TY - JOUR
T1 - Mapping disease regulatory circuits at cell-type resolution from single-cell multiomics data
AU - Chen, Xi
AU - Wang, Yuan
AU - Cappuccio, Antonio
AU - Cheng, Wan Sze
AU - Zamojski, Frederique Ruf
AU - Nair, Venugopalan D.
AU - Miller, Clare M.
AU - Rubenstein, Aliza B.
AU - Nudelman, German
AU - Tadych, Alicja
AU - Theesfeld, Chandra L.
AU - Vornholt, Alexandria
AU - George, Mary Catherine
AU - Ruffin, Felicia
AU - Dagher, Michael
AU - Chawla, Daniel G.
AU - Soares-Schanoski, Alessandra
AU - Spurbeck, Rachel R.
AU - Ndhlovu, Lishomwa C.
AU - Sebra, Robert
AU - Kleinstein, Steven H.
AU - Letizia, Andrew G.
AU - Ramos, Irene
AU - Fowler, Vance G.
AU - Woods, Christopher W.
AU - Zaslavsky, Elena
AU - Troyanskaya, Olga G.
AU - Sealfon, Stuart C.
N1 - Publisher Copyright:
© 2023, The Author(s).
PY - 2023/7
Y1 - 2023/7
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85165696755&partnerID=8YFLogxK
U2 - 10.1038/s43588-023-00476-5
DO - 10.1038/s43588-023-00476-5
M3 - Article
AN - SCOPUS:85165696755
SN - 2662-8457
VL - 3
SP - 644
EP - 657
JO - Nature Computational Science
JF - Nature Computational Science
IS - 7
ER -