A novel informatics approach to understanding complex muscle fiber phenotypes

一种理解复杂肌纤维表型的新信息学方法

• 批准号: 8929291
• 负责人: Xiaoyin Xu
• 金额: $ 36.04万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-28 至 2018-09-27
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8929291
• 关键词: Accounting; Affect; Algorithms; Amyotrophic Lateral Sclerosis; Becker Muscular Dystrophy; Bioinformatics; Biological Markers; Biological Process; Cardiac; Cardiomyopathies; Cardiopulmonary; Cause of Death; Cell Nucleus; Cell membrane; Cells; Cellular Membrane; Classification; Clinic; Clinical; Clinical Research; Communities; Complex; Computing Methodologies; Data Analyses; Data Set; Detection; Development; Diagnosis; Disease; Drug Kinetics; Duchenne muscular dystrophy; Dyes; Endomysium; Environment; Evaluation; Eye; Facioscapulohumeral Muscular Dystrophy; Fiber; Functional disorder; Head; Histopathology; Human; Hypertrophy; Image; Image Analysis; Inclusion Body Myositis; Inflammatory; Informatics; Intervention; Investigation; Jaw; Label; Laboratories; Laboratory Research; Limb structure; Location; Lung; Manuals; Measurement; Measures; Membrane; Membrane Proteins; Methods; Microscopic; Molecular; Motor Neurons; Movement; Muscle; Muscle Cells; Muscle Fibers; Muscle Weakness; Muscular Atrophy; Muscular Dystrophies; Myasthenia Gravis; Myopathy; Natural regeneration; Neurodegenerative Disorders; Pathologic Processes; Pathologist; Patients; Pharmacodynamics; Phenotype; Physiological Processes; Play; Population; Positioning Attribute; Process; Proteins; Public Health; Research; Research Personnel; Role; Slide; Source Code; Staining method; Stains; Structure; Survival Rate; Techniques; Therapeutic; Thick; Time; Tissue Sample; Treatment Efficacy; Variant; accurate diagnosis; base; behavior test; cellular imaging; clinical practice; data management; design; disease diagnosis; effective therapy; graphical user interface; image processing; imaging agent; improved; innovation; novel; open source; performance tests; relating to nervous system; statistics; tool

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.

描述（由申请人提供）：我们建议开发生物信息学工具箱，以处理和量化复杂的肌肉细胞图像以进行自动表型分析。该工具箱的目的是为临床实践者和实验室研究人员提供急需的能力，能够自动检测和确定在许多与肌肉相关的疾病中表现出来的病理特征和贝克尔肌肉营养不良以及炎症性肌肉损伤。其中许多情况没有有效的治疗和高死亡率。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，导致肌肉无力和萎缩增加，在五年内的存活率小于20％，并且该疾病每年影响更多的美国5,000人。在我们寻找与肌肉相关疾病的治疗时，缺乏客观和定量分析肌肉细胞图像的计算方法已成为率极限因子。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，人们必须详细的病理分析才能理解生物学过程。但是，唯一可用的方法是手动分析，它仅限于小型数据集并对图像的定性解释。由于人类观察的差异很大，可以通过手动分析遗漏重要的病理特征，也可能被遮盖。同样，由于长时间的时间，手动分析的结果还没有立即准备进行数据管理和分析。因此，我们确定了需要专门的工具箱来促进与肌肉相关的研究，这是我们用户社区证实的。 该工具箱具有新型成像处理算法，将定量分析肌肉细胞，整合了多个通道的结果，并导出定量结果，而用户干预很少。该工具箱将通过提供组织病理学特征的详细分析，例如细胞膜的完整，核的位置以及细胞的几何测量方法来推进临床和实验室研究。它将通过突出组织病理学中的小巧但重要的信息，减少人类错误，并使研究能够分析比目前能力的更多图像，从而促进发现的促进。该工具箱还将通过为用户提供高灵敏度，客观性和解释肌肉细胞图像的效率来改善诊所和实验室的工作流程。工具箱的量化功能将允许 用户以较高的信心水平比较治疗治疗。 总体而言，该项目将使大型生物医学社区受益于治疗和研究与肌肉有关的疾病。反过来，该项目将通过促进肌肉疾病的诊断和发现新疗法来使肌肉疾病患者受益。