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

Tsukada Y, Aoki K, Nakamura T, Sakumura Y, Matsuda M, Ishii S - PLoS Comput. Biol. (2008)

Bottom Line: By applying EET to fluorescence resonance energy transfer images of the Rho-family GTPases Rac1, Cdc42, and RhoA, we examined the cross-correlation between the local area difference and GTPase activity.The calculated correlations changed with time-shifts as expected, but surprisingly, the peak of the correlation coefficients appeared with a 6-8 min time shift of morphological changes and preceded the Rac1 or Cdc42 activities.Our method enables the quantification of the dynamics of local morphological change and local protein activity and statistical investigation of the relationship between them by considering time shifts in the relationship.

View Article: PubMed Central - PubMed

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

ABSTRACT
Advances in time-lapse fluorescence microscopy have enabled us to directly observe dynamic cellular phenomena. Although the techniques themselves have promoted the understanding of dynamic cellular functions, the vast number of images acquired has generated a need for automated processing tools to extract statistical information. A problem underlying the analysis of time-lapse cell images is the lack of rigorous methods to extract morphodynamic properties. Here, we propose an algorithm called edge evolution tracking (EET) to quantify the relationship between local morphological changes and local fluorescence intensities around a cell edge using time-lapse microscopy images. This algorithm enables us to trace the local edge extension and contraction by defining subdivided edges and their corresponding positions in successive frames. Thus, this algorithm enables the investigation of cross-correlations between local morphological changes and local intensity of fluorescent signals by considering the time shifts. By applying EET to fluorescence resonance energy transfer images of the Rho-family GTPases Rac1, Cdc42, and RhoA, we examined the cross-correlation between the local area difference and GTPase activity. The calculated correlations changed with time-shifts as expected, but surprisingly, the peak of the correlation coefficients appeared with a 6-8 min time shift of morphological changes and preceded the Rac1 or Cdc42 activities. Our method enables the quantification of the dynamics of local morphological change and local protein activity and statistical investigation of the relationship between them by considering time shifts in the relationship. Thus, this algorithm extends the value of time-lapse imaging data to better understand dynamics of cellular function.

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Comparison of morphodynamic analysis by EET with polar coordinate-based analysis.(A) Polar coordinate-based analysis was performed by setting the origin of coordinates at the mean mass center of the binary images. (B) Time-shifted cross-correlation analysis by polar coordinates and EET for the cell depicted in Figure 5. Both of the correlation profiles show positive correlations with negative time-shifts and low correlations with positive time-shifts. However, EET yields a higher correlation than the polar coordinate-based method for the negative time-shifts. (C) The same cells in Figure 7 were also analyzed by polar coordinate-based analysis. All panels show similar shapes to that in Figure 7; however, peaks in Cdc42 and Rac were lower with polar coordinate-based analysis than with EET.
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pcbi-1000223-g008: Comparison of morphodynamic analysis by EET with polar coordinate-based analysis.(A) Polar coordinate-based analysis was performed by setting the origin of coordinates at the mean mass center of the binary images. (B) Time-shifted cross-correlation analysis by polar coordinates and EET for the cell depicted in Figure 5. Both of the correlation profiles show positive correlations with negative time-shifts and low correlations with positive time-shifts. However, EET yields a higher correlation than the polar coordinate-based method for the negative time-shifts. (C) The same cells in Figure 7 were also analyzed by polar coordinate-based analysis. All panels show similar shapes to that in Figure 7; however, peaks in Cdc42 and Rac were lower with polar coordinate-based analysis than with EET.

Mentions: We compared EET analysis to polar coordinate-based analysis to further prove the utility of EET. We first performed polar coordinate-based analysis to the cell in Figure 5 for direct comparison with EET (Figure 8A). The polar coordinate-based analysis produced time-position maps of local activities and local morphological changes that were similar to the activity map and area difference map of EET (see Figure S6). As for EET, local activity was determined as a mean value within an ROI, which was a circle of radius r. We used the same r value for EET and the polar coordinate-based method. Both analyses produced similar maps (see Figure S6), and time-shifted cross-correlations were then calculated (Figure 8B). Both of the time-shifted cross-correlations showed similar patterns for the timing between local morphological changes and GTPase activities (Rac1), i.e., a high correlation with negative time-shifts and a low correlation with positive time-shifts. However, the EET analysis showed a higher correlation than that with the polar coordinate-based analysis at the time-shifts of −3 to −20 min.


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

Tsukada Y, Aoki K, Nakamura T, Sakumura Y, Matsuda M, Ishii S - PLoS Comput. Biol. (2008)

Comparison of morphodynamic analysis by EET with polar coordinate-based analysis.(A) Polar coordinate-based analysis was performed by setting the origin of coordinates at the mean mass center of the binary images. (B) Time-shifted cross-correlation analysis by polar coordinates and EET for the cell depicted in Figure 5. Both of the correlation profiles show positive correlations with negative time-shifts and low correlations with positive time-shifts. However, EET yields a higher correlation than the polar coordinate-based method for the negative time-shifts. (C) The same cells in Figure 7 were also analyzed by polar coordinate-based analysis. All panels show similar shapes to that in Figure 7; however, peaks in Cdc42 and Rac were lower with polar coordinate-based analysis than with EET.
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Related In: Results  -  Collection

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pcbi-1000223-g008: Comparison of morphodynamic analysis by EET with polar coordinate-based analysis.(A) Polar coordinate-based analysis was performed by setting the origin of coordinates at the mean mass center of the binary images. (B) Time-shifted cross-correlation analysis by polar coordinates and EET for the cell depicted in Figure 5. Both of the correlation profiles show positive correlations with negative time-shifts and low correlations with positive time-shifts. However, EET yields a higher correlation than the polar coordinate-based method for the negative time-shifts. (C) The same cells in Figure 7 were also analyzed by polar coordinate-based analysis. All panels show similar shapes to that in Figure 7; however, peaks in Cdc42 and Rac were lower with polar coordinate-based analysis than with EET.
Mentions: We compared EET analysis to polar coordinate-based analysis to further prove the utility of EET. We first performed polar coordinate-based analysis to the cell in Figure 5 for direct comparison with EET (Figure 8A). The polar coordinate-based analysis produced time-position maps of local activities and local morphological changes that were similar to the activity map and area difference map of EET (see Figure S6). As for EET, local activity was determined as a mean value within an ROI, which was a circle of radius r. We used the same r value for EET and the polar coordinate-based method. Both analyses produced similar maps (see Figure S6), and time-shifted cross-correlations were then calculated (Figure 8B). Both of the time-shifted cross-correlations showed similar patterns for the timing between local morphological changes and GTPase activities (Rac1), i.e., a high correlation with negative time-shifts and a low correlation with positive time-shifts. However, the EET analysis showed a higher correlation than that with the polar coordinate-based analysis at the time-shifts of −3 to −20 min.

Bottom Line: By applying EET to fluorescence resonance energy transfer images of the Rho-family GTPases Rac1, Cdc42, and RhoA, we examined the cross-correlation between the local area difference and GTPase activity.The calculated correlations changed with time-shifts as expected, but surprisingly, the peak of the correlation coefficients appeared with a 6-8 min time shift of morphological changes and preceded the Rac1 or Cdc42 activities.Our method enables the quantification of the dynamics of local morphological change and local protein activity and statistical investigation of the relationship between them by considering time shifts in the relationship.

View Article: PubMed Central - PubMed

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

ABSTRACT
Advances in time-lapse fluorescence microscopy have enabled us to directly observe dynamic cellular phenomena. Although the techniques themselves have promoted the understanding of dynamic cellular functions, the vast number of images acquired has generated a need for automated processing tools to extract statistical information. A problem underlying the analysis of time-lapse cell images is the lack of rigorous methods to extract morphodynamic properties. Here, we propose an algorithm called edge evolution tracking (EET) to quantify the relationship between local morphological changes and local fluorescence intensities around a cell edge using time-lapse microscopy images. This algorithm enables us to trace the local edge extension and contraction by defining subdivided edges and their corresponding positions in successive frames. Thus, this algorithm enables the investigation of cross-correlations between local morphological changes and local intensity of fluorescent signals by considering the time shifts. By applying EET to fluorescence resonance energy transfer images of the Rho-family GTPases Rac1, Cdc42, and RhoA, we examined the cross-correlation between the local area difference and GTPase activity. The calculated correlations changed with time-shifts as expected, but surprisingly, the peak of the correlation coefficients appeared with a 6-8 min time shift of morphological changes and preceded the Rac1 or Cdc42 activities. Our method enables the quantification of the dynamics of local morphological change and local protein activity and statistical investigation of the relationship between them by considering time shifts in the relationship. Thus, this algorithm extends the value of time-lapse imaging data to better understand dynamics of cellular function.

Show MeSH
Related in: MedlinePlus