Biophysical JournalCell BiologyPeer-reviewed journal article
Endogenous single-cell experiments and stochastic modeling reveal control mechanisms of glucocorticoid receptor dynamics and DUSP1 transcription
Eric RoniD, Alex PopingaiD, Jack FormaniD, Luis U. AguileraiD, Linda S. Forero-QuinteroiD, Brian E. MunskyiD
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Abstract
Glucocorticoids activate the glucocorticoid receptor (GR) to suppress inflammation, yet it remains unclear how GR transport dynamics and downstream gene regulation are coordinated within single cells. We combine immunocytochemistry (ICC) and single-molecule fluorescence in situ hybridization (smFISH) to quantify endogenous GR transport and DUSP1 transcription dynamics across thousands of individual cells following dexamethasone (Dex) stimulation. Performing multiple rounds of statistical inference based on Chemical Master Equations (CME), we determine the most likely mechanisms and reaction rates for Dex-driven GR nuclear import; compartment-specific GR degradation; GR-dependent control of the DUSP1 promoter; and DUSP1 transcription, elongation, transport, and degradation. Our inferred model suggests that nuclear GR degradation is the dominant mechanism of receptor clearance, that GR primarily regulates promoter activation, and that time-dependent AU-rich element (ARE)-mediated mRNA degradation contributes heavily to DUSP1 clearance. With these mechanisms, the fully-parameterized model quantitatively predicts joint distributions of GR translocation and decay dynamics, DUSP1 transcription site activity, and nuclear and cytoplasmic DUSP1 mRNA heterogeneity among clonal cells as functions of time and across seven orders of magnitude for Dex induction concentrations. Our results establish an integrated quantitative framework to link receptor dynamics to gene expression heterogeneity and predict single-cell hormone-responsive transcription programs.
- organism: human
- assay: spatial transcriptomics
- assay: fluorescence / live-cell microscopy
- research focus: cell type annotation
- research focus: gene regulatory network inference
- research focus: differential expression
- approach: statistical model
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