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

Nature Biotechnology volume 35, pages 640–646 (2017)Cite this article

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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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Figure 1: Schematic of SiC-seq workflow.
Figure 2: Microfluidic and biochemical workflow to generate a SiC-seq library.
Figure 3: SiC-seq performance on an artificial microbial community consisting of ten different cell species.
Figure 4: Application of SiC-seq to a marine community recovered from the San Francisco coastline.

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Sequence Read Archive

Referenced accessions

NCBI Reference Sequence

Change history

  • 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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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

  1. Adam R Abate

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

Authors and 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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  1. Freeman Lan
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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.

Corresponding author

Correspondence to Adam R Abate.

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Competing interests

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

Integrated supplementary information

Supplementary Figure 1 Drawings of microfluidic devices.

Schematics of microfluidic devices used to: a) generate barcode droplets and encapsulate cells in agarose microgels; b) re-encapsulate gels in tagmentation reagents; c) merge gel droplets with barcode droplets and PCR droplets.

Supplementary Figure 2 Characterizing diffusion of genomic DNA fragments in agarose microgels

a) Bacterial genomes were stained with SYBR Green I to visually monitor diffusion of genomes in microgels before and after tagmentation. b) Bioanalyzer traces of samples taken from microgel contents and the supernatant. The shift in fragment size is relatively minor as a result of the relatively low stoichiometric ratio of transposase to genome used. c) Bioanalyzer traces from microgel-encapsulated genomes reacted with a higher stoichiometric ratio of transposase to genome. After 1 hr, a peak centered at ~150 bp representing tagmented DNA is visible for the sample containing tagmention enzyme.

Supplementary Figure 3 Lorenz curves of barcode group coverage, by species.

The average depth and distribution of genome coverage of each barcode group plotted as a Lorenz curve for each species in the 10-cell control experiment.

Supplementary Figure 4 Genome size-normalized barcode group purity scores.

Genome size-normalized purity scores of barcode groups in the 10-cell control experiment. Genome size-normalized purity scores are calculated using the same method using the fraction of the genome sequenced for each respective species rather than the raw number of reads.

Supplementary Figure 5 Barcode group purities, by species.

Purity scores of barcode groups separately plotted for each species in the 10-cell control experiment.

Supplementary Figure 6 Barcode group purity scores for second-most abundant species.

Purity scores of the next-most abundant species in a) barcode groups of purity <80% and b) barcode groups of purity >80%. In barcode groups with <80% purity, the purity scores of the next-most abundant species tend to be high from ~20% to 50%, reflecting that those two species represent the majority of the reads in the barcode group, suggesting that these barcode groups represent double encapsulations. Barcode groups with 100% purity are not represented in the plots. Blue line represents cumulative barcode counts normalized to 1.

Supplementary Figure 7 SiC-seq performance on an artificially constructed microbial community consisting of Staphylococcus, Bacillus, and Saccharomyces.

Relative abundance estimates of each species are calculated using barcode counting (Barcode), marker gene counting without barcodes (Metaphlan), and manual counting under the microscope after cell encapsulation (Microscope count) and while in culture (Theoretical).

Supplementary Figure 8 Aggregate genomic coverage of all the barcode groups for species in the synthetic microbial community.

Species at low abundance show frequent dropouts characterized by dips in the graph, but instances of systematic bias characterized by sharp peaks are rarely observed.

Supplementary Figure 9 Read distribution across the Staphylococcus genome for individual barcode groups.

Genomic mapping positions of reads for randomly chosen individual barcode Staphylococcus groups with >2000 reads.

Supplementary Figure 10 Schematic depiction of the framework of the SiC-Reads database.

SiC-seq reads which contain properties such as sequence, read ID, and taxonomy are stored inside barcode groups which contain properties such as purity and taxonomy.

Supplementary Figure 11 Analysis of the marine microbial community used to demonstrate in silico cytometry.

a) Taxonomic abundance of the SiC-Reads database by barcode groups; b) Distribution of purity of barcode groups in the database at the genus level.

Supplementary Figure 12 Reference data obtained by simulating reads from genomic sequences of isolated strains for comparison against data in the marine microbial community.

a) Antibiotic resistance network for whole genome sequenced strains in public databases; b) Virulence factor ratios calculated for publically available strains.

Supplementary information

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Supplementary Figures 1–12 (PDF 5470 kb)

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Lan, F., Demaree, B., Ahmed, N. et al. Single-cell genome sequencing at ultra-high-throughput with microfluidic droplet barcoding. Nat Biotechnol 35, 640–646 (2017). https://doi.org/10.1038/nbt.3880

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