Department of Molecular Biotechnology, Faculty of Bioscience Engineering, Ghent University, Ghent 9000, Belgium.
Cytometry A. 2010 Jan;77(1):64-75. doi: 10.1002/cyto.a.20807.
The organization of proteins in space and time is essential to their function. To accurately quantify subcellular protein characteristics in a population of cells with regard for the stochasticity of events in a natural context, there is a fast-growing need for image-based cytometry. Simultaneously, the massive amount of data that is generated by image-cytometric analyses, calls for tools that enable pattern recognition and automated classification. In this article, we present a general approach for multivariate phenotypic profiling of individual cell nuclei and quantification of subnuclear spots using automated fluorescence mosaic microscopy, optimized image processing tools, and supervised classification. We demonstrate the efficiency of our analysis by determination of differential DNA damage repair patterns in response to genotoxic stress and radiation, and we show the potential of data mining in pinpointing specific phenotypes after transient transfection. The presented approach allowed for systematic analysis of subnuclear features in large image data sets and accurate classification of phenotypes at the level of the single cell. Consequently, this type of nuclear fingerprinting shows potential for high-throughput applications, such as functional protein assays or drug compound screening.
蛋白质在空间和时间上的组织对于其功能至关重要。为了在自然环境下准确地量化具有随机性事件的细胞群体中的亚细胞蛋白质特征，对基于图像的细胞计量术的需求正在迅速增长。同时，图像细胞计量分析所产生的大量数据也需要能够进行模式识别和自动分类的工具。在本文中，我们提出了一种使用自动化荧光镶嵌显微镜、优化的图像处理工具和监督分类来对单个细胞核的多维表型进行分析和亚核斑点进行定量的通用方法。我们通过测定对遗传毒性应激和辐射的 DNA 损伤修复模式的差异，证明了我们分析方法的效率，并通过瞬时转染后挖掘数据来确定特定表型，展示了数据挖掘的潜力。所提出的方法允许对大型图像数据集的亚核特征进行系统分析，并在单细胞水平上对表型进行准确分类。因此，这种核指纹分析具有高通量应用的潜力，例如功能蛋白测定或药物化合物筛选。
