Image-based multivariate profiling of drug responses from single cells
Image-based multivariate profiling of drug responses from single cells
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DOI：
10.1038/nmeth1032
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发表时间：
2007-05-01
期刊：
影响因子：
48
通讯作者：
Altschuler, Steven J.
中科院分区：
文献类型：
--
作者：
Loo, Lit-Hsin;Wu, Lani F.;Altschuler, Steven J.
发现新的化合物机制的定量分析方法是总结高通量，基于图像的药物筛选数据所必需的。在这里，我们提出了一种基于B300单细胞表型测量对未治疗和治疗的人类癌细胞进行分类的多变量方法。这种分类提供了一个分数，测量药物作用的大小，和一个向量，指示药物诱导的同时表型变化。这两个量用于表征化合物活性并鉴定剂量依赖性多相反应。对从100种复合图像数据纲要中提取的轮廓进行系统调查发现，仅需要10-15%的原始特征来检测复合效应。我们报告的最翔实的图像功能，为每个化合物和荧光标记集使用的方法，将是有用的，用于确定最小的药物筛选读数的集合。我们的方法提供了人类可解释的配置文件和自动确定的目标和脱靶效应。
Quantitative analytical approaches for discovering new compound mechanisms are required for summarizing high-throughput, image-based drug screening data. Here we present a multivariate method for classifying untreated and treated human cancer cells based on B300 single-cell phenotypic measurements. This classification provides a score, measuring the magnitude of the drug effect, and a vector, indicating the simultaneous phenotypic changes induced by the drug. These two quantities were used to characterize compound activities and identify dose-dependent multiphasic responses. A systematic survey of profiles extracted from a 100-compound compendium of image data revealed that only 10-15% of the original features were required to detect a compound effect. We report the most informative image features for each compound and fluorescence marker set using a method that will be useful for determining minimal collections of readouts for drug screens. Our approach provides human-interpretable profiles and automatic determination of on- and off-target effects.