Cell Type-specific DNA Methylation Research Articles

DNA Methylation-Based Assessment of Cell Composition in Human Pancreas and Islets.

Assessment of pancreas cell type composition is crucial to the understanding of the genesis of diabetes. Current approaches use immunodetection of protein markers, for example insulin as a marker of beta-cells. A major limitation of these methods is that protein content varies in physiological and pathological conditions, complicating the extrapolation to actual cell number. Here we demonstrate the use of cell type-specific DNA methylation markers for determining the fraction of specific cell types in human islet and pancreas specimens. We identified genomic loci that are uniquely demethylated in specific pancreatic cell types and applied targeted PCR to assess the methylation status of these loci in tissue samples, enabling inference of cell type composition. In islet preparations, normalization of insulin secretion to beta-cell DNA revealed similar beta-cell function in pre-T1D, T1D and T2D , which was significantly lower than in non-diabetic donors. In histological pancreas specimens from recent-onset T1D this assay showed beta-cell fraction within the normal range, suggesting a significant contribution of beta-cell dysfunction. In T2D pancreata we observed increased alpha-cell fraction and normal beta-cell fraction. Methylation-based analysis provides an accurate molecular alternative to immune detection of cell types in the human pancreas, with utility in the interpretation of insulin secretion assays and the assessment of pancreas cell composition in health and disease.

Exercise-Induced Cell-Free DNA Correlates with Energy Expenditure in Multiple Exercise Protocols.

Exercise-induced cell-free DNA (ei-cfDNA) has been studied in response to various types of exercise. Its correlation with exercise intensity and duration has been observed consistently. However, comprehensive measurements and exploration of the tissue of origin are lacking. The aim of this study is to establish precise connections between exercise variables and the distribution of tissue of origin, aiming to provide further evidence supporting its use as a biomarker for exercise. Twelve self-identified active adults (six men and six women) performed a crossover study starting with either endurance testing or resistance testing under different intensities and protocols. We obtained blood before and after each exercise session and measured the levels of cfDNA and determined its tissue of origin utilizing cell type-specific DNA methylation patterns in plasma. We found that when duration and intensity are fixed, ei-cfDNA fold change correlates with energy expenditure ( P = 0.001) in endurance testing and years trained ( P = 0.001) in resistance testing. Most of the ei-cfDNA comes from increases in white blood cells (~95%) where neutrophils make up the majority (~74%) and the distribution is different between exercise modalities and protocols. This study highlights the potential of exercise-induced cfDNA as a biomarker for exercise, showing correlations with energy expenditure and a consistent pattern of tissue origin. Additional research is needed to investigate potential sex differences in the response of cfDNA to exercise, further exploring its clinical implications.

Targeted DNA Methylation Analysis Facilitates Leukocyte Counts in Dried Blood Samples.

Cell-type specific DNA methylation (DNAm) can be employed to determine the numbers of leukocyte subsets in blood. In contrast to conventional methods for leukocyte counts, which are based on cellular morphology or surface marker protein expression, the cellular deconvolution based on DNAm levels is applicable for frozen or dried blood. Here, we further enhanced targeted DNAm assays for leukocyte counts in clinical application. DNAm profiles of 40 different studies were compiled to identify CG dinucleotides (CpGs) with cell-type specific DNAm using a computational framework, CimpleG. DNAm levels at these CpGs were then measured with digital droplet PCR in venous blood from 160 healthy donors and 150 patients with various hematological disorders. Deconvolution was further validated with venous blood (n = 75) and capillary blood (n = 31) that was dried on Whatman paper or on Mitra microsampling devices. In venous blood, automated cell counting or flow cytometry correlated well with epigenetic estimates of relative leukocyte counts for granulocytes (r = 0.95), lymphocytes (r = 0.97), monocytes (r = 0.82), CD4 T cells (r = 0.84), CD8 T cells (r = 0.94), B cells (r = 0.96), and NK cells (r = 0.72). Similar correlations and precisions were achieved for dried blood samples. Spike-in with a reference plasmid enabled accurate epigenetic estimation of absolute leukocyte counts from dried blood samples, correlating with conventional venous (r = 0.86) and capillary (r = 0.80) blood measurements. The advanced selection of cell-type specific CpGs and utilization of digital droplet PCR analysis provided accurate epigenetic blood counts. Analysis of dried blood facilitates self-sampling with a finger prick, thereby enabling easier accessibility to testing.

Dynamic DNA methylation reveals novel cis-regulatory elements in mouse hematopoiesis

Differentiation of hematopoietic stem and progenitor cells to terminally differentiated immune cells is accompanied by large-scale remodeling of the DNA methylation landscape. Although significant insights into the molecular mechanisms of hematopoietic tissue regeneration were derived from mouse models, profiling of DNA methylation has been hampered by high cost or low resolution using available methods. The recent development of the Infinium Mouse Methylation BeadChip (MMBC) array facilitates methylation profiling of the mouse genome at a single CpG resolution at affordable cost. We extended the RnBeads package to provide a computational framework for the analysis of MMBC data. This framework was applied to a newly generated reference map of mouse hematopoiesis encompassing nine different cell types. Analysis of dynamically regulated CpG sites showed progressive and unidirectional DNA methylation changes from hematopoietic stem and progenitor cells to differentiated hematopoietic cells and allowed the identification of lineage- and cell type-specific DNA methylation programs. Comparison with previously published catalogs of cis-regulatory elements (CREs) revealed 12,856 novel putative CREs that were dynamically regulated by DNA methylation (mdCREs). These mdCREs were predominantly associated with patterns of cell type-specific DNA hypomethylation and could be identified as epigenetic control regions regulating the expression of key hematopoietic genes during differentiation. In summary, we established an analysis pipeline for MMBC data sets and provide a DNA methylation atlas of mouse hematopoiesis. This resource allowed us to identify novel putative CREs involved in hematopoiesis and will serve as a platform to study epigenetic regulation of normal and malignant hematopoiesis.

Cell type-specific DNA methylome signatures reveal epigenetic mechanisms for neuronal diversity and neurodevelopmental disorder.

DNA methylation plays a critical function in establishing and maintaining cell identity in brain. Disruption of DNA methylation-related processes leads to diverse neurological disorders. However, the role of DNA methylation characteristics in neuronal diversity remains underexplored. Here, we report detailed context-specific DNA methylation maps for GABAergic, glutamatergic (Glu) and Purkinje neurons, together with matched transcriptome profiles. Genome-wide mCH levels are distinguishable, while the mCG levels are similar among the three cell types. Substantial CG-differentially methylated regions (DMRs) are also seen, with Glu neurons experiencing substantial hypomethylation events. The relationship between mCG levels and gene expression displays cell type-specific patterns, while genic CH methylation exhibits a negative effect on transcriptional abundance. We found that cell type-specific CG-DMRs are informative in terms of represented neuronal function. Furthermore, we observed that the identified Glu-specific hypo-DMRs have a high level of consistency with the chromatin accessibility of excitatory neurons and the regions enriched for histone modifications (H3K27ac and H3K4me1) of active enhancers, suggesting their regulatory potential. Hypomethylation regions specific to each cell type are predicted to bind neuron type-specific transcription factors. Finally, we show that the DNA methylation changes in a mouse model of Rett syndrome, a neurodevelopmental disorder caused by the de novo mutations in MECP2, are cell type- and brain region-specific. Our results suggest that cell type-specific DNA methylation signatures are associated with the functional characteristics of the neuronal subtypes. The presented results emphasize the importance of DNA methylation-mediated epigenetic regulation in neuronal diversity and disease.

P60. Meta-Analysis of Cell Type-Specific DNA Methylation of Childhood Attention-Deficit/Hyperactivity Disorder Symptoms

The interplay of genetic factors and environmental adversities in ADHD can be reflected by cell type-specific DNA methylation (DNAm). Previous methylome-wide association studies (MWAS) for ADHD have been performed in bulk tissue only, where meaningful DNAm signal could be left undetected. We aim to identify cell type-specific DNAm profiles associated with childhood ADHD symptoms.

Transcriptome-wide association study for postpartum depression implicates altered B-cell activation and insulin resistance

Postpartum depression (PPD) affects 1 in 7 women and has negative mental health consequences for both mother and child. However, the precise biological mechanisms behind the disorder are unknown. Therefore, we performed the largest transcriptome-wide association study (TWAS) for PPD (482 cases, 859 controls) to date using RNA-sequencing in whole blood and deconvoluted cell types. No transcriptional changes were observed in whole blood. B-cells showed a majority of transcriptome-wide significant results (891 transcripts representing 789 genes) with pathway analyses implicating altered B-cell activation and insulin resistance. Integration of other data types revealed cell type-specific DNA methylation loci and disease-associated eQTLs (deQTLs), but not hormones/neuropeptides (estradiol, progesterone, oxytocin, BDNF), serve as regulators for part of the transcriptional differences between cases and controls. Further, deQTLs were enriched for several brain region-specific eQTLs, but no overlap with MDD risk loci was observed. Altogether, our results constitute a convergence of evidence for pathways most affected in PPD with data across different biological mechanisms.

Low variability in the underlying cellular landscape adversely affects the performance of interaction-based approaches for conducting cell-specific analyses of DNA methylation in bulk samples.

Statistical methods that allow for cell type specific DNA methylation (DNAm) analyses based on bulk-tissue methylation data have great potential to improve our understanding of human disease and have created unprecedented opportunities for new insights using the wealth of publicly available bulk-tissue methylation data. These methodologies involve incorporating interaction terms formed between the phenotypes/exposures of interest and proportions of the cell types underlying the bulk-tissue sample used for DNAm profiling. Despite growing interest in such "interaction-based" methods, there has been no comprehensive assessment how variability in the cellular landscape across study samples affects their performance. To answer this question, we used numerous publicly available whole-blood DNAm data sets along with extensive simulation studies and evaluated the performance of interaction-based approaches in detecting cell-specific methylation effects. Our results show that low cell proportion variability results in large estimation error and low statistical power for detecting cell-specific effects of DNAm. Further, we identified that many studies targeting methylation profiling in whole-blood may be at risk to be underpowered due to low variability in the cellular landscape across study samples. Finally, we discuss guidelines for researchers seeking to conduct studies utilizing interaction-based approaches to help ensure that their studies are adequately powered.

High-resolution targeted bisulfite sequencing reveals blood cell type-specific DNA methylation patterns in IL13 and ORMDL3

BackgroundMethylation of DNA at CpG sites is an epigenetic modification and a potential modifier of disease risk, possibly mediating environmental effects. Currently, DNA methylation is commonly assessed using specific microarrays that sample methylation at a few % of all methylated sites.MethodsTo understand if significant information on methylation can be added by a more comprehensive analysis of methylation, we set up a quantitative method, bisulfite oligonucleotide-selective sequencing (Bs-OS-seq), and compared the data with microarray-derived methylation data. We assessed methylation at two asthma-associated genes, IL13 and ORMDL3, in blood samples collected from children with and without asthma and fractionated white blood cell types from healthy adult controls.ResultsOur results show that Bs-OS-seq can uncover vast amounts of methylation variation not detected by commonly used array methods. We found that high-density methylation information from even one gene can delineate the main white blood cell lineages.ConclusionsWe conclude that high-resolution methylation studies can yield clinically important information at selected specific loci missed by array-based methods, with potential implications for future studies of methylation-disease associations.

EMeth: An EM algorithm for cell type decomposition based on DNA methylation data

We introduce a new computational method named EMeth to estimate cell type proportions using DNA methylation data. EMeth is a reference-based method that requires cell type-specific DNA methylation data from relevant cell types. EMeth improves on the existing reference-based methods by detecting the CpGs whose DNA methylation are inconsistent with the deconvolution model and reducing their contributions to cell type decomposition. Another novel feature of EMeth is that it allows a cell type with known proportions but unknown reference and estimates its methylation. This is motivated by the case of studying methylation in tumor cells while bulk tumor samples include tumor cells as well as other cell types such as infiltrating immune cells, and tumor cell proportion can be estimated by copy number data. We demonstrate that EMeth delivers more accurate estimates of cell type proportions than several other methods using simulated data and in silico mixtures. Applications in cancer studies show that the proportions of T regulatory cells estimated by DNA methylation have expected associations with mutation load and survival time, while the estimates from gene expression miss such associations.

Diabetes changes gene expression but not DNA methylation in cardiac cells

BackgroundDiabetes mellitus is a worldwide epidemic that causes high mortality due to cardiovascular complications, in particular heart failure. Diabetes is associated with profound pathophysiological changes in the heart. The aim of this study was to investigate the impact of diabetes on gene expression and DNA methylation in cardiac cells. Methods and resultsTranscriptome analysis of heart tissue from mice with streptozotocin-induced diabetes revealed only 39 genes regulated, whereas cell type-specific analysis of the diabetic heart was more sensitive and more specific than heart tissue analysis and revealed a total of 3205 differentially regulated genes in five cell types. Whole genome DNA methylation analysis with basepair resolution of distinct cardiac cell types identified highly specific DNA methylation signatures of genic and regulatory regions. Interestingly, despite marked changes in gene expression, DNA methylation remained stable in streptozotocin-induced diabetes. Integrated analysis of cell type-specific gene expression enabled us to assign the particular contribution of single cell types to the pathophysiology of the diabetic heart. Finally, analysis of gene regulation revealed ligand-receptor pairs as potential mediators of heterocellular interaction in the diabetic heart, with fibroblasts and monocytes showing the highest degree of interaction. ConclusionIn summary, cell type-specific analysis reveals differentially regulated gene programs that are associated with distinct biological processes in diabetes. Interestingly, despite these changes in gene expression, cell type-specific DNA methylation signatures of genic and regulatory regions remain stable in diabetes. Analysis of heterocellular interactions in the diabetic heart suggest that the interplay between fibroblasts and monocytes is of pivotal importance.

P.039 Cell type-specific DNA methylation is associated with childhood attention-deficit hyperactivity disorder symptoms

We analyze the vertex-coloring problem restricted to planar graphs and propose to consider classic wheels and polyhedral wheels as basic patterns for the planar graphs. We analyze the colorability of the composition among wheels and introduce a novel algorithm based on three rules for the vertex-coloring problem. These rules are: 1) Selecting vertices in the frontier. 2) Processing subsumed wheels. 3) Processing centers of the remaining wheels. Our method forms a maximal independent set S1 ⊂ V (G) consisting of wheel's centers, and a maximum number of vertices in the frontier of the planar graph. Thus, we show that if the resulting graph G′ = (G − S1) is 3-colorable, then this implies the existence of a valid 4-coloring for G.

Methylation pattern of urinary DNA as a marker of kidney function decline in diabetes

IntroductionRenal tubular injury contributes to the decline in kidney function in patients with diabetes. Cell type-specific DNA methylation patterns have been used to calculate proportions of particular cell types. In...

Cell Types in Environmental Epigenetic Studies: Biological and Epidemiological Frameworks.

This article introduces the roles of perinatal DNA methylation in human health and disease, highlights the challenges of tissue and cellular heterogeneity to studying DNA methylation, summarizes approaches to overcome these challenges, and offers recommendations in conducting research in environmental epigenetics. Epigenetic modifications are essential for human development and are labile to environmental influences, especially during gestation. Epigenetic dysregulation is also a hallmark of multiple diseases. Environmental epigenetic studies routinely measure DNA methylation in readily available tissues. However, tissues and cell types exhibit specific epigenetic patterning and heterogeneity between samples complicates epigenetic studies. Failure to account for cell-type heterogeneity limits identification of biological mechanisms and biases study results. Tissue-level epigenetic measures represent a convolution of epigenetic signals from individual cell types. Tissue-specific epigenetics is an evolving field and the use of disease-affected target, surrogate, or multiple tissues has inherent trade-offs and affects inference. Likewise, experimental and bioinformatic approaches to accommodate cell-type heterogeneity have varying assumptions and inherent trade-offs that affect inference. The relationships between exposure, disease, tissue-level DNA methylation, cell type-specific DNA methylation, and cell-type heterogeneity must be carefully considered in study design and analysis. Causal diagrams can inform study design and analytic strategies. Properly addressing cell-type heterogeneity limits sources of potential bias, avoids misinterpretation of study results, and allows investigators to distinguish shifts in cell-type proportions from direct changes to cellular epigenetic programming, both of which provide insights into environmental disease etiology and aid development of novel methods for prevention and treatment.

Cell lineage-specific genome-wide DNA methylation analysis of patients with paediatric-onset systemic lupus erythematosus

ABSTRACT Patients with paediatric-onset systemic lupus erythematosus (SLE) often present with more severe clinical courses than adult-onset patients. Although genome-wide DNA methylation (DNAm) profiling has been performed in adult-onset SLE patients, parallel data on paediatric-onset SLE are not available. Therefore, we undertook a genome-wide DNAm study in paediatric-onset SLE patients across multiple blood cell lineages. The DNAm profiles of four purified immune cell lineages (CD4 + T cells, CD8 + T cells, B cells and neutrophils) and whole blood were compared in 16 Chinese patients with paediatric-onset SLE and 13 healthy controls using the Illumina HumanMethylationEPIC BeadChip. Comparison of DNAm in whole blood and within each independent cell lineage identified a consistent pattern of loss of DNAm at 21 CpG sites overlapping 15 genes, which represented a robust, disease-specific DNAm signature for paediatric-onset SLE in our cohort. In addition, cell lineage-specific changes, involving both loss and gain of DNAm, were observed in both novel genes and genes with well-described roles in SLE pathogenesis. This study also highlights the importance of studying DNAm changes in different immune cell lineages rather than only whole blood, since cell type-specific DNAm changes facilitated the elucidation of the cell type-specific molecular pathophysiology of SLE.

Cell type-specific DNA methylation in neonatal cord tissue and cord blood: a 850K-reference panel and comparison of cell types

ABSTRACTAccounting for cellular heterogeneity is essential in neonatal epigenome-wide association studies (EWAS) performed on heterogeneous tissues, such as umbilical cord tissue (CT) or cord blood (CB). Using a reference-panel-based statistical approach, the cell type composition of heterogeneous tissues can be estimated by comparison of whole tissue DNA methylation profiles with cell type-specific DNA methylation signatures. Currently, there is no adequate DNA methylation reference panel for CT, and existing CB panels have been generated on lower coverage Infinium HumanMethylation450 arrays. In this study, we generate a reference panel for CT and improve available CB panels by using the higher coverage Infinium MethylationEPIC arrays. We performed DNA methylation profiling of 9 cell types isolated from CT and CB samples from 14 neonates. In addition to these cell types, we profiled DNA methylation of unfractionated CT and CB. Cell type composition of these unfractionated tissue samples, as estimated by our reference panels, was in agreement with that obtained by flow cytometry. Expectedly, DNA methylation profiles from CT and CB were distinct, reflecting their mesenchymal and hematopoietic stem cell origins. Variable CpGs from both unfractionated CT and its isolated cell types were more likely to be located in open seas and intronic regions than those in CB. Cell type specific CpGs in CT were enriched in intercellular matrix pathways, while those from CB were enriched in immune-related pathways. This study provides an open source reference panel for estimation and adjustment of cellular heterogeneity in CT and CB, and broadens the scope of tissue utilization assessed in future neonatal EWAS studies.

Comparative DNA Methylation Profiling Reveals an Immunoepigenetic Signature of HIV-related Cognitive Impairment.

Monocytes/macrophages contribute to the neuropathogenesis of HIV-related cognitive impairment (CI); however, considerable gaps in our understanding of the precise mechanisms driving this relationship remain. Furthermore, whether a distinct biological profile associated with HIV-related CI resides in immune cell populations remains unknown. Here, we profiled DNA methylomes and transcriptomes of monocytes derived from HIV-infected individuals with and without CI using genome-wide DNA methylation and gene expression profiling. We identified 1,032 CI-associated differentially methylated loci in monocytes. These loci related to gene networks linked to the central nervous system (CNS) and interactions with HIV. Most (70.6%) of these loci exhibited higher DNA methylation states in the CI group and were preferentially distributed over gene bodies and intergenic regions of the genome. CI-associated DNA methylation states at 12 CpG sites associated with neuropsychological testing performance scores. CI-associated DNA methylation also associated with gene expression differences including CNS genes CSRNP1 (P = 0.017), DISC1 (P = 0.012), and NR4A2 (P = 0.005); and a gene known to relate to HIV viremia, THBS1 (P = 0.003). This discovery cohort data unveils cell type-specific DNA methylation patterns related to HIV-associated CI and provide an immunoepigenetic DNA methylation “signature” potentially useful for corroborating clinical assessments, informing pathogenic mechanisms, and revealing new therapeutic targets against CI.

Cell type specific DNA methylation in cord blood: A 450K-reference data set and cell count-based validation of estimated cell type composition

ABSTRACTEpigenome-wide association studies of prenatal exposure to different environmental factors are becoming increasingly common. These studies are usually performed in umbilical cord blood. Since blood comprises multiple cell types with specific DNA methylation patterns, confounding caused by cellular heterogeneity is a major concern. This can be adjusted for using reference data consisting of DNA methylation signatures in cell types isolated from blood. However, the most commonly used reference data set is based on blood samples from adult males and is not representative of the cell type composition in neonatal cord blood. The aim of this study was to generate a reference data set from cord blood to enable correct adjustment of the cell type composition in samples collected at birth. The purity of the isolated cell types was very high for all samples (>97.1%), and clustering analyses showed distinct grouping of the cell types according to hematopoietic lineage. We explored whether this cord blood and the adult peripheral blood reference data sets impact the estimation of cell type composition in cord blood samples from an independent birth cohort (MoBa, n = 1092). This revealed significant differences for all cell types. Importantly, comparison of the cell type estimates against matched cell counts both in the cord blood reference samples (n = 11) and in another independent birth cohort (Generation R, n = 195), demonstrated moderate to high correlation of the data. This is the first cord blood reference data set with a comprehensive examination of the downstream application of the data through validation of estimated cell types against matched cell counts.

Dnmt3a Regulates Proliferation of Muscle Satellite Cells via p57Kip2.

Cell differentiation status is defined by the gene expression profile, which is coordinately controlled by epigenetic mechanisms. Cell type-specific DNA methylation patterns are established by chromatin modifiers including de novo DNA methyltransferases, such as Dnmt3a and Dnmt3b. Since the discovery of the myogenic master gene MyoD, myogenic differentiation has been utilized as a model system to study tissue differentiation. Although knowledge about myogenic gene networks is accumulating, there is only a limited understanding of how DNA methylation controls the myogenic gene program. With an aim to elucidate the role of DNA methylation in muscle development and regeneration, we investigate the consequences of mutating Dnmt3a in muscle precursor cells in mice. Pax3 promoter-driven Dnmt3a-conditional knockout (cKO) mice exhibit decreased organ mass in the skeletal muscles, and attenuated regeneration after cardiotoxin-induced muscle injury. In addition, Dnmt3a-null satellite cells (SCs) exhibit a striking loss of proliferation in culture. Transcriptome analysis reveals dysregulated expression of p57Kip2, a member of the Cip/Kip family of cyclin-dependent kinase inhibitors (CDKIs), in the Dnmt3a-KO SCs. Moreover, RNAi-mediated depletion of p57Kip2 replenishes the proliferation activity of the SCs, thus establishing a role for the Dnmt3a-p57Kip2 axis in the regulation of SC proliferation. Consistent with these findings, Dnmt3a-cKO muscles exhibit fewer Pax7+ SCs, which show increased expression of p57Kip2 protein. Thus, Dnmt3a is found to maintain muscle homeostasis by epigenetically regulating the proliferation of SCs through p57Kip2.

Epigenetic age predictions based on buccal swabs are more precise in combination with cell type-specific DNA methylation signatures.

Aging is reflected by highly reproducible DNA methylation (DNAm) changes that open new perspectives for estimation of chronological age in legal medicine. DNA can be harvested non-invasively from cells at the inside of a person's cheek using buccal swabs – but these specimens resemble heterogeneous mixtures of buccal epithelial cells and leukocytes with different epigenetic makeup. In this study, we have trained an age predictor based on three age-associated CpG sites (associated with the genes PDE4C, ASPA, and ITGA2B) for swab samples to reach a mean absolute deviation (MAD) between predicted and chronological age of 4.3 years in a training set and of 7.03 years in a validation set. Subsequently, the composition of buccal epithelial cells versus leukocytes was estimated by two additional CpGs (associated with the genes CD6 and SERPINB5). Results of this “Buccal-Cell-Signature” correlated with cell counts in cytological stains (R2 = 0.94). Combination of cell type-specific and age-associated CpGs into one multivariate model enabled age predictions with MADs of 5.09 years and 5.12 years in two independent validation sets. Our results demonstrate that the cellular composition in buccal swab samples can be determined by DNAm at two cell type-specific CpGs to improve epigenetic age predictions.