Automated cell cluster analysis provides insight into multi-cell-type interactions between immune cells and their targets.

Understanding interactions between immune cells and their targets is an important step on the path to fully characterizing the immune system, and in doing so, learning how it combats disease. Many studies of these interactions have a narrow focus, often looking only at a binary result of whether or not a specific treatment was successful or only focusing on the interactions between two individual cells. Therefore, in an effort to more comprehensively study multicellular interactions among immune cells and their targets, we used in vitro longitudinal time-lapse imaging and developed an automated cell cluster analysis tool, or macro, to investigate the formation of cell clusters. In particular, we investigated the behavior of cancer-specific CD8+ and CD4+ T cells on how they interact around their targets: cancer cells and antigen-presenting cells. The macro that we established allowed us to examine these large-scale clustering behaviors taking place between those four cell types. Thus, we were able to distinguish directed immune cell clustering from random cell movement. Furthermore, this macro can be generalized to be applicable to systems consisting of any number of differently labeled species and can be used to track clustering behaviors and compare them to randomized simulations.