PT  - JOURNAL ARTICLE
AU  - Andriasyan, Vardan
AU  - Yakimovich, Artur
AU  - Georgi, Fanny
AU  - Petkidis, Anthony
AU  - Witte, Robert
AU  - Puntener, Daniel
AU  - Greber, Urs F.
TI  - Deep learning of virus infections reveals mechanics of lytic cells
AID  - 10.1101/798074
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 798074
4099  - http://biorxiv.org/content/early/2019/10/09/798074.short
4100  - http://biorxiv.org/content/early/2019/10/09/798074.full
AB  - 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.