Integrating high-content screening and ligand-target prediction to identify mechanism of action
Integrating high-content screening and ligand-target prediction to identify mechanism of action
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DOI：
10.1038/nchembio.2007.53
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发表时间：
2008-01-01
影响因子：
14.8
通讯作者：
Feng, Yan
中科院分区：
文献类型：
--
作者：
Young, Daniel W.;Bender, Andreas;Feng, Yan
高内涵筛选通过同时测量与化合物的治疗和毒性活性相关的细胞表型的多种特征来改变药物发现。高内容筛选研究通常会产生大量基于图像的表型信息数据集，如何最好地挖掘相关的表型数据是一个尚未解决的挑战。在这里，我们引入因子分析作为定义细胞表型和分析化合物活性的数据驱动工具。该方法允许大量的数据减少，同时保留相关信息，并且用于量化表型的数据衍生因子具有可辨别的生物学意义。我们使用因子分析的细胞染色的荧光标记的细胞周期状态的化合物库和聚类的命中到七个表型类别。然后，我们比较了活性化合物的表型特征、化学相似性和预测的蛋白质结合活性。通过整合这些测量和潜在生物活性的不同描述符，我们可以有效地得出作用机制的推论。
High-content screening is transforming drug discovery by enabling simultaneous measurement of multiple features of cellular phenotype that are relevant to therapeutic and toxic activities of compounds. High-content screening studies typically generate immense datasets of image-based phenotypic information, and how best to mine relevant phenotypic data is an unsolved challenge. Here, we introduce factor analysis as a data-driven tool for defining cell phenotypes and profiling compound activities. This method allows a large data reduction while retaining relevant information, and the data-derived factors used to quantify phenotype have discernable biological meaning. We used factor analysis of cells stained with fluorescent markers of cell cycle state to profile a compound library and cluster the hits into seven phenotypic categories. We then compared phenotypic profiles, chemical similarity and predicted protein binding activities of active compounds. By integrating these different descriptors of measured and potential biological activity, we can effectively draw mechanism-of-action inferences.