Highly scalable generation of DNA methylation profiles in single cells

Ryan M. Mulqueen, Dmitry Pokholok, Steven J. Norberg, Kristof A. Torkenczy, Andrew J. Fields, Duanchen Sun, John R. Sinnamon, Jay Shendure, Cole Trapnell, Brian J. O'Roak, Zheng Xia, Frank J. Steemers, Andrew C. Adey

Research output: Contribution to journalArticle

56 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)428-431
Number of pages4
JournalNature biotechnology
Volume36
Issue number5
DOIs
StatePublished - Jun 1 2018

ASJC Scopus subject areas

  • Biotechnology
  • Bioengineering
  • Applied Microbiology and Biotechnology
  • Molecular Medicine
  • Biomedical Engineering

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  • Cite this

    Mulqueen, R. M., Pokholok, D., Norberg, S. J., Torkenczy, K. A., Fields, A. J., Sun, D., Sinnamon, J. R., Shendure, J., Trapnell, C., O'Roak, B. J., Xia, Z., Steemers, F. J., & Adey, A. C. (2018). Highly scalable generation of DNA methylation profiles in single cells. Nature biotechnology, 36(5), 428-431. https://doi.org/10.1038/nbt.4112