Single-cell RNA-seq reveals dynamic paracrine control of cellular variation

Alex K. Shalek, Rahul Satija*, Joe Shuga, John J. Trombetta, Dave Gennert, Diana Lu, Peilin Chen, Rona S. Gertner, Jellert T. Gaublomme, Nir Yosef, Schraga Schwartz, Brian Fowler, Suzanne Weaver, Jing Wang, Xiaohui Wang, Ruihua Ding, Raktima Raychowdhury, Nir Friedman, Nir Hacohen, Hongkun ParkAndrew P. May, Aviv Regev

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

667 Scopus citations


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.

Original languageAmerican English
Pages (from-to)363-369
Number of pages7
Issue number7505
StatePublished - 2014

Bibliographical note

Funding Information:
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.KnuttsonforassistancewithC1experiments;andM.Ungerfordiscussions.Workwas 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.).


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