TY - JOUR T1 - Scalable and efficient single-cell DNA methylation sequencing by combinatorial indexing JF - bioRxiv DO - 10.1101/157230 SP - 157230 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 Y1 - 2017/01/01 UR - http://biorxiv.org/content/early/2017/06/28/157230.abstract N2 - 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. ER -