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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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Local activity and local morphological change distribution properties.(A) A scatter plot of the local activity and area difference of each segment. Each point represents the local activity and area difference of a single segment identified by EET. The overall property of all the segments in the dataset is portrayed, excluding temporal and positional information. (B) Histogram of GTPase activities (YFP/CFP ratio) approximated by Gaussian distribution. Vertical and horizontal axes denote the number of segments and local activity within each segment, respectively. (C) Histogram of area differences in each segment. Zero values occur frequently because the majority of edge segments do not move. (D) Time-shifted relationship between local area differences and GTPase activity. The top panels show the time-shifted scatter plots of the local area difference and the GTPase activity. Each point represents the mean local activity and summation of the area difference of the ancestry segments (see Materials and Methods). The same data are exhibited in different scales in (A) and (D) depending on the context; that is, (A) shows the detailed distribution of the activities and the area differences to provide clear comparisons with (B) and (C), while the upper panels in (D) show the differences between various time-shifts. The middle panels show the time-shifted area difference maps of the corresponding scatter plot in the top panel. The colored areas denote summation of the corresponding area differences at each shifted time. The numbers of columns are reduced with time-shifts because time-shift produces non-corresponding frames. GTPase activity maps without time-shifts are displayed in the bottom panels to illustrate their relation with the corresponding time-shifted area difference maps. Note that all activity maps in the bottom row are identical. A linear correlation appears with negative time-shifts (time-shift: −5 and −3 in the top scatter plots), whereas no correlation is observed with positive time-shifts (time-shift: 3 and 5 in the top scatter plots).
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pcbi-1000223-g006: Local activity and local morphological change distribution properties.(A) A scatter plot of the local activity and area difference of each segment. Each point represents the local activity and area difference of a single segment identified by EET. The overall property of all the segments in the dataset is portrayed, excluding temporal and positional information. (B) Histogram of GTPase activities (YFP/CFP ratio) approximated by Gaussian distribution. Vertical and horizontal axes denote the number of segments and local activity within each segment, respectively. (C) Histogram of area differences in each segment. Zero values occur frequently because the majority of edge segments do not move. (D) Time-shifted relationship between local area differences and GTPase activity. The top panels show the time-shifted scatter plots of the local area difference and the GTPase activity. Each point represents the mean local activity and summation of the area difference of the ancestry segments (see Materials and Methods). The same data are exhibited in different scales in (A) and (D) depending on the context; that is, (A) shows the detailed distribution of the activities and the area differences to provide clear comparisons with (B) and (C), while the upper panels in (D) show the differences between various time-shifts. The middle panels show the time-shifted area difference maps of the corresponding scatter plot in the top panel. The colored areas denote summation of the corresponding area differences at each shifted time. The numbers of columns are reduced with time-shifts because time-shift produces non-corresponding frames. GTPase activity maps without time-shifts are displayed in the bottom panels to illustrate their relation with the corresponding time-shifted area difference maps. Note that all activity maps in the bottom row are identical. A linear correlation appears with negative time-shifts (time-shift: −5 and −3 in the top scatter plots), whereas no correlation is observed with positive time-shifts (time-shift: 3 and 5 in the top scatter plots).

Mentions: We further investigated this spatio-temporal cross-correlation between morphological changes and Rho-family GTPase activity. First, we summarized their statistical characteristics to examine the cross-correlation. Figure 6A shows a scatter plot of the local activity and the local area difference for all identified segments. Because there were no non-linear relationships in this plot, we considered that common statistical analyses could be applied to these data. Next, we examined the histograms of the activity and area difference and found that the activities had a Gaussian distribution (Figure 6B); heavy tails were observed in some samples, but not in the area differences (Figure 6C). Although the activity histograms of a few samples exhibited one or two minor peaks in addition to the major peaks (data not shown), we assumed that they could still be approximated by Gaussian distribution for simplicity; in subsequent analyses, we used both Pearson's product-moment correlation coefficient and Spearman's rank correlation to confirm the cross-correlation data.


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)

Local activity and local morphological change distribution properties.(A) A scatter plot of the local activity and area difference of each segment. Each point represents the local activity and area difference of a single segment identified by EET. The overall property of all the segments in the dataset is portrayed, excluding temporal and positional information. (B) Histogram of GTPase activities (YFP/CFP ratio) approximated by Gaussian distribution. Vertical and horizontal axes denote the number of segments and local activity within each segment, respectively. (C) Histogram of area differences in each segment. Zero values occur frequently because the majority of edge segments do not move. (D) Time-shifted relationship between local area differences and GTPase activity. The top panels show the time-shifted scatter plots of the local area difference and the GTPase activity. Each point represents the mean local activity and summation of the area difference of the ancestry segments (see Materials and Methods). The same data are exhibited in different scales in (A) and (D) depending on the context; that is, (A) shows the detailed distribution of the activities and the area differences to provide clear comparisons with (B) and (C), while the upper panels in (D) show the differences between various time-shifts. The middle panels show the time-shifted area difference maps of the corresponding scatter plot in the top panel. The colored areas denote summation of the corresponding area differences at each shifted time. The numbers of columns are reduced with time-shifts because time-shift produces non-corresponding frames. GTPase activity maps without time-shifts are displayed in the bottom panels to illustrate their relation with the corresponding time-shifted area difference maps. Note that all activity maps in the bottom row are identical. A linear correlation appears with negative time-shifts (time-shift: −5 and −3 in the top scatter plots), whereas no correlation is observed with positive time-shifts (time-shift: 3 and 5 in the top scatter plots).
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1000223-g006: Local activity and local morphological change distribution properties.(A) A scatter plot of the local activity and area difference of each segment. Each point represents the local activity and area difference of a single segment identified by EET. The overall property of all the segments in the dataset is portrayed, excluding temporal and positional information. (B) Histogram of GTPase activities (YFP/CFP ratio) approximated by Gaussian distribution. Vertical and horizontal axes denote the number of segments and local activity within each segment, respectively. (C) Histogram of area differences in each segment. Zero values occur frequently because the majority of edge segments do not move. (D) Time-shifted relationship between local area differences and GTPase activity. The top panels show the time-shifted scatter plots of the local area difference and the GTPase activity. Each point represents the mean local activity and summation of the area difference of the ancestry segments (see Materials and Methods). The same data are exhibited in different scales in (A) and (D) depending on the context; that is, (A) shows the detailed distribution of the activities and the area differences to provide clear comparisons with (B) and (C), while the upper panels in (D) show the differences between various time-shifts. The middle panels show the time-shifted area difference maps of the corresponding scatter plot in the top panel. The colored areas denote summation of the corresponding area differences at each shifted time. The numbers of columns are reduced with time-shifts because time-shift produces non-corresponding frames. GTPase activity maps without time-shifts are displayed in the bottom panels to illustrate their relation with the corresponding time-shifted area difference maps. Note that all activity maps in the bottom row are identical. A linear correlation appears with negative time-shifts (time-shift: −5 and −3 in the top scatter plots), whereas no correlation is observed with positive time-shifts (time-shift: 3 and 5 in the top scatter plots).
Mentions: We further investigated this spatio-temporal cross-correlation between morphological changes and Rho-family GTPase activity. First, we summarized their statistical characteristics to examine the cross-correlation. Figure 6A shows a scatter plot of the local activity and the local area difference for all identified segments. Because there were no non-linear relationships in this plot, we considered that common statistical analyses could be applied to these data. Next, we examined the histograms of the activity and area difference and found that the activities had a Gaussian distribution (Figure 6B); heavy tails were observed in some samples, but not in the area differences (Figure 6C). Although the activity histograms of a few samples exhibited one or two minor peaks in addition to the major peaks (data not shown), we assumed that they could still be approximated by Gaussian distribution for simplicity; in subsequent analyses, we used both Pearson's product-moment correlation coefficient and Spearman's rank correlation to confirm the cross-correlation data.

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