Predicting bacterial infection outcomes using single cell RNA-sequencing analysis of human immune cells
| Autor: | Marije Oosting, Leo A. B. Joosten, Mihai G. Netea, Shelly Hen-Avivi, Roi Avraham, Natalia Levitin, Dror Yehezkel, Noa Bossel Ben-Moshe |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
Cell type Science Cell lnfectious Diseases and Global Health Radboud Institute for Molecular Life Sciences [Radboudumc 4] Predictive medicine General Physics and Astronomy 02 engineering and technology Disease Computational biology Biology Article General Biochemistry Genetics and Molecular Biology Cohort Studies 03 medical and health sciences All institutes and research themes of the Radboud University Medical Center Immune system Predictive Value of Tests Salmonella medicine Cluster Analysis Humans lcsh:Science Pathogen Cells Cultured Multidisciplinary Sequence Analysis RNA Gene Expression Profiling High-Throughput Nucleotide Sequencing RNA RNA sequencing General Chemistry 021001 nanoscience & nanotechnology Bacterial host response 3. Good health 030104 developmental biology medicine.anatomical_structure Immune System Host-Pathogen Interactions Salmonella Infections Natural Killer T-Cells lcsh:Q Bacterial infection Single-Cell Analysis 0210 nano-technology Algorithms Ex vivo |
| Zdroj: | Nature Communications, Vol 10, Iss 1, Pp 1-16 (2019) Nature Communications, 10 Nature Communications |
| ISSN: | 2041-1723 |
| Popis: | 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. 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. |
| Databáze: | OpenAIRE |
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