Han L, Wu HJ, Zhu H, Kim KY, Marjani SL, Riester M, Euskirchen G, Zi X, Yang J, Han J, Snyder M, Park IH, Irizarry R, Weissman SM, Michor F, Fan R, Pan X. 2017. Bisulfite-independent analysis of CpG island methylation enables genome-scale stratification of single cells. Nucleic acids research. 45(10):e77. Pubmed: 28126923 DOI:10.1093/nar/gkx026


Conventional DNA bisulfite sequencing has been extended to single cell level, but the coverage consistency is insufficient for parallel comparison. Here we report a novel method for genome-wide CpG island (CGI) methylation sequencing for single cells (scCGI-seq), combining methylation-sensitive restriction enzyme digestion and multiple displacement amplification for selective detection of methylated CGIs. We applied this method to analyzing single cells from two types of hematopoietic cells, K562 and GM12878 and small populations of fibroblasts and induced pluripotent stem cells. The method detected 21 798 CGIs (76% of all CGIs) per cell, and the number of CGIs consistently detected from all 16 profiled single cells was 20 864 (72.7%), with 12 961 promoters covered. This coverage represents a substantial improvement over results obtained using single cell reduced representation bisulfite sequencing, with a 66-fold increase in the fraction of consistently profiled CGIs across individual cells. Single cells of the same type were more similar to each other than to other types, but also displayed epigenetic heterogeneity. The method was further validated by comparing the CpG methylation pattern, methylation profile of CGIs/promoters and repeat regions and 41 classes of known regulatory markers to the ENCODE data. Although not every minor methylation differences between cells are detectable, scCGI-seq provides a solid tool for unsupervised stratification of a heterogeneous cell population.
© The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

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Franziska Michor uses the tools of theoretical evolutionary biology, applied mathematics, statistics, and computational biology to address important questions in cancer research.

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