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Nucleic Acids Res. 2017 Jun 2;45(10):e77. doi: 10.1093/nar/gkx026.

Bisulfite-independent analysis of CpG island methylation enables genome-scale stratification of single cells.

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Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA.
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02115, USA.
Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02215, USA.
Department of Genetics, Yale School of Medicine, New Haven, CT 06520, USA.
Department of Cell Biology, Second Military Medical University, Shanghai 200433, China.
Department of Genetics, Yale Stem Cell Center, Yale School of Medicine, 10 Amistad, 201B, New Haven, CT 06520, USA.
Department of Genetics, Stanford University, Palo Alto, CA 94305, USA.
Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, Guangzhou, China.
Guangdong Key Laboratory of Biochip Technology, Southern Medical University, Guangzhou 510515, Guangdong, China.


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.

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