Predicting bacterial infection outcomes using single cell RNA-sequencing analysis of human immune cells

Nat Commun. 2019 Jul 22;10(1):3266. doi: 10.1038/s41467-019-11257-y.

Abstract

Complex interactions between different host immune cell types can determine the outcome of pathogen infections. Advances in single cell RNA-sequencing (scRNA-seq) allow probing of these immune interactions, such as cell-type compositions, which are then interpreted by deconvolution algorithms using bulk RNA-seq measurements. However, not all aspects of immune surveillance are represented by current algorithms. Here, using scRNA-seq of human peripheral blood cells infected with Salmonella, we develop a deconvolution algorithm for inferring cell-type specific infection responses from bulk measurements. We apply our dynamic deconvolution algorithm to a cohort of healthy individuals challenged ex vivo with Salmonella, and to three cohorts of tuberculosis patients during different stages of disease. We reveal cell-type specific immune responses associated not only with ex vivo infection phenotype but also with clinical disease stage. We propose that our approach provides a predictive power to identify risk for disease, and human infection outcomes.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Cells, Cultured
  • Cluster Analysis
  • Cohort Studies
  • Gene Expression Profiling / methods*
  • High-Throughput Nucleotide Sequencing / methods*
  • Host-Pathogen Interactions / genetics
  • Humans
  • Immune System / cytology
  • Immune System / metabolism*
  • Immune System / microbiology
  • Natural Killer T-Cells / immunology
  • Natural Killer T-Cells / metabolism
  • Natural Killer T-Cells / microbiology
  • Predictive Value of Tests
  • Salmonella / physiology
  • Salmonella Infections / genetics
  • Salmonella Infections / microbiology
  • Sequence Analysis, RNA / methods*
  • Single-Cell Analysis / methods*