Santos Albert Francis, Zaltsman Alla Borisovna, Martin Rhian Clare, Kuzmin Alexandru, Alexandrov Yuriy, Roquemore Elizabeth Price, Jessop Robert Arnold, van Erck Monique Gertruida Maria, Verheijen Johan Hendrikus
GE Healthcare, Cardiff, Wales, UK.
Assay Drug Dev Technol. 2008 Oct;6(5):693-710. doi: 10.1089/adt.2008.146.
Angiogenesis is a general term describing formation of new tube-like microvessel sprouts that are the size of capillary blood vessels. Angiogenesis is fundamental in key stages of embryonic development, organ formation, and wound repair and is also involved in the development and progression of a variety of pathological conditions, including cancer (tumor growth and metastasis), cardiovascular disease, diabetic retinopathy, age-related macular degeneration, atherosclerosis, and rheumatoid arthritis. Because of its diverse roles in key physiological and pathological processes, angiogenesis is an important area of medical research, with a considerable number of angiogenic and anti-angiogenic drugs currently undergoing clinical trials. Cost-effective and efficient screening for potential lead compounds is therefore of prime importance. However, screening methodologies vary in their physiological relevance depending on how faithfully critical aspects of angiogenesis are represented. Cell-based in vitro angiogenesis assays are important tools for screening, which in many cases rely on imaging microscopy to ascertain drug effects. Unfortunately, such screens can be hampered by poorly defined biology, slow image acquisition by manual or semiautomated hardware, and slow data analysis by non-dedicated software. This article describes use of a 96-well microplate in vitro angiogenesis screening system as part of an integrated workflow, comprising (1) setting up the biology in a three-dimensional physiologically relevant system, (2) acquiring a series of image slices ("stacks") using an automated z-stage instrument, (3) collapsing the image stack series into sets of two-dimensional images, (4) segmenting objects of interest, and (5) analyzing the segmentation patterns in order to obtain statistically relevant data.
血管生成是一个通用术语,用于描述新的管状微血管芽的形成,这些微血管芽的大小与毛细血管相同。血管生成在胚胎发育、器官形成和伤口修复的关键阶段至关重要,并且还参与多种病理状况的发展和进程,包括癌症(肿瘤生长和转移)、心血管疾病、糖尿病视网膜病变、年龄相关性黄斑变性、动脉粥样硬化和类风湿性关节炎。由于其在关键生理和病理过程中的多种作用,血管生成是医学研究的一个重要领域,目前有相当数量的血管生成和抗血管生成药物正在进行临床试验。因此,对潜在先导化合物进行经济高效的筛选至关重要。然而,筛选方法的生理相关性各不相同,这取决于血管生成的关键方面被呈现的忠实程度。基于细胞的体外血管生成测定是筛选的重要工具,在许多情况下依赖成像显微镜来确定药物效果。不幸的是,这种筛选可能会受到生物学定义不明确、手动或半自动硬件图像采集缓慢以及非专用软件数据分析缓慢的阻碍。本文描述了使用96孔微孔板体外血管生成筛选系统作为集成工作流程的一部分,该工作流程包括:(1)在三维生理相关系统中建立生物学模型;(2)使用自动z轴仪器获取一系列图像切片(“堆栈”);(3)将图像堆栈系列合并为二维图像集;(4)分割感兴趣的对象;(5)分析分割模式以获得具有统计学意义的数据。