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

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

Author information

1
Department of Biological Regulation, Weizmann Institute of Science, 7610001, Rehovot, Israel.
2
Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, 6525, HP, Nijmegen, the Netherlands.
3
Department for Genomics & Immunoregulation, Life and Medical Sciences Institute (LIMES), University of Bonn, 53115, Bonn, Germany.
4
Department of Biological Regulation, Weizmann Institute of Science, 7610001, Rehovot, Israel. roi.avraham@weizmann.ac.il.

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.

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