Woehrmann Marcos H, Bray Walter M, Durbin James K, Nisam Sean C, Michael Alicia K, Glassey Emerson, Stuart Joshua M, Lokey R Scott
Department of Biomolecular Engineering, UC Santa Cruz, 1156 High Street, Santa Cruz, CA 95064, USA.
Mol Biosyst. 2013 Nov;9(11):2604-17. doi: 10.1039/c3mb70245f.
Cytological profiling (CP) is an unbiased image-based screening technique that uses automated microscopy and image analysis to profile compounds based on numerous quantifiable phenotypic features. We used CP to evaluate a library of nearly 500 compounds with documented mechanisms of action (MOAs) spanning a wide range of biological pathways. We developed informatics techniques for generating dosage-independent phenotypic "fingerprints" for each compound, and for quantifying the likelihood that a compound's CP fingerprint corresponds to its annotated MOA. We identified groups of features that distinguish classes with closely related phenotypes, such as microtubule poisons vs. HSP90 inhibitors, and DNA synthesis vs. proteasome inhibitors. We tested several cases in which cytological profiles indicated novel mechanisms, including a tyrphostin kinase inhibitor involved in mitochondrial uncoupling, novel microtubule poisons, and a nominal PPAR-gamma ligand that acts as a proteasome inhibitor, using independent biochemical assays to confirm the MOAs predicted by the CP signatures. We also applied maximal-information statistics to identify correlations between cytological features and kinase inhibitory activities by combining the CP fingerprints of 24 kinase inhibitors with published data on their specificities against a diverse panel of kinases. The resulting analysis suggests a strategy for probing the biological functions of specific kinases by compiling cytological data from inhibitors of varying specificities.
细胞图谱分析(CP)是一种基于图像的无偏筛选技术,它利用自动显微镜和图像分析,根据众多可量化的表型特征对化合物进行图谱分析。我们使用CP评估了一个包含近500种化合物的文库,这些化合物具有记录在案的作用机制(MOA),涵盖广泛的生物途径。我们开发了信息学技术,用于为每种化合物生成与剂量无关的表型“指纹”,并量化化合物的CP指纹与其注释的MOA相对应的可能性。我们确定了区分具有密切相关表型的类别(如微管毒物与HSP90抑制剂,以及DNA合成与蛋白酶体抑制剂)的特征组。我们测试了几个细胞学图谱表明新机制的案例,包括一种参与线粒体解偶联的酪氨酸激酶抑制剂、新型微管毒物,以及一种作为蛋白酶体抑制剂的名义PPAR-γ配体,使用独立的生化分析来确认CP特征预测的MOA。我们还应用最大信息统计,通过将24种激酶抑制剂的CP指纹与它们针对多种激酶的特异性的已发表数据相结合,来识别细胞学特征与激酶抑制活性之间的相关性。结果分析表明了一种通过汇编来自不同特异性抑制剂的细胞学数据来探究特定激酶生物学功能的策略。