Data-driven spatiotemporal characterization of cell death

The proper regulation of cell death is critical to support the normal development and physiology of all tissue and dysfunctional regulation of cell death can contribute to the development of degenerative diseases or cancer. Multiple different forms of cell death are now known to occur and have been identified through studies aimed at uncovering genetic pathways that control the death of individual cells. Emerging evidence now shows that different cell death mechanisms also have distinct effects on cell populations that extend beyond the consequences to individual cells. For example, we have shown that one particular form of cell death, called ferroptosis, spreads rapidly between the individual cells in a population, leading to the appearance of large waves of cell death that are regulated in a collective manner. Such population-scale effects of cell death mechanisms remain largely unexplored, due in large part to a lack of development of computational and imaging-based approaches that will allow for their systematic identification and study. The proposed research will use ferroptosis and another mode of cell death called apoptosis as a test case to develop advanced computational methods that will allow us to decipher how this collective fate is regulated. How information is processed to achieve collective cell death, versus cell-autonomous, or single-cell death, will be systematically studied through a combination of cell biological and computational approaches.