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Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells
Open accessAbstract
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 output, with important functional consequences. Existing studies of cellular heterogeneity, however, have typically measured only a few pre-selected RNAs or proteins simultaneously, because genomic profiling methods could not be applied to single cells until very recently. 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.
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“Second, because of both stochastic and regulated differences between the cells, each cell forms its own ‘perturbation system’, in which multiple regulators are subtly, or more strongly, perturbed, possibly reducing the need for additional experimental perturbations3,41.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [3]
Experimental — accuracy not yet measuredIn the citing paper
“DCs are mostly post-mitotic, and LPS synchronizes them (27) and causes dramatic regulatory changes from the expression of thousands of transcripts (22, 24, 25) to protein phosphorylation (26).”
From Dynamic profiling of the protein life cycle in response to pathogens (2015) · cites this paper as [27]
In the citing paper
“even cells of the same ‘type’ can exhibit substantial heterogeneity, reflecting finer sub-types, regulated functional variation, or inherent stochasticity1–6”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [3]
In the citing paper
“Across a set of cells, this manifests as bimodality of gene expression (with one mode at zero). While in some cases such bimodality is due to stochastic (‘bursty’) gene expression40 or more stable variation between cell types and states3, in many others it is dominated by false negatives70,85.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [3]
In the citing paper
“In analogy to simple observational approaches for inferring gene regulation, correlating the expression profiles of genes or proteins opens the way to elucidating the regulatory mechanisms that control them2,3,41,169,170.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [3]
In the citing paper
“Leveraging this insight, several studies have analyzed the covariation structure between transcripts or proteins across single cells to infer regulatory relations1–3,41,135,137,168–172.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [3]
In the citing paper
“For example, an analysis (N.Y., A.R. and colleagues3) of the covariation in the expression of ~600 transcripts across 18 dendritic cells detected a module of antiviral gene expression, which included two key transcription factors, STAT2 and IRF7.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [3]
In the citing paper
“These factors were predicted to control the module because variation in their protein expression levels was expected to propagate downstream and create variation in their targets’ mRNA expression, as well in as their own mRNA expression through autoregulation.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [3]
In the citing paper
“Similarly, simultaneous analysis of protein and RNA or of epigenomic profiles and RNA can provide mechanistic explanations that relate variations in the state of regulator proteins3 or cis-regulatory loci27,28 to variation in target transcripts.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [3]
In the citing paper
“Although single splicing events have been described for ≤200 single cells with statistical confidence 14,15 , full-length mRNA analyses for hundreds of cells have not been reported.”
From Single-cell isoform RNA sequencing characterizes isoforms in thousands of cerebellar cells (2018) · cites this paper as [15]
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Citations
Cites
RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome
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mRNA-Seq whole-transcriptome analysis of a single cell
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Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data
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Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells
Ramsköld, D., Luo, S., Wang, Y.-C., Li, R., Deng, Q., Faridani, O. R., Daniels, G. A., Khrebtukova, I., Loring, J. F., Laurent, L. C., et al. (2012) · Nature Biotechnology · doi:10.1038/nbt.2282 · Cited by 1,609
Analysis and design of RNA sequencing experiments for identifying isoform regulation
Katz, Y., Wang, E. T., Airoldi, E. M., & Burge, C. B. (2010) · Nature Methods · doi:10.1038/nmeth.1528 · Cited by 1,353
CEL-seq: single-cell RNA-seq by multiplexed linear amplification
Hashimshony, T., Wagner, F., Sher, N., & Yanai, I. (2012) · Cell Reports · doi:10.1016/j.celrep.2012.08.003 · Cited by 1,231
Inferring Cellular Networks Using Probabilistic Graphical Models
Friedman, N. (2004) · Science · doi:10.1126/science.1094068 · Cited by 1,211
Counting absolute numbers of molecules using unique molecular identifiers
Kivioja, T., Vähärautio, A., Karlsson, K., Bonke, M., Enge, M., Linnarsson, S., & Taipale, J. (2012) · Nature Methods · doi:10.1038/nmeth.1778 · Cited by 1,010
Cited by
Sorted by citations
Massively parallel digital transcriptional profiling of single cells
Zheng, G. X. Y., Terry, J. M., Belgrader, P., Ryvkin, P., Bent, Z. W., Wilson, R., Ziraldo, S. B., Wheeler, T. D., McDermott, G. P., Zhu, J., et al. (2017) · Nature Communications · doi:10.1038/ncomms14049 · Cited by 7,353
Trapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N. J., Livak, K. J., Mikkelsen, T. S., & Rinn, J. L. (2014) · Nature Biotechnology · doi:10.1038/nbt.2859 · Cited by 7,314
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110
Self-citationSpatial reconstruction of single-cell gene expression data
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A. (2015) · Nature Biotechnology · doi:10.1038/nbt.3192 · Cited by 7,055
“We used a modified, strand-specific, single-cell RNA-seq protocol based on the SMART template switching method 4,5,8 ( Fig.”
MethodSelf-citationBuenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y., & Greenleaf, W. J. (2013) · Nature Methods · doi:10.1038/nmeth.2688 · Cited by 6,422
Metadata
- Authors
- Shalek, A. K., Satija, R., Adiconis, X., Gertner, R. S., Gaublomme, J. T., Raychowdhury, R., Schwartz, S., Yosef, N., Malboeuf, C., Lu, D., Trombetta, J. J., Gennert, D., Gnirke, A., Goren, A., Hacohen, N., Levin, J. Z., Park, H., & Regev, A.
- Affiliations
- Harvard University (US)
- Broad Institute (US)
- Corresponding author
- Park, Hongkun
- Regev, Aviv
- ORCID
- Venue
- Nature
- Published
- 2013-05-19
- Volume
- 498
- Issue
- 7453
- Pages
- 236-240
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC3683364
- OpenAlex
- W2110768394
- Cited by
- 1,190
- References
- 30
- Citations by year
- 2026: 29 · 2025: 40 · 2024: 46 · 2023: 65 · 2022: 96 · 2021: 75 · 2020: 104 · 2019: 117 · 2018: 117 · 2017: 120 · 2016: 120 · 2015: 126 · 2014: 92 · 2013: 30
- FWCI
- 54.63
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion)
- PMC
- Funders
- Howard Hughes Medical Institute
- National Institutes of Health (DP1OD003893, 1dp2od002230-01, DP1OD003958)
- National Institute on Drug Abuse (DP1DA035083)
- National Human Genome Research Institute (P50HG006193)
- National Cancer Institute (DP1CA174427)
- National Institute of Allergy and Infectious Diseases (U54AI057159)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (F32HD075541)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- bimodal gene expression, single-cell transcriptomics, bone marrow-derived dendritic cells, lipopolysaccharide stimulation, alternative splicing, cellular heterogeneity, interferon pathway, STAT2, IRF7, dendritic cell maturation, cytokine expression, SMART-seq, RNA fluorescence in situ hybridization, gene regulatory circuits
- MeSH terms
- Animals, Bone Marrow Cells, Dendritic Cells, Gene Expression Regulation, Interferons, Lipopolysaccharides, RNA Splicing, RNA, Messenger, Viruses, Reproducibility of Results, In Situ Hybridization, Fluorescence, Sequence Analysis, RNA, Mice, Knockout, Protein Isoforms, Gene Expression Profiling, STAT2 Transcription Factor, Interferon Regulatory Factor-7, Mice, Single-Cell Analysis, Transcriptome
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