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

doi:10.1038/s41467-019-11257-y

Nature Communications (Jul 2019)

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

Affiliations

Noa Bossel Ben-Moshe: Department of Biological Regulation, Weizmann Institute of Science
Shelly Hen-Avivi: Department of Biological Regulation, Weizmann Institute of Science
Natalia Levitin: Department of Biological Regulation, Weizmann Institute of Science
Dror Yehezkel: Department of Biological Regulation, Weizmann Institute of Science
Marije Oosting: Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center
Leo A. B. Joosten: Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center
Mihai G. Netea: Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center
Roi Avraham: Department of Biological Regulation, Weizmann Institute of Science

DOI: https://doi.org/10.1038/s41467-019-11257-y
Journal volume & issue: Vol. 10, no. 1
pp. 1 – 16

Abstract

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Complex interactions between different host immune cell types can determine the outcome of pathogen infections. Here, Avraham and colleagues present a deconvolution algorithm that uses single-cell RNA and bulk RNA sequencing measurements of pathogen-infected cells to predict disease risk outcomes.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

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