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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We would like to thank B. DeRosa for culturing the primary fibroblast cell line for this project (Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, Oregon, USA). We would like to thank other members of the Adey laboratory for helpful suggestions and dialog pertaining to this work, particularly S. Vitak. We also thank G. Mandel for providing the mice used in this study and for helpful discussion and comments on the manuscript (Vollum Institute, Oregon Health & Science University, Portland, Oregon, USA). J.R.S. is supported by the Rett Syndrome Research Trust. A.C.A. is supported by an R35 from NIGMS (1R35GM124704-01), and the Knight Cardiovascular Institute. B.J.O. is supported a fellowship from the Sloan Foundation.
D.P., S.N., and F.J.S. are all employees of Illumina Inc. F.J.S., D.P., S.N., A.C.A., R.M.M., and J.S. all have one or more patents pertaining to one or more aspects of the technologies described here.
Supplementary Figures 1–25 (PDF 5847 kb)
Supplementary tables 1–5 (PDF 158 kb)
Supplementary Notes 1–2 (PDF 646 kb)
Supplementary Code: Custom scripts used to demultiplex fastq format files generated based on custom indexing strategy. Files included are text based barcodes, and perl scripts for demultiplexing and deduplicated reads. (ZIP 5 kb)
sciMET Transposase-loaded Oligos (5′-3′) design. (XLSX 13 kb)
sci-MET on Human Cell Line Mix metadata, summary statistics, and quality control metrics. (XLSX 183 kb)
sci-MET on Mouse Cortex metadata, summarystatistics and quality control metrics. (XLSX 83 kb)
Non-binary CG enrichment across genomic annotations and transcription factor binding sites. Pearson's paired chisquared test was performed between non-binary and binary sites per feature per collapsed cluster. (XLSX 77 kb)
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Mulqueen, R., Pokholok, D., Norberg, S. et al. Highly scalable generation of DNA methylation profiles in single cells. Nat Biotechnol 36, 428–431 (2018). https://doi.org/10.1038/nbt.4112
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