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

doi:10.1038/nbt.4112

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 journal › Article › peer-review

160 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 language	English (US)
Pages (from-to)	428-431
Number of pages	4
Journal	Nature biotechnology
Volume	36
Issue number	5
DOIs	https://doi.org/10.1038/nbt.4112
State	Published - Jun 1 2018

ASJC Scopus subject areas

Applied Microbiology and Biotechnology
Bioengineering
Molecular Medicine
Biotechnology
Biomedical Engineering

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10.1038/nbt.4112

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title = "Highly scalable generation of DNA methylation profiles in single cells",

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.",

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

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AU - Mulqueen, Ryan M.

AU - Pokholok, Dmitry

AU - Norberg, Steven J.

AU - Torkenczy, Kristof A.

AU - Fields, Andrew J.

AU - Sun, Duanchen

AU - Sinnamon, John R.

AU - Shendure, Jay

AU - Trapnell, Cole

AU - O'Roak, Brian J.

AU - Xia, Zheng

AU - Steemers, Frank J.

AU - Adey, Andrew C.

PY - 2018/6/1

Y1 - 2018/6/1

N2 - 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.

AB - 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.

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Highly scalable generation of DNA methylation profiles in single cells

Abstract

ASJC Scopus subject areas

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