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PreprintCited by 1Cell Image Analysis TechniquesCellular transport and secretionAdvanced Fluorescence Microscopy Techniques

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

Open access
Chouaib, Abed Alrahman, Chang, Hsin-Fang, Khamis, Omnia M., Echeverry, Santiago, Demeersseman, Lucie, Elizarova, Sofia, Daniel, James A, Valitutti, Salvatore, Barg, Sebastian, Pape, Constantin, Shaib, Ali H., Becherer, Ute·2024·bioRxiv (Cold Spring Harbor Laboratory)
DOI 10.1101/2024.08.02.606323

Preprint — published as 10.1038/s41467-025-61579-3 · Paper page

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Abstract

Vesicle exocytosis is a fundamental component of intercellular communication, in all organisms. It has been studied for decades, using various imaging tools. Nevertheless, exocytosis research is still limited by the lack of reliable automated analysis procedures. To address this, we developed the Intelligent Vesicle Exocytosis Analysis Platform (IVEA), a nearly universal solution for analyzing exocytosis acquired with live cell imaging. IVEA is applicable to a wide variety of experimental model systems, microscopes and reporter fluorophores. IVEA combines state-of-the-art deep-learning and computer vision regimes to enable fully automated analysis of large data. IVEA runs as a FIJI plugin and does not require prior training or human intervention. IVEA is 60 times faster than manual analysis and is able to detect rare events often missed by the human eye. Overall, IVEA represents a breakthrough in the analysis of cellular secretory mechanisms and has a transformative potential for the exocytosis imaging field.

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Research works printed but not linked: 43

  1. 2023, Lytic granule exocytosis at immune synapses: lessons from neuronal synapses, Front Immunol, 14, 1177670, 10.3389/fimmu.2023.1177670 · DOI
  2. 2012, Regulated exocytosis in chromaffin cells and cytotoxic T lymphocytes: how similar are they?, Cell Calcium, 52, 303, 10.1016/j.ceca.2012.04.002 · DOI
  3. 10.1038/s41586-022-04436-3 · DOI
  4. 2022, Identification of distinct cytotoxic granules as the origin of supramolecular attack particles in T lymphocytes, Nat Commun, 13, 1029, 10.1038/s41467-022-28596-y · DOI
  5. 2023, Genetic disorders of neurotransmitter release machinery, Front Synaptic Neurosci, 15, 1148957, 10.3389/fnsyn.2023.1148957 · DOI
  6. 10.1016/j.jmb.2019.12.012 · DOI
  7. 10.1111/apa.16013 · DOI
  8. 10.1038/28190 · DOI
  9. 10.1038/83949 · DOI
  10. 10.1085/jgp.201711757 · DOI
  11. 10.1038/nn1087 · DOI
  12. 10.1038/41329 · DOI
  13. 10.1523/JNEUROSCI.1589-13.2013 · DOI
  14. 10.1016/j.neuroscience.2017.01.006 · DOI
  15. 2006, Spatio-temporal analysis of constitutive exocytosis in epithelial cells, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 3, 17, 10.1109/TCBB.2006.11 · DOI
  16. 2014, Observer-Independent Quantification of Insulin Granule Exocytosis and Pre-Exocytotic Mobility by TIRF Microscopy, Microscopy and Microanalysis, 20, 206, 10.1017/S1431927613013767 · DOI
  17. Urbina, F. & Gupton, S.L. Automated Detection and Analysis of Exocytosis. J Vis Exp (2021). · DOI
  18. Liu, J. , Verweij, F.J. , Niel, G.V. , Danglot, L. & Bun, P. ExoJ: an ImageJ2/Fiji plugin for automated spatiotemporal detection of exocytosis. bioRxiv, 2022.2009.2005.506585 (2022). · DOI
  19. 10.1002/jemt.20711 · DOI
  20. Schmied, C. , Soykan, T. , Bolz, S. , Haucke, V. & Lehmann, M. SynActJ: Easy-to-Use Automated Analysis of Synaptic Activity. Frontiers in Computer Science 3 (2021). · DOI
  21. Dosovitskiy, A. et al. An Image is Worth 16×16 Words: Transformers for Image Recognition at Scale. International Conference on Learning Representations 2010.11929 (2021).
  22. 10.1162/neco.1997.9.8.1735 · DOI · Paper page
  23. 2020, Imaging of Monoamine Neurotransmitters with Fluorescent Nanoscale Sensors, Chempluschem, 85, 1465, 10.1002/cplu.202000248 · DOI
  24. 10.1073/pnas.2202842119 · DOI
  25. Bulumulla, C. et al. Visualizing synaptic dopamine efflux with a 2D composite nanofilm. Elife 11 (2022). · DOI
  26. 2019, Multivariate LSTM-FCNs for time series classification, Neural Netw, 116, 237, 10.1016/j.neunet.2019.04.014 · DOI · Paper page
  27. 10.1038/s41592-022-01663-4 · DOI · Paper page
  28. 2022, Prediction of corneal back surface power - Deep learning algorithm versus multivariate regression, Ophthalmic Physiol Opt, 42, 185, 10.1111/opo.12909 · DOI
  29. O’Shaughnessy, E.C. et al. pHusion - a robust and versatile toolset for automated detection and analysis of exocytosis. J Cell Sci 137 (2024). · DOI
  30. 2018, Paralogs of the Calcium-Dependent Activator Protein for Secretion Differentially Regulate Synaptic Transmission and Peptide Secretion in Sensory Neurons, Front Cell Neurosci, 12, 304, 10.3389/fncel.2018.00304 · DOI
  31. 2022, Localization of the Priming Factors CAPS1 and CAPS2 in Mouse Sensory Neurons Is Determined by Their N-Termini, Front Mol Neurosci, 15, 674243, 10.3389/fnmol.2022.674243 · DOI
  32. 1965, On Convergence of K-Means and Partitions with Minimum Average Variance, Ann Math Stat, 36, 1084
  33. 10.1038/323533a0 · DOI · Paper page
  34. 10.1016/j.neuron.2006.06.008 · DOI
  35. 10.1038/nn756 · DOI
  36. 10.1038/nmeth.4605 · DOI
  37. Chitirala, P. et al. Studying the biology of cytotoxic T lymphocytes in vivo with a fluorescent granzyme B-mTFP knock-in mouse. Elife 9 (2020). · DOI
  38. 2013, Visualization of cytolytic T cell differentiation and granule exocytosis with T cells from mice expressing active fluorescent granzyme B, PLoS One, 8, e67239, 10.1371/journal.pone.0067239 · DOI · Paper page
  39. 10.1186/s12896-024-00839-4 · DOI
  40. Estl, M. et al. Various Stages of Immune Synapse Formation Are Differently Dependent on the Strength of the TCR Stimulus. Int J Mol Sci 21 (2020). · DOI
  41. 10.1371/journal.pone.0000505 · DOI
  42. 10.1016/S0171-9335(99)80017-X · DOI
  43. 10.2337/diabetes.49.3.424 · DOI

Metadata

Authors
Chouaib, A. A., Chang, H.-F., Khamis, O. M., Echeverry, S., Demeersseman, L., Elizarova, S., Daniel, J. A., Valitutti, S., Barg, S., Pape, C., Shaib, A. H., & Becherer, U.
Affiliations
  • Saarland University (DE)
  • Uppsala University (SE)
  • Inserm (FR)
  • Max Planck Institute for Multidisciplinary Sciences (DE)
  • Teva Pharmaceuticals (Australia) (AU)
  • Multiscale Bioimaging (DE)
  • Universitätsmedizin Göttingen (DE)
Corresponding author
  • Shaib, Ali H.
  • Becherer, Ute
ORCID
Venue
bioRxiv (Cold Spring Harbor Laboratory)
Publisher
openRxiv
Published
2024-08-06
DOI
10.1101/2024.08.02.606323
OpenAlex
W4401406813
Cited by
1
References
2
Citations by year
2026: 1
Type
posted content
Language
en
Open access
Licence
Locations
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • GoeScholar The Publication Server of the Georg-August-Universität Göttingen (Georg-August-Universität Göttingen) (submittedVersion, other-oa)
  • MPG.PuRe (Max Planck Society) (submittedVersion, cc-by)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › Cellular transport and secretion
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Advanced Fluorescence Microscopy Techniques
Keywords
vesicle exocytosis, automated image analysis, deep learning, computer vision, live-cell imaging, Fiji plugin, rare event detection

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