Cell Image Analysis Techniques
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Abstract
Abstract
Abstract Analysis of multimodal and multidimensional data capturing dynamic interactions between diverse cell populations is a current challenge in bioimaging, especially in the context of immunology and immunotherapy research. Here, we introduce Celldetective, an open-source Python-based software tool designed for high-performance, end-to-end analysis of image-based in vitro immune and immunotherapy assays. Celldetective is purpose-built for multicondition, 2D multi-channel time-lapse microscopy of mixed cell populations. Although it is optimised for the needs of immunology assays, it is nevertheless broadly applicable to any biological system involving interacting cell populations. The software seamlessly integrates AI-based segmentation, tracking, and automated single-cell event detection, all within an intuitive graphical interface that supports interactive visualisation, annotation, and training options. We showcase its capabilities with original datasets of single immune effector cell interactions with an activating surface mediated by bispecific antibodies, and pairwise interactions in antibody-dependent cell cytotoxicity events.
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@article{Torro2026Celldetective,
title = {Celldetective, an AI-enhanced image analysis tool for unraveling dynamic cell interactions},
author = {Rémy Torro and Beatriz Díaz-Bello and Dalia El Arawi and Ksenija Dervanova and Lorna Ammer and Florian Dupuy and Patrick Chames and Kheya Sengupta and Laurent Limozin},
journal = {eLife},
year = {2026},
doi = {10.7554/elife.105302.3},
url = {https://doi.org/10.7554/elife.105302.3}
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