Natural Killer (NK) cells are crucial in the immune system’s battle against cancer, with their cytotoxic activity serving as a key determinant of therapeutic success. However, the functional heterogeneity among individual NK cells, ranging from inactive cells to highly potent "serial killers", remains poorly understood due to technical limitations of current assays. Population-based analyses obscure the nuanced performance of individual cells, while droplet and microwell-based methods are confined to small-scale datasets, missing the dynamic behavior of larger NK cell populations.

To overcome these limitations, we developed MSparkles, an advanced analysis platform based on MATLAB, designed to detect, track, and analyze single NK cell behavior alongside target cell death. Central to this system is the apoptosis-reporter pCasper3GR. On this basis, we have developed a single-cell cytotoxicity assay, which uses changes in fluorescence to distinguish between apoptosis, necrosis, and living cells. MSparkles enables time-resolved tracking of killer-target interactions, detailed motion and contact analyses, alongside classification of cell death events. Furthermore, the platform integrates fluorescence profiling of individual cells, enabling e.g. robust analysis of intracellular calcium signaling in NK cells during cytotoxic events. MSparkles provides direct correlation of signaling dynamics with other key parameters, such as contact time and killing phenotypes.

By bridging the gap between high-throughput approaches and single-cell resolution, MSparkles provides a transformative tool for studying NK cell heterogeneity. Its ability to uncover functional outliers, such as "super killer" NK cells with exceptional cytotoxic capacities, holds significant implications for both basic and translational research. MSparkles not only advances our understanding of NK cell biology but also supports the development of next-generation immunotherapies. By identifying and characterizing the most effective immune effector cells, MSparkles paves the way for more personalized and effective treatments for cancer.