TY - JOUR
T1 - Neural network-assisted humanisation of COVID-19 hamster transcriptomic data reveals matching severity states in human disease
AU - Friedrich, Vincent D.
AU - Pennitz, Peter
AU - Wyler, Emanuel
AU - Adler, Julia M.
AU - Postmus, Dylan
AU - Müller, Kristina
AU - Teixeira Alves, Luiz Gustavo
AU - Prigann, Julia
AU - Pott, Fabian
AU - Vladimirova, Daria
AU - Hoefler, Thomas
AU - Goekeri, Cengiz
AU - Landthaler, Markus
AU - Goffinet, Christine
AU - Saliba, Antoine Emmanuel
AU - Scholz, Markus
AU - Witzenrath, Martin
AU - Trimpert, Jakob
AU - Kirsten, Holger
AU - Nouailles, Geraldine
PY - 2024/9/23
Y1 - 2024/9/23
N2 - 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.
AB - 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.
KW - COVID-19
KW - Cross-species analysis
KW - Deep learning
KW - Disease state matching
KW - Hamster model
KW - Single-cell RNA-seq
U2 - 10.1016/j.ebiom.2024.105312
DO - 10.1016/j.ebiom.2024.105312
M3 - Article
SN - 2352-3964
VL - 108
SP - e105312
JO - eBioMedicine
JF - eBioMedicine
M1 - 105312
ER -
