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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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Time-shifted cross-correlation between GTPase activities and area differences.Rho family small GTPases Cdc42, Rac1 and RhoA were analyzed in terms of the time-shifted cross-correlation. We examined several cells for each GTPase. Each boxplot shows the first quartile (bottom of the box), third quartile (top of the box), median (red line) and outliers (red plus marks) for several cells (N = 9 for Cdc42, N = 6 for Rac1 and N = 6 for RhoA). Where there were no outliers, a red dot is shown at the bottom of the whisker. For Cdc42 and Rac1, the time-shifted correlation is significantly increased with negative time-shifts (results of the permutation test are shown in Table S1).
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pcbi-1000223-g007: Time-shifted cross-correlation between GTPase activities and area differences.Rho family small GTPases Cdc42, Rac1 and RhoA were analyzed in terms of the time-shifted cross-correlation. We examined several cells for each GTPase. Each boxplot shows the first quartile (bottom of the box), third quartile (top of the box), median (red line) and outliers (red plus marks) for several cells (N = 9 for Cdc42, N = 6 for Rac1 and N = 6 for RhoA). Where there were no outliers, a red dot is shown at the bottom of the whisker. For Cdc42 and Rac1, the time-shifted correlation is significantly increased with negative time-shifts (results of the permutation test are shown in Table S1).

Mentions: We calculated time-shifted cross-correlations between the local activities of Cdc42/Rac1/RhoA and local morphological changes, as shown in Figure 7. As expected, there were strong correlations between Cdc42/Rac1 activities and morphological changes, but the peaks of the correlation coefficients were slightly time-shifted. Moreover, and surprisingly, the peaks indicated that the local morphological changes preceded changes in local activity, which can be seen in Figure 6D. We confirmed statistical significance of the difference between negative (−6 min), zero and positive (+6 min) time-shifts by performing permutation tests (see Table S1). The number of samples used to calculate the cross-correlations was sufficiently large (see Figure S2 and Figure S3). Although there are some conspicuous morphological events seen in the EET profile (Figure 5C), such as the protrusion around 6–16 min and the retraction around 12–18 min, the cross-correlation based on the EET analysis was designed to be robust against such local events arising in limited sites in the cell. In this specific case of Rac1 activity in HT1080 cell, our finding that the cross-correlation profile is highly correlated with minus time-shift values is unchangeable, even when these conspicuous morphological events are replaced by normal morphological events (see Figure S4). Note that the Spearman's rank correlation also reduces the bias effect of large values (events) on statistical values.


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)

Time-shifted cross-correlation between GTPase activities and area differences.Rho family small GTPases Cdc42, Rac1 and RhoA were analyzed in terms of the time-shifted cross-correlation. We examined several cells for each GTPase. Each boxplot shows the first quartile (bottom of the box), third quartile (top of the box), median (red line) and outliers (red plus marks) for several cells (N = 9 for Cdc42, N = 6 for Rac1 and N = 6 for RhoA). Where there were no outliers, a red dot is shown at the bottom of the whisker. For Cdc42 and Rac1, the time-shifted correlation is significantly increased with negative time-shifts (results of the permutation test are shown in Table S1).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2573959&req=5

pcbi-1000223-g007: Time-shifted cross-correlation between GTPase activities and area differences.Rho family small GTPases Cdc42, Rac1 and RhoA were analyzed in terms of the time-shifted cross-correlation. We examined several cells for each GTPase. Each boxplot shows the first quartile (bottom of the box), third quartile (top of the box), median (red line) and outliers (red plus marks) for several cells (N = 9 for Cdc42, N = 6 for Rac1 and N = 6 for RhoA). Where there were no outliers, a red dot is shown at the bottom of the whisker. For Cdc42 and Rac1, the time-shifted correlation is significantly increased with negative time-shifts (results of the permutation test are shown in Table S1).
Mentions: We calculated time-shifted cross-correlations between the local activities of Cdc42/Rac1/RhoA and local morphological changes, as shown in Figure 7. As expected, there were strong correlations between Cdc42/Rac1 activities and morphological changes, but the peaks of the correlation coefficients were slightly time-shifted. Moreover, and surprisingly, the peaks indicated that the local morphological changes preceded changes in local activity, which can be seen in Figure 6D. We confirmed statistical significance of the difference between negative (−6 min), zero and positive (+6 min) time-shifts by performing permutation tests (see Table S1). The number of samples used to calculate the cross-correlations was sufficiently large (see Figure S2 and Figure S3). Although there are some conspicuous morphological events seen in the EET profile (Figure 5C), such as the protrusion around 6–16 min and the retraction around 12–18 min, the cross-correlation based on the EET analysis was designed to be robust against such local events arising in limited sites in the cell. In this specific case of Rac1 activity in HT1080 cell, our finding that the cross-correlation profile is highly correlated with minus time-shift values is unchangeable, even when these conspicuous morphological events are replaced by normal morphological events (see Figure S4). Note that the Spearman's rank correlation also reduces the bias effect of large values (events) on statistical values.

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