Single-Cell Genome-Wide Bisulfite Sequencing for Assessing Epigenetic Heterogeneity

By Sébastien A. Smallwood, Heather J. Lee, Christof Angermueller, Felix Krueger, Heba Saadeh, Julian R. Peat, Simon R. Andrews, Oliver Stegle, Wolf Reik, G. Kelsey. Nature Methods, 2014

1,047 citations recorded by the source corpus.

We report a single-cell bisulfite sequencing (scBS-seq) method that can be used to accurately measure DNA methylation at up to 48.4% of CpG sites. Embryonic stem cells grown in serum or in 2i medium displayed epigenetic heterogeneity, with '2i-like' cells present in serum culture. Integration of 12 individual mouse oocyte datasets largely recapitulated the whole DNA methylome, which makes scBS-seq a versatile tool to explore DNA methylation in rare cells and heterogeneous populations.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/dfaaa549bfe5bb7f9ac157f84878b36609e6a0b0

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Citations in the corpus

The technology and biology of single-cell RNA sequencing. (2015, background): “…genomics methods will provide another level of data orthogonal to the transcriptome. scRNA-seq and bisulfite sequencing (Guo et al., 2013; Smallwood et al., 2014) from the same cell will give insight into gene expression regulation through the interaction between the epigenome and the…”
Current best practices in single‐cell RNA‐seq analysis: a tutorial (2019, background): “Future single-cell platforms will have to be able to deal with different data sources such as DNA methylation (Smallwood et al , 2014), chromatin accessibility (Buenrostro et al , 2015), or protein abundance (Stoeckius et al , 2017), and include tools that integrate these modalities.”
Mapping and elucidating the function of modified bases in DNA (2017, methodology): “These methods result in increased uniformity of coverage and an adapted version of the latter technique is now the standard method for analyzing DNA methylation in single cells.(82) In this adaptation of PBAT, bisulfite conversion is performed on single cells prior to five rounds of random priming t”
DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning (2016, methodology): “We evaluated DeepCpG on mouse embryonic stem cells profiled using whole-genome single-cell methylation profiling (scBS-seq [5]), as well as on human and mouse cells profiled using a reduced representation protocol (scRRBS-seq [8]).”
Defining cell types and states with single-cell genomics (2015, background): “…and gene expression Dey et al. (2015); Macaulay et al. (2015) In situ RNA-seq Gene expression, subcellular mRNA localization Larsson et al. (2010); Lee et al. (2014) Bisulfitesequencing DNA methylation Guo et al. (2013); Smallwood et al. (2014) Hi-C Chromatin conformation Nagano et al. (2013)”
The Human Cell Atlas (2017, neutral): “, 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.”