Article

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

1] Department of Chemistry and Chemical Biology and Department of Physics, Harvard University, 12 Oxford Street, Cambridge, Massachusetts 02138, USA [2].
Nature (Impact Factor: 42.35). 05/2013; DOI: 10.1038/nature12172
Source: PubMed

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 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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Available from: Alex K Shalek, May 19, 2014
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