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

骨骼肌纤维横截面积和代谢率的自动分析

• 批准号: 8061464
• 负责人: PATRICK M MCDONOUGH
• 金额: $ 55.08万
• 依托单位: VALA SCIENCES, INC.
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-07-07 至 2013-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8061464
• 关键词: Agriculture; Algorithms; Animals; Antibodies; Area; Biological Assay; Biomedical Research; Breeding; Businesses; C Fiber; Cell Nucleus; Cells; Cellular Structures; Collaborations; Computer software; Computers; Contracts; Devices; Disease; Ecology; Energy-Generating Resources; Exercise; Exercise Physiology; Family suidae; Fiber; Fluorescence; Fluorescence Microscopy; Grant; Health; Human; Image; Image Analysis; Indiana; Inflammation; Intramuscular; Label; Laminin; Link; Lipids; Location; Marketing; Masks; Medical; Metabolic; Metabolism; Methods; Microscope; Microscopy; Monoclonal Antibodies; Morphology; Mus; Muscle; Muscle Fibers; Muscle Proteins; Muscle function; Muscular Dystrophies; Myosin ATPase; Natural regeneration; Nuclear; Obesity; Optics; Performance; Phase; Physiology; Procedures; Production; Protein Isoforms; Proteins; Quantum Dots; Reagent; Research; Research Personnel; Sampling; Science; Skeletal Muscle; Small Business Technology Transfer Research; Space Flight; Speed; Staining method; Stains; Structure; Subcellular structure; Tablets; Techniques; Time; Tissues; Universities; Work; base; cellular imaging; digital; fluorophore; improved; indexing; interest; macrophage; medical schools; muscle metabolism; muscle regeneration; muscular structure; nanocrystal; novel; nutrition; programs; skeletal

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.

描述（由申请人提供）：骨骼肌形态的准确定量在各种医学领域都是需要的，例如肌肉再生、肌营养不良、运动生理学和营养学。对于这样的研究，骨骼肌通常被固定，切片，并标记以可视化肌肉纤维的边界，并进行数字拍照。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识别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，以快速计算CSA，并定量肌肉内的单一肌球蛋白亚型。对于第二阶段，我们建议：1）开发用于鉴定肌球蛋白亚型的单克隆抗体（MAbs）（慢，IIa，IIb，和IIx），层粘连蛋白，和OXPAT，并用有机荧光团或纳米晶体标记单克隆抗体（又称量子点）用于直接免疫细胞荧光程序，2）使CyteSeer能够进行多通道分析以分析多种肌球蛋白同种型，纤维内细胞核分布的分析（对于检测再生纤维或炎症很重要），或与肥胖相关的肌细胞内脂质和蛋白质的分析，以及3）提高CyteSeer（R）表征健康和受损肌肉中纤维的能力，特别是关于肌营养不良症。该研究将开发试剂和软件，这将大大提高骨骼肌分析的准确性和速度，这在各种健康环境中都是非常重要的课题。 公共卫生关系：该研究将开发新的试剂和PC兼容的图像分析程序，这将有助于研究肌肉健康的研究人员。试剂将选择性地标记某些肌纤维类型，这取决于所发现的肌肉类型（慢收缩与快收缩）。该计划将提供非常快速的分析肌肉纤维的结构和代谢特征，从连接到数码相机的显微镜从肌肉获得的图像。这将是感兴趣的医学研究人员研究运动，营养，肥胖，太空飞行，和肌肉萎缩症。该项目开发的方法将改善生物医学研究中最常见的动物（小鼠）的肌肉特征，并大大提高肌肉纤维类型分析的速度和质量。