Highly scalable generation of DNA methylation profiles in single cells.
- Resource Type
- Academic Journal
- Authors
- Mulqueen RM; Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, Oregon, USA.; Pokholok D; Illumina, Inc., San Diego, California, USA.; Norberg SJ; Illumina, Inc., San Diego, California, USA.; Torkenczy KA; Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, Oregon, USA.; Fields AJ; Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, Oregon, USA.; Sun D; Department of Molecular Microbiology & Immunology, Oregon Health & Science University, Portland, Oregon, USA.; Computational Biology Program, Oregon Health & Science University, Portland, Oregon, USA.; Sinnamon JR; Vollum Institute, Oregon Health & Science University, Portland, Oregon, USA.; Shendure J; Department of Genome Sciences, University of Washington, Seattle, Washington, USA.; Howard Hughes Medical Institute, Seattle, Washington, USA.; Trapnell C; Department of Genome Sciences, University of Washington, Seattle, Washington, USA.; O'Roak BJ; Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, Oregon, USA.; Xia Z; Department of Molecular Microbiology & Immunology, Oregon Health & Science University, Portland, Oregon, USA.; Computational Biology Program, Oregon Health & Science University, Portland, Oregon, USA.; Steemers FJ; Illumina, Inc., San Diego, California, USA.; Adey AC; Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, Oregon, USA.; Knight Cardiovascular Institute, Portland, Oregon, USA.
- Source
- Publisher: Nature America Publishing Country of Publication: United States NLM ID: 9604648 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1546-1696 (Electronic) Linking ISSN: 10870156 NLM ISO Abbreviation: Nat Biotechnol Subsets: MEDLINE
- Subject
- Language
- English
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.