The regulation of muscle stem cell (MuSC) asymmetric division plays an essential role in controlling the growth and repair of skeletal muscle. Perturbations in MuSC function have been demonstrated in disease and aging contexts such as Duchenne’s Muscular Dystrophy (DMD) and sarcopenia. We developed and optimized a high content analysis platform combining lineage tracing, myofiber culture, imaging, and bioinformatic analysis to determine modulators of muscle stem cell division. We discover kinase domain receptor (KDR) as a positive modulator of MuSC asymmetric division and confirmed its expression in satellite cells by ddPCR and immunofluorescence. Knockdown of KDR significantly reduces the numbers of asymmetric divisions, whereas ligand stimulation of KDR increases the numbers of asymmetric divisions. KDR signaling is impaired in dystrophin- deficient satellite cells and requires a polarized cell environment established by the dystrophin glycoprotein complex (DGC) to direct asymmetric division. Mice lacking KDR in MuSCs exhibit reduced numbers of satellite cells due to precocious differentiation, and deficits in regeneration consistent with impaired asymmetric division and reduced generation of progenitors. Therefore, our experiments identify KDR signaling as playing an essential role in MuSC function in muscle regeneration. These findings further our understanding of muscle stem cell biology, and in particular, the role of asymmetric division under homeostatic and regenerative conditions.

<p>High-content screening (HCS) produces an immense amount of data, often on the scale of Terabytes. This requires considerable processing power resulting in long analysis time. As a result, HCS with a single-core processor system is an inefficient option because it takes a huge amount of time, storage and processing power. The situation is even worse because most of the image processing software is developed in high-level languages which make customization, flexibility and multi-processing features very challenging. Therefore, the goal of the project is to develop a multithreading model in C language. This model will be used to extract subcellular localization features, such as threshold adjacency statistics (TAS) from the HCS data. The first step of the research was to identify an appropriate dye for use in staining the MCF-7 cell line. The cell line has been treated with staurosporin kinase inhibitor, which can provide important physiological and morphological imaging information. The process of identifying a suitable dye involves treating cells with different dye options, capturing the fluorescent images of the treated cells with the Opera microscope, and analyzing the imaging properties of the stained cells. Several dyes were tested, and the most suitable dye to stain the cellular membrane was determined to be Di4-Anepps. The second part of the thesis was to design and develop a parallel program in C that can extract TAS features from the stained cellular images. The program reads the input cell images captured by Opera microscopes, converts it to TIFF format from the proprietary Opera format, identifies the region-of-interest contours of each cell, and computes the TAS features. A significant increase in speed in the order of four fold was obtained using the customized program. Different scalability tests using the developed software were compared against software developed in Acapella scripting language. The result of the test shows that the computational time is proportional to number of cells in the image and is inversely proportional to number of cores in a processor.</p> / Master of Applied Science (MASc)

In 2001 the human proteome organization (HUPO) was created with the ambition to identify and characterize all proteins encoded in the human genome according to several criteria; their expression levels in different tissues and under different conditions; the sub-cellular localization; post-translational modifications; interactions, and if possible also the relationship between their structure and function.When the knowledge of different proteins and their potential interactions increases, so does the need for methods able to unravel the nature of molecular processes in cells and organized tissues, and ultimately for clinical use in samples obtained from patients. The in situ proximity ligation assay (in situ PLA) was developed to provide localized detection of proteins, post-translational modifications and protein-protein interactions in fixed cells and tissues. Dual recognition of the target or interacting targets is a prerequisite for the creation of a circular reporter DNA molecule, which subsequently is locally amplified for visualization of individual protein molecules in single cells. These features offer the high sensitivity and selectivity required for detection of even rare target molecules. Herein in situ PLA was first established and then employed as a tool for detection of both interactions and post-translational modifications in cultured cells and tissue samples. In situ PLA was also adapted to high content screening (HCS) for therapeutic effects, where it was applied for cell-based drug screening of inhibitors influencing post-translational modifications. This was performed using primary cells, paving the way for evaluation of drug effects on cells from patient as a diagnostic tool in personalized medicine. In conclusion, this thesis describes the development and applications of in situ PLA as a tool to study proteins, post-translational modifications and protein-protein interactions in genetically unmodified cells and tissues, and for clinical interactomics.