Here, we describe a robust and flexible method for analyzing the infection of single cells with a fluorescent-reporter virus, with real-time tracking of infected cells through microscopy. We subsequently generate quantitative data from the resulting time-lapse microscopy, and harness these data to generate biologically meaningful parameters at scale. Our methodology offers a practical solution for researchers seeking to understand the complexities and variability of virus-host interactions at a granular level.
Keywords: Fluorescence microscopy; Image analysis; Infection dynamics; Poxvirus; Single cell; Single-cell isolation; Time series data modeling; Vaccinia virus.
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