Automated Analysis of FRET Figure 5. The global RhoA activity in migrating and stationary HeLa cells at different time points before and after EGF stimulation. The RhoA activity averaged over four ROIs spanning the whole cell body was used to represent the global RhoA activity. The error bars represent the standard error and the asterisks mark the time points where the average RhoA activities were significantly different (t-test, p,0.05) between migrating and stationary cells. doi:10.1371/journal.pone.0004082.g005 Figure 3B). This difference between migrating and stationary cells continued to increase in time after EGF stimulation (Figure 5). These results indicate that the RhoA activity in migrating cells upon EGF stimulation is differentially down-regulated from that in stationary cells, possibly due to the relatively high RhoA activity at the back of migrating cells after EGF stimulation. Cluster Analysis We further studied the geometric features of the subcellular regions with relatively high RhoA activity after EGF stimulation. The intracellular pixels in the FRET image was divided into six clusters according to their FRET ratio values. As shown in Figure 6A, the pixels in the image were color-coded by the cluster they belong to, ordered from blue to red according to the mean RhoA FRET ratio in each cluster ranging from low to high. The top three clusters with the highest FRET ratio were selected and monitored to examine the distribution of high RhoA activity and their effect on cell behaviors, e.g. migration (Figures 6 and Movie S3). A MATLAB program was developed to select and quantify the area of these regions automatically (Figure 6). The results revealed that the area size of these selected regions increased in time after EGF stimulation, particularly so at the back of the migrating cells (Figure 6C). To quantitatively analyze the size of these regions with high RhoA activities, a polar coordinate system was established, centered at the centroid of each cell. The cell was then divided into 18 angular wedges with 20u increments (Figure 6B). The number of pixels in the entire wedge, n0, was calculated as well as the number of pixels within the three high- RhoA clusters in the wedge, n. The normalized number of pixels in each wedge was calculated by taking the ratio of n=n0. The normalized number of pixels in each wedge was plotted against the angle of the wedge to represent the polarity of RhoA activity. As shown in Figure 6D, this polarity curve of a representative migrating HeLa cell at different time points clearly demonstrates an increased spatial polarity of RhoA initiated by EGF stimulation. The pixels with relatively high RhoA activity appeared to accumulate in the region opposite to the migration direction, approximately at 220u in coordinates in Figure 6B. A similar cluster analysis was conducted on stationary cells. A representative stationary cell with six clusters is color-coded by the FRET ratio and depicted in Figure 7A, with the highest three clusters and their time-lapse images shown in Figures 7B and 7C, respectively. The normalized number of wedge pixels at different angles was quantified and plotted against angle in Figure 7D. The result indicates that there was no significant polarization process upon EGF stimulation in stationary cells. The averaged polarity results from multiple migrating cells were calculated by aligning their direction of motion toward the same angle. At 52 minutes after EGF, the normalized number of pixels with high RhoA activity in each angular wedge clearly displayed a polarized pattern, with more pixels accumulating at the back of the migrating cells (Figure 8A). In contrast, the distribution of the regions with higher RhoA activity appeared more random in stationary cells (Figure 8B). All these results suggest that the polarization of RhoA activity in HeLa cells plays important roles in regulating the cell migration and its directions. Discussion The recent advancement of live-cell imaging technologies has provided a variety of powerful tools and allowed the collection of a vast amount of image data/movies which requires significant time and effort on image analysis and quantification. However, there is a lack of user-friendly and automated image-analysis tools with high throughput capability. We have developed image analysis algorithms and methods based on the image registration PLoS ONE | www.plosone.org 7 December 2008 | Volume 3 | Issue 12 | e4082