Scalable and efficient single-cell DNA methylation sequencing by combinatorial indexing.
Ryan M. MulqueeniD, Dmitry Pokholok, Steven J Norberg, Kristof A. TorkenczyiD, Andrew J. FieldsiD, Duanchen SuniD, John R. SinnamoniD, Jay ShendureiD, Cole TrapnelliD, Brian J. O’RoakiD, Zheng XiaiD, Frank J. Steemers, Andrew AdeyiD
Abstract
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.
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