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

Author

Listed:
  • 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
    University of Trieste)

  • Salvatore Valitutti

    (INSERM U1037)

  • Sebastian Barg

    (Uppsala University)

  • Constantin Pape

    (Georg-August University Göttingen
    Georg-August-University Göttingen)

  • Ali H. Shaib

    (University Medical Center Göttingen)

  • Ute Becherer

    (Saarland University)

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.

Suggested Citation

  • 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. Fornasie, 2025. "Highly adaptable deep-learning platform for automated detection and analysis of vesicle exocytosis," Nature Communications, Nature, vol. 16(1), pages 1-18, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-61579-3
    DOI: 10.1038/s41467-025-61579-3
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