Here, we describe a novel system for the quantification of focal adhesion dynamics. This approach utilizes high-resolution (60x oil-immersion) time-series images of living cells generated with TIRF. Image sequences are processed through an analysis system that identifies individual adhesions based on user-defined criteria, tracks their movement through time and collects associated properties concerning their location, shape, size and intensity. As adhesion properties throughout the lifetime of each adhesion are quantified in this approach, a thorough picture of global adhesion spatiotemporal behavior is captured. To demonstrate the capabilities of this computational approach, we focus on characterizing adhesions via the molecular scaffold protein Paxillin, a core constituent of focal adhesions commonly used in adhesion imaging [17]. Specifically, in this study we use our image analysis system to characterize FAs labeled with EGFP- Paxillin, generating high-resolution data sets of adhesion distribu- tion, morphology, and turnover in migrating NIH 3T3 fibroblasts. The results demonstrate that we can analyze adhesions in an unbiased manner, with 103–104 adhesions analyzed per cell. With wild-type Paxillin as a baseline for comparison, we use our system to detect alterations in adhesion spatiotemporal properties in response to the S178A mutation on Paxillin. Through this analysis we show that the loss of this single phosphorylation site affects adhesion site formation, size and assembly rates. We also verify the broad applicability of the analysis system by also applying the methods to examine time-lapse movies of EGFP-FAK. We are also making the analysis system available under an open source license, to allow the community to use our methods to analyze new experimental systems. These results illustrate the benefit of automated large-scale characterization of adhesion properties and behaviors, allowing both large and subtle differences to be readily detected. Results Quantitative Analysis of Focal Adhesion TIRF Images To quantify aspects of focal adhesion spatiotemporal dynamic behavior, we generated an NIH 3T3 fibroblast cell line expressing both EGFP-Paxillin, to label FAs, and a myristoylated-Red Fluorescent Protein (myr-RFP), to identify the cell edge. Cells were plated on fibronectin and imaged with TIRF for 1–4 hours. We then quantified FA dynamics through a multistage image analysis pipeline (Figure 1). Briefly, after high-pass filtering, FAs were identified and segmented with a watershed-based algorithm (see Methods). Characteristics of adhesions identified and quantified at each timepoint included properties such as area, position and fluorescent Paxillin intensity. Dynamic properties of adhesions, such as velocities and changes in fluorescent intensity, were also determined by tracking and measuring adhesion properties across time steps/images. At each consecutive time step the appearance of new adhesions, called birth events, and the disappearance of adhesions, called death events, were similarly identified and recorded by the software. An example of the segmentation results and characteristic properties are shown in Figure 2. The segmentation methods successfully identify the adhesions in each image regardless of the background Paxillin fluorescence intensity (Figure 2A, B). The dynamic nature of the adhesions during this experiment is clear when all the adhesions identified are shown superimposed in a single image (Figure 2C). The results also show several general properties of the adhesions in wild-type cells (Figure 2D). In general, adhesions are less than 0.2 mm2 in size, have axial ratios less than 3 and exist for less than ten minutes, although there are many adhesions that live longer. Both Paxillin fluorescence Quantifying Focal Adhesion Dynamics Figure 1. Automating the analysis of focal adhesion images requires a multi-stage pipeline. The first row shows several representative images of fluorescently labeled Paxillin using TIRF microscopy. In the second row, a cartoon depiction of the segmented adhesions and the cell edge are shown. Identification of the adhesions in each image allows a set of characteristic morphological and fluorescence intensity-based features to be extracted. The third row shows a single adhesion (highlighted in red) being tracked through the short sample time course. The properties of each adhesion are tracked over time, allowing the large scale dynamics of FA to be determined. doi:10.1371/journal.pone.0022025.g001 intensity and the position of the adhesion centroids with respect to the cell edge have skewed distributions. These results demonstrate the capabilities of our system to provide high- resolution and unbiased assessment of FA behavior. Kinetics of FA Assembly and Disassembly Of particular importance for understanding FA functions is the assessment of adhesion behavior through time. Figure 3A–D shows the methods used in determining FA assembly and disassembly rates for individual adhesions. Figure 3A depicts an image series of a single adhesion (highlighted in green) from birth, through maturation and stability, and on to death. Using time series information, we quantified the normalized intensity of each adhesion over its lifespan (Figure 3B). Readily apparent are the log-linear assembly and disassembly phases, which are automat- ically fit to a log-linear model (see Methods for details). Our results are consistent with previous work showing that adhesions assemble and disassemble with log-linear progression [7]. Specifically, we found that the log-linear fits for most of the adhesions produced R2 values above 0.7 (Figure S1). Note that the smaller number of adhesions analyzed relative to Figure 2 is due to the need for a minimum adhesion lifetime (.20 minutes) as well as other requirements needed for the accurate quantification of assembly and disassembly rates (see Methods). In the example shown in Figure 3, a log-linear approximation describes 90.5% and 96.1% of the variance in the rates of intensity increase and decrease, respectively (Figure 3C, D). In between these two phases we define PLoS ONE | www.plosone.org 2 July 2011 | Volume 6 | Issue 7 | e22025