Smart hybrid microscopy for cell-friendly detection of rare events
Fluorescence microscopy offers unparalleled access to the spatial organization and dynamics of biological events in living samples, yet capturing rare processes over extended durations remains challenging due to trade-offs between exposure to excitation light and sample health. Here, we introduce hybrid-EDA, an event-driven acquisition (EDA) framework that combines the gentleness and contextual richness of phase-contrast with the functional specificity of fluorescence. We develop surveillance for events of interest in label-free microscopy using dynamics-informed neural networks that trigger smart fluorescence acquisitions upon detection. This allows us to dramatically reduce phototoxic damage while obtaining specific and functional information from fluorescence when beneficial. We demonstrate how hybrid-EDA enables improved imaging acquisitions of organelle contacts and mitochondrial divisions. We envision that hybrid-EDA will enable insights into a range of dynamic and rare biological processes, providing a powerful and general strategy for cell-friendly imaging.
Authors
- Martin Weigert (ORCID: https://orcid.org/0000-0002-7780-9057)
- Willi L. Stepp (ORCID: https://orcid.org/0000-0001-9818-0377)
- Suliana Manley (ORCID: https://orcid.org/0000-0002-4755-4778)
- Sonia Álvarez (ORCID: https://orcid.org/0000-0003-4951-4566)
- Kyle M. Douglass (ORCID: https://orcid.org/0000-0003-4694-0922)
- Efkan Durmuş
- Giorgio Tortarolo (ORCID: https://orcid.org/0000-0002-1182-2478)
- Juan C. Landoni (ORCID: https://orcid.org/0000-0002-0318-6707)
Institutions
- German Research Centre for Artificial Intelligence (DE)
- Center for Scalable Data Analytics and Artificial Intelligence (DE)
- École Polytechnique Fédérale de Lausanne (CH)
- Technische Universität Dresden (DE)
Publication Details
- Journal
- Nature Communications
- Published
- 2026-01-07
- DOI
- https://doi.org/10.1038/s41467-025-68168-4
- Citations
- 2
- Primary Topic
- Digital Holography and Microscopy
- Type
- article
- Field-Weighted Citation Impact
- 62.78
Funders
- National Science Foundation
- European Commission
- École Polytechnique Fédérale de Lausanne
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
- Horizon 2020 Framework Programme