When we observe living cells under a microscope, we capture hours of dynamic behaviour but typically reduce this to static measurements. How much of the information are we throwing away? Using 4D lattice light-sheet microscopy, we tracked thousands of neutrophils as they underwent NETosis, a form of immune cell death. We measured how cell morphology changes over time and uncovered treatment effects that were not immediately apparent to conventional endpoint analysis. Different stimuli don’t just push cells to different endpoints; they change how cells get there. We present a framework for extracting temporal information that can be applied to other live imaging experiments and introduce early work with self-supervised deep learning to push these approaches further.