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Predicting bacterial infection outcomes using single cell RNA-sequencing analysis of human immune cells.
Bossel Ben-Moshe, Noa; Hen-Avivi, Shelly; Levitin, Natalia; Yehezkel, Dror; Oosting, Marije; Joosten, Leo A B; Netea, Mihai G; Avraham, Roi.
Afiliación
  • Bossel Ben-Moshe N; Department of Biological Regulation, Weizmann Institute of Science, 7610001, Rehovot, Israel.
  • Hen-Avivi S; Department of Biological Regulation, Weizmann Institute of Science, 7610001, Rehovot, Israel.
  • Levitin N; Department of Biological Regulation, Weizmann Institute of Science, 7610001, Rehovot, Israel.
  • Yehezkel D; Department of Biological Regulation, Weizmann Institute of Science, 7610001, Rehovot, Israel.
  • Oosting M; Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, 6525, HP, Nijmegen, the Netherlands.
  • Joosten LAB; Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, 6525, HP, Nijmegen, the Netherlands.
  • Netea MG; Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, 6525, HP, Nijmegen, the Netherlands.
  • Avraham R; Department for Genomics & Immunoregulation, Life and Medical Sciences Institute (LIMES), University of Bonn, 53115, Bonn, Germany.
Nat Commun ; 10(1): 3266, 2019 07 22.
Article en En | MEDLINE | ID: mdl-31332193
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.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Análisis de Secuencia de ARN / Perfilación de la Expresión Génica / Análisis de la Célula Individual / Secuenciación de Nucleótidos de Alto Rendimiento / Sistema Inmunológico Tipo de estudio: Etiology_studies / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Nat Commun Asunto de la revista: BIOLOGIA / CIENCIA Año: 2019 Tipo del documento: Article País de afiliación: Israel

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Análisis de Secuencia de ARN / Perfilación de la Expresión Génica / Análisis de la Célula Individual / Secuenciación de Nucleótidos de Alto Rendimiento / Sistema Inmunológico Tipo de estudio: Etiology_studies / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Nat Commun Asunto de la revista: BIOLOGIA / CIENCIA Año: 2019 Tipo del documento: Article País de afiliación: Israel