Nature CommunicationsGenomicsPeer-reviewed journal article
High-coverage allele-resolved single-cell DNA methylation profiling by scDEEP-mC reveals cell lineage, X-inactivation state, and replication dynamics
Nathan J. SpixiD, Walid Abi HabibiD, Zhouwei ZhangiD, Emily Eugster, Hsiao-yun Milliron, David Sokol, KwangHo Lee, Paula A. Nolte, Jamie L. Endicott, Kelly F. Krzyzanowski, Toshinori HinoueiD, Jacob MorrisoniD, Benjamin K. JohnsoniD, Wanding ZhouiD, Hui SheniD, Peter W. LairdiD
Abstract
DNA methylation is a relatively stable epigenetic mark with important roles in development and disease.1 Since cell-to-cell variation in epigenetic programming can reflect important differences in cell state and fate, it is clear that single-cell methods are essential to understanding this key epigenetic mark in heterogeneous tissues. Existing single-cell whole-genome bisulfite sequencing (scWGBS) methods2 have significant shortcomings, including very low CpG coverage3-7 or inefficient library generation requiring extremely deep sequencing.8 These methods offer limited insight into focal regulatory regions and generally preclude direct cell-to-cell comparisons. To address these shortcomings, we have developed an improved method, scDEEP-mC (single-cell Deep and Efficient Epigenomic Profiling of methyl-C). We show that high-coverage promoter methylation profiling by scDEEP-mC can identify multiple cell types, while allele-resolved methylation calls allow assessment of X-inactivation state in single cells and identification of transcription factor binding sites (TFBS) enriched for hemi-methylation. We also use scDEEP-mC to profile single-cell copy number alterations, identify actively replicating cells, and track DNA methylation dynamics during and after DNA replication. The high coverage of scDEEP-mC creates an exceptional opportunity to explore DNA methylation biology in individual cells.
- organism: human
- assay: DNA methylation
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: trajectory inference
- approach: deep learning
- approach: statistical model
- Citations
- 9
- FWCI
- 2.69
- Field percentile
- top 9%
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