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Abstract:
Transcription of almost all mammalian genes occurs in stochastic bursts, however the fundamental control mechanisms that allow appropriate single-cell responses remain unresolved. Here we utilise single cell genomics data and stochastic models of transcription to perform global analysis of the toll-like receptor (TLR)-induced gene expression variability. Based on analysis of more than 2000 TLR-response genes across multiple experimental conditions we demonstrate that the single-cell, gene-by-gene expression variability can be empirically described by a linear function of the population mean. We show that response heterogeneity of individual genes can be characterised by the slope of the mean-variance line, which captures how cells respond to stimulus and provides insight into evolutionary differences between species. We further demonstrate that linear relationships theoretically determine the underlying transcriptional bursting kinetics, revealing different regulatory modes of TLR response heterogeneity. Stochastic modelling of temporal scRNA-seq count distributions demonstrates that increased response variability is associated with larger and more frequent transcriptional bursts, which emerge via increased complexity of transcriptional regulatory networks between genes and different species. Overall, we provide a methodology relying on inference of empirical mean-variance relationships from single cell data and new insights into control of innate immune response variability.

Abstract:
Single-cell gene expression is inherently variable, but how this variability is controlled in response to stimulation remains unclear. Here, we use single-cell RNA-seq and single-molecule mRNA counting (smFISH) to study inducible gene expression in the immune toll-like receptor system. We show that mRNA counts of tumor necrosis factor α conform to a standard stochastic switch model, while transcription of interleukin-1β involves an additional regulatory step resulting in increased heterogeneity. Despite different modes of regulation, systematic analysis of single-cell data for a range of genes demonstrates that the variability in transcript count is linearly constrained by the mean response over a range of conditions. Mathematical modeling of smFISH counts and experimental perturbation of chromatin state demonstrates that linear constraints emerge through modulation of transcriptional bursting along with gene-specific relationships. Overall, our analyses demonstrate that the variability of the inducible single-cell mRNA response is constrained by transcriptional bursting.

Keywords:
cellular heterogeneity, transcriptional bursting, stochastic gene expression, toll-like receptor, single-cell transcriptomics, stochastic modeling, TNF-α, IL-1β

Abstract:
Toll-like receptor (TLR) signaling regulates macrophage activation and effector cytokine propagation in the constrained environment of a tissue. In macrophage populations, TLR4 stimulates the dose-dependent transcription of nuclear factor κB (NF-κB) target genes. However, using single-RNA counting, we found that individual cells exhibited a wide range (three orders of magnitude) of expression of the gene encoding the proinflammatory cytokine tumor necrosis factor–α (TNF-α). The TLR4-induced TNFA transcriptional response correlated with the extent of NF-κB signaling in the cells and their size. We compared the rates of TNF-α production and uptake in macrophages and mouse embryonic fibroblasts and generated a mathematical model to explore the heterogeneity in the response of macrophages to TLR4 stimulation and the propagation of the TNF-α signal in the tissue. The model predicts that the local propagation of the TLR4-dependent TNF-α response and cellular NF-κB signaling are limited to small distances of a few cell diameters between neighboring tissue-resident macrophages. In our predictive model, TNF-α propagation was constrained by competitive uptake of TNF-α from the environment, rather than by heterogeneous production of the cytokine. We propose that the highly constrained architecture of tissues enables effective localized propagation of inflammatory cues while avoiding out-of-context responses at longer distances.