Neural network-assisted humanisation of COVID-19 hamster transcriptomic data reveals matching severity states in human diseaseResearch in context
Vincent D. Friedrich,
Peter Pennitz,
Emanuel Wyler,
Julia M. Adler,
Dylan Postmus,
Kristina Müller,
Luiz Gustavo Teixeira Alves,
Julia Prigann,
Fabian Pott,
Daria Vladimirova,
Thomas Hoefler,
Cengiz Goekeri,
Markus Landthaler,
Christine Goffinet,
Antoine-Emmanuel Saliba,
Markus Scholz,
Martin Witzenrath,
Jakob Trimpert,
Holger Kirsten,
Geraldine Nouailles
Affiliations
Vincent D. Friedrich
University of Leipzig, Institute for Medical Informatics, Statistics, and Epidemiology, Leipzig, Germany; Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), Leipzig, Germany
Peter Pennitz
Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Infectious Diseases, Respiratory Medicine and Critical Care, Berlin, Germany
Emanuel Wyler
Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), Berlin, Germany
Julia M. Adler
Freie Universität Berlin, Institut für Virologie, Berlin, Germany
Dylan Postmus
Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Virology, Berlin, Germany; Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany; Liverpool School of Tropical Medicine, Department of Tropical Disease Biology, Liverpool, United Kingdom
Kristina Müller
University of Leipzig, Institute for Medical Informatics, Statistics, and Epidemiology, Leipzig, Germany
Luiz Gustavo Teixeira Alves
Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), Berlin, Germany
Julia Prigann
Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Virology, Berlin, Germany; Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany; Gladstone Institutes, San Francisco, USA
Fabian Pott
Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Virology, Berlin, Germany; Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany
Daria Vladimirova
Freie Universität Berlin, Institut für Virologie, Berlin, Germany
Thomas Hoefler
Freie Universität Berlin, Institut für Virologie, Berlin, Germany
Cengiz Goekeri
Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Infectious Diseases, Respiratory Medicine and Critical Care, Berlin, Germany; Cyprus International University, Faculty of Medicine, Nicosia, Cyprus
Markus Landthaler
Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), Berlin, Germany; Humboldt-Universität zu Berlin, Institut fuer Biologie, Berlin, Germany
Christine Goffinet
Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Virology, Berlin, Germany; Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany; Liverpool School of Tropical Medicine, Department of Tropical Disease Biology, Liverpool, United Kingdom
Antoine-Emmanuel Saliba
Faculty of Medicine, Institute of Molecular Infection Biology (IMIB), University of Würzburg, Würzburg, Germany; Helmholtz Institute for RNA-based Infection Research (HIRI), Helmholtz Center for Infection Research (HZI), Würzburg, Germany
Markus Scholz
University of Leipzig, Institute for Medical Informatics, Statistics, and Epidemiology, Leipzig, Germany; University of Leipzig, Faculty of Mathematics and Computer Science, Leipzig, Germany
Martin Witzenrath
Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Infectious Diseases, Respiratory Medicine and Critical Care, Berlin, Germany; German Center for Lung Research (DZL), Berlin, Germany
Jakob Trimpert
Freie Universität Berlin, Institut für Virologie, Berlin, Germany
Holger Kirsten
University of Leipzig, Institute for Medical Informatics, Statistics, and Epidemiology, Leipzig, Germany; Corresponding author. Institute for Medical Informatics, Statistics, and Epidemiology (IMISE), Haertelstraße 16-18, 04107, Leipzig, Germany.
Geraldine Nouailles
Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Infectious Diseases, Respiratory Medicine and Critical Care, Berlin, Germany; Corresponding author. Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Infectious Diseases, Respiratory Medicine and Critical Care, Charitéplatz 1, 10117, Berlin, Germany.
Summary: Background: Translating 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. Methods: Single-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. Findings: Integration 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. Interpretation: Consistently, 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. Funding: This 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.
