Highly adaptable deep-learning platform for automated detection and analysis of vesicle exocytosis
Activity recognition in live-cell imaging is labor-intensive and requires significant human effort. Existing automated analysis tools are largely limited in versatility. We present the Intelligent Vesicle Exocytosis Analysis (IVEA) platform, an ImageJ plugin for automated, reliable analysis of fluorescence-labeled vesicle fusion events and other burst-like activity. IVEA includes three specialized modules for detecting: (1) synaptic transmission in neurons, (2) single-vesicle exocytosis in any cell type, and (3) nano-sensor-detected exocytosis. Each module uses distinct techniques, including deep learning, allowing the detection of rare events often missed by humans at a speed estimated to be approximately 60 times faster than manual analysis. IVEA's versatility can be expanded by refining or training new models via an integrated interface. With its impressive speed and remarkable accuracy, IVEA represents a seminal advancement in exocytosis image analysis and other burst-like fluorescence fluctuations applicable to a wide range of microscope types and fluorescent dyes.
Authors
- Sebastian Barg (ORCID: https://orcid.org/0000-0003-4661-5724)
- Nadia Alawar (ORCID: https://orcid.org/0000-0002-8591-2513)
- Ute Becherer (ORCID: https://orcid.org/0000-0001-6005-7517)
- Santiago Echeverry (ORCID: https://orcid.org/0000-0001-7145-9802)
- Xiao Yu Tian (ORCID: https://orcid.org/0000-0003-3472-9898)
- Salvatore Valitutti (ORCID: https://orcid.org/0000-0002-6432-4421)
- Constantin Pape (ORCID: https://orcid.org/0000-0001-6562-7187)
- Hsin‐Fang Chang (ORCID: https://orcid.org/0000-0002-7691-4090)
- Sofia Elizarova (ORCID: https://orcid.org/0000-0002-5757-764X)
- Eugenio F. Fornasiero
- James Daniel (ORCID: https://orcid.org/0000-0002-2781-4544)
- Peter Lipp (ORCID: https://orcid.org/0000-0003-4728-9174)
- Abed Alrahman Chouaib
- Lucie Demeersseman (ORCID: https://orcid.org/0000-0001-8404-4761)
- Ali H. Shaib (ORCID: https://orcid.org/0009-0007-2656-3820)
- Omnia M. Khamis
Institutions
- Uppsala University (SE)
- University of Trieste (IT)
- Inserm (FR)
- Centre de Recherches en Cancérologie de Toulouse (FR)
- Universitätsmedizin Göttingen (DE)
- Max Planck Institute of Experimental Medicine (DE)
- University of Göttingen (DE)
- Saarland University (DE)
Publication Details
- Journal
- Nature Communications
- Published
- 2025-07-12
- DOI
- https://doi.org/10.1038/s41467-025-61579-3
- Citations
- 4
- Primary Topic
- Lipid Membrane Structure and Behavior
- Type
- article
- Field-Weighted Citation Impact
- 3.22
Funders
- European Commission
- Deutsche Forschungsgemeinschaft
- Institut National de la Santé et de la Recherche Médicale
- Lunds Universitet
- Universität des Saarlandes