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Ex vivo modelling of lung tissue resident antimicrobial responses

GSE306406 Homo sapiens; Mus musculus Expression profiling by high throughput sequencing 39 samples 2026/03/26 GPL24247GPL24676
Summary
Tissue resident host responses to microbial infections in the respiratory tract are highly dynamic in space and time and rely on the interaction of a multitude of cell types. To model these multicellular responses reliably in cell culture, we compare here the global transcriptional antimicrobial response to infection with influenza A virus (IAV) in precision cut lung slices (PCLS), volume defined organ discs largely maintaining the cellular composition and 3D architecture of the donor lung. To permit a fair comparison of host responses in an isogenic background we first challenged mice in vivo and murine PCLS (mPCLS) and assess host transcriptomic changes by unbiased RNAseq. While core antiviral responses overlapped substantially, mPCLS lacked certain features—such as type II interferon expression—likely due to the absence of infiltrating immune cells responses. Importantly, when expanding our findings to immune experienced human precision cut lung slices (hPCLS), we find a much broader antiviral response after IAV challenge, including type I, II and III interferons, suggesting the presence of responsive tissue resident lymphocytes. To prove specificity of this response we infected hPCLS with Streptococcus pneumoniae. Ex vivo tissues responded with a distinct proinflammatory gene profile including IL1A, IL1B and IL17 expression. Blocking of IL-1β signaling partially inhibited the proinflammatory response, suggesting cellular crosstalk and a complex and specific antimicrobial reaction in this ex vivo model. In conclusion diversified tissue resident immune cell compartment distinguishes the human ex vivo model, making it an ideal system for microbiological and immunological research.
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NCBI GEO page ↗ Paper (PMID 41989160) ↗ {# Names what the click gives you. "Open in finder" meant nothing to a visitor who arrived from a search engine and has never seen the tool. #} Find more RNA-seq datasets →
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