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Modeling the Human Immune Response to Severe Influenza Infection in an Immunocompetent Lung-on-Chip

GSE294626 Homo sapiens Expression profiling by high throughput sequencing 4 samples Submitted 2025/07/13 Platform GPL24676
Summary
Severe influenza affects 3-5 million people worldwide each year, resulting in more than 300,000 deaths annually. However, standard-of-care antiviral therapeutics have limited effectiveness in these patients. Current preclinical models of severe influenza fail to recapitulate the human immune response to severe viral infection accurately. Here, we develop an immune-competent, microvascularized, human lung-on-chip device to model the small airways, successfully demonstrating the cytokine storm, immune cell activation, epithelial cell damage, and other cellular- and tissue-level human immune responses to severe H1N1 infection. We find that IL-1β and TNF-α play opposing roles in the initiation and regulation of the cytokine storm associated with severe influenza. Furthermore, we discover the critical stromal-immune CXCL12-CXCR4 interaction and its role in immune response to infection. Our results underscore the importance of stromal cells and immune cells in microphysiological models of severe lung disease, describing a scalable model for severe influenza research. We expect the human lung-on-chip device to enable critical discoveries in respiratory host-pathogen interactions, therapeutic side effects, vaccine potency evaluation, and crosstalk between systemic and mucosal immunity in human lung.
Published in
An immune-competent lung-on-a-chip for modelling the human severe influenza infection response
Ringquist R, Bhatia E, Chatterjee P et al. · Nature biomedical engineering 2026 · PMID 40987954 · doi:10.1038/s41551-025-01491-9
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Direct links to NCBI, no account and no request form: the whole study as GSE294626_RAW.tar, processed values as the series matrix, the supplementary file directory, and per-sample supplementary files for any of the 4 samples. Raw sequencing reads are also available from ENA.

Also filed as BioProject PRJNA1249536 and SRA study SRP578108. Searching any of these in the dataset finder brings you back here.

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