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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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Schematic view of edge evolution tracking.(A) Identification of morphodynamic properties. Solid lines denote cellular edge at each frame and the shaded regions A, B, and C indicate area differences between consecutive frames. We define two properties for a local morphological status transition: segments and anchor points. The segments are subdivided along the cellular edges, which are determined by the area differences between neighboring frames. The anchor points are segment terminals (closed circles) and are projected into the previous frame (open circles). Open squares l and r represent the edge terminals. (B) All of the segments identified and anchor points are mapped two-dimensionally. Horizontal and vertical axes denote the time and position along the cell edge, respectively. Connections between anchor points (dashed lines) illustrate the corresponding points between neighboring frames. (C) We can then construct a graph to represent segment evolution. A node and link denote each segment and the connection between temporally consecutive segments. (D) Flow chart of the EET algorithm. (E) All colored nodes show the ancestry of the colored node at ‘T→T+1.’ The ancestry nodes in the different frames are identified by referring to the graph shown in (C); therefore, the time course of area differences stemming from a specific segment can be identified by applying simple algebra to the ancestry node map at each time point (see Materials and Methods). The plot shows the time course of area differences corresponding to the colored ancestry. Each node includes a time course of area difference that we have defined.
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pcbi-1000223-g003: Schematic view of edge evolution tracking.(A) Identification of morphodynamic properties. Solid lines denote cellular edge at each frame and the shaded regions A, B, and C indicate area differences between consecutive frames. We define two properties for a local morphological status transition: segments and anchor points. The segments are subdivided along the cellular edges, which are determined by the area differences between neighboring frames. The anchor points are segment terminals (closed circles) and are projected into the previous frame (open circles). Open squares l and r represent the edge terminals. (B) All of the segments identified and anchor points are mapped two-dimensionally. Horizontal and vertical axes denote the time and position along the cell edge, respectively. Connections between anchor points (dashed lines) illustrate the corresponding points between neighboring frames. (C) We can then construct a graph to represent segment evolution. A node and link denote each segment and the connection between temporally consecutive segments. (D) Flow chart of the EET algorithm. (E) All colored nodes show the ancestry of the colored node at ‘T→T+1.’ The ancestry nodes in the different frames are identified by referring to the graph shown in (C); therefore, the time course of area differences stemming from a specific segment can be identified by applying simple algebra to the ancestry node map at each time point (see Materials and Methods). The plot shows the time course of area differences corresponding to the colored ancestry. Each node includes a time course of area difference that we have defined.

Mentions: The traced cell boundary is divided into segments according to the area differences between two consecutive frames (see Figure 3A).


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)

Schematic view of edge evolution tracking.(A) Identification of morphodynamic properties. Solid lines denote cellular edge at each frame and the shaded regions A, B, and C indicate area differences between consecutive frames. We define two properties for a local morphological status transition: segments and anchor points. The segments are subdivided along the cellular edges, which are determined by the area differences between neighboring frames. The anchor points are segment terminals (closed circles) and are projected into the previous frame (open circles). Open squares l and r represent the edge terminals. (B) All of the segments identified and anchor points are mapped two-dimensionally. Horizontal and vertical axes denote the time and position along the cell edge, respectively. Connections between anchor points (dashed lines) illustrate the corresponding points between neighboring frames. (C) We can then construct a graph to represent segment evolution. A node and link denote each segment and the connection between temporally consecutive segments. (D) Flow chart of the EET algorithm. (E) All colored nodes show the ancestry of the colored node at ‘T→T+1.’ The ancestry nodes in the different frames are identified by referring to the graph shown in (C); therefore, the time course of area differences stemming from a specific segment can be identified by applying simple algebra to the ancestry node map at each time point (see Materials and Methods). The plot shows the time course of area differences corresponding to the colored ancestry. Each node includes a time course of area difference that we have defined.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1000223-g003: Schematic view of edge evolution tracking.(A) Identification of morphodynamic properties. Solid lines denote cellular edge at each frame and the shaded regions A, B, and C indicate area differences between consecutive frames. We define two properties for a local morphological status transition: segments and anchor points. The segments are subdivided along the cellular edges, which are determined by the area differences between neighboring frames. The anchor points are segment terminals (closed circles) and are projected into the previous frame (open circles). Open squares l and r represent the edge terminals. (B) All of the segments identified and anchor points are mapped two-dimensionally. Horizontal and vertical axes denote the time and position along the cell edge, respectively. Connections between anchor points (dashed lines) illustrate the corresponding points between neighboring frames. (C) We can then construct a graph to represent segment evolution. A node and link denote each segment and the connection between temporally consecutive segments. (D) Flow chart of the EET algorithm. (E) All colored nodes show the ancestry of the colored node at ‘T→T+1.’ The ancestry nodes in the different frames are identified by referring to the graph shown in (C); therefore, the time course of area differences stemming from a specific segment can be identified by applying simple algebra to the ancestry node map at each time point (see Materials and Methods). The plot shows the time course of area differences corresponding to the colored ancestry. Each node includes a time course of area difference that we have defined.
Mentions: The traced cell boundary is divided into segments according to the area differences between two consecutive frames (see Figure 3A).

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