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<p>Single-cell DNA methylation (scDNAm) sequencing offers unprecedented opportunities for dissecting epigenetic heterogeneity and gene regulatory mechanisms. However, most existing analytical methods are adapted from conventional single-cell sequencing frameworks, such as single-cell RNA sequencing, and fail to account for the unique characteristics of scDNAm data, including the widespread presence of explicit missing values and their distinct distribution patterns. To enable systematic exploration of DNA methylation-derived epigenetic variation at single-cell resolution, we propose scMethCraft, a versatile analytical toolkit. scMethCraft integrates multi-perspective genomic sequence features and position information of methylated regions via a hybrid neural network, and iteratively quantifies the cell-to-cell associations, enabling accurate modeling of scDNAm data. scMethCraft facilitates accurate reconstruction of DNA methylation landscapes and supports a spectrum of downstream analyses, including cell embedding, multi-source data integration, cell type annotation, epigenetic signal enhancement, and identification of differentially methylated regions, thereby significantly facilitating the identification and characterization of scDNAm heterogeneity. The integration of scMethCraft with complementary analytical workflows, including biological function enrichment, tissue-specific expression analysis, and partitioned heritability assessment, enables the discovery of biological insights into specific cellular subpopulations. Furthermore, scMethCraft identified oligodendrocyte-associated genes that are not well characterized in existing databases, providing an epigenetic perspective for investigating underlying biological mechanisms.</p>

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Dissecting epigenetic heterogeneity in single-cell DNA methylomes with a unified framework

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月15日

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  • Songming Tang,
  • Siyu Li,
  • Guangxin Zhang,
  • Aoran Lyu,
  • Han Li,
  • Shengquan Chen

摘要

Single-cell DNA methylation (scDNAm) sequencing offers unprecedented opportunities for dissecting epigenetic heterogeneity and gene regulatory mechanisms. However, most existing analytical methods are adapted from conventional single-cell sequencing frameworks, such as single-cell RNA sequencing, and fail to account for the unique characteristics of scDNAm data, including the widespread presence of explicit missing values and their distinct distribution patterns. To enable systematic exploration of DNA methylation-derived epigenetic variation at single-cell resolution, we propose scMethCraft, a versatile analytical toolkit. scMethCraft integrates multi-perspective genomic sequence features and position information of methylated regions via a hybrid neural network, and iteratively quantifies the cell-to-cell associations, enabling accurate modeling of scDNAm data. scMethCraft facilitates accurate reconstruction of DNA methylation landscapes and supports a spectrum of downstream analyses, including cell embedding, multi-source data integration, cell type annotation, epigenetic signal enhancement, and identification of differentially methylated regions, thereby significantly facilitating the identification and characterization of scDNAm heterogeneity. The integration of scMethCraft with complementary analytical workflows, including biological function enrichment, tissue-specific expression analysis, and partitioned heritability assessment, enables the discovery of biological insights into specific cellular subpopulations. Furthermore, scMethCraft identified oligodendrocyte-associated genes that are not well characterized in existing databases, providing an epigenetic perspective for investigating underlying biological mechanisms.