Quantifying Focal Adhesion Dynamics Figure 8. The lengths of the assembly and disassembly phases in S178A mutant FAs are significantly different from those in the wild-type, while the stability phase lengths are unaffected. The phase length values include all adhesions where the log-linear models fit with a p-value of 0.05 or less. Error bars indicate 99% confidence intervals on the mean phase length as determined through 50,000 bootstrap samples. A double asterisk (**) indicates p,1025 and single asterisk (*) indicates p,0.05. Wild-type N Values: Assembly (1068), Stability (465), Disassembly (1392); S178A N Values: Assembly (2106), Stability (870), Disassembly (1802). doi:10.1371/journal.pone.0022025.g008 The computational tools presented allow the entire FA life span to be analyzed. These tools include an automated adhesion detection, segmentation and tracking system; extracting a range of properties valuable for understanding FA development. All of these methods were tested using simulated data that replicated many of the observed experimental processes, confirming these methods are able to accurately quantify adhesion properties under controlled conditions (see Figure S11, Figure S12, Figure S13 and methods). The differences detected between the wild-type and S178A mutants are robust, being preserved through a range of parameter choices for the adhesion detection limit and the minimum length of the assembly and disassembly phases. The rate at which images were taken in this work (1 sample/min) also appears to be over the sampling rate needed to accurately measure the assembly and disassembly rates of long-lived adhesions (Figure S9 and Figure S10). Our analysis system integrates methods for automatically identifying and extracting rates of FA assembly and disassembly. We find that the assembly and disassembly rates detected using these automated methods encompass the rates determined using manual methods [7], while quantifying vastly greater numbers of adhesions. We also find that adhesions labeled with an alternate adhesion marker, FAK, also allows a similar number of adhesions to be quantified and that these adhesions are similar to those detected using fluorescently labeled Paxillin. Differences in the mean rates detected by manual versus automated searches can be attributed to several factors. First, the rates determined using manual methods originate from user-specified adhesions of interest. Such adhesions may be chosen based on specific localization properties, such as selecting only those adhesions found within particular cell regions, while the presented results do not make any distinction between adhesions present in different cellular structures a priori (though the properties of adhesions at particular locations can be determined a posteriori). In addition, due to our emphasis on observing the birth, death and taking multiple samples during the assembly and disassembly phase of an individual adhesion, our rate analysis focused on long-lived adhesions, which might have different properties than those measured in studies encompassing primarily short-lived adhesions. Finally, as our software analyzes all adhesions regardless of the brightness of the adhesion, we avoid biases that may occur through, for example, preferential selection for analysis of large or highly visible adhesions. Thus, the automated methods described here greatly extend the types of adhesions that can be readily analyzed, as well as the range of properties that can be quantified. The spatial properties of FA birth and death suggest that FAs have distinct regions where assembly and disassembly events are most concentrated. These assembly and disassembly regions overlap, but remain distinct. The greatest concentration of assembly events occurs within 5 mm of the cell edge. Previous PLoS ONE | www.plosone.org 8 July 2011 | Volume 6 | Issue 7 | e22025