Highly adaptable deep-learning platform for automated detection and analysis of vesicle exocytosis

Abed Alrahman Chouaib, Hsin-Fang Chang, Omnia M. Khamis, Nadia Alawar, Santiago Echeverry, Lucie Demeersseman, Sofia Elizarova, James A. Daniel, Qinghai Tian, Peter Lipp, Eugenio F. Fornasiero, Salvatore Valitutti, Sebastian Barg, Constantin Pape, Ali H. Shaib () and Ute Becherer ()
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Abed Alrahman Chouaib: Saarland University
Hsin-Fang Chang: Saarland University
Omnia M. Khamis: Saarland University
Nadia Alawar: Saarland University
Santiago Echeverry: Uppsala University
Lucie Demeersseman: INSERM U1037
Sofia Elizarova: Max Planck Institute for Multidisciplinary Sciences
James A. Daniel: Max Planck Institute for Multidisciplinary Sciences
Qinghai Tian: Saarland University
Peter Lipp: Saarland University
Eugenio F. Fornasiero: University Medical Center Göttingen
Salvatore Valitutti: INSERM U1037
Sebastian Barg: Uppsala University
Constantin Pape: Georg-August University Göttingen
Ali H. Shaib: University Medical Center Göttingen
Ute Becherer: Saarland University

Nature Communications, 2025, vol. 16, issue 1, 1-18

Abstract: Abstract 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.

Date: 2025
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DOI: 10.1038/s41467-025-61579-3

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