Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Overview of Secretory Vesicles01:33

Overview of Secretory Vesicles

10.4K
Secretory vesicles, also known as dense core vesicles (DCVs), are membrane-bound vesicles that transport secretory proteins, such as hormones or neurotransmitters. Regulated secretory vesicles transport proteins from the trans-Golgi network to the exterior of the cell. Proteins present in regulated secretory vesicles are required to be rapidly exocytosed in large amounts upon a specific stimulus.
Various proteins regulate the aggregation of molecules inside the secretory vesicles. Chromogranins...
10.4K
Pinching-off of Coated Vesicles01:32

Pinching-off of Coated Vesicles

4.5K
Vesicle budding is orchestrated by distinct cytosolic proteins such as adaptor proteins, coat proteins, and GTPases. To initiate vesicle budding, membrane-bending proteins containing crescent-shaped BAR domains bind to the lipid heads in the bilayer and distort the membrane to form a protein-coated vesicle bud. Adaptors proteins such as AP2 for clathrin-coated vesicles can nucleate on the deformed membrane. Finally, coat proteins such as clathrin or COPI and COPII assemble into a coat forming...
4.5K
Fusion of Secretory Vesicles with the Plasma Membrane01:26

Fusion of Secretory Vesicles with the Plasma Membrane

20.0K
Proteins and neurotransmitters in secretory vesicles can be released from a cell upon vesicle docking, priming, and fusion with the plasma membrane. Vesicles are docked and primed in preparation for the quick exocytosis of their contents in response to a stimulus. The fusion process is mainly carried out by a SNAP Receptor or SNARE complex, consisting of synaptobrevin, syntaxin-1, and SNAP-25.
In 1993, Jim Rothman proposed that the antiparallel pairing of vesicular and transmembrane SNAREs, or...
20.0K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Decoding fibrosis: Transcriptomic and clinical insights via AI-derived collagen deposition phenotypes in MASLD.

Hepatology (Baltimore, Md.)·2026
Same author

Radical diffusion, not lifetime, determines the range of peroxidase-based proximity labelling.

Journal of cell science·2026
Same author

Accuracy of Diagnosis in Myeloproliferative Neoplasms With Splanchnic Vein Thrombosis (MPN-SVT).

American journal of hematology·2026
Same author

Enhancing liver fibrosis measurement: Deep learning and uncertainty analysis across multi-center cohorts.

Journal of pathology informatics·2026
Same author

Self-Supervised Voxel-Level Representation Rediscovers Subcellular Structures in Volume Electron Microscopy.

Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops·2026
Same author

Extracting Axial Depth and Trajectory Trend Using Astigmatism, Gaussian Fitting, and CNNs for Protein Tracking.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026
Same journal

LEARNABLE HIERARCHICAL VISUAL CONTEXTS FOR TUMOR SEGMENTATION IN COMPUTED TOMOGRAPHY IMAGES.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026
Same journal

DUAL CROSS-ATTENTION SIAMESE TRANSFORMER FOR RECTAL TUMOR REGROWTH ASSESSMENT IN WATCH-AND-WAIT ENDOSCOPY.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026
Same journal

LUMEN: LONGITUDINAL MULTI-MODAL RADIOLOGY MODEL FOR PROGNOSIS AND DIAGNOSIS.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026
Same journal

OVERVIEW OF THE CXR-LT 2026 CHALLENGE: MULTI-CENTER LONG-TAILED AND ZERO SHOT CHEST X-RAY CLASSIFICATION.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026
Same journal

CROSS-MODAL FINE-TUNING OF 3D CONVOLUTIONAL FOUNDATION MODELS FOR ADHD CLASSIFICATION WITH LOW-RANK ADAPTATION.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026
Same journal

AN IN SILICO STUDY OF LOW-INTENSITY FOCUSED ULTRASOUND DISPLACEMENT MAPPING WITH A 220 KHZ CLINICAL PHASED-ARRAY TRANSDUCER.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026
See all related articles

Related Experiment Video

Updated: Apr 14, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.7K

Short Trajectory Segmentation With 1D Unet Framework: Application To Secretory Vesicle Dynamics.

Mariia Dmitrieva1, Joël Lefebvre1, Kristofer Delas Peñas1,2

  • 1Department of Engineering Science, University of Oxford, Oxford, UK.

Proceedings. IEEE International Symposium on Biomedical Imaging
|April 13, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel 1D U-Net for segmenting protein vesicle trajectories in cells. This automated method accurately quantifies vesicle dynamics, even for short trajectories, improving cell biology research.

Keywords:
U-Netdeep learningprotein traffickingsliding windowtrajectory segmentation

More Related Videos

Probing Structural and Dynamic Properties of Trafficking Subcellular Nanostructures by Spatiotemporal Fluctuation Spectroscopy
08:17

Probing Structural and Dynamic Properties of Trafficking Subcellular Nanostructures by Spatiotemporal Fluctuation Spectroscopy

Published on: August 16, 2021

2.2K
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

11.4K

Related Experiment Videos

Last Updated: Apr 14, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.7K
Probing Structural and Dynamic Properties of Trafficking Subcellular Nanostructures by Spatiotemporal Fluctuation Spectroscopy
08:17

Probing Structural and Dynamic Properties of Trafficking Subcellular Nanostructures by Spatiotemporal Fluctuation Spectroscopy

Published on: August 16, 2021

2.2K
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

11.4K

Area of Science:

  • Cellular biology
  • Biophysics
  • Microscopy imaging

Background:

  • Automated techniques are crucial for studying protein transport dynamics within secretory vesicles in living cells.
  • Quantitative analysis of vesicle dynamics necessitates accurate trajectory segmentation due to inconsistent movement patterns.

Purpose of the Study:

  • To introduce a novel 1D U-Net based framework for automated trajectory segmentation of secretory vesicles.
  • To overcome limitations of existing methods, particularly the requirement for long trajectories.

Main Methods:

  • Development and application of a 1D U-Net architecture for trajectory segmentation.
  • Utilizing data from spinning disk microscopy imaging of protein trafficking in *Drosophila* epithelial cells.
  • Segmentation is performed within sliding windows to capture short trajectory segments.

Main Results:

  • The proposed 1D U-Net framework achieves 77.7% accuracy in trajectory segmentation.
  • The method effectively segments both short (5 points) and long (135 points) trajectories.
  • Unlike traditional methods, it does not require long trajectories for effective segmentation.

Conclusions:

  • The novel 1D U-Net based approach provides an effective and automated method for quantifying vesicle dynamics through trajectory segmentation.
  • This framework enhances the study of protein transport by accurately capturing dynamics even from short cellular tracks.
  • The method offers a significant advancement for analyzing vesicle movement in cell biology research.