Single-cell RNA sequencing is used to investigate the transcriptional response of 18 mouse bone-marrow-derived dendritic cells after lipopolysaccharide stimulation; many highly expressed genes, such as key immune genes and cytokines, show bimodal variation in both transcript abundance and splicing patterns. This variation reflects differences in both cell state and usage of an interferon-driven pathway involving Stat2 and Irf7. 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. 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.

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