Institute for Research in Biomedicine, Faculty of Biomedical Sciences, USI, Lugano, Switzerland; Department of Information Technology and Electrical Engineering, ETH Zurich, Zürich, Switzerland
Diego Ulisse Pizzagalli
Institute for Research in Biomedicine, Faculty of Biomedical Sciences, USI, Lugano, Switzerland; Euler Institute, USI, Lugano, Switzerland
Institute of Cell Biology, University of Bern, Bern, Switzerland
Paul Lopez
University of Manitoba, Winnipeg, Canada
Romaniya Zayats
University of Manitoba, Winnipeg, Canada
Pau Carrillo-Barberà
Institute for Research in Biomedicine, Faculty of Biomedical Sciences, USI, Lugano, Switzerland; Instituto de Biotecnología y Biomedicina (BioTecMed), Universitat de València, Valencia, Spain
Paola Antonello
Institute for Research in Biomedicine, Faculty of Biomedical Sciences, USI, Lugano, Switzerland; Institute of Cell Biology, University of Bern, Bern, Switzerland
Miguel Palomino-Segura
Centro Nacional de Investigaciones Cardiovasculares, Madrid, Spain
Intravital microscopy has revolutionized live-cell imaging by allowing the study of spatial–temporal cell dynamics in living animals. However, the complexity of the data generated by this technology has limited the development of effective computational tools to identify and quantify cell processes. Amongst them, apoptosis is a crucial form of regulated cell death involved in tissue homeostasis and host defense. Live-cell imaging enabled the study of apoptosis at the cellular level, enhancing our understanding of its spatial–temporal regulation. However, at present, no computational method can deliver robust detection of apoptosis in microscopy timelapses. To overcome this limitation, we developed ADeS, a deep learning-based apoptosis detection system that employs the principle of activity recognition. We trained ADeS on extensive datasets containing more than 10,000 apoptotic instances collected both in vitro and in vivo, achieving a classification accuracy above 98% and outperforming state-of-the-art solutions. ADeS is the first method capable of detecting the location and duration of multiple apoptotic events in full microscopy timelapses, surpassing human performance in the same task. We demonstrated the effectiveness and robustness of ADeS across various imaging modalities, cell types, and staining techniques. Finally, we employed ADeS to quantify cell survival in vitro and tissue damage in mice, demonstrating its potential application in toxicity assays, treatment evaluation, and inflammatory dynamics. Our findings suggest that ADeS is a valuable tool for the accurate detection and quantification of apoptosis in live-cell imaging and, in particular, intravital microscopy data, providing insights into the complex spatial–temporal regulation of this process.
