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

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 dis

SUBMITTER: Bossel Ben-Moshe N 

PROVIDER: S-EPMC6646406 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature