A DNA methylation atlas of normal human cell types

Abstract

DNA methylation is a fundamental epigenetic mark that governs gene expression and chromatin organization, thus providing a window into cellular identity and developmental processes1. Current datasets typically include only a fraction of methylation sites and are often based either on cell lines that underwent massive changes in culture or on tissues containing unspecified mixtures of cells2–5. Here we describe a human methylome atlas, based on deep whole-genome bisulfite sequencing, allowing fragment-level analysis across thousands of unique markers for 39 cell types sorted from 205 healthy tissue samples. Replicates of the same cell type are more than 99.5% identical, demonstrating the robustness of cell identity programmes to environmental perturbation. Unsupervised clustering of the atlas recapitulates key elements of tissue ontogeny and identifies methylation patterns retained since embryonic development. Loci uniquely unmethylated in an individual cell type often reside in transcriptional enhancers and contain DNA binding sites for tissue-specific transcriptional regulators. Uniquely hypermethylated loci are rare and are enriched for CpG islands, Polycomb targets and CTCF binding sites, suggesting a new role in shaping cell-type-specific chromatin looping. The atlas provides an essential resource for study of gene regulation and disease-associated genetic variants, and a wealth of potential tissue-specific biomarkers for use in liquid biopsies.

Accurate age prediction from blood using of small set of DNA methylation sites and a cohort-based machine learning algorithm

“…As Fig. 8 shows, this further improved GP-age's predictions on WGBS datasets, yielding a median error of 1.75 years on the Jensen et al dataset 46 , and 2.29 years on the Loyfer et al dataset 45 . The difference in accuracy between the two datasets could be explained by the different ages in the two datasets (Supplemental Fig.…”

“…Notably, the improved predictions in WGBS samples using information from neighboring CpG sites, suggests that aging-related epigenetic changes occur - at least for some genomic loci - at DNA methylation blocks 42,45 , rather than at isolated independent CpGs (as may seem from DNA methylation array data). Further, this raises questions regarding the underlying processes of epigenetic aging.…”

“…These were then analyzed by GP-age for age prediction. We also augmented the methylation level estimation at each target CpG x by considering neighboring CpGs x i within the same DNA methylation block 45 , and averaging their methylation levels using an exponentially decaying Laplace kernel: with d being a parameter controlling the length scale of effect of CpG neighbors. Here, we used d =3.…”

“…Unlike methylation arrays, whole-genome bisulfite sequencing (WGBS) methylation data is relatively shallow with typically no more than 30 sequenced reads (fragments) covering each CpG (30x). Yet, neighboring CpG sites are also sequenced and could be an additional source of data, at least in genomic regions with block-like methylation patterns [42][43][44][45] . We therefore turned to test the performance of GP-age on such data.…”

“…Bam files were downloaded from dbGaP (accession number phs000846), and analyzed by wgbstools using the bam_to_pat function 64 . Second, we analyzed a dataset published by us 45 , including 23 WGBS white blood cells (WBC) samples from healthy donors.…”