Department of Pharmaceutical Chemistry, University of California San Francisco, San Fancisco, California 94158, United States.
ACS Chem Biol. 2023 Apr 21;18(4):679-685. doi: 10.1021/acschembio.2c00878. Epub 2023 Mar 15.
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 a 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.
高内涵显微镜提供了一种可扩展的方法，可以在单次筛选中针对多个靶标进行筛选。先前的工作主要集中在选择显微镜筛选中“最佳”细胞读出的方法上。然而，选择最佳细胞系模型的方法却受到了较少的关注。在这里，我们提供了一个如何选择最适合识别生物活性化合物及其作用机制 (MOA) 的细胞系或细胞系的路线图。我们在针对癌症相关途径的化合物上测试了我们的方法，对两条任务中的细胞系进行了排名：检测化合物活性（“表型活性”）和根据相似表型将具有相似 MOA 的化合物分组（“表型相似性”）。在对 3214 种经过充分注释的化合物进行的六种细胞系评估中，我们表明，最佳细胞系选择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）和化合物库中 MOA 的分布。给定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识别化合物库中命中所需的细胞系数量，并有助于加快早期药物发现的速度。
