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Automated analysis of skeletal muscle fiber crossectional area and metabolic type

骨骼肌纤维横截面积和代谢类型的自动分析

基本信息

批准号：
7481871
负责人：
PATRICK M MCDONOUGH
金额：
$ 15.39万
依托单位：
VALA SCIENCES, INC.
依托单位国家：
美国
项目类别：
财政年份：
2008
资助国家：
美国
起止时间：
2008-07-07 至 2010-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7481871
关键词：
Aging Agriculture Algorithms Antibodies Area Atrophic Biological Assay Biology Biomedical Research Businesses Cell membrane Cells Characteristics Collaborations Communities Compatible Computer software Computers Cultured Cells Development Diabetic Diet Disease Effectiveness Exercise Exercise Physiology Extracellular Matrix Extracellular Matrix Proteins Family suidae Fiber Gene Expression Goals Grant Health Hindlimb Image Image Analysis Indiana Intervention Java Knockout Mice Label Laminin Lead Metabolic Methodology Methods Morphology Mus Muscle Muscle Cells Muscle Fibers Muscle denervation procedure Muscular Dystrophies Myopathy Myosin ATPase Myosin Type I Obesity Pattern Phase Physiology Principal Investigator Proteins Purpose Rattus Reagent Research Research Personnel Sampling Science Skeletal Muscle Slice Small Business Technology Transfer Research Space Flight Speed Staining method Stains Superoxide Dismutase Sus scrofa Techniques Time Tissue Sample Tissues Travel Universities Work base computer program design desire image processing indexing interest medical schools muscle regeneration nutrition programs protein expression size

项目摘要

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.

描述（由申请人提供）：骨骼肌形态的精确量化是研究人员研究各种健康问题所期望的，这些健康问题诸如衰老、肌肉去神经支配、肌肉再生、肌肉营养不良、运动生理学、营养和太空飞行。对于这样的研究，将从骨骼肌样品制备的组织切片固定，染色以使肌纤维的边界可视化，并数码拍照。然后，研究人员使用费力耗时的技术来跟踪每个图像中的肌纤维轮廓，以计算纤维的横截面积，这是研究界非常感兴趣的参数。该I期STTR提案的目标是开发染色和软件方法，以半自动定量方式定量骨骼肌纤维的肌纤维横截面积和代谢纤维类型。在以前的工作中，Vala Sciences Inc开发了一种软件程序，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。我们与印第安纳州大学西北医学院的Tatiana Kostrominova博士合作，计划修改我们的软件，使其能够准确地处理骨骼肌组织切片样本。这将涉及确定产生最佳肌纤维轮廓的适当标记试剂，并修改我们现有的软件，以便准确识别肌纤维边界。此外，我们将标记肌球蛋白I型（慢纤维型）的组织切片，并开发以半自动方式定量每个组织中慢纤维百分比的方法。该研究将能够开发用于骨骼肌的试剂和软件包，这将大大提高对这些样本进行形态学和基因表达分析的准确性和速度。这些工具包和软件将引起希望量化各种实验干预措施在改变肌肉生理和健康方面的影响的研究人员的高度兴趣。叙述：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内的肌细胞的大小和代谢特征，这是运动、肌肉萎缩症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费力。拟议的研究将能够开发一种Windows兼容的计算机程序，用于自动分析来自这些样本的图像，大大提高了检测的吞吐量，这将有助于生物医学研究开发肌肉疾病和其他疾病的治疗方法。