The accurate quantification of skeletal muscle morphology is desired in a wide variety of medical areas such as muscle regeneration, muscular dystrophy, exercise physiology, and nutrition. For such studies, skeletal muscle is often fixed, sectioned, and labeled 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 to calculate the cross-sectional area (CSA). Investigators also label muscle tissues to identify the expression of certain myosin subtypes, but current reagents do not work well in the mouse, the most widely utilized experimental animal. In Phase I of this STTR project, an algorithm was developed and incorporated into Vala's CyteSeer(R) cell image analysis program, to enable rapid calculation of CSA, and quantification of a single myosin isoform within the muscle. For Phase II, we propose: 1) to develop monoclonal antibodies (MAbs) for identification of myosin subtypes (slow, IIa, IIb, and IIx), laminin, and OXPAT in the mouse, and to label the MAbs with organic fluorophores or nanocrystals (aka quantum dots) for use in direct immunocytofluorescence procedures, 2) to enable CyteSeer(R) to perform multichannel analysis for the analysis of multiple myosin isoforms, the analyze of distribution of nuclei within the fibers (important to detect regenerating fibers or inflammation), or analysis of intramyocellular lipids and proteins associated with obesity, and 3) to improve the ability of CyteSeer(R) to characterize fibers in healthy and damaged muscle, especially with regard to muscular dystrophy. The research will develop reagents and software which will greatly increase the accuracy and speed with which skeletal muscle can be analyzed a subject of great importance in a variety of health contexts.

Public Health Relevance

The research will develop novel reagents and a PC-compatible image analysis program which will be useful to researchers working on muscle health. Reagents will selectively label certain muscle fiber types, depending upon the type of muscle that is found (slow vs. fast contracting). The program will provide for very fast analysis of the structure and metabolic character of muscle fibers, from images obtained from the muscle by microscopes linked to digital cameras. This will be of interest to medical researchers studying exercise, nutrition, obesity, space-flight, and muscular dystrophies. The methods developed by this project will improve the way muscle is characterized in the most common animal used in biomedical research (the mouse), and greatly increase the speed and quality of the analysis of muscle fiber types.

National Institute of Health (NIH)
National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)
Small Business Technology Transfer (STTR) Grants - Phase II (R42)
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Special Emphasis Panel (ZRG1-MOSS-Q (90))
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Boyce, Amanda T
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Vala Sciences, Inc.
San Diego
United States
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Kostrominova, Tatiana Y; Reiner, David S; Haas, Richard H et al. (2013) Automated methods for the analysis of skeletal muscle fiber size and metabolic type. Int Rev Cell Mol Biol 306:275-332