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

Microtubules01:35

Microtubules

97.6K
There are three types of cytoskeletal structures in eukaryotic cells—microfilaments, intermediate filaments, and microtubules. With a diameter of about 25 nm, microtubules are the thickest of these fibers. Microtubules carry out a variety of functions that include cell structure and support, transport of organelles, cell motility (movement), and the separation of chromosomes during cell division.
97.6K
Microtubules01:18

Microtubules

10.0K
Microtubules are the thickest cytoskeletal filaments with a diameter of 25 nm. In prokaryotic organisms, microtubules are commonly found in locomotory appendages like cilia and flagella. In eukaryotic cells, microtubules form specialized extensions for moving fluid over the surface, like those found in cells lining the intestine.
Microtubules have two structurally similar globular protein subunits: α and β tubulins. In the cytosol, the α and β tubulins form a heterodimer....
10.0K
Microtubules in Cell Motility01:24

Microtubules in Cell Motility

4.4K
Microtubules are thick hollow cylindrical proteins that help form the cytoskeleton. Microtubules have varied roles in the cell. These filaments help form cellular appendages like cilia and flagella, which are responsible for locomotion. The cilia arise from basal bodies, separated from the main body by a membrane-like structure forming the transition zone. This zone is the gate for the entry of lipids and proteins, creating a unique composition of lipids and proteins in the ciliary membrane and...
4.4K
Microtubule Instability02:17

Microtubule Instability

5.9K
Microtubules are hollow cylindrical filaments having a diameter of approximately 25 nm and a length that varies from 200 nm to 25 μm. GTP-bound tubulin subunits form αβ-heterodimers for microtubule assembly. These core building blocks interact longitudinally, polymerizing into protofilaments. The protofilaments then interact with one another through lateral bonding forces to form stable cylindrical microtubules. These cylindrical filaments are dynamic as they undergo repeated...
5.9K
Microtubule Formation01:23

Microtubule Formation

7.3K
Microtubules are dynamic structures that undergo continuous assembly and disassembly. They originate from specialized multi-protein complexes known as microtubule organizing centers or MTOCs. Within the MTOC, the point of origin of the microtubule is known as the minus end, while the end radiating outward is the plus end. Microtubules serve two primary functions — the organization of spindle complexes to separate sister chromatids during mitotic or meiotic cell division and the formation...
7.3K
Microtubule Associated Motor Proteins01:32

Microtubule Associated Motor Proteins

9.9K
Eukaryotic cells have different motor proteins for transporting various cargo within the cell. These motor proteins differ based on the filament they associate with, the direction they move within the cell, and the type of cargo they transport. Motor proteins that associate with microtubules are known as microtubule-associated motor proteins. There are two families of microtubule-associated motor proteins —Kinesins and Dyneins. Both these proteins assist in the transport of cellular...
9.9K

You might also read

Related Articles

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

Sort by
Same author

Deep Learning for Assessment of Cardiac Chamber Enlargement on Anteroposterior Chest Radiographs.

Radiology. Cardiothoracic imaging·2026
Same author

REN: Anatomically-Informed Mixture-of-Experts for Interstitial Lung Disease Diagnosis.

IEEE transactions on medical imaging·2026
Same author

Multimodal AI for early prediction of adverse clinical outcomes in acute pancreatitis.

Abdominal radiology (New York)·2026
Same author

A Predictive MRI Radiomics Model for Histologic Differentiation in Soft Tissue Sarcomas.

Cancers·2026
Same author

Diverse image generation with diffusion models and cross class label learning for polyp classification.

Scientific reports·2026
Same author

Evaluating the Predictive Value of Post-Treatment Superb Microvascular Imaging for Complete Response to Neoadjuvant Chemotherapy in Invasive Breast Cancer.

Bioengineering (Basel, Switzerland)·2026
Same journal

BrainCL: Transformer-Based Brain Network Contrastive Learning with Multi-Order Topology and Salience Masking.

IEEE transactions on medical imaging·2026
Same journal

LLM-enhanced Neuron Segmentation and Reconstruction in Complex Mouse Brain Images.

IEEE transactions on medical imaging·2026
Same journal

Matrixed-Spectrum Decomposition Accelerated Linear Boltzmann Transport Equation Solver for Fast Scatter Correction in Multi-Spectral CT.

IEEE transactions on medical imaging·2026
Same journal

The Ritz Adjoint Method for MRI Pulse Design.

IEEE transactions on medical imaging·2026
Same journal

Physiology-guided Self-supervised Learning for Simultaneous Dual-Tracer PET Separation.

IEEE transactions on medical imaging·2026
Same journal

Informed-Exploration Reinforcement Learning for Automated Virtual Coronary Intervention Planning.

IEEE transactions on medical imaging·2026
See all related articles

Related Experiment Video

Updated: Dec 31, 2025

Preparation of Segmented Microtubules to Study Motions Driven by the Disassembling Microtubule Ends
12:20

Preparation of Segmented Microtubules to Study Motions Driven by the Disassembling Microtubule Ends

Published on: March 15, 2014

14.8K

Instance-Level Microtubule Tracking.

Samira Masoudi, Afsaneh Razi, Cameron H G Wright

    IEEE Transactions on Medical Imaging
    |January 7, 2020
    PubMed
    Summary
    This summary is machine-generated.

    We developed a new deep learning method for tracking individual microtubules (MTs) in videos, significantly improving velocity estimation accuracy and reducing errors. This advances biological studies of MT interactions.

    More Related Videos

    Measurement of Microtubule Dynamics by Spinning Disk Microscopy in Monopolar Mitotic Spindles
    08:31

    Measurement of Microtubule Dynamics by Spinning Disk Microscopy in Monopolar Mitotic Spindles

    Published on: November 15, 2019

    6.6K
    High-resolution Imaging and Analysis of Individual Astral Microtubule Dynamics in Budding Yeast
    10:23

    High-resolution Imaging and Analysis of Individual Astral Microtubule Dynamics in Budding Yeast

    Published on: April 20, 2017

    9.9K

    Related Experiment Videos

    Last Updated: Dec 31, 2025

    Preparation of Segmented Microtubules to Study Motions Driven by the Disassembling Microtubule Ends
    12:20

    Preparation of Segmented Microtubules to Study Motions Driven by the Disassembling Microtubule Ends

    Published on: March 15, 2014

    14.8K
    Measurement of Microtubule Dynamics by Spinning Disk Microscopy in Monopolar Mitotic Spindles
    08:31

    Measurement of Microtubule Dynamics by Spinning Disk Microscopy in Monopolar Mitotic Spindles

    Published on: November 15, 2019

    6.6K
    High-resolution Imaging and Analysis of Individual Astral Microtubule Dynamics in Budding Yeast
    10:23

    High-resolution Imaging and Analysis of Individual Astral Microtubule Dynamics in Budding Yeast

    Published on: April 20, 2017

    9.9K

    Area of Science:

    • Cell Biology
    • Biophysics
    • Computational Biology

    Background:

    • Accurate tracking of individual microtubules (MTs) in time-lapse imaging is crucial for understanding cellular processes.
    • Existing methods often struggle with precise velocity estimation and distinguishing individual MT trajectories.

    Purpose of the Study:

    • To introduce a novel deep learning algorithm for instance-level MT tracking in time-lapse image series.
    • To enhance the precision of MT velocity estimation and reduce false negative rates.

    Main Methods:

    • A recurrent attention-based deep learning algorithm was developed to segment individual MTs in each frame.
    • Segmentation results were used to establish correspondences between MTs across successive frames, generating unique path trajectories.
    • Simulated MT time-lapse image series, based on real gliding assay data, were used for pre-training and hyperparameter optimization.

    Main Results:

    • The proposed method drastically improved MT instance velocity estimation precision to 71.3% from a baseline of 29.3%.
    • Inclusion of temporal information reduced false negative rates from 67.8% (baseline) to 28.7%.

    Conclusions:

    • The novel deep learning approach offers a significant advancement in MT tracking accuracy and reliability.
    • This method is expected to aid biologists in characterizing MT spatial arrangements and MT-MT interactions.