Automated analysis of skeletal muscle fiber crossectional area and metabolic type

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DESCRIPTION (provided by applicant): The accurate quantification of skeletal muscle morphology is desired by researcher investigating a wide variety of health issues such as aging, muscle denervation, muscle regeneration, muscular dystrophy, exercise physiology, nutrition, and space flight. For such studies, tissue sections prepared from skeletal muscle samples are fixed, stained to visualize the borders of the muscle fibers, and digitally photographed. Investigators then use laborious time-consuming techniques to trace the outline of muscle fibers within each image to calculate the cross-sectional area of the fibers, a parameter of high interest to the research community. The goal of this Phase I STTR proposal is to develop staining and software methods to quantify muscle fiber cross sectional area and metabolic fiber type of skeletal muscle fibers in a semi- automated quantitative fashion. In previous work Vala Sciences Inc has developed a software program that automatically recognizes and outlines cell borders in images obtained from confluent cultured cells. Working in collaboration with Dr. Tatiana Kostrominova of Indiana University School of Medicine Northwest, we plan to modify our software so that it performs accurately with samples from tissue sections obtained from skeletal muscle. This will involve identifying the appropriate labeling reagents that yield the optimal outline of the muscle fibers and modifying our existing software so that it accurately identifies the muscle fiber boundaries. Furthermore, we will label the tissue sections for myosin type I (slow fiber type), and develop the methodology to quantify the percentage of slow fibers within each tissue in a semi-automated fashion. The research will enable development of reagent and software kits for use with skeletal muscle, which will greatly increase the accuracy and speed with such samples can be analyzed for morphology and gene expression. The kits and software will be of high interest among researchers wishing to quantify the effects of various experimental interventions in altering muscle physiology and health. Narrative: We propose to develop a technique to analyze, in an automated fashion, the size and metabolic characteristics of muscle cells within slices of tissue obtained from skeletal muscle, which is an important determination in studies of exercise, muscular dystrophy, and related health issues. Currently, techniques to do this are very time consuming and laborious. The proposed research will enable development of a Windows-compatible computer program for automatically analyzing images derived from these samples, greatly increasing the throughput of the assay, which will facilitate biomedical research into developing cures for muscle disorders and other diseases.