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

Noa Bossel Ben-Moshe ¹ Shelly Hen-Avivi ¹ Natalia Levitin ¹ Dror Yehezkel ¹ Marije Oosting ² Leo A.b. Joosten ² Mihai G. Netea ^2,3 Roi Avraham ¹

¹Department of Biological Regulation, Weizmann Institute of Science, Israel
²Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Netherlands
³Department for Genomics & Immunoregulation, Life and Medical Sciences Institute (LIMES), University of Bonn, Germany

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.