Measurement of cellular adhesions and adhesion protein dynamics using tracking paired with spatio-temporal image correlation spectroscopy

Proteins are ubiquitous in biological systems and while much is known about protein structure, less is known about the movement of these proteins. The phenomenon of movement also occurs at the cellular level through cell migration and, in particular, cells depend upon the movement of proteins to enable cell motility. More precisely, cell migration is dependent upon cytoskeletal structures, including focal adhesions, complexes which include multiple proteins and enable cells to exert forces upon the underlying substrate and migrate. The basic structure and components of the cytoskeleton are fairly well known, yet much less is known about their dynamic assembly and disassembly. Motile cells are known to be involved in numerous biological processes and thus studying the flow of proteins involved in cell migration has the potential to clarify their roles and lead to a more advanced understanding of cell migration. Major protein components that play a role in the formation of focal adhesions have been identified. Using genetically engineered fluorescent variants of these proteins, we can image cells expressing fluorescently-tagged proteins via fluorescence microscopy, and thereby obtain quantitative results on the location and movement of key proteins of interest in migrating cells. In this work, a correlation analysis was performed on the measured fluorescence fluctuations within image series in order to determine the magnitude and direction of protein flows within sub-regions of migrating cells. The correlation analysis technique known as STICS (spatiotemporal image correlation spectroscopy) was utilized and accomplishes this by using the full spatiotemporal correlation function. It is important that fluorescence variations in space throughout the cell as well as variations through time be investigated in the image series. STICS is well suited for this as it provides vector map image series of the fluorescent protein flows. A principal aspect of this thesis is performing the STICS correlation analysis on image series of cells containing fluorescently-tagged versions of 5 key adhesion proteins with cells on substrates of different rigidities. Another principal aspect is to track and measure the development of adhesions simultaneously with the STICS analysis of protein flows that take part in focal adhesion development. In order to designate the STICS flows detected in the neighborhood of adhesions to specific adhesions correctly, image processing methods were employed including filtering of noise in space and in time, adhesion segmentation and adhesions tracking. Image series were treated for noise sources with image filtering in the spatial domain to remove background noise and in the temporal domain using a Butterworth filter to remove lower frequencies that obscure the signal of interest from adhesion protein populations exhibiting directed flow. For a large number of focal adhesions, local flows were obtained throughout trajectories using STICS correlation analysis of fluorescence fluctuations, while also measuring the physical properties of the focal adhesions. The adhesion analysis protocol developed for this thesis tracks all adhesions detected from the cell's expressed fluorescent proteins, and provides a neighbourhood STICS flow at each frame for tracked adhesions along their trajectories. As well, physical properties including adhesion area, major axis length, and adhesion speeds are obtained for each frame along the trajectories of tracked adhesions. Distributions of adhesion physical properties and local protein flow speeds were obtained for adhesions across multiple NIH3T3 cells for five adhesion proteins: paxillin, vinculin, talin, actin, and α-actinin. A substrate most resembling glass was first used, followed by a substrate with a greater concentration of the endogenous fibronectin to decrease substrate rigidity. This is not the full abstract.

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