Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity
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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.
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Regions with active enhancer marks have a high variance in DNA methylation between single cells.
“showing that regions with active enhancer marks have a high variance in DNA methylation between single cells [73]”
From Mapping gene regulatory networks from single-cell omics data (2018) · cites this paper as [73]
Differences between sc-methylation approaches can be because of technical dropouts, variation in detection power (scRRBS detects 10% of CpG sites, scBS-seq up to 48%) and different analysis approaches.
“Differences can be because of (different levels of) technical dropouts, variation in detection power (e.g. scRRBS is able to detect 10% of CpG sites [68], whereas scBS-seq can detect up to 48% [73]) and different analysis approaches.”
From Mapping gene regulatory networks from single-cell omics data (2018) · cites this paper as [73]
DNA methylation can be measured in single cells by plate-based sorting of individual nuclei, followed by bisulfite conversion and multiplexed sequencing, capturing up to ~30% of genome per cell.
“DNA methylation can be measured in single cells by plate-based sorting of individual nuclei, followed by bisulfite conversion of DNA and multiplexed sequencing (Luo et al., 2017; Mulqueen et al., 2018; Smallwood et al., 2014), capturing up to ~30% of the genome per cell (Luo et al., 2018a).”
From Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes (2021) · cites this paper as Smallwood et al., 2014
Single-cell omics techniques are particularly apposite for developmental biology.
“Recently developed single‐cell ‐omics techniques (Tang et al, 2009; Smallwood et al, 2014; Buenrostro et al, 2015b; Heath et al, 2016), by contrast, are particularly apposite for developmental biology, transferring high‐throughput molecular techniques onto the correct scale for understanding cellular decision‐making.”
From Using single-cell genomics to understand developmental processes and cell fate decisions (2018) · cites this paper as Smallwood et al, 2014
scM&T-seq was the first WGBS-based multi-omics method; it used G&T-seq and scBS-seq to assess methylation heterogeneity and Smart-seq2 to obtain RNA data together with genome-wide methylation.
“The first method of WGBS multi-omics, single-cell genome-wide methylome and transcriptome sequencing (scM&T-seq)28, borrowed already-established single-omics techniques from G&T-seq21 and scBS-seq26 to measure DNA methylation heterogeneity in single cells, combined with Smart-seq2 to generate transcriptional data concurrently with genome-wide methylation data.”
From The technological landscape and applications of single-cell multi-omics (2023) · cites this paper as [26]
scTRIO-seq and scNMT-seq can simultaneously sample genomic CNVs, DNA methylome, nucleosome occupancy and transcriptome of single cells.
“Methods discussed above such as scTRIO-seq28 and scNMT-seq26 can be used to simultaneously sample genomic copy number variations, the DNA methylome, nucleosome occupancy and the transcriptome of single cells to reveal new cell types and their roles in a studied lineage.”
From The technological landscape and applications of single-cell multi-omics (2023) · cites this paper as [26]
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- Authors
- Smallwood, S. A., Lee, H. J., Angermueller, C., Krueger, F., Saadeh, H., Peat, J., Andrews, S. R., Stegle, O., Reik, W., & Kelsey, G.
- Affiliations
- Babraham Institute (GB)
- European Bioinformatics Institute (GB)
- Corresponding author
- Reik, Wolf
- Kelsey, Gavin
- ORCID
- Venue
- Nature Methods
- Published
- 2014-07-20
- Volume
- 11
- Issue
- 8
- Pages
- 817-820
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC4117646
- OpenAlex
- W2070339716
- Cited by
- 1,125
- References
- 26
- Citations by year
- 2026: 50 · 2025: 75 · 2024: 119 · 2023: 102 · 2022: 97 · 2021: 111 · 2020: 102 · 2019: 102 · 2018: 98 · 2017: 112 · 2016: 78 · 2015: 64 · 2014: 14
- FWCI
- 36.65
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature Methods (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Apollo (University of Cambridge) (submittedVersion)
- Apollo
- Funders
- Wellcome Trust (095645/Z/11/Z, 095645)
- Medical Research Council (MR/K011332/1)
- Biotechnology and Biological Sciences Research Council (BBS/E/B/000C0400, BB/J004499/1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Syndromes and Imprinting
- Keywords
- single-cell bisulfite sequencing, DNA methylation, epigenetic heterogeneity, embryonic stem cells, mouse oocytes, methylome, rare cell populations
- MeSH terms
- Animals, Sulfites, Genome, DNA Methylation, Epigenesis, Genetic, Mice
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