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Single-cell genome sequencing at ultra-high-throughput with microfluidic droplet barcoding

Nature Biotechnology volume 35, pages 640646 (2017) | Download Citation

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Abstract

The application of single-cell genome sequencing to large cell populations has been hindered by technical challenges in isolating single cells during genome preparation. Here we present single-cell genomic sequencing (SiC-seq), which uses droplet microfluidics to isolate, fragment, and barcode the genomes of single cells, followed by Illumina sequencing of pooled DNA. We demonstrate ultra-high-throughput sequencing of >50,000 cells per run in a synthetic community of Gram-negative and Gram-positive bacteria and fungi. The sequenced genomes can be sorted in silico based on characteristic sequences. We use this approach to analyze the distributions of antibiotic-resistance genes, virulence factors, and phage sequences in microbial communities from an environmental sample. The ability to routinely sequence large populations of single cells will enable the de-convolution of genetic heterogeneity in diverse cell populations.

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  • 21 June 2017

    In the version of this article initially published, in Figure 3c, bars on the x axis were labeled S. epidermidis rather than S. enterica. The error has been corrected for the print, PDF and HTML versions of this article.

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References

  1. 1.

    Does the cell number 10(9) still really fit one gram of tumor tissue? Cell Cycle 8, 505–506 (2009).

  2. 2.

    & Viral abundance in aquatic systems: a comparison between marine and fresh waters. Mar. Ecol. Prog. Ser. 121, 217–226 (1995).

  3. 3.

    & Optical tweezers for single cells. J. R. Soc. Interface 5, 671–690 (2008).

  4. 4.

    et al. Obtaining genomes from uncultivated environmental microorganisms using FACS-based single-cell genomics. Nat. Protoc. 9, 1038–1048 (2014).

  5. 5.

    , & Dissecting the clonal origins of childhood acute lymphoblastic leukemia by single-cell genomics. Proc. Natl. Acad. Sci. USA 111, 17947–17952 (2014).

  6. 6.

    et al. Robust high-performance nanoliter-volume single-cell multiple displacement amplification on planar substrates. Proc. Natl. Acad. Sci. USA 113, 8484–8489 (2016).

  7. 7.

    & Single gene-based distinction of individual microbial genomes from a mixed population of microbial cells. Front. Microbiol. 6, 195 (2015).

  8. 8.

    et al. Targeted access to the genomes of low-abundance organisms in complex microbial communities. Appl. Environ. Microbiol. 73, 3205–3214 (2007).

  9. 9.

    , , & Virtual microfluidics for digital quantification and single-cell sequencing. Nat. Methods 13, 759–762 (2016).

  10. 10.

    , , & Droplet barcoding for massively parallel single-molecule deep sequencing. Nat. Commun. 7, 11784 (2016).

  11. 11.

    et al. Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state. Nat. Biotechnol. 33, 1165–1172 (2015).

  12. 12.

    et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201 (2015).

  13. 13.

    et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

  14. 14.

    et al. High-throughput single-cell labeling (Hi-SCL) for RNA-seq using drop-based microfluidics. PLoS One 10, e0116328 (2015).

  15. 15.

    et al. Single-cell multiplex gene detection and sequencing with microfluidically generated agarose emulsions. Angew. Chem. Int. Edn Engl. 50, 390–395 (2011).

  16. 16.

    et al. Formation of monodisperse bubbles in a microfluidic flow-focusing device. Appl. Phys. Lett. 85, 2649–2651 (2004).

  17. 17.

    et al. Haplotype-resolved whole-genome sequencing by contiguity-preserving transposition and combinatorial indexing. Nat. Genet. 46, 1343–1349 (2014).

  18. 18.

    et al. A quantitative comparison of single-cell whole genome amplification methods. PLoS One 9, e105585 (2014).

  19. 19.

    et al. Massively parallel polymerase cloning and genome sequencing of single cells using nanoliter microwells. Nat. Biotechnol. 31, 1126–1132 (2013).

  20. 20.

    & Antibiotic resistance--problems, progress, and prospects. N. Engl. J. Med. 371, 1761–1763 (2014).

  21. 21.

    Processes controlling the transmission of bacterial pathogens in the environment. Res. Microbiol. 158, 195–202 (2007).

  22. 22.

    & Gene transfer by transduction in the marine environment. Appl. Environ. Microbiol. 64, 2780–2787 (1998).

  23. 23.

    & Genes lost and genes found: evolution of bacterial pathogenesis and symbiosis. Science 292, 1096–1099 (2001).

  24. 24.

    Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012).

  25. 25.

    et al. Geospatial resolution of human and bacterial diversity with city-scale metagenomics. Cell Syst. 1, 72–87 (2015).

  26. 26.

    et al. Untangling genomes from metagenomes: revealing an uncultured class of marine Euryarchaeota. Science 335, 587–590 (2012).

  27. 27.

    et al. Reproducible copy number variation patterns among single circulating tumor cells of lung cancer patients. Proc. Natl. Acad. Sci. USA 110, 21083–21088 (2013).

  28. 28.

    & ARDB—Antibiotic Resistance Genes Database. Nucleic Acids Res. 37 (Suppl. 1), D443–D447 (2009).

  29. 29.

    , , , & VFDB 2012 update: toward the genetic diversity and molecular evolution of bacterial virulence factors. Nucleic Acids Res. 40, D641–D645 (2012).

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Acknowledgements

We are grateful for K. Stedman, R. Malmstrom, R. Andino, K. Pollard, and M. Fischbach for very helpful discussion of and advice on the manuscript. We thank C. O'Loughlin at UCSF for providing microbial strains. This work was supported by the National Science Foundation through a CAREER Award (grant number DBI-1253293); the National Institutes of Health (NIH) (grant numbers HG007233-01, R01-EB019453-01, 1R21HG007233, DP2-AR068129-01, R01-HG008978); and the Defense Advanced Research Projects Agency Living Foundries Program (contract numbers HR0011-12-C-0065, N66001-12-C-4211, HR0011-12-C-0066). Funding for open access charge: (NIH grant number DP2-AR068129-01). F.L. is supported by a PGS-D grant from the National Science and Engineering Research Council of Canada (NSERC).

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Author notes

    • Adam R Abate

    Present address: Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California, USA.

Affiliations

  1. Department of Bioengineering and Therapeutic Sciences, California Institute for Quantitative Biosciences, University of California, San Francisco, California, USA.

    • Freeman Lan
    • , Benjamin Demaree
    • , Noorsher Ahmed
    •  & Adam R Abate
  2. UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco, California, USA.

    • Freeman Lan
    • , Benjamin Demaree
    •  & Adam R Abate
  3. Chan Zuckerberg Biohub, San Francisco, California, USA.

    • Adam R Abate

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Contributions

F.L. and A.R.A. conceived of the SiC-seq method. F.L., B.D., and N.A. designed and performed the experiments, and analyzed data. F.L. and A.R.A. wrote the manuscript.

Competing interests

Patents pertaining to this workflow may be licensed to Mission Bio, of which A.R.A. is a shareholder.

Corresponding author

Correspondence to Adam R Abate.

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https://doi.org/10.1038/nbt.3880

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