Development of Novel Image Analysis and Modeling Tools to Examine Mechanisms of Muscle Damage in Duchenne Muscular Dystrophy

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Miller, Emily, Biomedical Engineering - School of Engineering and Applied Science, University of Virginia
Blemker, Silvia, EN-Biomed Engr Dept, University of Virginia

DMD is a devastating X-linked recessive musculoskeletal disorder that effects 1 in 3500 boys. DMD is caused by the lack of a functional dystrophin protein, a structural protein that mechanically links muscle fibers to the extracellular material. Without dystrophin, muscle fibers are more susceptible to contraction induced damage.Therefore, everyday movements such as walking, talking, and breathing result in cycles of muscle degeneration and regeneration, ultimately leaving affected individuals wheelchair users by their early teens, and at high risk for respiratory or cardiac failure in their second or third decade of life. Muscle fibers, the central cellular unit of muscle, change their morphologies and geometric arrangements in ways visibly discernible using immunofluorescence microscopy in response to external stimuli or changing functional demands. Therefore, analysis of these geometrical differences can provide insight into the structure-function relationships present in skeletal muscle.

Computational models provide a powerful paradigm to understand muscle degeneration and explore possible treatment approaches for Duchenne muscular dystrophy (DMD). This thesis contains two sections. First, I designed, developed, and validated a new skeletal muscle image processing algorithm to detect muscle fiber boundaries in skeletal muscle histological cross-sections. The algorithm is capable of whole muscle cross-section microstructure analysis, and was validated against a standard muscle histological manual analysis and two open-sourced currently available skeletal muscle analysis software programs. Then, I utilized this algorithm to build micromechanical finite element models of real skeletal muscle microstructures of both dystrophic and healthy full muscle-cross-sections to explore how muscle fiber morphologies and geometric arrangements affect the
susceptibility of dystrophic and healthy muscles to contraction induced-damage. The models predicted that decreased muscle fiber cross-sectional areas, increased muscle fiber circularity and increased variability of muscle fiber cross-sectional areas, increase the susceptibility to contraction-induced damage of a given muscle.

Ultimately, I have developed in this thesis a skeletal muscle image analysis tool that outperforms the current available programs and has already been adapted by two other research groups at UVA and developed micromechanical models that can be used to investigate the role of muscle microstructure in DMD pathogenesis. This work provides a framework to determine micro-scale damage from microstructure images and could be used to model the effect of pharmacological treatments on DMD damage susceptibility and therefore lays the groundwork for future work in in silico testing of therapeutics for DMD.

MS (Master of Science)
Muscle, Image Analysis, FE Modeling
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