We present a highly scalable assay for whole-genome methylation profiling of single cells. We use our approach, single-cell combinatorial indexing for methylation analysis (sci-MET), to produce 3,282 single-cell bisulfite sequencing libraries and achieve read alignment rates of 68 ± 8%. We apply sci-MET to discriminate the cellular identity of a mixture of three human cell lines and to identify excitatory and inhibitory neuronal populations from mouse cortical tissue.
Gene Expression Omnibus
We would like to thank B. DeRosa for culturing the primary fibroblast cell line for this project (Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, Oregon, USA). We would like to thank other members of the Adey laboratory for helpful suggestions and dialog pertaining to this work, particularly S. Vitak. We also thank G. Mandel for providing the mice used in this study and for helpful discussion and comments on the manuscript (Vollum Institute, Oregon Health & Science University, Portland, Oregon, USA). J.R.S. is supported by the Rett Syndrome Research Trust. A.C.A. is supported by an R35 from NIGMS (1R35GM124704-01), and the Knight Cardiovascular Institute. B.J.O. is supported a fellowship from the Sloan Foundation.
sciMET Transposase-loaded Oligos (5′-3′) design.
sci-MET on Human Cell Line Mix metadata, summary statistics, and quality control metrics.
sci-MET on Mouse Cortex metadata, summarystatistics and quality control metrics.
Non-binary CG enrichment across genomic annotations and transcription factor binding sites. Pearson's paired chisquared test was performed between non-binary and binary sites per feature per collapsed cluster.