Microscopy deep learning predicts virus infections and reveals mechanics of lytic-infected cells

Imaging across scales reveals disease mechanisms in organisms, tissues, and cells. Yet, particular infection phenotypes, such as virus-induced cell lysis, have remained difficult to study. Here, we developed imaging modalities and deep learning procedures to identify herpesvirus and adenovirus (AdV) infected cells without virus-specific stainings. Fluorescence microscopy of vital DNA-dyes and live-cell imaging revealed learnable virus-specific nuclear patterns transferable to related viruses of the same family. Deep learning predicted two major AdV infection outcomes, non-lytic (nonspreading) and lytic (spreading) infections, up to about 20 hr prior to cell lysis. Using these predictive algorithms, lytic and non-lytic nuclei had the same levels of green fluorescent protein (GFP)-tagged virion proteins but lytic nuclei enriched the virion proteins faster, and collapsed more extensively upon laser-rupture than non-lytic nuclei, revealing impaired mechanical properties of lytic nuclei. Our algorithms may be used to infer infection phenotypes of emerging viruses, enhance single cell biology, and facilitate differential diagnosis of non-lytic and lytic infections. [Display omitted] •Artificial intelligence identifies HSV- and AdV-infected cells without specific probes.•Imaging lytic-infected cells reveals nuclear envelope rupture and AdV dissemination.•Live cell imaging and neural networks presciently pinpoint lytic-infected cells.•Lytic-infected cell nuclei have mechanical properties distinct from non-lytic nuclei. Virology; Machine learning