ArticleEBioMedicine2024
Neural network-assisted humanisation of COVID-19 hamster transcriptomic data reveals matching severity states in human disease.
Article in EBioMedicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Cross-species integration of single-cell data reveals conserved pathology-associated cell populations across animal models and human samples.Cell reports methods · 2026Article
- Adapted Live SARS-CoV-2 Vaccine Elicits Rapid Mucosal Immunity, Protects From Disease, and Reduces Shedding of XBB.1.5.European journal of immunology · 2026Article
- Strong immunogenicity and protection against SARS-CoV-2 in hamsters induced by heterologous boost vaccination with an MVA-based COVID-19 vaccine candidate.The Journal of general virology · 2025Article
- Deep learning-aided inter-species-comparison reveals shared and distinct molecular patterns in cynomolgus monkey and humans following non-specific T cell activation.Frontiers in immunology · 2025Article
- Bridging gaps: a neural network approach for cross-species scRNA-seq analysis in COVID-19.EBioMedicine · 2024Article
- A single-dose MCMV-based vaccine elicits long-lasting immune protection in mice against distinct SARS-CoV-2 variants.Frontiers in immunology · 2024Article
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Abstract
backgroundTranslating findings from animal models to human disease is essential for dissecting disease mechanisms, developing and testing precise therapeutic strategies. The coronavirus disease 2019 (COVID-19) pandemic has highlighted this need, particularly for models showing disease severity-dependent immune responses.
methodsSingle-cell transcriptomics (scRNAseq) is well poised to reveal similarities and differences between species at the molecular and cellular level with unprecedented resolution. However, computational methods enabling detailed matching are still scarce. Here, we provide a structured scRNAseq-based approach that we applied to scRNAseq from blood leukocytes originating from humans and hamsters affected with moderate or severe COVID-19.
findingsIntegration of data from patients with COVID-19 with two hamster models that develop moderate (Syrian hamster, Mesocricetus auratus) or severe (Roborovski hamster, Phodopus roborovskii) disease revealed that most cellular states are shared across species. A neural network-based analysis using variational autoencoders quantified the overall transcriptomic similarity across species and severity levels, showing highest similarity between neutrophils of Roborovski hamsters and patients with severe COVID-19, while Syrian hamsters better matched patients with moderate disease, particularly in classical monocytes. We further used transcriptome-wide differential expression analysis to identify which disease stages and cell types display strongest transcriptional changes.
interpretationConsistently, hamsters' response to COVID-19 was most similar to humans in monocytes and neutrophils. Disease-linked pathways found in all species specifically related to interferon response or inhibition of viral replication. Analysis of candidate genes and signatures supported the results. Our structured neural network-supported workflow could be applied to other diseases, allowing better identification of suitable animal models with similar pathomechanisms across species.
fundingThis work was supported by German Federal Ministry of Education and Research, (BMBF) grant IDs: 01ZX1304B, 01ZX1604B, 01ZX1906A, 01ZX1906B, 01KI2124, 01IS18026B and German Research Foundation (DFG) grant IDs: 14933180, 431232613.
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