GSE178429
Multi-omics gene regulatory network analysis of resting and stimulated PBMCs [stimulation scRNA-seq]
Cells require coordinated control over gene expression changes when responding to environmental stimuli. Recent advancements in single-cell technologies have enabled studies of regulatory dynamics associated with cellular perturbation response in immune cells. However, associating these changes to underlying differences in epigenetic regulation through integration of multi-omic data has not been achieved at single-cell resolution. Here, we apply single-cell ATAC-seq (scATAC-seq) and scRNA-seq to assay chromatin accessibility and gene expression in resting and stimulated human blood cells. Collectively, we generate ~91,000 high-coverage single-cell profiles, allowing us to probe the cis-regulatory landscape of immunological response across cell types, stimuli and time. Advancing tools to integrate multiomic data, we connect distal cis-regulatory elements to genes, identifying key regulatory hubs associated with immune signaling and stress response. Subsequently, we design FigR - a method to infer gene regulatory networks (GRNs) to identify candidate TF regulators across single cells. Importantly, construction of a GRN using stimulus response data identifies TF regulatory activity associated with  genetic variants in inflammatory diseases. Lastly, we assess the time-dependent regulatory dynamics of immune stimulation, where we find that cells alter chromatin accessibility prior to productive gene expression, a process occurring at time scales of minutes. Extending this concept of chromatin-mediated priming, and incorporating regulatory relationships defined by FigR, we detect a rare subpopulation of monocytes marked by a primed chromatin state predictive of enhanced immunological response. Overall, we build upon computational tools for GRN construction and demonstrate the utility of GRNs for analysis of multi-omic single cell data. Overall design: Human peripheral blood mononuclear cells from 3-4 donors were stimulated with either Interferon gamma, LPS or PMA/ionomycin for 1 hour and 6 hours and then profiled either using the BioRad SureCell single-cell RNA-seq platform. Control samples (DMSO) are included for both the 1 hour and 6 hours time points. In addition, the 6 hours stimulations were performed in the presence of a Golgi inhibitor for all 3 stimulants and control.