Abstract 3714: Microscopic mayhem: Serial killing and other immune cell dynamics captured in situ

Critical to advancing immunotherapy and cell therapy in cancer is developing a deeper understanding pf the dynamics of immune cell-mediated cytotoxicity. The results from the multidisciplinary effort reported here include numerous movies of immune cell-mediated cytotoxicity with striking examples of serial killing, foraging, path-tracking, cytokine gradients at tumor margins, and killing dynamics, in some cases revealing peak apoptotic signatures just minutes after T Cell engagement. In vitro studies of immune cell killing are traditionally performed using time-lapse imaging and biochemical assays, but these methods are often limited by spatial and temporal resolution, throughput, and the ability to extract the dynamics of cellular interactions. This study integrates high-resolution and high-speed laser scanning confocal microscopy with artificial intelligence (AI), and machine learning (ML) approaches to provide a high-resolution data-driven analysis of immune cell killing dynamics in vitro. We have engineered a perfusion-enabled 3D culture system integrated microscopy to assess cellular dynamics for extended periods of time. Perfusion culture maintains the interstitial flow of liquid culture media, clearing the microenvironment of toxic metabolites and reactive oxygen species. This platform uses a Liquid-Like Solids (LLS) to mimic the transport dynamics of a capillary bed. Integrated microscopy allows in situ quantification of spatiotemporal cytokine concentrations, immune cell tracking, immune cell killing dynamics, and invasion dynamics. In one example, we used Human CAR T cells with an anti-cd19.28z CAR that included a fluorescent mCherry protein fused to the carboxy terminus of the CAR vs. target cancer cells (Burkitt lymphoma) that express GFP. The media contained a 0.1% concentration of Propidium Iodide as a terminal maker of cell death. Such multi-channel data highlights the critical stages of immune-mediated cytotoxicity; however, the complexity and volume of confocal imaging data necessitates advanced computational tools for analysis. The ML algorithm used in this study comes from computer vision using object detection, which is a cornerstone technique in medical imaging. We utilized the YOLOv8X model, which is distinguished by its high accuracy and efficiency in object detection tasks. The YOLO (You Only Look Once) architecture is designed to predict both bounding boxes and class probabilities in a single forward pass, making it exceptionally fast and suitable for real-time applications. Finally, by integrating robotics, imaging, and AI/ML detection we are able to capture immune cells of interest in situ. This integrated analysis of complex imaging data from confocal microscopy has been significantly accelerated, enabling the identification of subtle patterns and dynamic interactions between immune cells and their targets, and the actual collection of cells of interest. Citation Format: W. Gregory Sawyer, Diego Pedro, Alfonso Pepe, Reginald Atkins, Chandler Gatenbee, Luis Felipe, Carlos Garcia Fernandez, Soraya Zorro Manrique, Said Cifuentes Maury, Athena Tsingelis, Duy Nguyen, Alexander Anderson, Erin George, Gilmer Valdes, Fredrick Locke, Patrick Hwu. Microscopic mayhem: Serial killing and other immune cell dynamics captured in situ [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3714.