Mapping disease-associated regulatory circuits by cell type from single-cell multiomics data (S. aureus scRNA-seq)

Resolving chromatin remodeling-linked gene expression changes is important for understanding disease states. We describe MAGICAL (Multiome Accessible Gene Integration Calling And Looping), a hierarchical Bayesian approach that leverages paired scRNA-seq and scATAC-seq data from different conditions to map disease-associated transcription factors, regulatory sites and genes as regulatory circuits. By introducing hidden Bayesian variables to allow modeling noise and signal variation across cells and conditions in both transcriptome and chromatin data, in systemic evaluations MAGICAL achieved high accuracy on circuit prediction at cell-type resolution. We applied MAGICAL to study Staphylococcus aureus sepsis from peripheral blood mononuclear single cell data that we generated from infected subjects and healthy uninfected controls. MAGICAL identified sepsis-associated regulatory circuits predominantly in CD14 monocytes, known to be sepsis activated. We addressed the challenging problem of distinguishing methicillin-resistant- (MRSA) and methicillin-sensitive Staphylococcus aureus (MSSA) infections, where differential expression analysis failed to show predictive value. MAGICAL, however, identified epigenetic circuit biomarkers that distinguished MRSA from MSSA. scRNA sequencing of PBMCs from 10 samples with MRSAs, 11 samples with MSSAs and 23 healthy controls