| A pan-tissue DNA methylation atlas enables in silico decomposition of human tissue methylomes at cell-type resolution |
| 論文作者 |
Zhu, TY; Liu, J; Beck, S; Pan, S; Capper, D; Lechner, M; Thirlwell, C; Breeze, CE; Teschendorff, AE |
| 期刊/會(huì)議名稱 |
NATURE METHODS |
| 論文年度 |
2022 |
| 論文類別 |
Article |
| 摘要 |
Bulk-tissue DNA methylomes represent an average over many different cell types, hampering our understanding of cell-type-specific contributions to disease development. As single-cell methylomics is not scalable to large cohorts of individuals, cost-effective computational solutions are needed, yet current methods are limited to tissues such as blood. Here we leverage the high-resolution nature of tissue-specific single-cell RNA-sequencing datasets to construct a DNA methylation atlas defined for 13 solid tissue types and 40 cell types. We comprehensively validate this atlas in independent bulk and single-nucleus DNA methylation datasets. We demonstrate that it correctly predicts the cell of origin of diverse cancer types and discovers new prognostic associations in olfactory neuroblastoma and stage 2 melanoma. In brain, the atlas predicts a neuronal origin for schizophrenia, with neuron-specific differential DNA methylation enriched for corresponding genome-wide association study risk loci. In summary, the DNA methylation atlas enables the decomposition of 13 different human tissue types at a high cellular resolution, paving the way for an improved interpretation of epigenetic data. This resource presents an in silico generated DNA methylation atlas that can be used for cell-type deconvolution of human tissues. |
| 期 |
3 |
| 卷 |
19 |
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