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

Author

Listed:
  • Alex K. Shalek

    (Harvard University, 12 Oxford Street, Cambridge, Massachusetts 02138, USA
    Harvard University, 17 Oxford Street, Cambridge, Massachusetts 02138, USA
    Broad Institute of MIT and Harvard, 7 Cambridge Center)

  • Rahul Satija

    (Broad Institute of MIT and Harvard, 7 Cambridge Center)

  • Joe Shuga

    (Fluidigm Corporation, 7000 Shoreline Court, Suite 100, South San Francisco, California 94080, USA)

  • John J. Trombetta

    (Broad Institute of MIT and Harvard, 7 Cambridge Center)

  • Dave Gennert

    (Broad Institute of MIT and Harvard, 7 Cambridge Center)

  • Diana Lu

    (Broad Institute of MIT and Harvard, 7 Cambridge Center)

  • Peilin Chen

    (Fluidigm Corporation, 7000 Shoreline Court, Suite 100, South San Francisco, California 94080, USA)

  • Rona S. Gertner

    (Harvard University, 12 Oxford Street, Cambridge, Massachusetts 02138, USA
    Harvard University, 17 Oxford Street, Cambridge, Massachusetts 02138, USA)

  • Jellert T. Gaublomme

    (Harvard University, 12 Oxford Street, Cambridge, Massachusetts 02138, USA
    Harvard University, 17 Oxford Street, Cambridge, Massachusetts 02138, USA)

  • Nir Yosef

    (Broad Institute of MIT and Harvard, 7 Cambridge Center)

  • Schraga Schwartz

    (Broad Institute of MIT and Harvard, 7 Cambridge Center)

  • Brian Fowler

    (Fluidigm Corporation, 7000 Shoreline Court, Suite 100, South San Francisco, California 94080, USA)

  • Suzanne Weaver

    (Fluidigm Corporation, 7000 Shoreline Court, Suite 100, South San Francisco, California 94080, USA)

  • Jing Wang

    (Fluidigm Corporation, 7000 Shoreline Court, Suite 100, South San Francisco, California 94080, USA)

  • Xiaohui Wang

    (Fluidigm Corporation, 7000 Shoreline Court, Suite 100, South San Francisco, California 94080, USA)

  • Ruihua Ding

    (Harvard University, 12 Oxford Street, Cambridge, Massachusetts 02138, USA
    Harvard University, 17 Oxford Street, Cambridge, Massachusetts 02138, USA)

  • Raktima Raychowdhury

    (Broad Institute of MIT and Harvard, 7 Cambridge Center)

  • Nir Friedman

    (School of Computer Science and Engineering, Hebrew University, 91904 Jerusalem, Israel)

  • Nir Hacohen

    (Broad Institute of MIT and Harvard, 7 Cambridge Center
    Massachusetts General Hospital)

  • Hongkun Park

    (Harvard University, 12 Oxford Street, Cambridge, Massachusetts 02138, USA
    Harvard University, 17 Oxford Street, Cambridge, Massachusetts 02138, USA
    Broad Institute of MIT and Harvard, 7 Cambridge Center)

  • Andrew P. May

    (Fluidigm Corporation, 7000 Shoreline Court, Suite 100, South San Francisco, California 94080, USA)

  • Aviv Regev

    (Broad Institute of MIT and Harvard, 7 Cambridge Center
    Howard Hughes Medical Institute, Massachusetts Institute of Technology)

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.

Suggested Citation

  • 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 W, 2014. "Single-cell RNA-seq reveals dynamic paracrine control of cellular variation," Nature, Nature, vol. 510(7505), pages 363-369, June.
    • Handle: RePEc:nat:nature:v:510:y:2014:i:7505:d:10.1038_nature13437
    DOI: 10.1038/nature13437
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    Citations

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    Cited by:

    1. Sylvie Rato & Antonio Rausell & Miguel Muñoz & Amalio Telenti & Angela Ciuffi, 2017. "Single-cell analysis identifies cellular markers of the HIV permissive cell," PLOS Pathogens, Public Library of Science, vol. 13(10), pages 1-23, October.
    2. Yael Korem & Pablo Szekely & Yuval Hart & Hila Sheftel & Jean Hausser & Avi Mayo & Michael E Rothenberg & Tomer Kalisky & Uri Alon, 2015. "Geometry of the Gene Expression Space of Individual Cells," PLOS Computational Biology, Public Library of Science, vol. 11(7), pages 1-27, July.
    3. Shengfei Tang & Yanmei Shi & Qi Zhang, 2023. "Bias-Corrected Inference of High-Dimensional Generalized Linear Models," Mathematics, MDPI, vol. 11(4), pages 1-14, February.
    4. Md Tauhidul Islam & Jen-Yeu Wang & Hongyi Ren & Xiaomeng Li & Masoud Badiei Khuzani & Shengtian Sang & Lequan Yu & Liyue Shen & Wei Zhao & Lei Xing, 2022. "Leveraging data-driven self-consistency for high-fidelity gene expression recovery," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    5. Shaojie Qin & Yi Zhang & Mingying Shi & Daiyu Miao & Jiansen Lu & Lu Wen & Yu Bai, 2024. "In-depth organic mass cytometry reveals differential contents of 3-hydroxybutanoic acid at the single-cell level," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    6. Peizhuo Wang & Xiao Wen & Han Li & Peng Lang & Shuya Li & Yipin Lei & Hantao Shu & Lin Gao & Dan Zhao & Jianyang Zeng, 2023. "Deciphering driver regulators of cell fate decisions from single-cell transcriptomics data with CEFCON," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    7. Manikandan Narayanan & Andrew J Martins & John S Tsang, 2016. "Robust Inference of Cell-to-Cell Expression Variations from Single- and K-Cell Profiling," PLOS Computational Biology, Public Library of Science, vol. 12(7), pages 1-33, July.
    8. Irene Robles-Rebollo & Sergi Cuartero & Adria Canellas-Socias & Sarah Wells & Mohammad M. Karimi & Elisabetta Mereu & Alexandra G. Chivu & Holger Heyn & Chad Whilding & Dirk Dormann & Samuel Marguerat, 2022. "Cohesin couples transcriptional bursting probabilities of inducible enhancers and promoters," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    9. Jingtao Wang & Gregory J. Fonseca & Jun Ding, 2024. "scSemiProfiler: Advancing large-scale single-cell studies through semi-profiling with deep generative models and active learning," Nature Communications, Nature, vol. 15(1), pages 1-27, December.

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