RT Journal Article
SR Electronic
T1 Deep learning of virus infections reveals mechanics of lytic cells
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 798074
DO 10.1101/798074
A1 Andriasyan, Vardan
A1 Yakimovich, Artur
A1 Georgi, Fanny
A1 Petkidis, Anthony
A1 Witte, Robert
A1 Puntener, Daniel
A1 Greber, Urs F.
YR 2019
UL http://biorxiv.org/content/early/2019/10/09/798074.abstract
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.