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来处理和量化复杂的肌肉细胞图像，用于自动表型分析。该工具箱旨在为临床从业者和实验室研究人员提供急需的功能，以自动检测和识别许多肌肉相关疾病的病理特征，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)以及炎症性肌肉损伤。其中许多疾病没有有效的治疗方法，病死率很高。例如，肌萎缩侧索硬化症是一种进行性神经退行性疾病，由运动神经元死亡引起，导致肌肉无力和萎缩加剧，五年内存活率不到20%，在美国，这种疾病每年影响5000多人。在我们寻找肌肉相关疾病的治疗方法时，缺乏计算方法来客观和定量地分析肌肉细胞图像已经成为一个速度限制因素。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了解生物过程是必要的。然而，唯一可用的方法是人工分析，这仅限于小数据集和图像的定性解释。重要的病理特征可能会被人工分析遗漏，或者由于人类观察的巨大差异而被模糊。此外，人工分析的结果不能立即用于数据管理和分析，因为这需要很长的时间。因此，我们确定了需要一个专用工具箱来促进与肌肉相关的研究，这一点得到了我们的用户社区的确认。 该工具箱采用了新的成像处理算法，将对肌肉细胞进行定量分析，整合来自多个渠道的结果，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对组织病理学特征的详细分析，如细胞膜的完整性、细胞核的位置和细胞的几何测量，来推进临床和实验室研究。它将通过突出组织病理学中微妙但重要的信息，减少人为错误，并使研究能够分析比目前能够分析的更多数量的图像，从而促进发现。该工具箱还将为用户提供高灵敏度、客观性和高效率的肌肉细胞图像解释，从而改善诊所和实验室的工作流程。工具箱的量化能力将允许 让用户以较高的置信度比较治疗方法。 总体而言，该项目将使治疗和研究肌肉相关疾病的大型生物医学界受益。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.