0.05). Our results demonstrate that epigenetic variation in easily obtainable leukocyte DNA, manifested by reproducible methylation differences, may be used to detect PaC patients. The methylation differences at certain CpG sites are partially attributable to genetic variation. This study strongly supports future epigenome-wide association study using leukocyte DNA for biomarker discovery in human diseases.     

Epigenetics,heritability and longitudinal analysis

Background

Longitudinal data and repeated measurements in epigenome-wide association studies (EWAS) provide a rich resource for understanding epigenetics. We summarize 7 analytical approaches to the GAW20 data sets that addressed challenges and potential applications of phenotypic and epigenetic data. All contributions used the GAW20 real data set and employed either linear mixed effect (LME) models or marginal models through generalized estimating equations (GEE). These contributions were subdivided into 3 categories: (a) quality control (QC) methods for DNA methylation data; (b) heritability estimates pretreatment and posttreatment with fenofibrate; and (c) impact of drug response pretreatment and posttreatment with fenofibrate on DNA methylation and blood lipids.

Results

Two contributions addressed QC and identified large statistical differences with pretreatment and posttreatment DNA methylation, possibly a result of batch effects. Two contributions compared epigenome-wide heritability estimates pretreatment and posttreatment, with one employing a Bayesian LME and the other using a variance-component LME. Density curves comparing these studies indicated these heritability estimates were similar. Another contribution used a variance-component LME to depict the proportion of heritability resulting from a genetic and shared environment. By including environmental exposures as random effects, the authors found heritability estimates became more stable but not significantly different. Two contributions investigated treatment response. One estimated drug-associated methylation effects on triglyceride levels as the response, and identified 11 significant cytosine-phosphate-guanine (CpG) sites with or without adjusting for high-density lipoprotein. The second contribution performed weighted gene coexpression network analysis and identified 6 significant modules of at least 30 CpG sites, including 3 modules with topological differences pretreatment and posttreatment.

Conclusions

Four conclusions from this GAW20 working group are: (a) QC measures are an important consideration for EWAS studies that are investigating multiple time points or repeated measurements; (b) application of heritability estimates between time points for individual CpG sites is a useful QC measure for DNA methylation studies; (c) drug intervention demonstrated strong epigenome-wide DNA methylation patterns across the 2 time points; and (d) new statistical methods are required to account for the environmental contributions of DNA methylation across time. These contributions demonstrate numerous opportunities exist for the analysis of longitudinal data in future epigenetic studies.

     

Assessment of RainDrop BS-seq as a method for large-scale,targeted bisulfite sequencing

We present a systematic assessment of RainDrop BS-seq, a novel method for large-scale, targeted bisulfite sequencing using microdroplet-based PCR amplification coupled with next-generation sequencing. We compared DNA methylation levels at 498 target loci (1001 PCR amplicons) in human whole blood, osteosarcoma cells and an archived tumor tissue sample. We assessed the ability of RainDrop BS-seq to accurately measure DNA methylation over a range of DNA quantities (from 10 to 1500 ng), both with and without whole-genome amplification (WGA) following bisulfite conversion. DNA methylation profiles generated using at least 100 ng correlated well (median R = 0.92) with those generated on Illumina Infinium HumanMethylation450 BeadChips, currently the platform of choice for epigenome-wide association studies (EWAS). WGA allowed for testing of samples with a starting DNA amount of 10 and 50 ng, although a reduced correlation was observed (median R = 0.79). We conclude that RainDrop BS-seq is suitable for measuring DNA methylation levels using nanogram quantities of DNA, and can be used to study candidate epigenetic biomarker loci in an accurate and high-throughput manner, paving the way for its application to routine clinical diagnostics.     

Assessment of RainDrop BS-seq as a method for large-scale,targeted bisulfite sequencing

We present a systematic assessment of RainDrop BS-seq, a novel method for large-scale, targeted bisulfite sequencing using microdroplet-based PCR amplification coupled with next-generation sequencing. We compared DNA methylation levels at 498 target loci (1001 PCR amplicons) in human whole blood, osteosarcoma cells and an archived tumor tissue sample. We assessed the ability of RainDrop BS-seq to accurately measure DNA methylation over a range of DNA quantities (from 10 to 1500 ng), both with and without whole-genome amplification (WGA) following bisulfite conversion. DNA methylation profiles generated using at least 100 ng correlated well (median R = 0.92) with those generated on Illumina Infinium HumanMethylation450 BeadChips, currently the platform of choice for epigenome-wide association studies (EWAS). WGA allowed for testing of samples with a starting DNA amount of 10 and 50 ng, although a reduced correlation was observed (median R = 0.79). We conclude that RainDrop BS-seq is suitable for measuring DNA methylation levels using nanogram quantities of DNA, and can be used to study candidate epigenetic biomarker loci in an accurate and high-throughput manner, paving the way for its application to routine clinical diagnostics.     

Specific Role of Each Human Leukocyte Type in Viral Infections I. Monocyte as Host Cell for Vesicular Stomatitis Virus Replication In Vitro

Each major leukocyte type of the peripheral blood of healthy donors was studied in vitro for its ability to support vesicular stomatitis virus (VSV) replication. Purified cultures of each white blood cell type were prepared by the selective adsorption and elution of cells from silicone-treated glass beads. It was found that monocytes and macrophages (derived from the rapid transformation of monocytes in vitro) were the principal host cells for VSV replication. Interferon added to mixed leukocyte cultures, prior to virus inoculation, reduced virus yields and prevented destruction of macrophages. Cultures of small lymphocytes, containing no detectable monocytes or macrophages, produced amounts of virus equivalent to 1% of that produced in leukocyte cultures which contained 7% monocytes. Small lymphocytes did not undergo demonstrable cytopathic alterations in virus-infected cultures. VSV neither replicated nor produced cytopathic effects in polymorphonuclear leukocytes.     

Adjustment of Cell-Type Composition Minimizes Systematic Bias in Blood DNA Methylation Profiles Derived by DNA Collection Protocols

Differences in DNA collection protocols may be a potential confounder in epigenome-wide association studies (EWAS) using a large number of blood specimens from multiple biobanks and/or cohorts. Here we show that pre-analytical procedures involved in DNA collection can induce systematic bias in the DNA methylation profiles of blood cells that can be adjusted by cell-type composition variables. In Experiment 1, whole blood from 16 volunteers was collected to examine the effect of a 24 h storage period at 4°C on DNA methylation profiles as measured using the Infinium HumanMethylation450 BeadChip array. Our statistical analysis showed that the P-value distribution of more than 450,000 CpG sites was similar to the theoretical distribution (in quantile-quantile plot, λ = 1.03) when comparing two control replicates, which was remarkably deviated from the theoretical distribution (λ = 1.50) when comparing control and storage conditions. We then considered cell-type composition as a possible cause of the observed bias in DNA methylation profiles and found that the bias associated with the cold storage condition was largely decreased (λ_adjusted = 1.14) by taking into account a cell-type composition variable. As such, we compared four respective sample collection protocols used in large-scale Japanese biobanks or cohorts as well as two control replicates. Systematic biases in DNA methylation profiles were observed between control and three of four protocols without adjustment of cell-type composition (λ = 1.12–1.45) and no remarkable biases were seen after adjusting for cell-type composition in all four protocols (λ_adjusted = 1.00–1.17). These results revealed important implications for comparing DNA methylation profiles between blood specimens from different sources and may lead to discovery of disease-associated DNA methylation markers and the development of DNA methylation profile-based predictive risk models.