PT - JOURNAL ARTICLE AU - Ryan M. Mulqueen AU - Dmitry Pokholok AU - Steve Norberg AU - Andrew J. Fields AU - Duanchen Sun AU - Kristof A. Torkenczy AU - Jay Shendure AU - Cole Trapnell AU - Brian J. O’Roak AU - Zheng Xia AU - Frank J. Steemers AU - Andrew C. Adey TI - Scalable and efficient single-cell DNA methylation sequencing by combinatorial indexing AID - 10.1101/157230 DP - 2017 Jan 01 TA - bioRxiv PG - 157230 4099 - http://biorxiv.org/content/early/2017/06/28/157230.short 4100 - http://biorxiv.org/content/early/2017/06/28/157230.full AB - Here we present a novel method: single-cell combinatorial indexing for methylation analysis (sci-MET), which is the first highly scalable assay for whole genome methylation profiling of single cells. We use sci-MET to produce 2,697 total single-cell bisulfite sequencing libraries and achieve read alignment rates of 69 ± 7%, comparable to those of bulk cell methods. As a proof of concept, we applied sci-MET to successfully deconvolve the cellular identity of a mixture of three human cell lines.