Brief Communication

Highly scalable generation of DNA methylation profiles in single cells

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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.

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Acknowledgements

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.

Author information

Affiliations

  1. Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, Oregon, USA.

    • Ryan M Mulqueen
    • , Kristof A Torkenczy
    • , Andrew J Fields
    • , Brian J O'Roak
    •  & Andrew C Adey

  2. Illumina, Inc., San Diego, California, USA.

    • Dmitry Pokholok
    • , Steven J Norberg
    •  & Frank J Steemers

  3. Department of Molecular Microbiology & Immunology, Oregon Health & Science University, Portland, Oregon, USA.

    • Duanchen Sun
    •  & Zheng Xia

  4. Computational Biology Program, Oregon Health & Science University, Portland, Oregon, USA.

    • Duanchen Sun
    •  & Zheng Xia

  5. Vollum Institute, Oregon Health & Science University, Portland, Oregon, USA.

    • John R Sinnamon

  6. Department of Genome Sciences, University of Washington, Seattle, Washington, USA.

    • Jay Shendure
    •  & Cole Trapnell

  7. Howard Hughes Medical Institute, Seattle, Washington, USA.

    • Jay Shendure

  8. Knight Cardiovascular Institute, Portland, Oregon, USA.

    • Andrew C Adey

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Contributions

A.C.A. and R.M.M. conceived the sci-MET assay. R.M.M. carried out all sci-MET preparations with contributions from A.J.F. A.C.A., R.M.M., F.J.S., D.P., and S.N. designed the sci-MET adaptors and primers and reduced the assay to practice. R.M.M., F.J.S., D.P., and S.N. carried out all sequencing. R.M.M. led the data analysis. D.S. and Z.X. performed the NMF-tSNE analysis. K.A.T. provided additional analyses. J.R.S. performed mouse cortex dissection. F.J.S., J.S., C.T., and B.J.O. contributed to analysis design and edited the manuscript. A.C.A. supervised all aspects of the study. All authors approved the manuscript.

Competing interests

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.

Corresponding author

Correspondence to Andrew C Adey.

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–25

  2. 2.

    Life Sciences Reporting Summary

  3. 3.

    Supplementary Tables

    Supplementary tables 1–5

  4. 4.

    Supplementary Notes

    Supplementary Notes 1–2

Zip files

  1. 1.

    Supplementary Code

    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.

Excel files

  1. 1.

    Supplementary Dataset 1

    sciMET Transposase-loaded Oligos (5′-3′) design.

  2. 2.

    Supplementary Dataset 2

    sci-MET on Human Cell Line Mix metadata, summary statistics, and quality control metrics.

  3. 3.

    Supplementary Dataset 3

    sci-MET on Mouse Cortex metadata, summarystatistics and quality control metrics.

  4. 4.

    Supplementary Dataset 4

    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.

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