Dataset Information

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

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.

SUBMITTER: Bossel Ben-Moshe N

PROVIDER: S-EPMC6646406 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Bossel Ben-Moshe Noa N Hen-Avivi Shelly S Levitin Natalia N Yehezkel Dror D Oosting Marije M Joosten Leo A B LAB Netea Mihai G MG Avraham Roi R

Nature communications 20190722 1

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 dev ...[more]

PMID: 31332193

Similar Datasets

Project description:BackgroundCaenorhabditis elegans is an excellent research model whose populations have been used in many studies to address various biological questions. Although worm-to-worm phenotypic variations in isogenic populations have been persistently observed, they are not well understood and are often ignored or averaged out in studies, masking the impacts of such variations on data collection and interpretation. Single-worm RNA sequencing that profiles the transcriptomes of individual animals has the power to examine differences between individuals in a worm population, but this approach has been understudied. The integrity of the starting RNA, the quality of the library and sequence data, as well as the transcriptome-profiling effectiveness of single-worm RNA-seq remain unclear. Therefore, more studies are needed to improve this technique and its application in research.ResultsIn this study, we aimed to develop a single-worm RNA-seq method that includes five steps: worm lysis and RNA extraction, cDNA synthesis, library preparation, sequencing, and sequence data analysis. We found that the mechanical lysis of worms using a Qiagen TissueLyser maintained RNA integrity and determined that the quality of our single-worm libraries was comparable to that of standard RNA-seq libraries based on assessments of a variety of parameters. Furthermore, analysis of pathogen infection-induced gene expression using single-worm RNA-seq identified a core set of genes and biological processes relating to the immune response and metabolism affected by infection. These results demonstrate the effectiveness of our single-worm RNA-seq method in transcriptome profiling and its usefulness in addressing biological questions.ConclusionsWe have developed a single-worm RNA-seq method to effectively profile gene expression in individual C. elegans and have applied this method to study C. elegans responses to pathogen infection. Key aspects of our single-worm RNA-seq libraries were comparable to those of standard RNA-seq libraries. The single-worm method captured the core set of, but not all, infection-affected genes and biological processes revealed by the standard method, indicating that there was gene regulation that is not shared by all individuals in a population. Our study suggests that combining single-worm and standard RNA-seq approaches will allow for detecting and distinguishing shared and individual-specific gene activities in isogenic populations.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets