The FA analysis methods could be expanded in several ways. These improvements and additions are driven by developments in methods for probing the FA structures themselves and the protein signalling networks related to FA. Possible areas of future work involve adapt- ing the analysis methods to FA images gathered using new methodologies, better relating FA structures to the spatial environment and integration of information about the signalling network that control FA development.
The current methods for segmentation of FA are tailored to the analysis of TIRF images, but this is not the only imaging methodology that can capture FA dynamics. Epifluorescence imaging is a simpler and more widely available imaging methodology, which can be used to observe FA dynamics. Compared with TIRF imaging, epifluorescence images suffer from lower signal to noise ratios and a higher likelyhood of fluorophore photobleaching. Exten- sions to the FA analysis software could alleviate these issues through additional filtering and application of photobleaching corrections. Confocal imaging can be used to gather FA image sets, increasing the spatial resolution at the cost of typically lower signal to noise ratios. I have attempted to analyze two sets of confocal FA images and developed some extensions to the segmentation methods to deal with the properties of confocal images, but these extensions need to be verified and tested using more FA image sets. The development of high-resolution imaging methodologies, such as 3D-SIM [105], STED [106] and PALM [107], have made it possible to gather biological images at higher resolution than the diffraction limit of stan- dard microscopy. PALM has already been used to study the spacial organization of fixed FA complexes in remarkable detail [108]. If high-resolution methods can be adapted to work in migrating cells, it would be possible to dynamically observe the recruitment and local- ization of FA proteins to different portions of developing FA complexes. Since the methods described here treat each FA as essentially a globular object, without internal features, expand-
ing the software to deal with a dynamic variegated FA substructure would be an interesting and worthwhile challenge.
FA are inherently three dimensional structures, but the methods described in this thesis are tailored towards treating FA as two dimensional globular objects. This focus on two dimensions is a product of two dimensional imaging being the only readily available method for measuring FA dynamics. Assuming the signal to noise and resolution issues associated with measuring FA structures in three dimensions could be overcome, the methods described here could be readily adapted to work in three dimensions. The minimum-size watershed segmentation method can be adapted to work in three dimensions, as can the object overlap and centroid distance tracking methods. One significant issue that would need to be solved would be dealing with volume of data produced by three dimensional imaging methods. The Arp2/3 depletion results presented in Chapter 3, required the analysis of 272 time-lapse image sets, which would have taken approximately 2.5 days to process on the FA analysis server, assuming no other processing jobs were being run and all detection parameters were already determined. If the third dimension was added to these image sets, with 10 Z-sections per image set, the amount of hard drive and processing time would in turn be expected to by nearly a factor of 10. These issues would be need to be addressed by identification of the processing bottlenecks and development of either more efficient algorithms or conversion of the existing methods to faster implementations.
In addition to expanding the range of imaging methods that could be used with the FA analysis software, there are several types of FA properties which might help to quantify FA behavior. One area deals with with quantifying the FA spatial properties. The internal envi- ronment of the cell is highly varied with different areas within the cell experiencing a range of internal and external signals. These signals must be converted through signalling path- ways to FA behavior which varies on a spatial scale, but the tools developed here only use gross morphological distinctions, such as leading or trailing, to subdivide FA analysis in a spatial manner. More sophisticated spatial clustering methods, such as K-means or hierar-
chical, could possibly discern local cellular regions where FA dynamics are being perturbed compared to the rest of the cell. Integration of these methods with either spatial activation of specific signalling cascades, such as a recently described photoactivatable RAC [109], or exposure to controlled spatial gradients of signalling molecules could help to clarify funda- mental aspects of cell migration.
The FA analysis software could also be expanded to integrate the analysis of other dynamic intracellular signals. One source for gathering additional signals are biosensors that have been developed to quantify intracellular protein activity levels. The location and development of FA could be used as a marker to link biosensor activity levels across multiple experiments, analogous to how the biosensors developed in the Hahn lab were analyzed using the motion of the cell edge to link the activity of biosensors across experiments [28].