Janssen Pharmaceutica N.V., Turnhoutseweg 30, 2340, Beerse, Belgium.
Open Analytics N.V., Antwerp, Belgium.
Sci Rep. 2020 Aug 6;10(1):13262. doi: 10.1038/s41598-020-69354-8.
Phenomic profiles are high-dimensional sets of readouts that can comprehensively capture the biological impact of chemical and genetic perturbations in cellular assay systems. Phenomic profiling of compound libraries can be used for compound target identification or mechanism of action (MoA) prediction and other applications in drug discovery. To devise an economical set of phenomic profiling assays, we assembled a library of 1,008 approved drugs and well-characterized tool compounds manually annotated to 218 unique MoAs, and we profiled each compound at four concentrations in live-cell, high-content imaging screens against a panel of 15 reporter cell lines, which expressed a diverse set of fluorescent organelle and pathway markers in three distinct cell lineages. For 41 of 83 testable MoAs, phenomic profiles accurately ranked the reference compounds (AUC-ROC ≥ 0.9). MoAs could be better resolved by screening compounds at multiple concentrations than by including replicates at a single concentration. Screening additional cell lineages and fluorescent markers increased the number of distinguishable MoAs but this effect quickly plateaued. There remains a substantial number of MoAs that were hard to distinguish from others under the current study's conditions. We discuss ways to close this gap, which will inform the design of future phenomic profiling efforts.
表型谱是一组高维的读出值,可以全面捕捉化学和遗传扰动对细胞测定系统的生物学影响。化合物库的表型谱分析可用于化合物靶标鉴定或作用机制 (MoA) 预测以及药物发现中的其他应用。为了设计一套经济的表型谱分析检测方法,我们人工注释了 1008 种已批准药物和经过充分验证的工具化合物,将它们组装成一个文库,并在活细胞、高内涵成像筛选中以四个浓度对 15 个报告细胞系进行分析,这些细胞系在三个不同的细胞谱系中表达了一组多样化的荧光细胞器和途径标记物。对于 83 个可测试 MoA 中的 41 个,表型谱能够准确地对参考化合物进行排序(AUC-ROC≥0.9)。通过在多个浓度下筛选化合物而不是在单个浓度下包含重复样本,可以更好地解析 MoA。筛选更多的细胞谱系和荧光标记物增加了可区分的 MoA 数量,但这种效果很快就达到了饱和。在当前研究条件下,仍然有大量的 MoA 难以与其他 MoA 区分开来。我们讨论了缩小这一差距的方法,这将为未来的表型谱分析工作提供信息。