Automatic identification of subcellular phenotypes on human cell arrays
Automatic identification of subcellular phenotypes on human cell arrays
复制标题
DOI：
10.1101/gr.2383804
复制
发表时间：
2004-06-01
期刊：
影响因子：
7
通讯作者：
Eils, R
中科院分区：
文献类型：
--
作者：
Conrad, C;Erfle, H;Eils, R
细胞形态的光学显微镜分析提供了细胞功能和蛋白质定位的高内涵读数。对培养细胞的细胞阵列和微孔转染测定使细胞表型分析可用于高通量实验。蛋白质组中每种蛋白质的定位以及单个基因的 RNAi 敲低对细胞形态的影响都可以通过手动检查显微图像来分析。然而，使用形态学读数进行功能基因组学需要快速、自动地识别复杂的细胞表型。在这里，我们提出了一个用于高通量细胞表型筛选的全自动平台，结合了人类活细胞阵列、筛选显微镜和基于机器学习的分类方法。通过对 GFP 标记蛋白标记的 11 种亚细胞模式进行分类，证明了该平台的效率。我们的分类方法几乎可以适用于任何基于细胞形态的显微检测，从而开辟了广泛的应用范围，包括人类细胞中的大规模 RNAi 筛选。
Light microscopic analysis of cell morphology provides a high-content readout of cell function and protein localization. Cell arrays and microwell transfection assays on cultured cells have made cell phenotype analysis accessible to high-through put experiments. Both the localization of each protein in the proteome and the effect of RNAi knock-down of individual genes on cell morphology can be assayed by manual inspection of microscopic images. However, the use of morphological readouts for functional genomics requires fast and automatic identification of complex cellular phenotypes. Here, we present a fully automated platform for high-throughput cell phenotype screening combining human live cell arrays, screening microscopy, and machine-learning-based classification methods. Efficiency of this platform is demonstrated by classification of eleven subcellular patterns marked by GFP-tagged proteins. Our classification method can be adapted to virtually any microscopic assay based on cell morphology, opening a wide range of applications including large-scale RNAi screening in human cells.