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A pan-tissue DNA methylation atlas enables in silico decomposition of human tissue methylomes at cell-type resolution.


ABSTRACT: 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.

SUBMITTER: Zhu T 

PROVIDER: S-EPMC8916958 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

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A pan-tissue DNA methylation atlas enables in silico decomposition of human tissue methylomes at cell-type resolution.

Zhu Tianyu T   Liu Jacklyn J   Beck Stephan S   Pan Sun S   Capper David D   Lechner Matt M   Thirlwell Chrissie C   Breeze Charles E CE   Teschendorff Andrew E AE  

Nature methods 20220311 3


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  ...[more]

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