Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Letter
  • Published:

Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells

Nature volume 498, pages 236–240 (2013)Cite this article

Subjects

This article has been updated

Abstract

Recent molecular studies have shown that, even when derived from a seemingly homogenous population, individual cells can exhibit substantial differences in gene expression, protein levels and phenotypic output1,2,3,4,5, with important functional consequences4,5. Existing studies of cellular heterogeneity, however, have typically measured only a few pre-selected RNAs1,2 or proteins5,6 simultaneously, because genomic profiling methods3 could not be applied to single cells until very recently7,8,9,10. Here we use single-cell RNA sequencing to investigate heterogeneity in the response of mouse bone-marrow-derived dendritic cells (BMDCs) to lipopolysaccharide. We find extensive, and previously unobserved, bimodal variation in messenger RNA abundance and splicing patterns, which we validate by RNA-fluorescence in situ hybridization for select transcripts. In particular, hundreds of key immune genes are bimodally expressed across cells, surprisingly even for genes that are very highly expressed at the population average. Moreover, splicing patterns demonstrate previously unobserved levels of heterogeneity between cells. Some of the observed bimodality can be attributed to closely related, yet distinct, known maturity states of BMDCs; other portions reflect differences in the usage of key regulatory circuits. For example, we identify a module of 137 highly variable, yet co-regulated, antiviral response genes. Using cells from knockout mice, we show that variability in this module may be propagated through an interferon feedback circuit, involving the transcriptional regulators Stat2 and Irf7. Our study demonstrates the power and promise of single-cell genomics in uncovering functional diversity between cells and in deciphering cell states and circuits.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Single-cell RNA-Seq of LPS-stimulated BMDCs reveals extensive transcriptome heterogeneity.
Figure 2: Bimodal variation in expression levels across single cells.
Figure 3: Variation in isoform usage between single cells.
Figure 4: Analysis of co-variation in single-cell mRNA expression levels reveals distinct maturity states and an antiviral cell circuit.

Similar content being viewed by others

Accession codes

Accessions

Gene Expression Omnibus

Data deposits

Data have been deposited in GEO under accession number GSE41265.

Change history

  • 12 June 2013

    Minor changes were made to the spelling of authors S.S. and J.J.T. Also, an accession number for GEO was added.

References

  1. Bengtsson, M. Gene expression profiling in single cells from the pancreatic islets of Langerhans reveals lognormal distribution of mRNA levels. Genome Res. 15, 1388–1392 (2005)

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  3. Kalisky, T., Blainey, P. & Quake, S. R. Genomic analysis at the single-cell level. Ann. Rev. Gen. 45, 431–445 (2011)

    Article  CAS  Google Scholar 

  4. Feinerman, O. et al. Single-cell quantification of IL-2 response by effector and regulatory T cells reveals critical plasticity in immune response. Mol. Sys. Biol. 6, 1–16 (2010)

    Google Scholar 

  5. Cohen, A. A. et al. Dynamic proteomics of individual cancer cells in response to a drug. Science 322, 1511–1516 (2008)

    Article  ADS  CAS  Google Scholar 

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

    Article  ADS  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  10. Hashimshony, T., Wagner, F., Sher, N. & Yanai, I. CEL-Seq: single-cell RNA-seq by multiplexed linear amplification. Cell Rep. 2, 666–673 (2012)

    Article  CAS  Google Scholar 

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

    Article  ADS  CAS  Google Scholar 

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

    Article  ADS  CAS  Google Scholar 

  13. Li, G.-W. & Xie, X. S. Central dogma at the single-molecule level in living cells. Nature 475, 308–315 (2011)

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  15. Bar-Even, A. et al. Noise in protein expression scales with natural protein abundance. Nature Genet. 38, 636–643 (2006)

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  17. Sigal, A. et al. Variability and memory of protein levels in human cells. Nature 444, 643–646 (2006)

    Article  ADS  CAS  Google Scholar 

  18. Katz, Y., Wang, E. T., Airoldi, E. M. & Burge, C. B. Analysis and design of RNA sequencing experiments for identifying isoform regulation. Nature Methods 7, 1009–1015 (2010)

    Article  CAS  Google Scholar 

  19. Kivioja, T. et al. Counting absolute numbers of molecules using unique molecular identifiers. Nature Methods 9, 72–74 (2012)

    Article  CAS  Google Scholar 

  20. Waks, Z., Klein, A. M. & Silver, P. A. Cell-to-cell variability of alternative RNA splicing. Mol. Syst. Biol. 7, 1–12 (2011)

    Article  Google Scholar 

  21. Gurskaya, N. G. et al. Analysis of alternative splicing of cassette exons at single-cell level using two fluorescent proteins. Nucleic Acids Res. 40, e57 (2012)

    Article  CAS  Google Scholar 

  22. Jiang, A. et al. Disruption of E-cadherin-mediated adhesion induces a functionally distinct pathway of dendritic cell maturation. Immunity 27, 610–624 (2007)

    Article  Google Scholar 

  23. Friedman, N. Inferring cellular networks using probabilistic graphical models. Science 303, 799–805 (2004)

    Article  ADS  CAS  Google Scholar 

  24. Ning, S., Huye, L. E. & Pagano, J. S. Regulation of the transcriptional activity of the IRF7 promoter by a pathway independent of interferon signaling. J. Biol. Chem. 280, 12262–12270 (2005)

    Article  CAS  Google Scholar 

  25. Zhao, M., Zhang, J., Phatnani, H., Scheu, S. & Maniatis, T. Stochastic expression of the interferon-β gene. PLoS Biol. 10, e1001249 (2012)

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  27. Änkö, M.-L. et al. The RNA-binding landscapes of two SR proteins reveal unique functions and binding to diverse RNA classes. Genome Biol. 13, R17 (2012)

    Article  Google Scholar 

  28. Sachs, K., Perez, O., Pe’er, D., Lauffenburger, D. A. & Nolan, G. P. Causal protein-signaling networks derived from multiparameter single-cell data. Science 308, 523–529 (2005)

    Article  ADS  CAS  Google Scholar 

  29. Cabili, M. N. et al. Integrative annotation of human large intergenic noncoding RNAs reveals global properties and specific subclasses. Genes Dev. 25, 1915–1927 (2011)

    Article  CAS  Google Scholar 

  30. Cai, L., Dalal, C. K. & Elowitz, M. B. Frequency-modulated nuclear localization bursts coordinate gene regulation. Nature 455, 485–490 (2008)

    Article  ADS  CAS  Google Scholar 

Download references

Acknowledgements

We thank N. Chevrier, C. Villani, M. Jovanovic, M. Bray and J. Shuga for scientific discussions; N. Friedman and E. Lander for comments on the manuscript; B. Tilton, T. Rogers and M. Tam for assistance with cell sorting; J. Bochicchio, E. Shefler and C. Guiducci for project management; the Broad Genomics Platform for all sequencing work; K. Fitzgerald for the Irf7−/− bone marrow; and L. Gaffney for help with artwork. Work was supported by a National Institutes of Health (NIH) Postdoctoral Fellowship (1F32HD075541-01, to R.S.), a Charles H. Hood Foundation Postdoctoral Fellowship (to A. Goren), an NIH grant (U54 AI057159, to N.H.), an NIH New Innovator Award (DP2 OD002230, to N.H.), an NIH CEGS Award (1P50HG006193-01, to H.P., A.R. and N.H.), NIH Pioneer Awards (5DP1OD003893-03 to H.P., DP1OD003958-01 to A.R.), the Broad Institute (to H.P. and A.R.), HHMI (to A.R.), and the Klarman Cell Observatory at the Broad Institute (to A.R.).

Author information

Author notes

  1. Alex K. Shalek and Rahul Satija: These authors contributed equally to this work.

Authors and Affiliations

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

    Alex K. Shalek, Rona S. Gertner, Jellert T. Gaublomme & Hongkun Park

  2. Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, 02142, Massachuestts, USA

    Rahul Satija, Xian Adiconis, Raktima Raychowdhury, Schraga Schwartz, Nir Yosef, Christine Malboeuf, Diana Lu, John J. Trombetta, Dave Gennert, Andreas Gnirke, Alon Goren, Nir Hacohen, Joshua Z. Levin, Hongkun Park & Aviv Regev

  3. Department of Pathology & Center for Systems Biology and Center for Cancer Research, Massachusetts General Hospital, Charlestown, 02129, Massachusetts, USA

    Alon Goren

  4. Center for Immunology and Inflammatory Diseases & Department of Medicine, Massachusetts General Hospital, Charlestown, 02129, Massachuestts, USA

    Nir Hacohen

  5. Department of Biology, Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, 02140, Massachusetts, USA

    Aviv Regev

Authors
  1. Alex K. Shalek
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rahul Satija
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xian Adiconis
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rona S. Gertner
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jellert T. Gaublomme
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Raktima Raychowdhury
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Schraga Schwartz
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Nir Yosef
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Christine Malboeuf
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Diana Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. John J. Trombetta
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Dave Gennert
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Andreas Gnirke
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Alon Goren
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Nir Hacohen
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Joshua Z. Levin
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Hongkun Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Aviv Regev
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

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

Corresponding authors

Correspondence to Hongkun Park or Aviv Regev.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Information

This file contains a Supplementary Discussion, Supplementary Methods, Supplementary Figures 1-20 and additional references. (PDF 5465 kb)

Supplementary Data

This zipped file contains Supplementary Tables 1-7. Supplementary Table 1 shows sequencing metrics for single cell, population, and molecularly barcoded RNA-seq libraries. Supplementary Table 2 shows transcript per million (TPM) levels for all UCSC genes (rows) for 18 single cells and 3 population replicates (columns), along with annotation of which genes are 'Housekeeping' and which are 'LPS Response'. Supplementary Table 3 shows single cell variability measures for 523 highly expressed (population average) genes, along with annotation of which genes are 'Housekeeping' and which are 'LPS Response'. Supplementary Table 4 shows percent spliced in (PSI) estimates for all genes that are very highly expressed (TPM>250) in at least one single cell, only PSI estimates for the highly expressing cells were used to generate Figure 3b (see Supplementary Information file). Supplementary Table 5 shows clustering assignments and principal component scores for 633 genes induced in response to LPS stimulation. Supplementary Table 6 contains gene list and PCR primer pairs used for the Fluidigm single cell qPCR codeset. Supplementary Table 7 contains TPM estimates and unique molecular identifier counts for each gene in the 3 libraries prepared using the modified SMARTer protocol. (ZIP 4525 kb)

PowerPoint slides

Rights and permissions

Reprints and permissions

About this article

Cite this article

Shalek, A., Satija, R., Adiconis, X. et al. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature 498, 236–240 (2013). https://doi.org/10.1038/nature12172

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nature12172

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing