High-coverage allele-resolved single-cell DNA methylation profiling reveals cell lineage, X-inactivation state, and replication dynamics

Nathan J. Spix, Walid Abi Habib, Zhouwei Zhang, Emily Eugster, Hsiao-yun Milliron, David Sokol, KwangHo Lee, Paula A. Nolte, Jamie L. Endicott, Kelly F. Krzyzanowski, Toshinori Hinoue, Jacob Morrison, Benjamin K. Johnson, Wanding Zhou, Hui Shen () and Peter W. Laird ()
Additional contact information
Nathan J. Spix: Van Andel Institute
Walid Abi Habib: Van Andel Institute
Zhouwei Zhang: Van Andel Institute
Emily Eugster: Van Andel Institute
Hsiao-yun Milliron: Van Andel Institute
David Sokol: Van Andel Institute
KwangHo Lee: Van Andel Institute
Paula A. Nolte: Van Andel Institute
Jamie L. Endicott: Van Andel Institute
Kelly F. Krzyzanowski: Van Andel Institute
Toshinori Hinoue: Van Andel Institute
Jacob Morrison: Van Andel Institute
Benjamin K. Johnson: Van Andel Institute
Wanding Zhou: Van Andel Institute
Hui Shen: Van Andel Institute
Peter W. Laird: Van Andel Institute

Nature Communications, 2025, vol. 16, issue 1, 1-17

Abstract: Abstract DNA methylation patterns at crucial short sequence features, such as enhancers and promoters, may convey key information about cell lineage and state. The need for high-resolution single-cell DNA methylation profiling has therefore become increasingly apparent. Existing single-cell whole-genome bisulfite sequencing (scWGBS) studies have both methodological and analytical shortcomings. Inefficient library generation and low CpG coverage mostly preclude direct cell-to-cell comparisons and necessitate the use of cluster-based analyses, imputation of methylation states, or averaging of DNA methylation measurements across large genomic bins. Such summarization methods obscure the interpretation of methylation states at individual regulatory elements and limit our ability to discern important cell-to-cell differences. We report an improved scWGBS method, single-cell Deep and Efficient Epigenomic Profiling of methyl-C (scDEEP-mC), which offers efficient generation of high-coverage libraries. scDEEP-mC allows for cell type identification, genome-wide profiling of hemi-methylation, and allele-resolved analysis of X-inactivation epigenetics in single cells. Furthermore, we combine methylation and copy-number data from scDEEP-mC to identify single, actively replicating cells and profile DNA methylation maintenance dynamics during and after DNA replication. These analyses unlock further avenues for exploring DNA methylation regulation and dynamics and illustrate the power of high-complexity, highly efficient scWGBS library construction as facilitated by scDEEP-mC.

Date: 2025
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DOI: 10.1038/s41467-025-61589-1

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