Single-Cell DNA Methylome Sequencing and Bioinformatic Inference of Epigenomic Cell-State Dynamics
By Matthias Farlik, Nathan C. Sheffield, Angelo Nuzzo, Paul Datlinger, Andreas Schönegger, Johanna Klughammer, Christoph Bock. Cell Reports, 2015
399 citations recorded by the source corpus.
Summary Methods for single-cell genome and transcriptome sequencing have contributed to our understanding of cellular heterogeneity, whereas methods for single-cell epigenomics are much less established. Here, we describe a whole-genome bisulfite sequencing (WGBS) assay that enables DNA methylation mapping in very small cell populations (μWGBS) and single cells (scWGBS). Our assay is optimized for profiling many samples at low coverage, and we describe a bioinformatic method that analyzes collections of single-cell methylomes to infer cell-state dynamics. Using these technological advances, we studied epigenomic cell-state dynamics in three in vitro models of cellular differentiation and pluripotency, where we observed characteristic patterns of epigenome remodeling and cell-to-cell heterogeneity. The described method enables single-cell analysis of DNA methylation in a broad range of biological systems, including embryonic development, stem cell differentiation, and cancer. It can also be used to establish composite methylomes that account for cell-to-cell heterogeneity in complex tissue samples.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/7ff91637901a53e4885264427cf465582d2cb1e4
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In Molecular Biology
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Citations in the corpus
- DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning (2016, background): “These protocols have already provided unprecedented insights into the regulation and the dynamics of DNA methylation in single cells [6, 9], and have uncovered new linkages between epigenetic and transcriptional heterogeneity [8, 10, 11].”
- chromVAR: Inferring transcription factor-associated accessibility from single-cell epigenomic data (2017, methodology): “In this vein, and building on previous work from our group and others 4,8,9 , we have developed chromVAR, a versatile R package for analyzing sparse chromatin accessibility data by measuring the gain or loss of chromatin accessibility within sets of genomic features while controlling for known…”
- Advances and Trends in Omics Technology Development (2022, background): “Epigenomic sequencing technologies have enabled, for example and among others, non-invasive epigenomic molecular phenotyping of the human brain (127), rapid epigenomic diagnosis of brain cancers (128), and inference of epigenomic cell-state dynamics (129).”
- The Human Cell Atlas (2017, background): “…(single-cell ATAC-Seq (Buenrostro et al., 2015; Cusanovich et al., 2015)), or likewise characterize DNA methylation patterns (single-cell DNAme-Seq (Farlik et al., 2015; Guo et al., 2013; Mooijman et al., 2016; Smallwood et al., 2014)) or 3D organization (single-cell Hi-C (Nagano et al., 2013;…”
- Next-Generation Sequencing — An Overview of the History, Tools, and “Omic” Applications (2016, background): “Bisulfite DNA sequencing is used widely for DNA methylation profiling in various organisms as well as humans for assessing disease genes [23, 27, 29].”