A novel informatics approach to understanding complex muscle fiber phenotypes

描述（由申请人提供）：我们建议开发一个生物信息学工具箱，以处理和量化复杂的肌细胞图像，用于自动表型分析。该工具箱旨在为临床从业人员和实验室研究人员提供急需的能力，以自动检测和识别在许多肌肉相关疾病中表现出来的病理特征，如心肌病，肌肉肥大，肌萎缩侧索硬化症（ALS）（通常称为Lou Gehrig病），肌营养不良症如Duchenne肌营养不良症和Becker肌营养不良症，和炎症性肌肉损伤其中许多疾病没有有效的治疗方法，死亡率很高。例如，ALS是由运动神经元死亡引起的进行性神经退行性疾病，并导致肌肉无力和萎缩增加，在五年内存活率低于20%，并且该疾病每年影响美国5,000多人。在我们寻求治疗肌肉相关疾病的过程中，缺乏客观和定量分析肌肉细胞图像的计算方法已经成为限制因素。随着临床医生和研究人员越来越多地研究疾病的细胞和分子机制，详细的病理分析对于人们理解生物学过程是必要的。然而，唯一可用的方法是手动分析，这是局限于小数据集和定性解释的图像。人工分析可能会遗漏重要的病理学特征，或者由于人类观察的巨大差异而使其模糊。此外，由于需要很长时间，手动分析的结果不能立即用于数据管理和分析。因此，我们确定需要一个专用工具箱来促进肌肉相关的研究，这得到了我们的用户社区的证实。 该工具箱采用新颖的成像处理算法，将定量分析肌肉细胞，整合多个通道的结果，并导出定量结果，几乎不需要用户干预。该工具箱将通过提供组织病理学特征的详细分析来推进临床和实验室研究，这些特征包括细胞膜的完整性、细胞核的位置和细胞的几何测量。它将通过突出组织病理学中细微但重要的信息，减少人为错误，并使研究能够分析比目前更多的图像来促进发现。该工具箱还将通过为用户提供解释肌肉细胞图像的高灵敏度，客观性和效率来改善诊所和实验室的工作流程。工具箱的量化能力将允许 使用者比较具有高置信度的治疗方法。 总的来说，该项目将有利于治疗和研究肌肉相关疾病的大型生物医学界。反过来，该项目将通过促进肌肉疾病的诊断和新疗法的发现，使肌肉疾病患者受益。

DESCRIPTION (provided by applicant): We propose to develop a bioinformatics toolbox to process and quantify complex muscle cell images for automated phenotype analysis. The toolbox is aimed to provide both clinical practitioners and laboratory investigators with the much-needed capability to automatically detect and identify pathological features that manifest themselves in many muscle-related diseases such as cardiomyopathy, muscular hypertrophy, amyotrophic lateral sclerosis (ALS) (often known as Lou Gehrig's disease), muscular dystrophies such as Duchenne muscular dystrophy and Becker muscular dystrophy, and inflammatory muscle damage. Many of these conditions have no effective treatment and high fatality rates. For example, ALS is progressive neurodegenerative disease that is caused by the death of motor neurons and results in increasing muscle weakness and atrophy, has a survival rate less than twenty percent over a five-year period, and the disease affects more 5,000 people in the U.S. each year. In our search for treatment for the muscle-related diseases, lack of computational method to objectively and quantitatively analyze muscle cell images has become a rate limit factor. As clinicians and researchers are increasingly looking into the cellular and molecular mechanisms of the diseases, detailed pathological analysis is necessary for people to understand the biological processes. Yet, the only available approach is manual analysis which is confined to small datasets and qualitatively interpretation of the images. Important pathological features may be missed by manual analysis or obscured due to the large variation in human observation. Also results of manual analysis are not immediately ready for data management and analysis because of the long time it takes. Hence we identified the need for a dedicated toolbox to facilitate muscle-related research, which was confirmed by our user community. Featuring novel imaging processing algorithms, the toolbox will quantitatively analyze muscle cells, integrate results from multiple channels, and export quantitative results, with little user intervention. The toolbox will advance clinical and laboratory research by providing detailed analysis of histopathological features such as the intact of cellular membrane, the location of nuclei, and geometric measurements of the cells. It will facilitate discovery by highlighting subtl yet important information in histopathology, reducing human errors, and enabling research to analyze a larger number of images than they currently are able to. The toolbox will also improve workflow in clinics and laboratories by providing users with high sensitivity, objectivity, and efficiency in interpreting muscle cell images. The quantifying capability of the toolbox will allow users to compare therapeutic treatments with a high confidence level. Overall the project will benefit the large biomedical community of treating and researching muscle-related diseases. In turn, the project will benefit the patients of muscular diseases by facilitating diagnosis of muscular disorders and discovery of new therapies.