ExoJ – a Fiji/ImageJ2 plugin for automated spatiotemporal detection and analysis of exocytosis

Exocytosis is a dynamic physiological process that enables the release of biomolecules to the surrounding environment via the fusion of membrane compartments to the plasma membrane. Understanding its mechanisms is crucial, as defects can compromise essential biological functions. The development of pH-sensitive optical reporters alongside fluorescence microscopy enables the assessment of individual vesicle exocytosis events at the cellular level. Manual annotation represents, however, a time-consuming task that is prone to selection biases and human operational errors. Here, we introduce ExoJ, an automated plugin based on Fiji/ImageJ2 software. ExoJ identifies user-defined genuine populations of exocytosis events, recording quantitative features including intensity, apparent size and duration. We designed ExoJ to be fully user-configurable, making it suitable for studying distinct forms of vesicle exocytosis regardless of the imaging quality. Our plugin demonstrates its capabilities by showcasing distinct exocytic dynamics among tetraspanins and vesicular SNARE protein reporters. Assessment of performance on synthetic data shows that ExoJ is a robust tool that is capable of correctly identifying exocytosis events independently of signal-to-noise ratio conditions. We propose ExoJ as a standard solution for future comparative and quantitative studies of exocytosis.

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Publication Details

Journal
Journal of Cell Science
Published
2024-09-02
DOI
https://doi.org/10.1242/jcs.261938
Citations
5
Primary Topic
Advanced Fluorescence Microscopy Techniques
Type
article
Field-Weighted Citation Impact
3.31

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article

ExoJ – a Fiji/ImageJ2 plugin for automated spatiotemporal detection and analysis of exocytosis

Thierry Galli, Guillaume van Niel, Philippe Bun, Lydia Danglot et al.
5 citations
Journal of Cell Science
Advanced Fluorescence Microscopy Techniques
3.31
article

ExoJ – a Fiji/ImageJ2 plugin for automated spatiotemporal detection and analysis of exocytosis

Thierry Galli, Guillaume van Niel, Philippe Bun, Lydia Danglot, Frederik J. Verweij, Junjun Liu
article en
5 citations

Abstract

Exocytosis is a dynamic physiological process that enables the release of biomolecules to the surrounding environment via the fusion of membrane compartments to the plasma membrane. Understanding its mechanisms is crucial, as defects can compromise essential biological functions. The development of pH-sensitive optical reporters alongside fluorescence microscopy enables the assessment of individual vesicle exocytosis events at the cellular level. Manual annotation represents, however, a time-consuming task that is prone to selection biases and human operational errors. Here, we introduce ExoJ, an automated plugin based on Fiji/ImageJ2 software. ExoJ identifies user-defined genuine populations of exocytosis events, recording quantitative features including intensity, apparent size and duration. We designed ExoJ to be fully user-configurable, making it suitable for studying distinct forms of vesicle exocytosis regardless of the imaging quality. Our plugin demonstrates its capabilities by showcasing distinct exocytic dynamics among tetraspanins and vesicular SNARE protein reporters. Assessment of performance on synthetic data shows that ExoJ is a robust tool that is capable of correctly identifying exocytosis events independently of signal-to-noise ratio conditions. We propose ExoJ as a standard solution for future comparative and quantitative studies of exocytosis.

Journal of Cell ScienceVol. 137(20)
Inserm (FR), Utrecht University (NL), Université Paris Cité (FR), Jinan Central Hospital (CN), Institut de Psychiatrie et Neurosciences de Paris (FR), Shandong First Medical University (CN)
European Molecular Biology Organization, Infrastructures en Biologie Santé et Agronomie, Agence Nationale de la Recherche, Institut National de la Santé et de la Recherche Médicale, Fondation pour la Recherche Médicale, Fondation ARC pour la Recherche sur le Cancer, Institut National Du Cancer
Openalex Percentile: Top 8%
Advanced Fluorescence Microscopy Techniques
3.31
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