Analysis of cellular heterogeneity in activated macrophages reveals hidden modes of state-specific gene regulation

Authors

• AJ Martins
• M Narayanan
• T Prustel
• B Fixsen
• Y Lu
• RA Gottschalk
• C Pfannkoch
• W Lau
• K Wendelsdorf
• JS Tsang

Abstract

Macrophages are critical innate immune cells, known for diverse physiological roles and dynamic phenotypic adaptations to external signals. However, the underlying gene regulatory programs that control cellular behavior in these distinct adaptive states are typically examined by utilizing average changes across conditions, ignoring heterogeneity between individual cells within a particular state. We hypothesized that utilizing the natural heterogeneity across single cells to construct state-specific gene regulatory networks would reveal hidden functional connections missed by approaches relying on cross-state comparisons. We have examined state-specific gene-gene correlation networks in human macrophages using 96-plex microfluidic qPCR. We observed striking changes in gene-gene correlation networks in different activation conditions. Some “hub” genes in these networks, often involving factors not previously known to play a role in these conditions, exhibit substantial differences in connectivity across conditions without a concomitant change in average gene expression. These include activating transcription factor 2 (ATF2), a component of the activating protein-1 complex, which we find to be a hub specific to interleukin (IL)-10-activated macrophages. Microscopy and flow cytometry experiments revealed a higher level of phosphorylated nuclear ATF2 after IL-10 activation. ChIP-Seq analysis shows increased ATF2 binding and enhancer activity near ATF2-connected genes after IL-10 treatment, suggesting that an epigenetic mechanism mediates the increased correlation. Our results show how inference of state-specific gene networks based on cellular heterogeneity profiling can reveal novel gene-gene connections, and illustrate how variation may differentially propagate independent of average expression changes. This research was supported by the Intramural Research Program of the NIH, NIAID.

Scientific Focus Area: Systems Biology

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