Unraveling cause-and-effect relationships in the nervous system is challenging because some biological processes begin stochastically, take a significant amount of time to unfold, and affect small neuronal subpopulations that can be difficult to isolate and measure. Single-cell approaches are slow, subject to user bias, and sometimes too laborious to achieve sample sizes large enough to detect important effects. Here, we describe an automated imaging and analysis system that enables us to follow the fates of individual cells and intracellular proteins over time. Observations can be quantified in a high-throughput manner with minimal user bias. We have adapted survival analysis methods to determine whether and how factors measured during longitudinal analysis predict a particular biological outcome. The ability to monitor complex processes at single-cell resolution quickly, quantitatively, and over long intervals should have wide applications for biology.