Transformer-based spatial–temporal detection of apoptotic cell death in live-cell imaging
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....
| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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eLife Sciences Publications Ltd
2024-03-01
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| Series: | eLife |
| Subjects: | |
| Online Access: | https://elifesciences.org/articles/90502 |
| _version_ | 1797254452724367360 |
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| author | Alain Pulfer Diego Ulisse Pizzagalli Paolo Armando Gagliardi Lucien Hinderling Paul Lopez Romaniya Zayats Pau Carrillo-Barberà Paola Antonello Miguel Palomino-Segura Benjamin Grädel Mariaclaudia Nicolai Alessandro Giusti Marcus Thelen Luca Maria Gambardella Thomas T Murooka Olivier Pertz Rolf Krause Santiago Fernandez Gonzalez |
| author_facet | Alain Pulfer Diego Ulisse Pizzagalli Paolo Armando Gagliardi Lucien Hinderling Paul Lopez Romaniya Zayats Pau Carrillo-Barberà Paola Antonello Miguel Palomino-Segura Benjamin Grädel Mariaclaudia Nicolai Alessandro Giusti Marcus Thelen Luca Maria Gambardella Thomas T Murooka Olivier Pertz Rolf Krause Santiago Fernandez Gonzalez |
| author_sort | Alain Pulfer |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T21:50:11Z |
| format | Article |
| id | doaj.art-7e7d13c61fdf4ad4b1ddfbc78e28f7a6 |
| institution | Directory Open Access Journal |
| issn | 2050-084X |
| language | English |
| last_indexed | 2024-04-24T21:50:11Z |
| publishDate | 2024-03-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj.art-7e7d13c61fdf4ad4b1ddfbc78e28f7a62024-03-20T15:22:12ZengeLife Sciences Publications LtdeLife2050-084X2024-03-011210.7554/eLife.90502Transformer-based spatial–temporal detection of apoptotic cell death in live-cell imagingAlain Pulfer0https://orcid.org/0009-0004-3780-1642Diego Ulisse Pizzagalli1Paolo Armando Gagliardi2https://orcid.org/0000-0002-4818-035XLucien Hinderling3https://orcid.org/0000-0002-3956-9363Paul Lopez4Romaniya Zayats5Pau Carrillo-Barberà6Paola Antonello7Miguel Palomino-Segura8Benjamin Grädel9https://orcid.org/0000-0002-1995-0263Mariaclaudia Nicolai10Alessandro Giusti11Marcus Thelen12https://orcid.org/0000-0002-3443-1605Luca Maria Gambardella13Thomas T Murooka14Olivier Pertz15https://orcid.org/0000-0001-8579-4919Rolf Krause16Santiago Fernandez Gonzalez17https://orcid.org/0000-0003-4166-7664Institute for Research in Biomedicine, Faculty of Biomedical Sciences, USI, Lugano, Switzerland; Department of Information Technology and Electrical Engineering, ETH Zurich, Zürich, SwitzerlandInstitute for Research in Biomedicine, Faculty of Biomedical Sciences, USI, Lugano, Switzerland; Euler Institute, USI, Lugano, SwitzerlandInstitute of Cell Biology, University of Bern, Bern, SwitzerlandInstitute of Cell Biology, University of Bern, Bern, SwitzerlandUniversity of Manitoba, Winnipeg, CanadaUniversity of Manitoba, Winnipeg, CanadaInstitute for Research in Biomedicine, Faculty of Biomedical Sciences, USI, Lugano, Switzerland; Instituto de Biotecnología y Biomedicina (BioTecMed), Universitat de València, Valencia, SpainInstitute for Research in Biomedicine, Faculty of Biomedical Sciences, USI, Lugano, Switzerland; Institute of Cell Biology, University of Bern, Bern, SwitzerlandCentro Nacional de Investigaciones Cardiovasculares, Madrid, SpainInstitute of Cell Biology, University of Bern, Bern, SwitzerlandEuler Institute, USI, Lugano, SwitzerlandDalle Molle Institute for Artificial Intelligence, IDSIA, Lugano, SwitzerlandInstitute for Research in Biomedicine, Faculty of Biomedical Sciences, USI, Lugano, SwitzerlandDalle Molle Institute for Artificial Intelligence, IDSIA, Lugano, SwitzerlandUniversity of Manitoba, Winnipeg, CanadaInstitute of Cell Biology, University of Bern, Bern, SwitzerlandEuler Institute, USI, Lugano, SwitzerlandInstitute for Research in Biomedicine, Faculty of Biomedical Sciences, USI, Lugano, SwitzerlandIntravital 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.https://elifesciences.org/articles/90502cell culturelymph nodespleen |
| spellingShingle | Alain Pulfer Diego Ulisse Pizzagalli Paolo Armando Gagliardi Lucien Hinderling Paul Lopez Romaniya Zayats Pau Carrillo-Barberà Paola Antonello Miguel Palomino-Segura Benjamin Grädel Mariaclaudia Nicolai Alessandro Giusti Marcus Thelen Luca Maria Gambardella Thomas T Murooka Olivier Pertz Rolf Krause Santiago Fernandez Gonzalez Transformer-based spatial–temporal detection of apoptotic cell death in live-cell imaging eLife cell culture lymph node spleen |
| title | Transformer-based spatial–temporal detection of apoptotic cell death in live-cell imaging |
| title_full | Transformer-based spatial–temporal detection of apoptotic cell death in live-cell imaging |
| title_fullStr | Transformer-based spatial–temporal detection of apoptotic cell death in live-cell imaging |
| title_full_unstemmed | Transformer-based spatial–temporal detection of apoptotic cell death in live-cell imaging |
| title_short | Transformer-based spatial–temporal detection of apoptotic cell death in live-cell imaging |
| title_sort | transformer based spatial temporal detection of apoptotic cell death in live cell imaging |
| topic | cell culture lymph node spleen |
| url | https://elifesciences.org/articles/90502 |
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