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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 marker-tracking-based analysis.Marker-tracking-based analysis was performed using virtually-defined markers, and their movements perpendicular to the cellular edge were measured. (A) Cellular edges changing with time (blue: 6 min; indigo: 7 min; light blue: 8 min; green: 9 min; yellow: 10 min; red: 11 min). The cell analyzed was the same as that used in Figure 5. Black lines show traces of virtually defined markers. (B) Closed subsection of the lower right area in (A). Black dots show the positions of the markers. The uniform distribution of the markers (dots on the blue line) changed into a non-uniform distribution accompanied by persistent protrusion (dots on the red line). (C) Time-shifted cross-correlation analysis by the marker-tracking-based method and EET on the cell in Figure 5. Both of the correlation profiles show strong positive correlations in negative time-shifts and weak correlations in positive time-shifts. However, EET yielded higher correlations than the marker-tracking-based method in the negative time-shifts. (D) The same cells as in Figures 7 and 8 were also analyzed by the marker-tracking-based analysis. All three panels show similar shapes to those in Figures 7 and 8, but the peaks in Cdc42 and Rac were lower with marker-tracking-based analysis than with EET.
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pcbi-1000223-g009: Comparison of morphodynamic analysis by EET with marker-tracking-based analysis.Marker-tracking-based analysis was performed using virtually-defined markers, and their movements perpendicular to the cellular edge were measured. (A) Cellular edges changing with time (blue: 6 min; indigo: 7 min; light blue: 8 min; green: 9 min; yellow: 10 min; red: 11 min). The cell analyzed was the same as that used in Figure 5. Black lines show traces of virtually defined markers. (B) Closed subsection of the lower right area in (A). Black dots show the positions of the markers. The uniform distribution of the markers (dots on the blue line) changed into a non-uniform distribution accompanied by persistent protrusion (dots on the red line). (C) Time-shifted cross-correlation analysis by the marker-tracking-based method and EET on the cell in Figure 5. Both of the correlation profiles show strong positive correlations in negative time-shifts and weak correlations in positive time-shifts. However, EET yielded higher correlations than the marker-tracking-based method in the negative time-shifts. (D) The same cells as in Figures 7 and 8 were also analyzed by the marker-tracking-based analysis. All three panels show similar shapes to those in Figures 7 and 8, but the peaks in Cdc42 and Rac were lower with marker-tracking-based analysis than with EET.

Mentions: We also compared EET analysis with simple implementation of marker-tracking-based analysis. In this marker-tracking-based analysis, virtually defined markers were aligned uniformly along the spline-fitted cellular edge in the first frame of time-lapse FRET images. Then, the movements of markers in the direction perpendicular to the cellular edge during a single time-frame were measured according to the current marker position and the intersection of the perpendicular axes of the current cellular edge and the next cellular edge (Figure 9A and 9B). Figure 9A and 9B show time-lapse cellular edges of the same cell as in Figure 5, colored from blue (6 min) to red (11 min), with virtually defined markers (black dots) and movements of the markers (black lines). Topological violations of the markers (crossing the black lines) are indicated in Figure 9B, which is probably due to the highly complex morphological changes in the edges. Such complex changes could affect the marker movement maps (although the map obtained by the marker-tracking-based method was comparable to that obtained by EET and by polar coordinate-based analysis; see Figure S6), but our statistical analysis was not affected. Instead, the changes in marker distribution from a uniform (black dots on the blue line in Figure 9B) to a non-uniform alignment (black dots on the red line in Figure 9B) would have non-negligible influences on the time-shifted statistical analysis (e.g., Figure 1C). As with EET and the polar coordinate-based method, the local activity was determined as a mean value within an ROI, which was a circle of radius r. We used the same r value in EET, the polar coordinate-based and the marker-tracking-based methods. All analyses produced similar maps (see Figure S6), and time-shifted cross-correlations were then calculated (Figure 9C).


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 marker-tracking-based analysis.Marker-tracking-based analysis was performed using virtually-defined markers, and their movements perpendicular to the cellular edge were measured. (A) Cellular edges changing with time (blue: 6 min; indigo: 7 min; light blue: 8 min; green: 9 min; yellow: 10 min; red: 11 min). The cell analyzed was the same as that used in Figure 5. Black lines show traces of virtually defined markers. (B) Closed subsection of the lower right area in (A). Black dots show the positions of the markers. The uniform distribution of the markers (dots on the blue line) changed into a non-uniform distribution accompanied by persistent protrusion (dots on the red line). (C) Time-shifted cross-correlation analysis by the marker-tracking-based method and EET on the cell in Figure 5. Both of the correlation profiles show strong positive correlations in negative time-shifts and weak correlations in positive time-shifts. However, EET yielded higher correlations than the marker-tracking-based method in the negative time-shifts. (D) The same cells as in Figures 7 and 8 were also analyzed by the marker-tracking-based analysis. All three panels show similar shapes to those in Figures 7 and 8, but the peaks in Cdc42 and Rac were lower with marker-tracking-based analysis than with EET.
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Related In: Results  -  Collection

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

pcbi-1000223-g009: Comparison of morphodynamic analysis by EET with marker-tracking-based analysis.Marker-tracking-based analysis was performed using virtually-defined markers, and their movements perpendicular to the cellular edge were measured. (A) Cellular edges changing with time (blue: 6 min; indigo: 7 min; light blue: 8 min; green: 9 min; yellow: 10 min; red: 11 min). The cell analyzed was the same as that used in Figure 5. Black lines show traces of virtually defined markers. (B) Closed subsection of the lower right area in (A). Black dots show the positions of the markers. The uniform distribution of the markers (dots on the blue line) changed into a non-uniform distribution accompanied by persistent protrusion (dots on the red line). (C) Time-shifted cross-correlation analysis by the marker-tracking-based method and EET on the cell in Figure 5. Both of the correlation profiles show strong positive correlations in negative time-shifts and weak correlations in positive time-shifts. However, EET yielded higher correlations than the marker-tracking-based method in the negative time-shifts. (D) The same cells as in Figures 7 and 8 were also analyzed by the marker-tracking-based analysis. All three panels show similar shapes to those in Figures 7 and 8, but the peaks in Cdc42 and Rac were lower with marker-tracking-based analysis than with EET.
Mentions: We also compared EET analysis with simple implementation of marker-tracking-based analysis. In this marker-tracking-based analysis, virtually defined markers were aligned uniformly along the spline-fitted cellular edge in the first frame of time-lapse FRET images. Then, the movements of markers in the direction perpendicular to the cellular edge during a single time-frame were measured according to the current marker position and the intersection of the perpendicular axes of the current cellular edge and the next cellular edge (Figure 9A and 9B). Figure 9A and 9B show time-lapse cellular edges of the same cell as in Figure 5, colored from blue (6 min) to red (11 min), with virtually defined markers (black dots) and movements of the markers (black lines). Topological violations of the markers (crossing the black lines) are indicated in Figure 9B, which is probably due to the highly complex morphological changes in the edges. Such complex changes could affect the marker movement maps (although the map obtained by the marker-tracking-based method was comparable to that obtained by EET and by polar coordinate-based analysis; see Figure S6), but our statistical analysis was not affected. Instead, the changes in marker distribution from a uniform (black dots on the blue line in Figure 9B) to a non-uniform alignment (black dots on the red line in Figure 9B) would have non-negligible influences on the time-shifted statistical analysis (e.g., Figure 1C). As with EET and the polar coordinate-based method, the local activity was determined as a mean value within an ROI, which was a circle of radius r. We used the same r value in EET, the polar coordinate-based and the marker-tracking-based methods. All analyses produced similar maps (see Figure S6), and time-shifted cross-correlations were then calculated (Figure 9C).

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