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

By A. Shalek, Rahul Satija, Xian Adiconis, Rona S. Gertner, Jellert T. Gaublomme, R. Raychowdhury, Schragi Schwartz, Nir Yosef, C. Malboeuf, Diana Lu and 8 more. Nature, 2013

1,193 citations recorded by the source corpus.

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.

Subfields: Cancer Research, Immunology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/a0fcd51d7b55ccab72f4f7a48d9204f3d349fdf4

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Citations in the corpus

Single-cell Genomics Unveils Critical Regulators of Th17 cell Pathogenicity (2015, positive): “Single-cell RNA-seq (Shalek et al., 2013; Shalek et al., 2014; Trapnell et al., 2014) opens the way for a more unbiased interrogation of cell states, including in limited in vivo samples.”
Mapping the Mouse Cell Atlas by Microwell-Seq. (2018, neutral): “…2011; Guo et al., 2010; Han et al., 2017), single-cell mass cytometry (Bendall et al., 2011), and singlecell mRNA-seq (Hashimshony et al., 2012; Ramsköld et al., 2012; Shalek et al., 2013; Tang et al., 2009; Treutlein et al., 2014) allow for dissection of cell heterogeneity at high resolution.”
Development of RNA profiling tools and the implementation in forensic casework (2014, background): “Although previous studies have discussed the possible cross reactivity of these markers with other mucus-like membranes, such as mouth epithelia, resulting in non-specific signals for saliva [40], tissue inference may be possible if the saliva markers do not show”
Exponential scaling of single-cell RNA-seq in the past decade (2017, background): “Some large scale studies made use of a large number of IFC’s to create big data sets (Shalek et al., 2013; Zeisel et al., 2015).”
CRISPR-Cas9 Knockin Mice for Genome Editing and Cancer Modeling (2014, background): “Together, these results suggest that Cas9 mice empower a wide range of biological and disease modeling applications.”
Design and Analysis of Single-Cell Sequencing Experiments. (2015, background): “…et al., 2014; Zeisel et al., 2015), to study differentiation dynamics (Treutlein et al., 2014), to discover rare cell types (Gru¨n et al., 2015), to investigate the transcriptome response upon external signals (Shalek et al., 2013, 2014), or to proﬁle tumor heterogeneity (Patel et al., 2014).”
A Single-Cell Transcriptome Atlas of the Human Pancreas (2016, neutral): “Initial single-cell transcriptomic studies were performed on cultured cells (Deng et al., 2014; Hashimshony et al., 2012; Islam et al., 2011; Klein et al., 2015; Shalek et al., 2013; Tang et al., 2010).”
Single-cell RNA-seq reveals changes in cell cycle and differentiation programs upon aging of hematopoietic stem cells (2015, methodology): “2012), have opened the way to characterize distinct functional states of individual cells, even within seemingly homogeneous immune cell populations (Shalek et al. 2013; Mahata et al. 2014; Shalek et al. 2014).”