Wolff Christopher, Neuenschwander Martin, Beese Carsten Jörn, Sitani Divya, Ramos Maria C, Srovnalova Alzbeta, Varela María José, Polishchuk Pavel, Skopelitou Katholiki E, Škuta Ctibor, Stechmann Bahne, Brea José, Clausen Mads Hartvig, Dzubak Petr, Fernández-Godino Rosario, Genilloud Olga, Hajduch Marian, Loza María Isabel, Lehmann Martin, Peter von Kries Jens, Sun Han, Schmied Christopher
Leibniz-Forschungsinstitut für Molekulare Pharmakologie im Forschungsverbund Berlin e.V. (FMP), Campus Berlin-Buch, Robert-Roessle-Str. 10, 13125 Berlin, Germany.
Fundación MEDINA, Centro de Excelencia en Investigación de Medicamentos Innovadores en Andalucía. Parque Tecnológico Ciencias de la Salud, Avda. del Conocimiento 34, 18016 Armilla, Granada, Spain.
iScience. 2025 Apr 16;28(5):112445. doi: 10.1016/j.isci.2025.112445. eCollection 2025 May 16.
Morphological profiling with the Cell Painting assay has emerged as a promising method in drug discovery research. The assay captures morphological changes across various cellular compartments enabling the rapid prediction of compound bioactivity. We present a comprehensive morphological profiling resource using the carefully curated and well-annotated EU-OPENSCREEN Bioactive compounds. The data were generated across four imaging sites with high-throughput confocal microscopes using the Hep G2 as well as the U2 OS cell lines. We employed an extensive assay optimization process to achieve high data quality across the different sites. An analysis of the extracted profiles validates the robustness of the generated data. We used this resource to compare the morphological features of the different cell lines. By correlating the profiles with overall activity, cellular toxicity, several specific mechanisms of action (MOAs), and protein targets, we demonstrate the dataset's potential for facilitating more extensive exploration of MOAs.
细胞绘画分析的形态学分析已成为药物发现研究中一种很有前景的方法。该分析可捕捉各个细胞区室的形态变化,从而能够快速预测化合物的生物活性。我们使用精心策划且注释完善的欧盟开放筛选生物活性化合物,提供了一个全面的形态学分析资源。数据是使用高通量共聚焦显微镜在四个成像位点,以Hep G2和U2 OS细胞系生成的。我们采用了广泛的分析优化过程,以在不同位点实现高质量数据。对提取的图谱进行分析验证了所生成数据的稳健性。我们利用这一资源比较了不同细胞系的形态特征。通过将图谱与总体活性、细胞毒性、几种特定作用机制(MOA)和蛋白质靶点相关联,我们证明了该数据集在促进更广泛的作用机制探索方面的潜力。