High-content Image Analysis and Modeling for Neuron Assay Based Screening

基于神经元分析的筛选的高内涵图像分析和建模

• 批准号: 7493946
• 负责人: STEPHEN TC WONG
• 金额: $ 29.18万
• 依托单位: METHODIST HOSPITAL RESEARCH INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-15 至 2011-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7493946
• 关键词: Affect; Algorithms; Alzheimer' s Disease; Amyloid; Amyloid beta-Protein; Antibodies; Architecture; Area; Bioinformatics; Biological Assay; Biological Models; Biology; Brain; Cell Nucleus; Cells; Cessation of life; Chemicals; Class; Classification; Comprehension; Computational algorithm; Computer Simulation; Computer software; Data; Data Set; Descriptor; Disease; Disease model; Hippocampus (Brain); Image; Image Analysis; Imagery; Immunofluorescence Immunologic; Informatics; Label; Lead; Life; Methods; Microscopy; Modeling; Mus; Nerve Degeneration; Neurites; Neurodegenerative Disorders; Neurons; Neuroprotective Agents; Patients; Peptides; Performance; Pharmaceutical Preparations; Phenotype; Population; Preclinical Drug Evaluation; Preparation; Prevention; Process; Property; Research Personnel; Resolution; Sampling; Score; Scoring Method; Screening Result; Screening procedure; Spinal cord injury; Staining method; Stains; Structure; Synapses; System; Techniques; Testing; base; beta Tubulin; bioimaging; cellular imaging; cognitive function; data modeling; detector; drug development; high throughput screening; image processing; in vitro Model; in vivo; innovation; mathematical model; neuron loss; novel; programs; relating to nervous system; research study; sample fixation; small molecule libraries; success; tool; vector

DESCRIPTION (provided by applicant): The aim of this proposal is to develop NCELLIQ, Neuron and Cellular Imaging Quantitator, in order to assess quantitatively neurite loss and outgrowth screened by high throughput, automated fluorescent microscopy imaging. Such a tool will be essential in high content screening (HCS) of neuro-based assays. Loss of neuronal projections in Alzheimer's disease (AD) can be modeled in vitro in primary mouse cortical neuron cultures treated with the amyloid beta peptide, which has been shown to be a major cause of neurodegeneration in AD patients. As is the case in vivo, neurite loss precedes neuronal death in this disease model, which can be assessed visually either in live neurons through bright field microscopy, or through immunofluorescence following fixation and staining with neuronal marker class III tubulin beta antibody. Given recent advances in automated microscopy, the later visualization technique could be adapted for use in HCS of chemical libraries in order to identify compounds that can specifically suppress amyloid-induced neurite damage and loss. The hypothesis of this proposal is that the HCS informatics system developed, NCELLIQ, will be an important image processing and analytic tool to help identify possible drugs in treating Alzheimer's disease. Using multi-channel image data obtained from hippocampal neurons, we integrate and develop techniques to screen for potential drug leads in AD treatment. To test the hypothesis, we aim to define the high throughput image processing pipeline of NCELLIQ, develop automated algorithms for neurite centerline extraction and cellular image analysis, implement computational modeling tools, and evaluate the utility of the NCELLIQ with established, novel, and well-defined biology-driven experiments. NCELLIQ provides three key technical contributions. First, NCELLIQ will provide an integrated neural image processing pipeline using advanced computational algorithms to extract image contents of neuron- based screening assay automatically. Second, it will develop an innovative, effective, and fully automatic neurite centerline extraction methods using detector of curvilinear structures and dynamic programming. Third, it will develop mathematical representation of compound vector, as well as an innovative and effective scoring method to allow intuitive comprehension of the HCS results. The success of NCELLIQ will lead to a new class of bioinformatics tools for identifying quality hits in AD drug development and for determining cyptological profile of neurites. Upon the successful completion of the project, we will set up a website to disseminate the NCELLIQ software and sample image datasets.

描述（由申请人提供）：本提案的目的是开发NCELLIQ、神经元和细胞成像定量仪，以便定量评估通过高通量自动荧光显微镜成像筛选的神经突丢失和生长。 这样的工具将是必不可少的高内容筛选（HCS）的神经为基础的测定。 阿尔茨海默病（AD）中神经元投射的丧失可以在用淀粉样蛋白β肽处理的原代小鼠皮质神经元培养物中体外建模，淀粉样蛋白β肽已被证明是AD患者神经变性的主要原因。 与体内情况一样，在该疾病模型中神经突损失先于神经元死亡，这可以通过明视野显微镜在活神经元中视觉评估，或者通过固定后的免疫荧光并用神经元标记物III类微管蛋白β抗体染色来评估。 鉴于自动化显微镜的最新进展，后来的可视化技术可以适用于HCS的化学库，以确定化合物，可以特异性地抑制淀粉样蛋白诱导的神经突损伤和损失。 该提案的假设是，开发的HCS信息学系统NCELLIQ将成为一个重要的图像处理和分析工具，以帮助确定治疗阿尔茨海默病的可能药物。 使用从海马神经元获得的多通道图像数据，我们整合并开发了筛选AD治疗中潜在药物线索的技术。 为了验证这一假设，我们的目标是定义NCELLIQ的高通量图像处理管道，开发神经突中心线提取和细胞图像分析的自动化算法，实施计算建模工具，并评估NCELLIQ的实用性，建立新的，定义明确的生物学驱动的实验。 NCELLIQ提供了三个关键的技术贡献。 首先，NCELLIQ将提供一个集成的神经图像处理管道，使用先进的计算算法自动提取基于神经元的筛选试验的图像内容。 第二，利用曲线结构检测器和动态规划，开发一种创新的、有效的、全自动的神经突中心线提取方法。 第三，它将开发复合向量的数学表示，以及一种创新和有效的评分方法，以直观地理解HCS结果。 NCELLIQ的成功将导致一类新的生物信息学工具，用于识别AD药物开发中的质量命中和确定神经突的密码学特征。 在项目成功完成后，我们将建立一个网站，发布NCELLIQ软件和样本图像数据集。