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

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.

Automated summary

Bulk-tissue DNA methylomes represent an average over many different cell types, limiting our understanding of cell-type-specific contributions to disease development. This study leverages high-resolution tissue-specific single-cell RNA-sequencing datasets to construct a DNA methylation atlas for 13 solid tissue types and 40 cell types. The atlas accurately predicts the cell of origin for various cancer types and discovers new prognostic associations in olfactory neuroblastoma and stage 2 melanoma.