Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Abramson Family Cancer Research Institute, Department of Pathology & Laboratory Medicine, Penn Sarcoma Program, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Cell Syst. 2022 Sep 21;13(9):724-736.e9. doi: 10.1016/j.cels.2022.08.003. Epub 2022 Sep 2.
Identifying the chemical regulators of biological pathways is a time-consuming bottleneck in developing therapeutics and research compounds. Typically, thousands to millions of candidate small molecules are tested in target-based biochemical screens or phenotypic cell-based screens, both expensive experiments customized to each disease. Here, our uncustomized, virtual, profile-based screening approach instead identifies compounds that match to pathways based on the phenotypic information in public cell image data, created using the Cell Painting assay. Our straightforward correlation-based computational strategy retrospectively uncovered the expected, known small-molecule regulators for 32% of positive-control gene queries. In prospective, discovery mode, we efficiently identified new compounds related to three query genes and validated them in subsequent gene-relevant assays, including compounds that phenocopy or pheno-oppose YAP1 overexpression and kill a Yap1-dependent sarcoma cell line. This image-profile-based approach could replace many customized labor- and resource-intensive screens and accelerate the discovery of biologically and therapeutically useful compounds.
鉴定生物途径的化学调节剂是开发治疗药物和研究化合物的一个耗时的瓶颈。通常，数以千计到数百万种候选小分子在基于靶标的生化筛选或基于表型的细胞筛选中进行测试，这两种实验都是针对每种疾病进行定制的昂贵实验。在这里，我们的非定制的、虚拟的、基于谱型的筛选方法，而是根据公共细胞图像数据中的表型信息，基于细胞画测定法创建，识别与途径匹配的化合物。我们基于相关性的简单计算策略，回顾性地发现了 32%的阳性对照基因查询的预期的、已知的小分子调节剂。在探索性的发现模式中，我们有效地鉴定了与三个查询基因相关的新化合物，并在随后的基因相关测定中进行了验证，包括能够模拟或拮抗 Yap1 过表达并杀死 Yap1 依赖性肉瘤细胞系的化合物。这种基于图像谱型的方法可以替代许多定制的、耗费人力和资源的筛选方法，并加速具有生物学和治疗用途的化合物的发现。
