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Quantification of local morphodynamics and local GTPase activity by edge evolution tracking.

Yuki Tsukada¹, Kazuhiro Aoki, Takeshi Nakamura

¹Laboratory for Systems Biology, Graduate School of Information Science, Nara Institute of Science and Technology, Nara, Japan.

Plos Computational Biology

|November 15, 2008

Summary

We developed edge evolution tracking (EET), a new algorithm for analyzing time-lapse cell images. EET quantifies cell edge dynamics and their relationship with protein activity, revealing insights into cellular functions.

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Area of Science:

Cellular dynamics
Biophysics
Microscopy image analysis

Background:

Time-lapse fluorescence microscopy generates vast image data.
Automated tools are needed to extract statistical information from cellular dynamics.
Existing methods lack rigorous approaches for morphodynamic property extraction.

Purpose of the Study:

To develop a novel algorithm for quantifying morphodynamic properties from time-lapse microscopy images.
To investigate the relationship between local cellular morphology and local protein activity.
To analyze cross-correlations between edge dynamics and GTPase activity with time shifts.

Main Methods:

Developed the edge evolution tracking (EET) algorithm.
Traced local edge extension and contraction using subdivided edges across successive frames.

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Applied EET to fluorescence resonance energy transfer (FRET) images of Rho-family GTPases (Rac1, Cdc42, RhoA).

Main Results:

EET quantifies local morphological changes and local fluorescence intensities.
Cross-correlation analysis revealed a 6-8 minute time lag between morphological changes and Rac1/Cdc42 activity.
The study demonstrates the ability to statistically investigate relationships between dynamics and protein activity.

Conclusions:

The edge evolution tracking (EET) algorithm provides a robust method for analyzing time-lapse cell imaging data.
EET enables quantification of local morphological dynamics and protein activity.
This approach enhances the understanding of cellular function dynamics by linking morphology and molecular activity over time.