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

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.

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Autores principales:	Noa Bossel Ben-Moshe, Shelly Hen-Avivi, Natalia Levitin, Dror Yehezkel, Marije Oosting, Leo A. B. Joosten, Mihai G. Netea, Roi Avraham
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/4934fef129ce45bf965afdfe39cd13bc
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spelling	oai:doaj.org-article:4934fef129ce45bf965afdfe39cd13bc2021-12-02T15:35:58ZPredicting bacterial infection outcomes using single cell RNA-sequencing analysis of human immune cells10.1038/s41467-019-11257-y2041-1723https://doaj.org/article/4934fef129ce45bf965afdfe39cd13bc2019-07-01T00:00:00Zhttps://doi.org/10.1038/s41467-019-11257-yhttps://doaj.org/toc/2041-1723Complex 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.Noa Bossel Ben-MosheShelly Hen-AviviNatalia LevitinDror YehezkelMarije OostingLeo A. B. JoostenMihai G. NeteaRoi AvrahamNature PortfolioarticleScienceQENNature Communications, Vol 10, Iss 1, Pp 1-16 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Noa Bossel Ben-Moshe Shelly Hen-Avivi Natalia Levitin Dror Yehezkel Marije Oosting Leo A. B. Joosten Mihai G. Netea Roi Avraham Predicting bacterial infection outcomes using single cell RNA-sequencing analysis of human immune cells
description	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.
format	article
author	Noa Bossel Ben-Moshe Shelly Hen-Avivi Natalia Levitin Dror Yehezkel Marije Oosting Leo A. B. Joosten Mihai G. Netea Roi Avraham
author_facet	Noa Bossel Ben-Moshe Shelly Hen-Avivi Natalia Levitin Dror Yehezkel Marije Oosting Leo A. B. Joosten Mihai G. Netea Roi Avraham
author_sort	Noa Bossel Ben-Moshe
title	Predicting bacterial infection outcomes using single cell RNA-sequencing analysis of human immune cells
title_short	Predicting bacterial infection outcomes using single cell RNA-sequencing analysis of human immune cells
title_full	Predicting bacterial infection outcomes using single cell RNA-sequencing analysis of human immune cells
title_fullStr	Predicting bacterial infection outcomes using single cell RNA-sequencing analysis of human immune cells
title_full_unstemmed	Predicting bacterial infection outcomes using single cell RNA-sequencing analysis of human immune cells
title_sort	predicting bacterial infection outcomes using single cell rna-sequencing analysis of human immune cells
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/4934fef129ce45bf965afdfe39cd13bc
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Predicting bacterial infection outcomes using single cell RNA-sequencing analysis of human immune cells

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