Your browser doesn't support javascript.
loading
Mapping disease regulatory circuits at cell-type resolution from single-cell multiomics data.
Chen, Xi; Wang, Yuan; Cappuccio, Antonio; Cheng, Wan-Sze; Zamojski, Frederique Ruf; Nair, Venugopalan D; Miller, Clare M; Rubenstein, Aliza B; Nudelman, German; Tadych, Alicja; Theesfeld, Chandra L; Vornholt, Alexandria; George, Mary-Catherine; Ruffin, Felicia; Dagher, Michael; Chawla, Daniel G; Soares-Schanoski, Alessandra; Spurbeck, Rachel R; Ndhlovu, Lishomwa C; Sebra, Robert; Kleinstein, Steven H; Letizia, Andrew G; Ramos, Irene; Fowler, Vance G; Woods, Christopher W; Zaslavsky, Elena; Troyanskaya, Olga G; Sealfon, Stuart C.
Afiliação
  • Chen X; Center for Computational Biology, Flatiron Institute, New York, NY, USA.
  • Wang Y; Lewis-Sigler Institute of Integrative Genomics, Princeton University, Princeton, NJ, USA.
  • Cappuccio A; Department of Computer Science, Princeton University, Princeton, NJ, USA.
  • Cheng WS; Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Zamojski FR; Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Nair VD; Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Miller CM; Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Rubenstein AB; Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Nudelman G; Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Tadych A; Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Theesfeld CL; Lewis-Sigler Institute of Integrative Genomics, Princeton University, Princeton, NJ, USA.
  • Vornholt A; Lewis-Sigler Institute of Integrative Genomics, Princeton University, Princeton, NJ, USA.
  • George MC; Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Ruffin F; Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Dagher M; Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, NC, USA.
  • Chawla DG; Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, NC, USA.
  • Soares-Schanoski A; Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA.
  • Spurbeck RR; Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Ndhlovu LC; Battelle Memorial Institute, Columbus, OH, USA.
  • Sebra R; Division of Infectious Diseases, Department of Medicine, Weill Cornell Medicine, New York, NY, USA.
  • Kleinstein SH; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Letizia AG; Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA.
  • Ramos I; Department of Pathology and Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA.
  • Fowler VG; Naval Medical Research Center, Silver Spring, MD, USA.
  • Woods CW; Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Zaslavsky E; Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Troyanskaya OG; Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, NC, USA.
  • Sealfon SC; Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, NC, USA.
Nat Comput Sci ; 3(7): 644-657, 2023 Jul.
Article em En | MEDLINE | ID: mdl-37974651
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.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Nat Comput Sci Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Nat Comput Sci Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos