Cellular behavior analysis from live-cell imaging of TCR T cell-cancer cell interactions.

T cell therapies, such as chimeric antigen receptor (CAR) T cells and T cell receptor (TCR) T cells, are a growing class of anti-cancer treatments. However, expansion to novel indications and beyond last-line treatment requires engineering cells' dynamic population behaviors. Here we develop the tools for of T cells from live-cell imaging, a common and inexpensive experimental setup used to evaluate engineered T cells. We first develop a state-of-the-art segmentation and tracking pipeline, , based on human-in-the-loop deep learning. We then build the pipeline to collect a catalog of phenotypes that characterize cell populations, morphology, movement, and interactions in co-cultures of modified T cells and antigen-presenting tumor cells. We use Caliban and Occident to interrogate how interactions between T cells and cancer cells differ when beneficial knock-outs of and are introduced into TCR T cells. We apply spatiotemporal models to quantify T cell recruitment and proliferation after interactions with cancer cells. We discover that, compared to a safe harbor knockout control, knockout T cells have longer interaction times with cancer cells leading to greater T cell activation and killing efficacy, while knockout T cells have increased proliferation rates leading to greater numbers of T cells for hunting. Together, segmentation and tracking from Caliban and phenotype quantification from Occident enable cellular behavior analysis to better engineer T cell therapies for improved cancer treatment.