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.