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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Gene Expression Omnibus

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Data are deposited in GEO under accession number GSE48968.


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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.


  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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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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