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

IF 14.7 1区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
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, Peter W. Laird
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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.

Abstract Image

高覆盖率等位基因分辨单细胞DNA甲基化分析揭示细胞谱系,x失活状态和复制动力学
DNA甲基化模式在关键的短序列特征上,如增强子和启动子,可能传递关于细胞谱系和状态的关键信息。因此,对高分辨率单细胞DNA甲基化分析的需求变得越来越明显。现有的单细胞全基因组亚硫酸氢盐测序(scWGBS)研究在方法学和分析上都存在缺陷。低效的文库生成和低CpG覆盖率大多妨碍了直接的细胞间比较,需要使用基于聚类的分析,甲基化状态的输入,或在大基因组箱中平均DNA甲基化测量。这种总结方法模糊了个体调控元件甲基化状态的解释,限制了我们辨别重要细胞间差异的能力。我们报告了一种改进的scWGBS方法,即甲基c的单细胞深度和高效表观基因组分析(scDEEP-mC),它提供了高覆盖率文库的高效生成。scDEEP-mC允许细胞类型鉴定,半甲基化的全基因组分析,以及单细胞中x失活表观遗传学的等位基因解析分析。此外,我们结合scDEEP-mC的甲基化和拷贝数数据来鉴定单细胞、主动复制细胞,并描述DNA复制期间和之后的DNA甲基化维持动态。这些分析为探索DNA甲基化调控和动力学开辟了进一步的途径,并说明了scDEEP-mC促进的高复杂性,高效率的scWGBS文库构建的力量。
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来源期刊
Nature Communications
Nature Communications Biological Science Disciplines-
CiteScore
24.90
自引率
2.40%
发文量
6928
审稿时长
3.7 months
期刊介绍: Nature Communications, an open-access journal, publishes high-quality research spanning all areas of the natural sciences. Papers featured in the journal showcase significant advances relevant to specialists in each respective field. With a 2-year impact factor of 16.6 (2022) and a median time of 8 days from submission to the first editorial decision, Nature Communications is committed to rapid dissemination of research findings. As a multidisciplinary journal, it welcomes contributions from biological, health, physical, chemical, Earth, social, mathematical, applied, and engineering sciences, aiming to highlight important breakthroughs within each domain.
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