Highly adaptable deep-learning platform for automated detection and analysis of vesicle exocytosis
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...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-61579-3 |
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| author | 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 Ute Becherer |
| author_facet | 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 Ute Becherer |
| author_sort | Abed Alrahman Chouaib |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-941ce5dafbfe4bc599e5bf611da79cdf |
| institution | DOAJ |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-941ce5dafbfe4bc599e5bf611da79cdf2025-08-20T03:05:10ZengNature PortfolioNature Communications2041-17232025-07-0116111810.1038/s41467-025-61579-3Highly adaptable deep-learning platform for automated detection and analysis of vesicle exocytosisAbed Alrahman Chouaib0Hsin-Fang Chang1Omnia M. Khamis2Nadia Alawar3Santiago Echeverry4Lucie Demeersseman5Sofia Elizarova6James A. Daniel7Qinghai Tian8Peter Lipp9Eugenio F. Fornasiero10Salvatore Valitutti11Sebastian Barg12Constantin Pape13Ali H. Shaib14Ute Becherer15Department of Cellular Neurophysiology, Center for Integrative Physiology and Molecular Medicine (CIPMM), Saarland UniversityDepartment of Cellular Neurophysiology, Center for Integrative Physiology and Molecular Medicine (CIPMM), Saarland UniversityDepartment of Cellular Neurophysiology, Center for Integrative Physiology and Molecular Medicine (CIPMM), Saarland UniversityDepartment of Cellular Neurophysiology, Center for Integrative Physiology and Molecular Medicine (CIPMM), Saarland UniversityMedical Cell Biology, Uppsala UniversityCancer Research Center of Toulouse, INSERM U1037Department of Molecular Neurobiology, Max Planck Institute for Multidisciplinary SciencesDepartment of Molecular Neurobiology, Max Planck Institute for Multidisciplinary SciencesCenter for Molecular Signaling (PZMS), Institute for Molecular Cell Biology, Research Center for Molecular Imaging and Screening, Medical Faculty, Saarland UniversityCenter for Molecular Signaling (PZMS), Institute for Molecular Cell Biology, Research Center for Molecular Imaging and Screening, Medical Faculty, Saarland UniversityDepartment of Neuro- and Sensory Physiology, University Medical Center GöttingenCancer Research Center of Toulouse, INSERM U1037Medical Cell Biology, Uppsala UniversityInstitute of Computer Science, Georg-August University GöttingenDepartment of Neuro- and Sensory Physiology, University Medical Center GöttingenDepartment of Cellular Neurophysiology, Center for Integrative Physiology and Molecular Medicine (CIPMM), Saarland UniversityAbstract 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.https://doi.org/10.1038/s41467-025-61579-3 |
| spellingShingle | 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 Ute Becherer Highly adaptable deep-learning platform for automated detection and analysis of vesicle exocytosis Nature Communications |
| title | Highly adaptable deep-learning platform for automated detection and analysis of vesicle exocytosis |
| title_full | Highly adaptable deep-learning platform for automated detection and analysis of vesicle exocytosis |
| title_fullStr | Highly adaptable deep-learning platform for automated detection and analysis of vesicle exocytosis |
| title_full_unstemmed | Highly adaptable deep-learning platform for automated detection and analysis of vesicle exocytosis |
| title_short | Highly adaptable deep-learning platform for automated detection and analysis of vesicle exocytosis |
| title_sort | highly adaptable deep learning platform for automated detection and analysis of vesicle exocytosis |
| url | https://doi.org/10.1038/s41467-025-61579-3 |
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