This study reveals extensive bimodal variation in gene expression and splicing patterns in bone marrow-derived dendritic cells (BMDCs) upon lipopolysaccharide (LPS) stimulation. Using single-cell RNA-Seq, the researchers identified that hundreds of key immune genes exhibit bimodal expression, even among highly expressed genes. Splicing patterns also showed significant heterogeneity between cells, with some genes predominantly expressing one isoform. These findings suggest that bimodal expression may reflect distinct cellular subtypes or differences in the activation of regulatory circuits. The study also identified a module of 137 co-regulated antiviral response genes that showed bimodal expression. The researchers further demonstrated that variability in this module may be propagated through an interferon feedback circuit involving the transcriptional regulators Stat2 and Irf7. They also found that splicing patterns varied significantly between cells, with some genes showing a preference for specific isoforms. These results highlight the power of single-cell genomics in uncovering functional diversity between cells and in deciphering cell states and circuits. The study also showed that single-cell variability in splicing patterns has rarely been studied for individual genes, and never been analyzed on a genomic scale. The researchers used RNA-FISH to verify that heterogeneity in their single-cell expression data reflected true biological differences rather than technical noise. They found that some genes, despite being highly expressed on average, showed significantly greater levels of heterogeneity. The study also revealed that bimodal expression was not a universal feature of immune response transcripts, as some key chemokines and cytokines were highly expressed in every cell. The researchers concluded that single-cell RNA-Seq can sensitively distinguish between closely related, yet distinct, developmental states, even within the same cell type. The study also demonstrated that co-variation across single cells can help identify and assemble regulatory circuits. The researchers identified a cluster of 137 genes that varied in a correlated way and were strongly discriminated by the second principal component. These genes included known antiviral master regulators and were highly enriched for members of the antiviral response. The study also showed that the expression of these genes was bimodal across single cells, despite being strongly expressed at the population level. The researchers validated these findings using single-cell qRT-PCR and RNA-FISH. They also found that the expression of these genes was affected by the absence of Irf7 and Ifnr, suggesting that Stat2 and Irf7 may act upstream or in parallel during the response. The study highlights the importance of single-cell genomics in understanding the functional diversity of cells and in deciphering cell states and circuits. It also shows that single-cell measurements can help facilitate the discovery and annotation of long non-coding RNAs. The study concludes that the variability seen through single-cell genomics may help determine new cell classification schemes, identify transitional states, discover previously unrecognized biological distinctions, and mapThis study reveals extensive bimodal variation in gene expression and splicing patterns in bone marrow-derived dendritic cells (BMDCs) upon lipopolysaccharide (LPS) stimulation. Using single-cell RNA-Seq, the researchers identified that hundreds of key immune genes exhibit bimodal expression, even among highly expressed genes. Splicing patterns also showed significant heterogeneity between cells, with some genes predominantly expressing one isoform. These findings suggest that bimodal expression may reflect distinct cellular subtypes or differences in the activation of regulatory circuits. The study also identified a module of 137 co-regulated antiviral response genes that showed bimodal expression. The researchers further demonstrated that variability in this module may be propagated through an interferon feedback circuit involving the transcriptional regulators Stat2 and Irf7. They also found that splicing patterns varied significantly between cells, with some genes showing a preference for specific isoforms. These results highlight the power of single-cell genomics in uncovering functional diversity between cells and in deciphering cell states and circuits. The study also showed that single-cell variability in splicing patterns has rarely been studied for individual genes, and never been analyzed on a genomic scale. The researchers used RNA-FISH to verify that heterogeneity in their single-cell expression data reflected true biological differences rather than technical noise. They found that some genes, despite being highly expressed on average, showed significantly greater levels of heterogeneity. The study also revealed that bimodal expression was not a universal feature of immune response transcripts, as some key chemokines and cytokines were highly expressed in every cell. The researchers concluded that single-cell RNA-Seq can sensitively distinguish between closely related, yet distinct, developmental states, even within the same cell type. The study also demonstrated that co-variation across single cells can help identify and assemble regulatory circuits. The researchers identified a cluster of 137 genes that varied in a correlated way and were strongly discriminated by the second principal component. These genes included known antiviral master regulators and were highly enriched for members of the antiviral response. The study also showed that the expression of these genes was bimodal across single cells, despite being strongly expressed at the population level. The researchers validated these findings using single-cell qRT-PCR and RNA-FISH. They also found that the expression of these genes was affected by the absence of Irf7 and Ifnr, suggesting that Stat2 and Irf7 may act upstream or in parallel during the response. The study highlights the importance of single-cell genomics in understanding the functional diversity of cells and in deciphering cell states and circuits. It also shows that single-cell measurements can help facilitate the discovery and annotation of long non-coding RNAs. The study concludes that the variability seen through single-cell genomics may help determine new cell classification schemes, identify transitional states, discover previously unrecognized biological distinctions, and map