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A RhoC biosensor reveals differences in the activation kinetics of RhoA and RhoC in migrating cells.

Zawistowski JS, Sabouri-Ghomi M, Danuser G, Hahn KM, Hodgson L - PLoS ONE (2013)

Bottom Line: To understand these differences, we developed and validated a biosensor of RhoC activation (RhoC FLARE).The two isoforms differed markedly in the kinetics of activation.During macropinocytosis, differences were observed during vesicle closure and in the area surrounding vesicle formation.

View Article: PubMed Central - PubMed

Affiliation: Department of Pharmacology and Lineberger Cancer Center, University of North Carolina School of Medicine, Chapel Hill, North Carolina, United States of America.

ABSTRACT
RhoA and RhoC GTPases share 92% amino acid sequence identity, yet play different roles in regulating cell motility and morphology. To understand these differences, we developed and validated a biosensor of RhoC activation (RhoC FLARE). This was used together with a RhoA biosensor to compare the spatio-temporal dynamics of RhoA and RhoC activity during cell protrusion/retraction and macropinocytosis. Both GTPases were activated similarly at the cell edge, but in regions more distal from the edge RhoC showed higher activation during protrusion. The two isoforms differed markedly in the kinetics of activation. RhoC was activated concomitantly with RhoA at the cell edge, but distally, RhoC activation preceded RhoA activation, occurring before edge protrusion. During macropinocytosis, differences were observed during vesicle closure and in the area surrounding vesicle formation.

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Correlation of RhoA/C activation and cell edge dynamics.(A) Top: 2.5 µm width, 0.9 µm depth sampling windows (yellow) are placed at fixed distances from the edge of a MEF expressing the RhoC biosensor. (B) Correlation of each biosensor and cell edge velocity as a function of time and distance from the edge. Inset legend indicates color code for spatial zones. RhoA: n=16 cells, 993 windows, RhoC: n=16 cells, 869 windows. Correlation coefficients (C) and time shifts (D) for RhoA (open) and RhoC (solid) at different distances from the edge. Error bars represent 95% confidence intervals, estimated by bootstrap analysis of variation in the correlation functions.
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pone-0079877-g003: Correlation of RhoA/C activation and cell edge dynamics.(A) Top: 2.5 µm width, 0.9 µm depth sampling windows (yellow) are placed at fixed distances from the edge of a MEF expressing the RhoC biosensor. (B) Correlation of each biosensor and cell edge velocity as a function of time and distance from the edge. Inset legend indicates color code for spatial zones. RhoA: n=16 cells, 993 windows, RhoC: n=16 cells, 869 windows. Correlation coefficients (C) and time shifts (D) for RhoA (open) and RhoC (solid) at different distances from the edge. Error bars represent 95% confidence intervals, estimated by bootstrap analysis of variation in the correlation functions.

Mentions: We focused on comparing RhoC and RhoA activation in the constitutive protrusions of migrating MEFs, where RhoA activity had previously been extensively characterized using the RhoA FLARE.sc biosensor [9,10]. To quantify differences in RhoA and RhoC activity, we turned to the computational multiplexing approach described in Machacek et al., 2009. This method uses cell edge velocity as a common reference to relate, in time and space, the activities of two different biosensors studied in separate experiments. The first step in computational multiplexing is to determine the spatiotemporal correlation between cell edge movement and each of the biosensors separately. As illustrated in Figure 3A and Movie 7 for RhoC, GTPase activity was probed in reporter windows which followed the edge during protrusion and retraction events. For each window we obtained a time series of edge velocity and a time series of GTPase activity, allowing us to determine by Pearson’s cross-correlation the tightness of coupling and the time lag between protein activity and edge motion. Importantly, as the correlation is computed locally, these relationships are captured despite the asynchronous motion of different edge sectors. This analysis is completely invariant with respect to cell shape and largely insensitive to the heterogeneity of morphodynamic behaviors between cells. However, the correlation analysis from only a single window would be too noisy to determine unambiguous relationships. Therefore, we take all windows from all cells and perform a cubic spline fit to obtain the mean correlation, followed by 2000 bootstrap samplings of the residuals from per-window correlations relative to spline, to obtain the confidence intervals about the mean (RhoA: n=16 cells, 993 windows, RhoC: n=16 cells, 869 windows). This procedure was repeated for windows at different distances from the edge, allowing us to determine how the correlation changes with the location of the signaling activity. The second step in computational multiplexing is to compare the correlation functions of multiple Rho GTPases. As each of the functions uses the edge velocity as a reference, the differences between the functions indicate directly spatiotemporal differences between the Rho GTPases.


A RhoC biosensor reveals differences in the activation kinetics of RhoA and RhoC in migrating cells.

Zawistowski JS, Sabouri-Ghomi M, Danuser G, Hahn KM, Hodgson L - PLoS ONE (2013)

Correlation of RhoA/C activation and cell edge dynamics.(A) Top: 2.5 µm width, 0.9 µm depth sampling windows (yellow) are placed at fixed distances from the edge of a MEF expressing the RhoC biosensor. (B) Correlation of each biosensor and cell edge velocity as a function of time and distance from the edge. Inset legend indicates color code for spatial zones. RhoA: n=16 cells, 993 windows, RhoC: n=16 cells, 869 windows. Correlation coefficients (C) and time shifts (D) for RhoA (open) and RhoC (solid) at different distances from the edge. Error bars represent 95% confidence intervals, estimated by bootstrap analysis of variation in the correlation functions.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3818223&req=5

pone-0079877-g003: Correlation of RhoA/C activation and cell edge dynamics.(A) Top: 2.5 µm width, 0.9 µm depth sampling windows (yellow) are placed at fixed distances from the edge of a MEF expressing the RhoC biosensor. (B) Correlation of each biosensor and cell edge velocity as a function of time and distance from the edge. Inset legend indicates color code for spatial zones. RhoA: n=16 cells, 993 windows, RhoC: n=16 cells, 869 windows. Correlation coefficients (C) and time shifts (D) for RhoA (open) and RhoC (solid) at different distances from the edge. Error bars represent 95% confidence intervals, estimated by bootstrap analysis of variation in the correlation functions.
Mentions: We focused on comparing RhoC and RhoA activation in the constitutive protrusions of migrating MEFs, where RhoA activity had previously been extensively characterized using the RhoA FLARE.sc biosensor [9,10]. To quantify differences in RhoA and RhoC activity, we turned to the computational multiplexing approach described in Machacek et al., 2009. This method uses cell edge velocity as a common reference to relate, in time and space, the activities of two different biosensors studied in separate experiments. The first step in computational multiplexing is to determine the spatiotemporal correlation between cell edge movement and each of the biosensors separately. As illustrated in Figure 3A and Movie 7 for RhoC, GTPase activity was probed in reporter windows which followed the edge during protrusion and retraction events. For each window we obtained a time series of edge velocity and a time series of GTPase activity, allowing us to determine by Pearson’s cross-correlation the tightness of coupling and the time lag between protein activity and edge motion. Importantly, as the correlation is computed locally, these relationships are captured despite the asynchronous motion of different edge sectors. This analysis is completely invariant with respect to cell shape and largely insensitive to the heterogeneity of morphodynamic behaviors between cells. However, the correlation analysis from only a single window would be too noisy to determine unambiguous relationships. Therefore, we take all windows from all cells and perform a cubic spline fit to obtain the mean correlation, followed by 2000 bootstrap samplings of the residuals from per-window correlations relative to spline, to obtain the confidence intervals about the mean (RhoA: n=16 cells, 993 windows, RhoC: n=16 cells, 869 windows). This procedure was repeated for windows at different distances from the edge, allowing us to determine how the correlation changes with the location of the signaling activity. The second step in computational multiplexing is to compare the correlation functions of multiple Rho GTPases. As each of the functions uses the edge velocity as a reference, the differences between the functions indicate directly spatiotemporal differences between the Rho GTPases.

Bottom Line: To understand these differences, we developed and validated a biosensor of RhoC activation (RhoC FLARE).The two isoforms differed markedly in the kinetics of activation.During macropinocytosis, differences were observed during vesicle closure and in the area surrounding vesicle formation.

View Article: PubMed Central - PubMed

Affiliation: Department of Pharmacology and Lineberger Cancer Center, University of North Carolina School of Medicine, Chapel Hill, North Carolina, United States of America.

ABSTRACT
RhoA and RhoC GTPases share 92% amino acid sequence identity, yet play different roles in regulating cell motility and morphology. To understand these differences, we developed and validated a biosensor of RhoC activation (RhoC FLARE). This was used together with a RhoA biosensor to compare the spatio-temporal dynamics of RhoA and RhoC activity during cell protrusion/retraction and macropinocytosis. Both GTPases were activated similarly at the cell edge, but in regions more distal from the edge RhoC showed higher activation during protrusion. The two isoforms differed markedly in the kinetics of activation. RhoC was activated concomitantly with RhoA at the cell edge, but distally, RhoC activation preceded RhoA activation, occurring before edge protrusion. During macropinocytosis, differences were observed during vesicle closure and in the area surrounding vesicle formation.

Show MeSH
Related in: MedlinePlus