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人员和实验室研究人员提供急需的功能，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's病）、肌肉营养不良症（如Duchenne肌营养不良症和Becker肌营养不良症）和炎症性肌肉损伤。其中许多病症没有有效治疗，死亡率很高。例如，肌萎缩性侧索硬化症（ALS）是一种由运动神经元死亡引起的进行性神经退行性疾病，其结果是肌肉无力和萎缩加剧，5年内的存活率不到20%，在美国每年有5000多人患有这种疾病。在我们寻求治疗肌肉相关疾病的过程中，缺乏客观、定量分析肌肉细胞图像的计算方法已经成为限制速度的因素。随着临床医生和研究人员越来越多地关注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学过程是必要的。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由于人类观察的巨大变化而模糊不清。此外，手工分析的结果不能立即用于数据管理和分析，因为它需要很长时间。因此，我们确定需要一个专门的工具箱来促进肌肉相关的研究，我们的用户社区也证实了这一点。