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Protein Dynamics in Living Cells

Protein Dynamics in Living Cells

Different fluorescence-based techniques are used to study the protein dynamics in living cells. These techniques include FRAP, FRET, and PET.
Fluorescent recovery after photobleaching (FRAP) is a fluorescent-protein-based detection technique used to quantify protein movement rates within the cell. This method exposes a small portion of the cell to an intense laser beam. The laser beam causes permanent photobleaching of the fluorophore-tagged proteins in the exposed region. As the bleached...

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Updated: May 2, 2026

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

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

Published on: November 15, 2019

Image-based computational tracking and analysis of spindle protein dynamics.

¹Department of Biomedical Engineering and Lane Center for Computational Biology, Mellon Institute 403, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, PA, 15213, USA, geyang@andrew.cmu.edu.

Methods in Molecular Biology (Clifton, N.J.)

|March 18, 2014

Summary

This chapter details computational methods for tracking spindle protein dynamics during cell division. Understanding these spatiotemporal behaviors is key to analyzing chromosome segregation and cell division mechanisms.

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Area of Science:

Cell Biology
Biophysics
Computational Biology

Background:

Cell division requires precise spatiotemporal regulation of spindle proteins for accurate chromosome segregation.
Understanding the dynamic behavior of these proteins is crucial for deciphering cell division mechanisms.

Purpose of the Study:

To introduce fundamental concepts and methodologies for computational tracking and analysis of spindle protein spatiotemporal dynamics.
To provide practical guidance on relevant software tools for analyzing fluorescence microscopy data.

Main Methods:

Computational tracking algorithms for analyzing protein movement in microscopy images.
Quantitative analysis of spatiotemporal dynamics of spindle proteins.
Utilizing fluorescence microscopy for data acquisition.

Main Results:

Demonstration of computational methods applied to real biological data.
Illustrative examples showcasing the utility of specific software tools.
Practical insights into analyzing protein localization and movement during cell division.

Conclusions:

Computational analysis of spindle protein dynamics is essential for understanding cell division.
Available software tools facilitate the quantitative characterization of spatiotemporal protein behavior.
This chapter serves as a practical guide for researchers in the field.