Automated Analysis of Skeletal Muscle Fiber Cross-sectional Area and Metabolic Ty

描述（由申请人提供）：骨骼肌形态学的精确定量在许多医学领域都是需要的，如肌肉再生，肌肉萎缩症，运动生理学和营养学。对于这样的研究，骨骼肌通常是固定的，切片，并标记，以可视化肌肉纤维的边界，并数码拍照。然后，研究人员使用费时费力的技术来追踪肌肉纤维的轮廓，以计算横截面积（CSA）。研究人员还标记肌肉组织以确定某些肌球蛋白亚型的表达，但目前的试剂在小鼠中效果不佳，小鼠是最广泛使用的实验动物。在该STTR项目的第一阶段，开发了一种算法并将其纳入Vala的CyteSeer(R)细胞图像分析程序，以实现CSA的快速计算和肌肉内单个肌球蛋白异构体的定量。就第二期而言，我们建议：1)开发单克隆抗体（mab）用于识别小鼠肌球蛋白亚型（slow, IIa， IIb和IIx），层粘连蛋白和OXPAT，并用有机荧光团或纳米晶体（又称量子点）标记单克隆抗体，用于直接免疫细胞荧光程序；2)使CyteSeer(R)能够进行多通道分析，用于分析多种肌球蛋白亚型，分析纤维内细胞核分布（对检测再生纤维或炎症很重要）；或分析与肥胖相关的细胞内脂质和蛋白质，以及3)提高CyteSeer(R)表征健康和受损肌肉纤维的能力，特别是在肌肉萎缩症方面。该研究将开发试剂和软件，大大提高骨骼肌分析的准确性和速度，骨骼肌分析在各种健康环境中都非常重要。

DESCRIPTION (provided by applicant): 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.