Leveraging automated time-lapse microscopy coupled with deep learning to automate colony forming assay - Klett, Anusha; Raith, Dennis; Silvestrini, Paula; Stingl, Matías; Bermeitinger, Jonas; Sapre, Avani; Condor, Martin; Melachrinos, Roman; Kusterer, Mira; Brand, Alexandra; Pisani, Guido; Ullrich, Evelyn; et al. (Frontiers in Oncology, 2025)
IntroductionThe colony forming assay (CFA) stands as a cornerstone technique for evaluating the clonal expansion ability of single cancer cells and is crucial for assessing drug efficacy. However, traditional CFAs rely on labor-intensive, endpoint manual counting, offering limited insights into the dynamic effects of treatment. To overcome these limitations, we developed an Artificial Intelligence (AI)-assisted automated CFA combining time-lapse microscopy for real-time tracking of colony formation.MethodsUsing B-acute lymphoblastic leukemia (B-ALL) cells from an E2A-PBX1 mouse model, we cultured them in a collagen-based 3D matrix with cytokines under static conditions in a low volume (60 µl) culture vessel and validated its comparability to methylcellulose-based media. No significant differences in final colony count or plating efficiency were observed. Our automated platform utilizes a deep learning and multi-object tracking approach for colony counting. Brightfield images were used to train a YOLOv8 object detection network, achieving a mAP50 score of 86% for identifying single cells, clusters, and colonies, and 97% accuracy for Z-stack colony identification with a multi-object tracking algorithm. The detection model accurately identified the majority of objects in the dataset.ResultsThis AI-assisted CFA was successfully applied for density optimization, enabling the determination of seeding densities that maximize plating efficiency (PE), and for IC50 determination, offering an efficient, less labor-intensive method for testing drug concentrations. In conclusion, our novel AI-assisted automated colony counting platform enables automated, high-throughput analysis of colony dynamics, significantly reducing labor and increasing accuracy. Furthermore, it allows detailed, long-term studies of cell-cell interactions and treatment responses using live-cell imaging and AI-assisted cell tracking. DiscussionFuture integration with a perfusion-based drug screening system promises to enhance personalized cancer therapy by optimizing broad drug screening approaches and enabling real-time evaluation of therapeutic efficacy.