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Nature communications
07 22, 2019;10 (1): 3266.

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

Noa Bossel Ben-Moshe, Shelly Hen-Avivi, Natalia Levitin, Dror Yehezkel, Marije Oosting, Leo A B Joosten, Mihai G Netea, Roi Avraham
Author Information
  1. Noa Bossel Ben-Moshe: Department of Biological Regulation, Weizmann Institute of Science, 7610001, Rehovot, Israel.
  2. Shelly Hen-Avivi: Department of Biological Regulation, Weizmann Institute of Science, 7610001, Rehovot, Israel.
  3. Natalia Levitin: Department of Biological Regulation, Weizmann Institute of Science, 7610001, Rehovot, Israel.
  4. Dror Yehezkel: Department of Biological Regulation, Weizmann Institute of Science, 7610001, Rehovot, Israel.
  5. Marije Oosting: Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, 6525, HP, Nijmegen, the Netherlands.
  6. Leo A B Joosten: Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, 6525, HP, Nijmegen, the Netherlands. ORCID
  7. Mihai G Netea: Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, 6525, HP, Nijmegen, the Netherlands. ORCID
  8. Roi Avraham: Department of Biological Regulation, Weizmann Institute of Science, 7610001, Rehovot, Israel. roi.avraham@weizmann.ac.il.
PMID: 31332193 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.

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MeSH Term

Algorithms
Cells, Cultured
Cluster Analysis
Cohort Studies
Gene Expression Profiling
High-Throughput Nucleotide Sequencing
Host-Pathogen Interactions
Humans
Immune System
Natural Killer T-Cells
Predictive Value of Tests
Salmonella
Salmonella Infections
Sequence Analysis, RNA
Single-Cell Analysis
Journal Article Research Support, Non-U.S. Gov't

Links to CNCB-NGDC Resources

GEN: GEND000125 (Prediction of bacterial infection outcome using single cell RNA-seq analysis of human immune cells [scRNA-seq ind.1])
GEN: GEND000127 (Prediction of bacterial infection outcome using single cell RNA-seq analysis of human immune cells [scRNA-seq ind. 2])

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