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

Naturevolume 498, pages236–240 (2013)Cite this article

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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-fluorescencein 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.

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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.

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Accession codes

Accessions

Gene Expression Omnibus

Data deposits

Data have been deposited in GEO under accession numberGSE41265.

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.

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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 theIrf7−/− 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

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  2. Rahul Satija

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  5. Jellert T. Gaublomme

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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 toHongkun Park orAviv Regev.

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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)

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Shalek, A., Satija, R., Adiconis, X.et al. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells.Nature498, 236–240 (2013). https://doi.org/10.1038/nature12172

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Editorial Summary

Heterogeneity in immune cells

Gene expression profiles are typically derived at cell-population level, yet there is growing evidence to suggest that seemingly identical individual cells can differ considerably in their gene expression. This paper describes the use of single-cell RNA sequencing (RNA-Seq) to analyse the transcriptional response of 18 mouse bone-marrow-derived dendritic cells after lipopolysaccharide stimulation. The authors find that even genes that are highly expressed at the population level — such as key immune genes and cytokines — are often bimodally expressed. They may be very highly expressed in one cell, and expressed hardly at all in another. This variation reflects differences in both cell state and usage of an interferon-driven pathway involving Stat2 and Irf7. The SMART-Seq technology used here could have wide application in the study of regulatory circuits at the single-cell level.

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