Automated analysis of skeletal muscle fiber crossectional area and metabolic type

描述(由申请人提供)： 研究各种健康问题，如衰老、肌肉失神经、肌肉再生、肌营养不良、运动生理学、营养和太空飞行等，都需要对骨骼肌形态进行准确的量化。在这样的研究中，从骨骼肌样本中制备的组织切片被固定，染色以显示肌肉纤维的边界，并进行数字摄影。然后，研究人员使用费时费力的技术来追踪每张图像中肌肉纤维的轮廓，以计算纤维的横截面面积，这是研究界非常感兴趣的一个参数。这个第一阶段STTR提案的目标是开发染色和软件方法，以半自动定量的方式量化骨骼肌纤维的肌肉纤维横截面积和代谢纤维类型。在之前的工作中，Vala Sciences Inc.开发了一个软件程序，可以自动识别并勾勒出从融合培养细胞获得的图像中的细胞边界。与印第安纳大学西北医学院的Tatiana Kostrominova博士合作，我们计划修改我们的软件，使其能够准确地处理从骨骼肌获得的组织切片样本。这将涉及确定适当的标记试剂，以产生肌肉纤维的最佳轮廓，并修改我们现有的软件，使其准确地识别肌肉纤维边界。此外，我们将标记肌球蛋白I型(慢纤维类型)的组织切片，并开发以半自动方式量化每个组织中慢纤维百分比的方法。这项研究将使用于骨骼肌的试剂和软件套件的开发成为可能，这将大大提高对此类样本进行形态和基因表达分析的准确性和速度。这些试剂盒和软件将引起希望量化各种实验干预在改变肌肉生理学和健康方面的影响的研究人员的高度兴趣。简介：我们建议开发一种技术，以自动化的方式分析从骨骼肌获得的组织切片中肌肉细胞的大小和代谢特征，这是运动、肌营养不良和相关健康问题研究中的重要决定。目前，这样做的技术非常耗时和费力。拟议的研究将使开发一种与Windows兼容的计算机程序来自动分析从这些样本获得的图像，极大地提高分析的吞吐量，这将促进生物医学研究开发肌肉疾病和其他疾病的治疗方法。

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.