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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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Obstacles to quantifying cell morphological changes.(A) General scheme of cellular morphological changes. The diagram shows part of a cell's edge expanding continuously over time (frame number) T−1 to T+3. We focus on the correlation timing between morphological change and a regulation signal (red region). (B) The kymograph approaches, including polar coordinate-based analysis, encounters problem caused by the fixed direction of the axis. Although it describes morphodynamics along the proper direction of the axis (solid arrow), lateral movements against this assigned direction (dotted arrow) cannot be quantified. (C) Marker-based analysis rearranges the marker positions depending on the rate and direction of morphological changes, so that the marker density cannot be conserved. Therefore, it is not suitable for persistently changing cell morphology such as neurite outgrowth.
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pcbi-1000223-g001: Obstacles to quantifying cell morphological changes.(A) General scheme of cellular morphological changes. The diagram shows part of a cell's edge expanding continuously over time (frame number) T−1 to T+3. We focus on the correlation timing between morphological change and a regulation signal (red region). (B) The kymograph approaches, including polar coordinate-based analysis, encounters problem caused by the fixed direction of the axis. Although it describes morphodynamics along the proper direction of the axis (solid arrow), lateral movements against this assigned direction (dotted arrow) cannot be quantified. (C) Marker-based analysis rearranges the marker positions depending on the rate and direction of morphological changes, so that the marker density cannot be conserved. Therefore, it is not suitable for persistently changing cell morphology such as neurite outgrowth.

Mentions: Although previous methodologies have successfully described the specific aspects of cellular morphodynamics, there remain challenges to quantify the relationship between morphodynamics and signaling events. One representative problem is the association between regions in different frames. To scrutinize the dynamic relationship between morphological change and molecular signaling, we need to cross-correlate them in a time-dependent manner (Figure 1A). A polar coordinate system does not ensure the association of time-shifted local domains (Figure 1B), and is unsuitable for non-circular cell shapes. The virtual marker tracking method satisfies this requirement for cells with broadly consistent shapes, but its fixed number of markers causes unequal distribution when a dramatic shape change such as the persistent growth of neurites in neurons, occurs (Figure 1C). Taking these problems into account, we perceive the need for a novel quantification method to better understand the mechanisms of morphodynamic regulation by molecular signaling.


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)

Obstacles to quantifying cell morphological changes.(A) General scheme of cellular morphological changes. The diagram shows part of a cell's edge expanding continuously over time (frame number) T−1 to T+3. We focus on the correlation timing between morphological change and a regulation signal (red region). (B) The kymograph approaches, including polar coordinate-based analysis, encounters problem caused by the fixed direction of the axis. Although it describes morphodynamics along the proper direction of the axis (solid arrow), lateral movements against this assigned direction (dotted arrow) cannot be quantified. (C) Marker-based analysis rearranges the marker positions depending on the rate and direction of morphological changes, so that the marker density cannot be conserved. Therefore, it is not suitable for persistently changing cell morphology such as neurite outgrowth.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1000223-g001: Obstacles to quantifying cell morphological changes.(A) General scheme of cellular morphological changes. The diagram shows part of a cell's edge expanding continuously over time (frame number) T−1 to T+3. We focus on the correlation timing between morphological change and a regulation signal (red region). (B) The kymograph approaches, including polar coordinate-based analysis, encounters problem caused by the fixed direction of the axis. Although it describes morphodynamics along the proper direction of the axis (solid arrow), lateral movements against this assigned direction (dotted arrow) cannot be quantified. (C) Marker-based analysis rearranges the marker positions depending on the rate and direction of morphological changes, so that the marker density cannot be conserved. Therefore, it is not suitable for persistently changing cell morphology such as neurite outgrowth.
Mentions: Although previous methodologies have successfully described the specific aspects of cellular morphodynamics, there remain challenges to quantify the relationship between morphodynamics and signaling events. One representative problem is the association between regions in different frames. To scrutinize the dynamic relationship between morphological change and molecular signaling, we need to cross-correlate them in a time-dependent manner (Figure 1A). A polar coordinate system does not ensure the association of time-shifted local domains (Figure 1B), and is unsuitable for non-circular cell shapes. The virtual marker tracking method satisfies this requirement for cells with broadly consistent shapes, but its fixed number of markers causes unequal distribution when a dramatic shape change such as the persistent growth of neurites in neurons, occurs (Figure 1C). Taking these problems into account, we perceive the need for a novel quantification method to better understand the mechanisms of morphodynamic regulation by molecular signaling.

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