While the development of the FRET biosensors has greatly advanced our knowledge of signaling transduction in live cells, the vast amount of imaging data produced by these biosensors requires automatic, intelligent, and objective image analysis tools to allow precise and efficient interpretation of biological information [18]. In particular, efficient algorithms are in great need to track the cell movement and to account for the difference in the shape and geometry of individual cells. Image registration has been widely applied in engineering and science for automatically tracking moving objects in time and for finding the pixel-wise correspondence between two images of similar objects [19,20]. In live-cell image analysis, this method has been used to align, via simple translation and rotation, the fluorescent images at different wavelengths [21–23] and the nucleus images at different time points of the same cell [24]. It has also been applied to track fluorescent particles inside the cells [25]. More sophisticated image registration methods have also been applied to analyze in situ gene expression in the mouse brain [20]. Other quantification methods have been developed to study the distribution of protein and the polarity of molecular activity by dividing the whole cell into small wedges in the polar coordinated system [23,26–28], to study the motility and energy of single sperms by tracking and trapping [29,30], or to extract data on 3D vascular anatomy [31]. However, automated whole-cell image registration of moving cells by pixel- wise tracking is still in great need for the high-resolution and efficient quantification of molecular signals in space and time. In this paper, we have developed automated image analysis methods to track the movement of single cells and quantify the FRET signals at subcellular regions utilizing the RhoA biosensor from Dr. Matsuda [12]. The spatiotemporal patterns of RhoA activation were analyzed in migratory and stationary HeLa cells in response to growth factor stimulation. Our results suggest that the global RhoA activity in migratory cells was down-regulated in response to growth factor stimulation to a lesser extent than that in stationary cells. The RhoA activity was more concentrated in the opposite direction of migration in migratory cells, but did not show preference in stationary cells. Materials and Methods Cell Lines and Culture HeLa cells (ATCC, Manassas, Virginia) were cultured in a humidified 95% air, 5% CO2 incubator at 37uC. The culture medium was Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum, 2 mM L-glutamine, 1 unit/ml penicillin, 100 mg/ml streptomycin, and 1 mM sodium pyruvate. The cell culture reagents were obtained from Invitrogen (San Diego, California). Transient Transfection and Microscope Imaging Since the Raichu-RhoA biosensor can be easily transfected and is well-characterized for HeLa cells, we used this biosensor to visualize the dynamics of RhoA activity in HeLa cells on fibronectin covered glass [12]. The Raichu-RhoA biosensor was transfected into HeLa using Lipofectamine (Invitrogen) [12] before starvation with 0.5% FBS for 36–48 hours. The cells were then suspended in trypsin-EDTA and seeded on fibronectin- coated glass-bottom dishes for 3–6 hours before EGF (50 ng/ml) stimulation. During imaging, the cells were kept in CO2- independent medium without serum (Invitrogen) at 37uC. The objective focus was aimed near the basal side of the cell. Images were collected by a Zeiss axiovert inverted microscope with a 420DF20 excitation filter, a 450DRLP dichroic mirror, two emission filters controlled by a filter changer (480DF30 for CFP Automated Analysis of FRET and 535DF25 for YFP), and a cooled charge-coupled device camera (Cascade 512B; Photometrics). The intensity of excitation light was controlled by adjustable neutral density filters to ensure minimal photobleaching during imaging. The CFP and YFP fluorescence intensity images were background subtracted. The FRET ratio images were computed by calculating the pixel-wise CFP/YFP ratio to represent RhoA activity in space and time, using MetaFluor 6.2 software (Universal Imaging) or our customized programs developed in MATLAB (MathWorks). Image Registration To automatically register the time-lapse images of a moving cell, we mapped these cell images into a unit disk, which served as a reference image both for the registration of the time-lapse image sequence of the same cell and for the comparison between different cells with varying shapes. It took four steps to map a cell to the unit disk. First, the cell body was determined by segmentation. Second, the control points on the cell were computed by Delaunay triangulation of the convex hull of the cell body. Third, the control points were mapped to the reference image (unit disk) by calculating the relative position of the points in the convex hull. Finally, the cells were mapped into the unit disk by a piecewise linear transformation [32] defined by the control points and the Delaunay triangulation (Figure 1). Otsu’s method [33] was used to compute a threshold of intensity for the segmentation of the cell body, because the fluorescent images recorded using the RhoA biosensor were bright and readily separable from the background. A list of nodes outlining the contour of the cell was generated based on the identified cell body. Inside the contour, a mask image was hence created to represent the shape of the cell. The images collected from the YFP channel were used for the detection of cell edge and the mask because they were brighter than those from the CFP channel. To register the shape of a cell by a unit disk without ambiguity, it is required that the cell shape be convex. Therefore, the convex hull circumscribing the cell mask image was calculated [34] and used as the boundary for computing the Delaunay triangulation [35]. The nodes in the triangulation were then used as the control points for the cell image. For each control point in the cell image, the corresponding control point in the unit disk was calculated based on its relative position in the convex hull. In brief, the centroid of the cell (C) was mapped to the center of the unit disk (C’) (Figure 1A). For any other control point (P), the corresponding point on the unit disk, P’, was calculated based on the relative position of the point P between the centroid and the edge of the convex hull. Suppose P has the polar coordinates of (jCPj,h), centered at the centroid C; and the ray from C to P crosses the edge of the convex hull at the point E. Then the polar coordinates of the corresponding point (P’) in the unit disk is given by (r,h) centered at C’, where r represents the relative distance from C to P, i.e. r~jCPj=jCEj (Figure 1A–B). Conversely, any point in the unit disk, P’~ðr,hÞ, can be mapped back onto different cells or the same cell with different shapes at different time points. The convex hull of the whole cell was mapped onto the unit disk by a piecewise linear transformation [32]. Within each triangle in the Delaunay triangulation, the transformation is linear and uniquely defined by the three control points which are nodes of the triangle (Figure 1B). The transformation is also continuous at the edges shared by two triangles. Tracking the Regions of Interest (ROIs) To track the regions of interest, the time-lapse images of a single cell were first transformed into a series of unit disks. One or multiple ROIs were then selected and defined by the user on the PLoS ONE | www.plosone.org 2 December 2008 | Volume 3 | Issue 12 | e4082