Droplet microfluidic profiling of NK cell cytotoxicity with machine learning-enabled target-cell death analysis

Abstract

Predicting the clinical efficacy of Natural Killer (NK) cell immunotherapies remains challenging due to functional heterogeneity within effector populations and tumor microenvironment (TME)-mediated suppression. Here, we present a droplet microfluidic platform that couples machine-learning-based, frame-wise K562 target-cell detection and live/dead classification with deterministic temporal event calling to map single-cell cytotoxicity trajectories at scale. These ML-derived target-cell trajectories were integrated with standardized morphology-based NK-cell annotation and effector-target attachment scoring. The resulting framework enabled standardized quantification of NK-cell cytotoxicity, serial-killing capacity, killing-time distributions, and attachment-linked outcomes across thousands of isolated NK-target microenvironments. Using matched donor-derived NK-cell states in defined single-effector droplets containing one to four K562 targets, we resolved how ex vivo expansion and ascites-mediated TME conditioning reshape individual NK-cell function. The results demonstrated that expanded NK cells (exNK) exhibited superior cytotoxic activity, serial killing, and rapid killing dynamics, whereas peripheral blood NK cells (pbNK), especially after exposure to ascites TME (pbNK-asc), showed reduced function across all cytotoxicity metrics. Notably, expanded NK cells exposed to ascites TME (exNK-asc) retained partial functionality, indicating that expansion provides resilience against suppressive factors. This single-cell platform provides insight into NK-cancer cell interactions and offers a scalable framework for optimizing off-the-shelf NK cell-based immunotherapies.