coverslips due to a spreading defect. Immediately before being transferred to a sealed imaging chamber, complete culture media was replaced with imaging media. Imaging experiments for all cells used in this study were conducted within the first 8 hours after plating. Imaging was performed on an Olympus IX81 motorized inverted microscope equipped with a ZDC focus drift compensa- tor and a TIRFM illuminator, a 60X 1.45 PlanApoN TIRFM objective, a cooled digital 12-bit CCD camera (CoolSnap, Roper Scientific), a 100 W Mercury arc lamp, and MetaMorph imaging software. The 488 nm laser line from a Krypton-Argon ion laser (Series 43, Omnichrome) was controlled with a custom laser launch/AOTF (LSM Technologies). Imaging of the cells express- ing EGFP-FAK was performed on a Nikon Eclipse Ti inverted microscope equipped with the Perfect Focus System, a TIRF illuminator, a 60X 1.45NA PlanApoN TIRF objective, a a cooled digital 16-bit EMCCD camera (QuantEM: 512SC, Photometrics), an Argon ion laser (Melles Griot) controlled with a custom laser launch/AOTF, and Nikon Elements imaging software. Images were acquired with 262 binning, except for images of EGFP-FAK expressing cells, which were acquired with 161 binning. Images were gathered once every minute. Illumination intensity was controlled with either the AOTF (TIRF excitation) or neutral density filters (epifluorescence excitation). Simultaneous TIRF images of EGFP and epifluorescence images of RFP were acquired using an 80/20 (TIRF/Epifluorescence) splitter mirror, a custom dichroic mirror (Chroma) and the following band-pass filters: EGFP (HQ 525/50); RFP (HQ580/30, HQ 630/40). In total, 21 EGFP-Paxillin, 9 EGFP-PaxillinS178A and 10 EGFP-FAK cells were included in this study. The EGFP-Paxillin experiments were conducted over four days, while the EGFP-PaxillinS178A experiments were conducted over three days and the EGFP- FAK experiments were conducted over three days. Image Processing Methods to identify individual FAs were adapted from [28], with some modification. Briefly, each image taken during an experiment was high pass filtered, using a round averaging filter with a radius of 11 pixels (4.95 mm diameter). The high pass filtered images were threshholded by an empirically determined value set to identify adhesion pixels. The water segmentation method was used as described, but with the following modifica- tions. When a pixel acts as bridge between two large adhesions, 2 where large is defined as 40 or more pixels (1.85 mm ), the bridge pixel is assigned to the adhesion whose centroid is closest to the bridge pixel. Also, holes in any single adhesion were filled using the same water segmentation algorithm. Between 200 and 600 adhesions were found in each image from the experimental data. The average signal-to-noise ratio was 6.04 as calculated by dividing the mean of the adhesion intensity by the standard deviation of the backgound pixels [29]. After each focal adhesion has been identified, characteristic adhesion properties, such as those in Figure 2, are then collected. Cell edges were found by analyzing the myr-RFP images using a method similar to that described in a prior publication [30]. This method automatically identifies a single threshold which splits the myr-RFP images into cell body and background regions. Briefly, a histogram of all the intensity values for a single image was collected and split into 1000 equal sized bins. The counts of each bin were then smoothed with the loess algorithm (Polynomial order 2, 5% of data included in each fit). This smoothed histogram has two peaks corresponding to the background region and the cell body. The local minima and maxima in the smoothed histogram are found and the two maxima at the lowest pixel intensity bins Quantifying Focal Adhesion Dynamics identified. The threshold for image segmentation is set to the minima between the set of maxima found in the prior step. After thresholding the image, the connected regions are identified and the regions less than 10 pixels in area are discarded. The cell edge is defined by the border pixels of the connected regions. FA Tracking With the focal adhesions identified in each image of the experimental data set, another series of algorithms were designed to track the focal adhesions through each sequential image. The tracking algorithm is based on a birth-death model of a FA lifetime (Figure S4). In each sequential image a FA can either be born, continue into the next time step, merge or die. The birth-death- merge processes are detected by examining the properties extracted from the segmented adhesions. The results of this tracking algorithm are assignments of the FAs identified in each image into lineages that track the development of the FAs during the course of the experiment. The tracking algorithm is initialized with all the adhesions detected in the first frame of the image sequence. The first step of the tracking algorithm attempts to locate FAs that correspond to one another in the next time step of the experimental data (Figure S4). This first step assumes that if a focal adhesion in the first frame overlaps with a focal adhesion in the subsequent frame, these overlapping adhesions correspond to one another. When an adhesion overlaps with more than one adhesion in the following frame, the adhesion with the greatest percentage of overlap is assigned as the match. If a FA does not overlap with any of the FAs in the following image, the FA closest to that adhesion in terms of the Euclidean distance between each adhesion’s centroid is assigned as a match. Adhesions in the next frame that are not selected via either of these methods, but still overlap with an adhesion in the current frame are marked as being created via a split birth event. Adhesion births that are the result of split events are dealt with in later filtering steps. All of the living focal adhesions are assigned a corresponding FA in the following image by these percentage overlap and centroid distance rules. This process of assigning live adhesions in one frame to corresponding adhesions in the following frame produces sets of adhesions that are predicted to merge. Some of these merge events are true merge events where one adhesion has joined with another, while others are adhesions which die, but are erroneously assigned as merge events. When a FA does not overlap with the FA it is predicted to become, this FA is assumed to have died and its lineage is ended. These adhesions are also marked as having undergone a death, which will be used in later filtering steps. For the remaining merge events where more than one adhesion has been predicted to merge in the next frame, one of the merging FA lineages is selected to continue, while the other FA lineage is predicted to end. When the adhesions predicted to merge differ in size by at least 10%, the larger adhesion’s lineage is continued. If the merging FA’s sizes do not differ by at least 10%, the lineage whose current centroid is closer to the adhesion centroid in the following image is predicted to continue. By this sequence of rules, each merge event is resolved so that corresponding FAs in experimental data images are determined. After tracking live adhesions and resolving the merge and death events, the last step involves starting lineages that correspond to newly born adhesions. New lineages are started for the adhesions that had no match in the prior frame (birth events). This process of tracking the live adhesions, resolving merge and death events and starting new lineages is repeated for each image in the experimental data sequence until adhesion data from all the images have been processed. On average 2128 adhesions were PLoS ONE | www.plosone.org 10 July 2011 | Volume 6 | Issue 7 | e22025