Mikheeva Alesya M, Bogomolov Mikhail A, Gasca Valentina A, Sementsov Mikhail V, Spirin Pavel V, Prassolov Vladimir S, Lebedev Timofey D
Department of Cancer Cell Biology, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 32 Vavilova str., Moscow, 119991, Russia.
Moscow Institute of Physics and Technology (National Research University), 9 Institutskiy per., Dolgoprudny, Moscow Region, 141701, Russia.
Cell Death Discov. 2024 Apr 18;10(1):181. doi: 10.1038/s41420-024-01950-3.
Imaging-based anticancer drug screens are becoming more prevalent due to development of automated fluorescent microscopes and imaging stations, as well as rapid advancements in image processing software. Automated cell imaging provides many benefits such as their ability to provide high-content data, modularity, dynamics recording and the fact that imaging is the most direct way to access cell viability and cell proliferation. However, currently most publicly available large-scale anticancer drugs screens, such as GDSC, CTRP and NCI-60, provide cell viability data measured by assays based on colorimetric or luminometric measurements of NADH or ATP levels. Although such datasets provide valuable data, it is unclear how well drug toxicity measurements can be integrated with imaging data. Here we explored the relations between drug toxicity data obtained by XTT assay, two quantitative nuclei imaging methods and trypan blue dye exclusion assay using a set of four cancer cell lines with different morphologies and 30 drugs with different mechanisms of action. We show that imaging-based approaches provide high accuracy and the differences between results obtained by different methods highly depend on drug mechanism of action. Selecting AUC metrics over IC50 or comparing data where significantly drugs reduced cell numbers noticeably improves consistency between methods. Using automated cell segmentation protocols we analyzed mitochondria activity in more than 11 thousand drug-treated cells and showed that XTT assay produces unreliable data for CDK4/6, Aurora A, VEGFR and PARP inhibitors due induced cell size growth and increase in individual mitochondria activity. We also explored several benefits of image-based analysis such as ability to monitor cell number dynamics, dissect changes in total and individual mitochondria activity from cell proliferation, and ability to identify chromatin remodeling drugs. Finally, we provide a web tool that allows comparing results obtained by different methods.
由于自动荧光显微镜和成像站的发展以及图像处理软件的快速进步，基于成像的抗癌药物筛选正变得越来越普遍。自动细胞成像具有许多优点，例如能够提供高内涵数据、模块化、动态记录，以及成像作为获取细胞活力和细胞增殖的最直接方式这一事实。然而，目前大多数公开可用的大规模抗癌药物筛选，如GDSC、CTRP和NCI - 60，提供的是通过基于比色法或发光法测量NADH或ATP水平的检测方法测得的细胞活力数据。尽管此类数据集提供了有价值的数据，但尚不清楚药物毒性测量与成像数据的整合程度如何。在此，我们使用一组具有不同形态的四种癌细胞系和30种具有不同作用机制的药物，探索了通过XTT检测、两种定量细胞核成像方法和台盼蓝染料排除法获得的药物毒性数据之间的关系。我们表明基于成像的方法具有很高的准确性，并且不同方法所得结果之间的差异高度依赖于药物作用机制。选择AUC指标而非IC50，或者比较药物显著减少细胞数量的数据，可显著提高方法之间的一致性。使用自动细胞分割协议，我们分析了超过1.1万个药物处理细胞中的线粒体活性，结果表明，由于诱导细胞大小增长和单个线粒体活性增加，XTT检测对于CDK4/6、Aurora A、VEGFR和PARP抑制剂产生不可靠的数据。我们还探索了基于图像分析的几个优点，例如监测细胞数量动态的能力、从细胞增殖中剖析总线粒体活性和单个线粒体活性变化的能力，以及识别染色质重塑药物的能力。最后，我们提供了一个网络工具，可用于比较不同方法获得的结果。
