Heinrich Louise, Kumbier Karl, Li Li, Altschuler Steven P, Wu Lani F
Department of Pharmaceutical Chemistry, University of California San Francisco, San Fancisco, California, 94158, United States.
bioRxiv. 2023 Jan 12:2023.01.11.523662. doi: 10.1101/2023.01.11.523662.
High-content microscopy offers a scalable approach to screen against multiple targets in a single pass. Prior work has focused on methods to select "optimal" cellular readouts in microscopy screens. However, methods to select optimal cell line models have garnered much less attention. Here, we provide a roadmap for how to select the cell line or lines that are best suited to identify bioactive compounds and their mechanism of action (MOA). We test our approach on compounds targeting cancer-relevant pathways, ranking cell lines in two tasks: detecting compound activity ("phenoactivity") and grouping compounds with similar MOA by similar phenotype ("phenosimilarity"). Evaluating six cell lines across 3214 well-annotated compounds, we show that optimal cell line selection depends on both the task of interest (e.g. detecting phenoactivity vs. inferring phenosimilarity) and distribution of MOAs within the compound library. Given a task of interest and set of compounds, we provide a systematic framework for choosing optimal cell line(s). Our framework can be used to reduce the number of cell lines required to identify hits within a compound library and help accelerate the pace of early drug discovery.
高内涵显微镜技术提供了一种可扩展的方法，能够一次性针对多个靶点进行筛选。先前的工作主要集中在显微镜筛选中选择“最佳”细胞读数的方法上。然而，选择最佳细胞系模型的方法却很少受到关注。在此，我们提供了一个路线图，说明如何选择最适合识别生物活性化合物及其作用机制（MOA）的细胞系。我们针对靶向癌症相关通路的化合物测试了我们的方法，在两项任务中对细胞系进行排名：检测化合物活性（“表型活性”）以及通过相似表型对具有相似MOA的化合物进行分组（“表型相似性”）。通过评估六种细胞系对3214种注释良好的化合物的反应，我们表明最佳细胞系的选择既取决于感兴趣的任务（例如检测表型活性与推断表型相似性），也取决于化合物库中MOA的分布。给定一个感兴趣的任务和一组化合物，我们提供了一个选择最佳细胞系的系统框架。我们的框架可用于减少在化合物库中识别活性化合物所需的细胞系数量，并有助于加快早期药物发现的步伐。
