Automated Analysis of FRET and after EGF stimulation. The inset highlights the position of the cluster with the highest FRET ratio, which is typically located at the very edge of the cell. (D) The cell was divided into 18 wedges, as shown in panel (B). The number of pixels within the three clusters of highest FRET ratio in each wedge was normalized by dividing the total pixel number of the wedge. This normalized number of high-FRET pixels per wedge is plotted versus the angle of the wedge. The dynamic behavior of the automatically detected clusters is also shown in Movie S3. doi:10.1371/journal.pone.0004082.g006 for tracking the complex movement of migrating cells. These methods were applied to automatically track and quantify the time-lapse FRET ratio of Raichu-RhoA biosensor at subcellular regions in live HeLa cells, and to analyze the polarity of intracellular regions with high RhoA activities. The results suggest that the subcellular distribution of high RhoA activity is differentially regulated in migrating and stationary cells. These methods can also be applied to analyze the general fluorescence images of signaling transduction in live cells and enable an automated, efficient and objective quantification of a large quantity of imaging data. The image analysis methods presented in this paper are completely automated. Because the ROIs were automatically tracked in moving cells, users only need to define the initial regions of interest in the first cell image. Each frame with four ROIs requires several seconds to quantify the FRET ratio with a personal computer. The automation of this kind of analysis is crucial because, at the current stage, the majority of data analysis for FRET imaging in live cells was performed manually with the contours of ROIs moved by a biologist if the cell moves or changes the shape. This manual tracking of ROIs for live cells can result in low-efficiency, tediousness, and inaccuracy. With our analysis method, the tracking is very reliable in general if the ROIs were selected inside the cell body and away from the cell edge. Because the convex hull or contour of the cell, but not the actual cell edge, was mapped to the unit disk, this method would become less accurate if a ROI was close to some cell edges which change their shapes between concave and convex. Further improvement of the automated tracking method would require the development and implementation of more sophisticated image registration algorithm. Nevertheless, the conversion of different cells into a uniform scheme, such as a unit disk, should be useful and important to standardize and compare the signals from each individual cell. The application of the novel cluster analysis method is useful for analyzing and quantifying the subcellular distribution and the polarity of RhoA activity. The absolute value of RhoA activity did not present an obvious pattern of polarity. However, there was a significantly increase in the number of pixels with relatively higher RhoA activity at the tail of migrating cells upon EGF stimulation, evidenced by the quantification using the automated cluster analysis (Figure 6D). This highly-coordinated distribution of RhoA activity in space can likely contribute and control the direction of cell migration. The concentrated accumulation of high RhoA activity at the cell rear can help the detachment of cell tail from the substrate during migrating [41], possibly through the activation of downstream molecule ROCK which controls the phosphorylation of myosin light chain kinase (MLCK) and phosphatase (MLCP) to regulate the actomyosin contractility [5]. The inhibited RhoA activity at cell migration direction can lead to the activation of Rac1 [42], a small GTPase controlling cell protrusion and lamellipodia formation [43]. As such, cells can be coordinately guided to migrate toward the direction opposite to the RhoA polarity. Comparing the results from migrating and stationary cells revealed distinct spatial patterns of RhoA activity in these two kinds of cells. In contrast to the polarized RhoA activity in migrating cells, the global down-regulation of the RhoA activity in stationary cells upon EGF stimulation may promote the protrusion in all directions without discrimination, which prevents a persistent and directional migration. The global change of RhoA activity upon EGF stimulation in migrating cells appeared less significant as compared to that in stationary cells. This is largely due to the consistent maintenance of a high RhoA activity at the trailing edge of migrating cells, but not in stationary cells. Further studies are needed to elucidate the detailed molecular mechanism governing the differential RhoA responses of stationary and migrating cells. In summary, our image analysis method can allow a high- throughput and automated means for quantifying and analyzing the spatiotemporal molecular signals in live cells. This is particularly powerful in analyzing a large quantity of signals from cells with changing shapes, e.g. migrating cells. We have further demonstrated the significance of this method by applying it to quantify the dynamic RhoA activity at subcellular levels in migrating and stationary HeLa cells. The results revealed that EGF induced a down-regulation of RhoA in both migrating and stationary HeLa cells. However, a polarized distribution of RhoA activity can be consistently observed only in migrating, but not stationary cells. Supporting Information Movie S1 The FRET images were mapped to a disk. The spatiotemporal dynamics of the FRET ratio representing RhoA activity upon EGF stimulation is shown in a typical migrating cell (left) and reference images mapped into a disk (right). Red and blue colors indicate high and low RhoA activity, respectively. The cell was shown to migrate in the view field, especially during the later part of the video (left), while the reference images of the cell remained within the static unit disk (right). Found at: doi:10.1371/journal.pone.0004082.s001 (9.81 MB MOV) Movie S2 Four regions of interest were tracked in a migrating cell. The regions moved and changed shape as the cell migrated forward. Found at: doi:10.1371/journal.pone.0004082.s002 (7.93 MB MOV) Movie S3 The geometric features of the six clusters (right) and three top clusters (left) in a migrating cell are shown in time. These regions are color-coded according to their RhoA activities, with cold and hot colors representing regions with low and high levels of RhoA activity, respectively. Found at: doi:10.1371/journal.pone.0004082.s003 (10.26 MB MOV) Acknowledgments We thank Dr. Michiyuki Matsuda for precious reagents and constructs. Author Contributions Conceived and designed the experiments: JPE SL YW. Performed the experiments: JX. Analyzed the data: JPE SL YW. Contributed reagents/ materials/analysis tools: JPE SL. Wrote the paper: JPE SL YW. PLoS ONE | www.plosone.org 9 December 2008 | Volume 3 | Issue 12 | e4082