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Deep learning of virus infections reveals mechanics of lytic cells

View ORCID ProfileVardan Andriasyan, View ORCID ProfileArtur Yakimovich, View ORCID ProfileFanny Georgi, Anthony Petkidis, View ORCID ProfileRobert Witte, Daniel Puntener, View ORCID ProfileUrs F. Greber
doi: https://doi.org/10.1101/798074
Vardan Andriasyan
aDepartment of Molecular Life Sciences, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
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Artur Yakimovich
bMRC Laboratory for Molecular Cell Biology, University College London, Gower St, London WC1E 6BT, UK
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Fanny Georgi
aDepartment of Molecular Life Sciences, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
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Anthony Petkidis
aDepartment of Molecular Life Sciences, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
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Robert Witte
aDepartment of Molecular Life Sciences, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
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Daniel Puntener
aDepartment of Molecular Life Sciences, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
cRoche Diagnostics International Ltd, Forrenstrasse 2, 6343 Rotkreuz, Switzerland
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Urs F. Greber
aDepartment of Molecular Life Sciences, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
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  • ORCID record for Urs F. Greber
  • For correspondence: urs.greber@imls.uzh.ch
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Abstract

Imaging across scales gives insight into disease mechanisms in organisms, tissues and cells. Yet, rare infection phenotypes, such as virus-induced cell lysis have remained difficult to study. Here, we developed fixed and live cell imaging modalities and a deep learning approach to identify herpesvirus and adenovirus infections in the absence of virus-specific stainings. Procedures comprises staining of infected nuclei with DNA-dyes, fluorescence microscopy, and validation by virus-specific live-cell imaging. Deep learning of multi-round infection phenotypes identified hallmarks of adenovirus-infected cell nuclei. At an accuracy of >95%, the procedure predicts two distinct infection outcomes 20 hours prior to lysis, nonlytic (nonspreading) and lytic (spreading) infections. Phenotypic prediction and live-cell imaging revealed a faster enrichment of GFP-tagged virion proteins in lytic compared to nonlytic infected nuclei, and distinct mechanics of lytic and nonlytic nuclei upon laser-induced ruptures. The results unleash the power of deep learning based prediction in unraveling rare infection phenotypes.

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  • https://viresnet.github.io/movies

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Posted October 09, 2019.
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Deep learning of virus infections reveals mechanics of lytic cells
Vardan Andriasyan, Artur Yakimovich, Fanny Georgi, Anthony Petkidis, Robert Witte, Daniel Puntener, Urs F. Greber
bioRxiv 798074; doi: https://doi.org/10.1101/798074
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Deep learning of virus infections reveals mechanics of lytic cells
Vardan Andriasyan, Artur Yakimovich, Fanny Georgi, Anthony Petkidis, Robert Witte, Daniel Puntener, Urs F. Greber
bioRxiv 798074; doi: https://doi.org/10.1101/798074

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