Abstract

High-throughput single-cell transcriptomics offers an unbiased approach for understanding the extent, basis and function of gene expression variation between seemingly identical cells. Here we sequence single-cell RNA-seq libraries prepared from over 1,700 primary mouse bone-marrow-derived dendritic cells spanning several experimental conditions. We find substantial variation between identically stimulated dendritic cells, in both the fraction of cells detectably expressing a given messenger RNA and the transcript’s level within expressing cells. Distinct gene modules are characterized by different temporal heterogeneity profiles. In particular, a ‘core’ module of antiviral genes is expressed very early by a few ‘precocious’ cells in response to uniform stimulation with a pathogenic component, but is later activated in all cells. By stimulating cells individually in sealed microfluidic chambers, analysing dendritic cells from knockout mice, and modulating secretion and extracellular signalling, we show that this response is coordinated by interferon-mediated paracrine signalling from these precocious cells. Notably, preventing cell-to-cell communication also substantially reduces variability between cells in the expression of an early-induced ‘peaked’ inflammatory module, suggesting that paracrine signalling additionally represses part of the inflammatory program. Our study highlights the importance of cell-to-cell communication in controlling cellular heterogeneity and reveals general strategies that multicellular populations can use to establish complex dynamic responses.

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Accessions

Primary accessions

Gene Expression Omnibus

Data deposits

Data are deposited in GEO under accession number GSE48968.

References

  1. 1.

    et al. Single-cell NF-κB dynamics reveal digital activation and analogue information processing. Nature 466, 267–271 (2010)

  2. 2.

    & Single-molecule approaches to stochastic gene expression. Ann. Rev. Biophys. 38, 255–270 (2009)

  3. 3.

    , , & Characterizing heterogeneous cellular responses to perturbations. Proc. Natl Acad. Sci. USA 105, 19306–19311 (2008)

  4. 4.

    et al. A chromatin-mediated reversible drug-tolerant state in cancer cell subpopulations. Cell 141, 69–80 (2010)

  5. 5.

    , , , & Non-genetic origins of cell-to-cell variability in TRAIL-induced apoptosis. Nature 459, 428–432 (2009)

  6. 6.

    et al. Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells. Science 329, 533–538 (2010)

  7. 7.

    & We are all individuals: causes and consequences of non-genetic heterogeneity in mammalian cells. Curr. Opin. Genet. Dev. 21, 753–758 (2011)

  8. 8.

    et al. Single-cell quantification of IL-2 response by effector and regulatory T cells reveals critical plasticity in immune response. Mol. Syst. Biol. 6, 437–453 (2010)

  9. 9.

    , & Bistability, epigenetics, and bet-hedging in bacteria. Ann. Rev. Microbiol. 62, 193–210 (2008)

  10. 10.

    , , , & Stochastic cytokine expression induces mixed T helper cell states. PLoS Biol. 11, e1001618 (2013)

  11. 11.

    et al. Interplay between gene expression noise and regulatory network architecture. Trends Genet. 28, 221–232 (2012)

  12. 12.

    et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332, 687–696 (2011)

  13. 13.

    , , & CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell Rep. 2, 666–673 (2012)

  14. 14.

    et al. Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Genome Res. 21, 1160–1167 (2011)

  15. 15.

    et al. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nature Biotechnol. 30, 777–782 (2012)

  16. 16.

    et al. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature 498, 236–240 (2013)

  17. 17.

    et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nature Methods 6, 377–382 (2009)

  18. 18.

    et al. Unbiased reconstruction of a mammalian transcriptional network mediating pathogen responses. Science 326, 257–263 (2009)

  19. 19.

    et al. Quantitative assessment of single-cell RNA-sequencing methods. Nature Methods 11, 41–46 (2014)

  20. 20.

    & Statistical significance for genomewide studies. Proc. Natl Acad. Sci. USA 100, 9440–9445 (2003)

  21. 21.

    et al. Data exploration, quality control and testing in single-cell qPCR-based gene expression experiments. Bioinformatics 29, 461–467 (2013)

  22. 22.

    et al. Highly multiplexed and strand-specific single-cell RNA 5′ end sequencing. Nature Protocols 7, 823–828 (2012)

  23. 23.

    et al. From single-cell to cell-pool transcriptomes: stochasticity in gene expression and RNA splicing. Genome Res. 24, 496–510 (2014)

  24. 24.

    et al. A high-throughput chromatin immunoprecipitation approach reveals principles of dynamic gene regulation in mammals. Mol. Cell 47, 810–822 (2012)

  25. 25.

    et al. Visualising individual sequence-specific protein-DNA interactions in situ. New Biotechnol. 29, 589–598 (2012)

  26. 26.

    et al. A noisy paracrine signal determines the cellular NFκB response to lipopolysaccharide. Sci. Signal. 2, ra65 (2009)

  27. 27.

    et al. Multi-layered stochasticity and paracrine signal propagation shape the type-I interferon response. Mol. Syst. Biol. 8, 584 (2012)

  28. 28.

    , , , & Stochastic expression of the interferon-β gene. PLoS Biol. 10, e1001249 (2012)

  29. 29.

    et al. Population context determines cell-to-cell variability in endocytosis and virus infection. Nature 461, 520–523 (2009)

  30. 30.

    , & Interferons as anti-inflammatory mediators. Sci. STKE 416, pe70 (2007)

  31. 31.

    , , & Keratins and the keratinocyte activation cycle. J. Invest. Dermatol. 116, 633–640 (2001)

  32. 32.

    et al. Metabolic labeling of RNA uncovers principles of RNA production and degradation dynamics in mammalian cells. Nature Biotechnol. 29, 436–442 (2011)

  33. 33.

    & Quorum sensing: cell-to-cell communication in bacteria. Ann. Rev. Cell Dev. Biol. 21, 319–346 (2005)

  34. 34.

    , & Interferon in systemic lupus erythematosus and other autoimmune diseases. Immunity 25, 383–392 (2006)

  35. 35.

    & Type I interferons: crucial participants in disease amplification in autoimmunity. Nature Rev. Rheumatol. 6, 40–49 (2010)

  36. 36.

    & RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011)

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Acknowledgements

We thank B. Tilton, T. Rogers and M. Tam for assistance with cell sorting; E. Shefler, C. Guiducci, D. Thompson, and O. Rozenblatt-Rosen for project management and discussions and the Broad Genomics Platform for sequencing. We thank J. West, R. Lebofsky, A. Leyrat, M. Thu, M. Wong, W. Yorza, D. Toppani, M. Norris and B. Clerkson for contributions to C1 system development; B. Alvarado, M. Ray and L. Knuttson for assistance with C1 experiments; and M. Unger for discussions. Work was supported by an NIH Postdoctoral Fellowship (1F32HD075541-01, R.S.), an NIH grant (U54 AI057159, N.H.), an NIH New Innovator Award (DP2 OD002230, N.H.), an NIH CEGS (1P50HG006193-01, H.P., N.H., A.R.), NIH Pioneer Awards (5DP1OD003893-03 to H.P., DP1OD003958-01 to A.R.), the Broad Institute (H.P. and A.R.), HHMI (A.R.), the Klarman Cell Observatory at the Broad Institute (A.R.), an ISF-Broad Grant (N.F.), and the ERC (N.F.).

Author information

Author notes

    • Alex K. Shalek
    • , Rahul Satija
    •  & Joe Shuga

    These authors contributed equally to this work.

Affiliations

  1. Department of Chemistry & Chemical Biology, Harvard University, 12 Oxford Street, Cambridge, Massachusetts 02138, USA

    • Alex K. Shalek
    • , Rona S. Gertner
    • , Jellert T. Gaublomme
    • , Ruihua Ding
    •  & Hongkun Park
  2. Department of Physics, Harvard University, 17 Oxford Street, Cambridge, Massachusetts 02138, USA

    • Alex K. Shalek
    • , Rona S. Gertner
    • , Jellert T. Gaublomme
    • , Ruihua Ding
    •  & Hongkun Park
  3. Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, Massachusetts 02142, USA

    • Alex K. Shalek
    • , Rahul Satija
    • , John J. Trombetta
    • , Dave Gennert
    • , Diana Lu
    • , Nir Yosef
    • , Schraga Schwartz
    • , Raktima Raychowdhury
    • , Nir Hacohen
    • , Hongkun Park
    •  & Aviv Regev
  4. Fluidigm Corporation, 7000 Shoreline Court, Suite 100, South San Francisco, California 94080, USA

    • Joe Shuga
    • , Peilin Chen
    • , Brian Fowler
    • , Suzanne Weaver
    • , Jing Wang
    • , Xiaohui Wang
    •  & Andrew P. May
  5. School of Computer Science and Engineering, Hebrew University, 91904 Jerusalem, Israel

    • Nir Friedman
  6. Center for Immunology and Inflammatory Diseases & Department of Medicine, Massachusetts General Hospital, Charlestown, Massachusetts 02129, USA

    • Nir Hacohen
  7. Howard Hughes Medical Institute, Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts 02140, USA

    • Aviv Regev

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Contributions

A.R., A.P.M., H.P., A.K.S., R.S. and J.S. conceived and designed the study. A.K.S., J.S., J.J.T., D.G., D.L., P.C., R.S.G., J.T.G., B.F., S.W., J.W., X.W., R.D. and R.R. performed experiments. R.S., A.K.S., S.S. and N.Y. performed computational analyses. R.S., A.K.S., J.S., N.F., H.P., A.P.M. and A.R. wrote the manuscript, with extensive input from all authors.

Competing interests

J.S., P.C., B.F., S.W., J.W., X.W. and A.P.M. declare competing financial interests as employees and/or stockholders in Fluidigm Corp.

Corresponding authors

Correspondence to Rahul Satija or Hongkun Park or Andrew P. May.

Extended data

Supplementary information

PDF files

  1. 1.

    Supplementary Information

    This file contains Supplementary Notes, Supplementary Methods and Supplementary References.

Excel files

  1. 1.

    Supplementary Table 1

    This table contains a summary of single cell RNA-Seq experiments.

  2. 2.

    Supplementary Table 2

    This table contains sequencing metrics for single cell and population RNA-seq libraries.

  3. 3.

    Supplementary Table 3

    This table shows cluster assignments and GO enrichments for the 813 genes that were induced by at least two-fold (compared to unstimulated cells) at a population level at any time point during the LPS time course.

  4. 4.

    Supplementary Table 4

    This table contains ingenuity analysis of the genes differentially regulated between a wildtype 4 hour LPS stimulation and either the "on-chip" stimulation, an Ifnar knockout, or a Stat1 knockout.

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DOI

https://doi.org/10.1038/nature13437

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