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

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

Nature communications (2019-07-25)
Noa Bossel Ben-Moshe, Shelly Hen-Avivi, Natalia Levitin, Dror Yehezkel, Marije Oosting, Leo A B Joosten, Mihai G Netea, Roi Avraham
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.

MATERIALS
Product Number
Brand
Product Description

Sigma-Aldrich
Percoll®, pH 8.5-9.5 (20 °C)