Wollman Roy, Stuurman Nico
Department of Molecular and Cellular Biology, University of California, Davis, CA, USA.
J Cell Sci. 2007 Nov 1;120(Pt 21):3715-22. doi: 10.1242/jcs.013623.
Technological advances in automated microscopy now allow rapid acquisition of many images without human intervention, images that can be used for large-scale screens. The main challenge in such screens is the conversion of the raw images into interpretable information and hence discoveries. This post-acquisition component of image-based screens requires computational steps to identify cells, choose the cells of interest, assess their phenotype, and identify statistically significant 'hits'. Designing such an analysis pipeline requires careful consideration of the necessary hardware and software components, image analysis, statistical analysis and data presentation tools. Given the increasing availability of such hardware and software, these types of experiments have come within the reach of individual labs, heralding many interesting new ways of acquiring biological knowledge.
自动显微镜技术的进步如今使得在无需人工干预的情况下就能快速获取大量图像,这些图像可用于大规模筛选。此类筛选中的主要挑战在于将原始图像转化为可解读的信息并进而获得发现。基于图像的筛选的这个采集后环节需要通过计算步骤来识别细胞、挑选出感兴趣的细胞、评估其表型以及识别具有统计学意义的“命中目标”。设计这样一个分析流程需要仔细考虑必要的硬件和软件组件、图像分析、统计分析及数据呈现工具。鉴于此类硬件和软件越来越容易获得,这些类型的实验已在各个实验室的能力范围之内,预示着获取生物学知识的许多有趣新方法。