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dc.contributor.author | Pulfer, Alain | |
dc.contributor.author | Pizzagalli, Diego Ulisse | |
dc.contributor.author | Gagliardi, Paolo Armando | |
dc.contributor.author | Hinderling, Lucien | |
dc.contributor.author | Lopez, Paul | |
dc.contributor.author | Zayats, Romaniya | |
dc.contributor.author | Carrillo-Barberà, Pau | |
dc.contributor.author | Antonello, Paola | |
dc.contributor.author | Palomino-Segura, Miguel | |
dc.contributor.author | Grädel, Benjamin | |
dc.contributor.author | Nicolai, Mariaclaudia | |
dc.contributor.author | Giusti, Alessandro | |
dc.contributor.author | Thelen, Marcus | |
dc.contributor.author | Gambardella, Luca Maria | |
dc.contributor.author | Murooka, Thomas T | |
dc.contributor.author | Pertz, Olivier | |
dc.contributor.author | Krause, Rolf | |
dc.contributor.author | Gonzalez, Santiago Fernandez | |
dc.date.accessioned | 2024-07-03T13:39:41Z | |
dc.date.available | 2024-07-03T13:39:41Z | |
dc.date.issued | 2024-03-18 | |
dc.identifier.citation | Elife. 2024 Mar 18:12:RP90502. | es_ES |
dc.identifier.uri | http://hdl.handle.net/20.500.12105/20033 | |
dc.description.abstract | 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. | es_ES |
dc.description.sponsorship | We thank Dr. Coral Garcia (IQS, Barcelona, Spain) for the support in generating graphical content. Moreover, we would like to acknowledge Gabriele Abbate (IDSIA, Lugano, Switzerland) for his help during an early implementation of the DL classifier. Suisse National Science Foundation grant 176124 (AP, DU, MP, SG); Swiss Cancer League grant KLS-4867-08-2019, Suisse National Science Foundation grant Div3; 310030_185376 and IZKSZ3_62195, Uniscientia Foundation (PG, LH, OP); SystemsX.ch grant iPhD2013124 (DU, RK, SG); Novartis Foundation for medical-biological Research, The Helmut Horten Foundation, SwissCancer League grant KFS-4223-08-2017-R (PA, MT); Canadian Institute for Health Research (CIHR) Project grants PJT-155951 (RZ, PL, TM); NCCR Robotics program of the Swiss National Science Foundation (AG, LG); Biolink grant 189699 (DU, PC, SG) | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | eLife Sciences Publications | es_ES |
dc.type.hasVersion | VoR | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject.mesh | Apoptosis | es_ES |
dc.subject.mesh | Microscopy | es_ES |
dc.subject.mesh | Humans | es_ES |
dc.subject.mesh | Animals | es_ES |
dc.subject.mesh | Mice | es_ES |
dc.subject.mesh | Cell Survival | es_ES |
dc.subject.mesh | Intravital Microscopy | es_ES |
dc.subject.mesh | Recognition, Psychology | es_ES |
dc.title | Transformer-based spatial-temporal detection of apoptotic cell death in live-cell imaging. | es_ES |
dc.type | journal article | es_ES |
dc.rights.license | Atribución 4.0 Internacional | * |
dc.identifier.pubmedID | 38497754 | es_ES |
dc.format.volume | 12 | es_ES |
dc.identifier.doi | 10.7554/eLife.90502 | es_ES |
dc.contributor.funder | Novartis Foundation | es_ES |
dc.contributor.funder | Canadian Institutes of Health Research | es_ES |
dc.contributor.funder | Swiss National Science Foundation | es_ES |
dc.description.peerreviewed | Sí | es_ES |
dc.identifier.e-issn | 2050-084X | es_ES |
dc.relation.publisherversion | 10.7554/eLife.90502 | es_ES |
dc.identifier.journal | eLife | es_ES |
dc.repisalud.orgCNIC | CNIC::Grupos de investigación::Imagen de la Inflamación Cardiovascular y la Respuesta Inmune | es_ES |
dc.repisalud.institucion | CNIC | es_ES |
dc.rights.accessRights | open access | es_ES |