ArticleNature communications2019
Predicting bacterial infection outcomes using single cell RNA-sequencing analysis of human immune cells.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
58 citing papers in PubMed.
- Leveraging Single-Cell and Spatial Transcriptomics in Gut Nutrition and Mucosal Immunology.The Journal of nutrition · 2026Article
- The cell with many faces: lung macrophage plasticity and function in response to environmental and pathogenic insults.Physiological reviews · 2026Review
- Review
- Decoding cell fate: integrated experimental and computational analysis at the single-cell level.Bioinformatics (Oxford, England) · 2025Review
- Backtracking metabolic dynamics in single cells predicts bacterial replication in human macrophages.Nature communications · 2025Article
- Genetic Susceptibility to Tuberculosis and the Utility of Polygenic Scores in Population Stratification.International journal of molecular sciences · 2025Review
- The Salmonella phage shock protein system is required for defense against host antimicrobial peptides.PLoS pathogens · 2025Article
- Advances in Single-Cell Sequencing for Infectious Diseases: Progress and Perspectives.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Single-cell RNA sequencing in tuberculosis: Application and future perspectives.Chinese medical journal · 2025Review
- Article
- Bioinformatics perspectives on transcriptomics: A comprehensive review of bulk and single-cell RNA sequencing analyses.Quantitative biology (Beijing, China) · 2025Review
- Advances in Necrotizing Fasciitis Animal Models: Current Trends and Future Perspectives.Journal of inflammation research · 2025Review
- Bacterial single-cell RNA sequencing captures biofilm transcriptional heterogeneity and differential responses to immune pressure.Nature communications · 2024Article
- Dichotomous outcomes of TNFR1 and TNFR2 signaling in NK cell-mediated immune responses during inflammation.Nature communications · 2024Article
- SELF-Former: multi-scale gene filtration transformer for single-cell spatial reconstruction.Briefings in bioinformatics · 2024Article
- Identification of intracellular bacteria from multiple single-cell RNA-seq platforms using CSI-Microbes.Science advances · 2024Article
- An advanced comprehensive muti-cell-type-specific model for predicting anti-PD-1 therapeutic effect in melanoma.Theranostics · 2024Article
- Relative effect size-based profiles as an alternative to differentiation analysis in multi-species single-cell transcriptional studies.PloS one · 2024Article
- From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning.Military Medical Research · 2023Review
- Decoding the immune landscape following hip fracture in elderly patients: unveiling temporal dynamics through single-cell RNA sequencing.Immunity & ageing : I & A · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.