Devising computational tools to quantify the actin cytoskeleton and pavement cell shape using network-based approaches

Abstract

Recent advances in microscopy have led to an improved visualization of different cell processes. Yet, this also leads to a higher demand of tools which can process images in an automated and quantitative fashion. Here, we present two applications that were developed to quantify different processes in eukaryotic cells which rely on the organization and dynamics of the cytoskeleton. In plant cells, microtubules and actin filaments form the backbone of the cytoskeleton. These structures support cytoplasmic streaming, cell wall organization and tracking of cellular material to and from the plasma membrane. To better understand the underlying mechanisms of cytoskeletal organization, dynamics and coordination, frameworks for the quantification are needed. While this is fairly well established for the microtubules, the actin cytoskeleton has remained difficult to study due to its highly dynamic behaviour. One aim of this thesis was therefore to provide an automated framework to quantify and describe actin organization and dynamics. We used the framework to represent actin structures as networks and examined the transport efficiency in Arabidopsis thaliana hypocotyl cells. Furthermore, we applied the framework to determine the growth mode of cotton fibers and compared the actin organization in wild-type and mutant cells of rice. Finally, we developed a graphical user interface for easy usage. Microtubules and the actin cytoskeleton also play a major role in the morphogenesis of epidermal leaf pavement cells. These cells have highly complex and interdigitated shapes which are hard to describe in a quantitative way. While the relationship between microtubules, the actin cytoskeleton and shape formation is the object of many studies, it is still not clear how and if the cytoskeletal components predefine indentations and protrusions in pavement cell shapes. To understand the underlying cell processes which coordinate cell morphogenesis, a quantitative shape descriptor is needed. Therefore, the second aim of this thesis was the development of a network-based shape descriptor which captures global and local shape features, facilitates shape comparison and can be used to evaluate shape complexity. We demonstrated that our framework can be used to describe and compare shapes from various domains. In addition, we showed that the framework accurately detects local shape features of pavement cells and outperform contending approaches. In the third part of the thesis, we extended the shape description framework to describe pavement cell shape features on tissue-level by proposing different network representations of the underlying imaging data.