BLINK: Behavioral Latent Modeling of NK Cell Cytotoxicity

2026-03-05

AI Summary

Natural killer (NK) cell cytotoxicity is difficult to assess accurately from single-frame images, necessitating modeling of their dynamic, long-term interactions with tumor cells. To address this, this work proposes BLINK—a trajectory-based recurrent state-space model that learns latent dynamics from partially observed NK–tumor interaction sequences to predict time-accumulated apoptosis increments and thereby infer final killing outcomes. This study introduces, for the first time, trajectory-level latent behavior modeling into NK cell cytotoxicity research, enabling interpretable segmentation of interaction phases, organization of behavioral patterns, and prediction of future killing efficacy. Evaluated on long-term time-lapse imaging data, BLINK significantly improves killing detection accuracy and yields structured, interpretable representations of NK cell behavior.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• NK cell cytotoxicity
• cellular interaction dynamics
• time-resolved microscopy
• apoptosis prediction
• single-cell behavior

Innovation

• trajectory-based modeling
• recurrent state-space model
• latent interaction dynamics
• NK cell cytotoxicity
• interpretable representation