A human DNA methylation atlas reveals principles of cell type-specific methylation and identifies thousands of cell type-specific regulatory elements
Bibliographic record
Abstract
Abstract DNA methylation is a fundamental epigenetic mark that governs chromatin organization, cell identity, and gene expression. 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 207 healthy tissue samples. Replicates of the same cell-type are >99.5% identical, demonstrating robustness of cell identity programs to genetic variation and environmental perturbation. Unsupervised clustering of the atlas recapitulates key elements of tissue ontogeny, and identifies methylation patterns retained since gastrulation. Loci uniquely unmethylated in an individual cell type often reside in transcriptional enhancers and contain DNA binding sites for tissue-specific transcriptional regulators. Uniquely hyper-methylated loci are rare and are enriched for CpG islands, polycomb targets, and CTCF binding sites, suggesting a novel role in shaping cell type-specific chromatin looping. The atlas provides an essential resource for interpretation of disease-associated genetic variants, and a wealth of potential tissue-specific biomarkers for use in liquid biopsies. Summary paragraph DNA methylation, a fundamental epigenetic mark, governs chromatin organization and gene expression 1 , thus defining the molecular identity of cells and providing a window into developmental processes with wide-ranging physiologic and clinical ramifications. Current DNA methylation datasets have limitations, typically including only a fraction of methylation sites, many from cell lines that underwent massive changes in culture or from tissues containing unspecified mixtures of cells 2–6 . We present a human methylome atlas based on deep whole-genome bisulfite sequencing of 39 sorted, primary cell types and use this dataset to address fundamental questions in developmental biology, physiology and pathology. Biological replicates are >99.5% identical, demonstrating unappreciated robustness to genetic variation and environmental perturbations. Clustering recapitulates key elements of tissue ontogeny, identifying methylation patterns retained since gastrulation. Loci uniquely unmethylated in individual cell types identify novel transcriptional enhancers and are enriched for tissue-specific transcription factors binding motifs. In contrast, loci uniquely hyper-methylated in specific cell types are rare, enriched for CpG islands and polycomb targets, and overlap CTCF binding sites, suggesting a novel role in shaping cell-type-specific chromatin looping. Finally, the atlas facilitates fragment-level deconvolution of tissue and plasma methylomes across thousands of cell-type specific regions to quantify their individual components at unprecedented resolution. The human cell-type-specific methylation atlas provides an essential resource for studying gene regulation by defining cell-type-specific distal enhancers and regulators of 3D organization, for identifying pathological changes in DNA methylation, and for the interpretation of methylation-based liquid biopsies. A deep methylation atlas of 39 human cell types, sorted from healthy samples Methylomes record developmental history of cells Thousands of novel cell type-specific methylation markers Hypo-methylation uncovers cell type-specific regulatory map of distal enhancers Hyper-methylation across CTCF sites Cell type-specific biomarkers facilitate fragment-level deconvolution of tissues and cfDNA
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".