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Cell Migration

Cell Migration

Cell migration is a process by which the cells move from one location to another, playing an essential role in embryological development, repair and regeneration, immune response, and metastasis. Cells migrate in response to chemical or mechanical signals generated by specific organs or tissues. The overall mechanism includes three steps - polarization, protrusion, and release. Polarization involves the formation of a distinct cell front and rear, which determines the direction of movement.

Cell Migration

Cell Migration

Cell migration, the process by which cells move from one location to another, is essential for the proper development and viability of organisms throughout their life. When cells are not able to migrate properly to their ordained locations, various disorders may occur. For example, disruption in cell migration causes chronic inflammatory diseases such as arthritis.

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Updated: Jun 2, 2026

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

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Published on: September 5, 2019

A Dynamic Contour Evolution Algorithm for Cell Segmentation and Synaptic Tracking under Occlusion.

Chen-Xi Zhang¹, Xin-Gui Yu², Ming-Kang Li³

¹School of Pharmacy, Nanjing Medical University, Nanjing 211166, P. R. China.

Chemical & Biomedical Imaging

|June 1, 2026

Summary

This summary is machine-generated.

We developed a new algorithm combining contour deformation networks and graph neural networks to accurately track single cells, even with challenging cell shapes and overlaps. This tool quantifies cellular dynamics and reveals how oxidative stress impacts cell migration.

Keywords:

Cell migration Cell morphology Cell segmentation Cell tracking Dynamic contour evolution FT-KAN

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Last Updated: Jun 2, 2026

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Area of Science:

Cell biology
Bioinformatics
Computational biology

Background:

Cell migration is crucial for biological processes but challenging to quantify due to cell variability and occlusion.
Dysregulation of cell migration is linked to various diseases.

Purpose of the Study:

To develop a robust algorithm for accurate single-cell segmentation and tracking.
To enable precise quantification of cellular dynamics under diverse conditions.

Main Methods:

A novel framework combining Dynamic Profile Evolution (DPE) contour deformation network and a graph neural network (GNN).
Algorithm designed to handle morphological variability and cell occlusion for reliable tracking.
Validation on HT22 cell datasets demonstrating improved recognition and identity continuity.

Main Results:

The DPE-GNN framework accurately segments and tracks cells, outperforming existing methods.
Analysis of over 4,000 cells revealed stable morphology and migration patterns under normal conditions.
Oxidative stress was observed to reduce cell motility and alter migration trajectories.

Conclusions:

The developed framework provides a versatile tool for precise quantification of cellular dynamics.
Enables long-term live-cell monitoring and high-content analysis of cell behaviors.
Offers insights into how environmental factors like oxidative stress affect cell migration patterns.