BLINK: Behavioral Latent Modeling of NK Cell Cytotoxicity

TL;DR

BLINK is a novel recurrent state-space model that captures the dynamics of NK cell cytotoxicity, improving outcome detection, forecasting, and interpretability of cellular interactions over time.

Contribution

It introduces BLINK, a trajectory-based model that learns latent interaction dynamics from time-lapse data, enabling better prediction and understanding of NK cell behavior.

Findings

Enhanced detection of cytotoxic outcomes.

Ability to forecast future NK-tumor interactions.

Interpretable latent representations of cellular behavior.

Abstract

Machine learning models of cellular interaction dynamics hold promise for understanding cell behavior. Natural killer (NK) cell cytotoxicity is a prominent example of such interaction dynamics and is commonly studied using time-resolved multi-channel fluorescence microscopy. Although tumor cell death events can be annotated at single frames, NK cytotoxic outcome emerges over time from cellular interactions and cannot be reliably inferred from frame-wise classification alone. We introduce BLINK, a trajectory-based recurrent state-space model that serves as a cell world model for NK-tumor interactions. BLINK learns latent interaction dynamics from partially observed NK-tumor interaction sequences and predicts apoptosis increments that accumulate into cytotoxic outcomes. Experiments on long-term time-lapse NK-tumor recordings show improved cytotoxic outcome detection and enable forecasting of future outcomes, together with an interpretable latent representation that organizes NK trajectories into coherent behavioral modes and temporally structured interaction phases. BLINK provides a unified framework for quantitative evaluation and structured modeling of NK cytotoxic behavior at the single-cell level.