Genome-wide base-resolution mapping of DNA methylation in single cells using single-cell bisulfite sequencing (scBS-seq)


DNA methylation (DNAme) is an important epigenetic mark in diverse species. Our current understanding of DNAme is based on measurements from bulk cell samples, which obscures intercellular differences and prevents analyses of rare cell types. Thus, the ability to measure DNAme in single cells has the potential to make important contributions to the understanding of several key biological processes, such as embryonic development, disease progression and aging. We have recently reported a method for generating genome-wide DNAme maps from single cells, using single-cell bisulfite sequencing (scBS-seq), allowing the quantitative measurement of DNAme at up to 50% of CpG dinucleotides throughout the mouse genome. Here we present a detailed protocol for scBS-seq that includes our most recent developments to optimize recovery of CpGs, mapping efficiency and success rate; reduce hands-on time; and increase sample throughput with the option of using an automated liquid handler. We provide step-by-step instructions for each stage of the method, comprising cell lysis and bisulfite (BS) conversion, preamplification and adaptor tagging, library amplification, sequencing and, lastly, alignment and methylation calling. An individual with relevant molecular biology expertise can complete library preparation within 3 d. Subsequent computational steps require 1–3 d for someone with bioinformatics expertise.

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Figure 1: Overview of scBS-seq library preparation protocol.
Figure 2: Bioanalyzer electropherograms of typical scBS-seq libraries.
Figure 3: Quality control criteria for scBS-seq libraries.


  1. 1

    Tang, F. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–382 (2009).

    CAS  Article  Google Scholar 

  2. 2

    Islam, S. et al. Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Genome Res. 21, 1160–1167 (2011).

    CAS  Article  Google Scholar 

  3. 3

    Sasagawa, Y. et al. Quartz-Seq: a highly reproducible and sensitive single-cell RNA sequencing method, reveals non-genetic gene-expression heterogeneity. Genome Biol. 14, R31 (2013).

    Article  Google Scholar 

  4. 4

    Hashimshony, T., Wagner, F., Sher, N. & Yanai, I. CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell Rep. 2, 666–673 (2012).

    CAS  Article  Google Scholar 

  5. 5

    Picelli, S. et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat. Methods 10, 1096–1098 (2013).

    CAS  Article  Google Scholar 

  6. 6

    Jaitin, D.A. et al. Massively parallel single cell RNA-Seq for marker-free decomposition of tissues into cell types. Science 343, 776–779 (2014).

    CAS  Article  Google Scholar 

  7. 7

    Klein, A.M. et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201 (2015).

    CAS  Article  Google Scholar 

  8. 8

    Macosko, E.Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. 9

    Clark, S.J., Lee, H.J., Smallwood, S.A., Kelsey, G. & Reik, W. Single-cell epigenomics: powerful new methods for understanding gene regulation and cell identity. Genome Biol. 17, 72 (2016).

    Article  Google Scholar 

  10. 10

    Smallwood, S.A. et al. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat. Methods 11, 817–820 (2014).

    CAS  Article  Google Scholar 

  11. 11

    Farlik, M. et al. Single-cell DNA methylome sequencing and bioinformatic inference of epigenomic cell-state dynamics. Cell Rep. 10, 1386–1397 (2015).

    CAS  Article  Google Scholar 

  12. 12

    Gravina, S., Dong, X., Yu, B. & Vijg, J. Single-cell genome-wide bisulfite sequencing uncovers extensive heterogeneity in the mouse liver methylome. Genome Biol. 17, 150 (2016).

    Article  Google Scholar 

  13. 13

    Nashun, B. et al. Continuous histone replacement by Hira is essential for normal transcriptional regulation and de novo DNA methylation during mouse oogenesis. Mol. Cell 60, 611–625 (2015).

    CAS  Article  Google Scholar 

  14. 14

    Macaulay, I.C. et al. G&T-seq: parallel sequencing of single-cell genomes and transcriptomes. Nat. Methods 12, 519–522 (2015).

    CAS  Article  Google Scholar 

  15. 15

    Angermueller, C. et al. Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity. Nat. Methods 13, 229–232 (2016).

    CAS  Article  Google Scholar 

  16. 16

    Miura, F., Enomoto, Y., Dairiki, R. & Ito, T. Amplification-free whole-genome bisulfite sequencing by post-bisulfite adaptor tagging. Nucleic Acids Res. 40, e136 (2012).

    CAS  Article  Google Scholar 

  17. 17

    Cokus, S.J. et al. Shotgun bisulphite sequencing of the Arabidopsis genome reveals DNA methylation patterning. Nature 452, 215–219 (2008).

    CAS  Article  Google Scholar 

  18. 18

    Shirane, K. et al. Mouse oocyte methylomes at base resolution reveal genome-wide accumulation of non-CpG methylation and role of DNA methyltransferases. PLoS Genet. 9, e1003439 (2013).

    CAS  Article  Google Scholar 

  19. 19

    Okae, H. et al. Genome-wide analysis of DNA methylation dynamics during early human development. PLoS Genet. 10, e1004868 (2014).

    Article  Google Scholar 

  20. 20

    Fisher, S. et al. A scalable, fully automated process for construction of sequence-ready human exome targeted capture libraries. Genome Biol. 12, R1 (2011).

    Article  Google Scholar 

  21. 21

    Li, Y. et al. The DNA methylome of human peripheral blood mononuclear cells. PLoS Biol. 8, e1000533 (2010).

    Article  Google Scholar 

  22. 22

    Bock, C. et al. DNA methylation dynamics during in vivo differentiation of blood and skin stem cells. Mol. Cell 47, 633–647 (2012).

    CAS  Article  Google Scholar 

  23. 23

    Yu, M. et al. Tet-assisted bisulfite sequencing of 5-hydroxymethylcytosine. Nat. Protoc. 7, 2159–2170 (2012).

    CAS  Article  Google Scholar 

  24. 24

    Mooijman, D., Dey, S.S., Boisset, J.-C., Crosetto, N. & van Oudenaarden, A. Single-cell 5hmC sequencing reveals chromosome-wide cell-to-cell variability and enables lineage reconstruction. Nat. Biotechnol. 34, 852–856 (2016).

    CAS  Article  Google Scholar 

  25. 25

    Booth, M.J. et al. Oxidative bisulfite sequencing of 5-methylcytosine and 5-hydroxymethylcytosine. Nat. Protoc. 8, 1841–1851 (2013).

    CAS  Article  Google Scholar 

  26. 26

    Hu, Y. et al. Simultaneous profiling of transcriptome and DNA methylome from a single cell. Genome Biol. 17, 88 (2016).

    Article  Google Scholar 

  27. 27

    Hou, Y. et al. Single-cell triple omics sequencing reveals genetic, epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas. Cell Res. 26, 304–319 (2016).

    CAS  Article  Google Scholar 

  28. 28

    Bronner, I.F., Quail, M.A., Turner, D.J. & Swerdlow, H. Improved protocols for Illumina sequencing. Curr. Protoc. Hum. Genet. 80 18.2.1 (2014).

  29. 29

    Habibi, E. et al. Whole-genome bisulfite sequencing of two distinct interconvertible DNA methylomes of mouse embryonic stem cells. Cell Stem Cell 13, 360–369 (2013).

    CAS  Article  Google Scholar 

  30. 30

    Macaulay, I.C. et al. Separation and parallel sequencing of the genomes and transcriptomes of single cells using G&T-seq. Nat Protoc. 11, 2081–2103 (2016).

    CAS  Article  Google Scholar 

  31. 31

    Guo, G. et al. Naive pluripotent stem cells derived directly from isolated cells of the human inner cell mass. Stem Cell Rep. 6, 437–446 (2016).

    CAS  Article  Google Scholar 

  32. 32

    von Meyenn, F. et al. Comparative principles of DNA methylation reprogramming during human and mouse in vitro primordial germ cell specification. Dev. Cell 39, 104–115 (2016).

    CAS  Article  Google Scholar 

  33. 33

    Eckersley-Maslin, M.A. et al. MERVL/Zscan4 network activation results in transient genome-wide DNA demethylation of mESCs. Cell Rep. 17, 179–192 (2016).

    CAS  Article  Google Scholar 

  34. 34

    Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10–12 (2011).

    Article  Google Scholar 

  35. 35

    Krueger, F. & Andrews, S.R. Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics 27, 1571–1572 (2011).

    CAS  Article  Google Scholar 

  36. 36

    Langmead, B. & Salzberg, S.L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    CAS  Article  Google Scholar 

  37. 37

    Guo, H. et al. Single-cell methylome landscapes of mouse embryonic stem cells and early embryos analyzed using reduced representation bisulfite sequencing. Genome Res. 23, 2126–2135 (2013).

    CAS  Article  Google Scholar 

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W.R. was supported by the Biotechnology and Biological Sciences Research Council (BBSRC), the Wellcome Trust, the EU Blueprint and EpiGeneSys. G.K. was supported by the BBSRC and the Medical Research Council (MRC). H.J.L. was supported by the EU Network of Excellence (NoE) EpiGeneSys.

Author information




S.J.C. developed the updated protocol and wrote the manuscript with input from all other authors; S.A.S. and H.J.L conceived the original protocol and developed the updated version; F.K. developed the bioinformatics pipeline; and W.R. and G.K. conceived of and jointly supervised the study.

Corresponding authors

Correspondence to Stephen J Clark or Wolf Reik or Gavin Kelsey.

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Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Table 1

iPCRTag primer sequences. Sequence obtained is the index that is read by the Illumina machine and is the reverse complement of index portion of the primer. Primers should be ordered HPLC-purified and resuspended in EB to 100 μM. (PDF 299 kb)

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Clark, S., Smallwood, S., Lee, H. et al. Genome-wide base-resolution mapping of DNA methylation in single cells using single-cell bisulfite sequencing (scBS-seq). Nat Protoc 12, 534–547 (2017).

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