Mapping disease regulatory circuits at cell-type resolution from single-cell multiomics data

Resolving chromatin-remodeling-linked gene expression changes at cell-type resolution is important for understanding disease states. Here we describe MAGICAL (Multiome Accessibility Gene Integration Calling and Looping), a hierarchical Bayesian approach that leverages paired single-cell RNA sequencing and single-cell transposase-accessible chromatin sequencing from different conditions to map disease-associated transcription factors, chromatin sites, and genes as regulatory circuits. By simultaneously modeling signal variation across cells and conditions in both omics data types, MAGICAL achieved high accuracy on circuit inference. We applied MAGICAL to study Staphylococcus aureus sepsis from peripheral blood mononuclear single-cell data that we generated from subjects with bloodstream infection and uninfected controls. MAGICAL identified sepsis-associated regulatory circuits predominantly in CD14 monocytes, known to be activated by bacterial sepsis. We addressed the challenging problem of distinguishing host regulatory circuit responses to methicillin-resistant and methicillin-susceptible S. aureus infections. Although differential expression analysis failed to show predictive value, MAGICAL identified epigenetic circuit biomarkers that distinguished methicillin-resistant from methicillin-susceptible S. aureus infections. A Bayesian method, called MAGICAL, that contrasts single cell multiomics data across conditions to accurately discover differences in gene regulatory circuits at cell type resolution is applied to specific host-based diagnosis of bacterial sepsis.

Automated summary

This study presents a method called MAGICAL, which combines single-cell RNA sequencing and single-cell chromatin accessibility sequencing to uncover the gene expression changes associated with chromatin remodeling in disease states. By integrating multiomic data and modeling signal variation across cells and conditions, MAGICAL accurately identifies disease-associated regulatory circuits. The method is successfully applied to study Staphylococcus aureus sepsis and distinguish methicillin-resistant from methicillin-susceptible infections.