Seal Srijit, Trapotsi Maria-Anna, Spjuth Ola, Singh Shantanu, Carreras-Puigvert Jordi, Greene Nigel, Bender Andreas, Carpenter Anne E
Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK.
Nat Methods. 2025 Feb;22(2):254-268. doi: 10.1038/s41592-024-02528-8. Epub 2024 Dec 5.
Modern quantitative image analysis techniques have enabled high-throughput, high-content imaging experiments. Image-based profiling leverages the rich information in images to identify similarities or differences among biological samples, rather than measuring a few features, as in high-content screening. Here, we review a decade of advancements and applications of Cell Painting, a microscopy-based cell-labeling assay aiming to capture a cell's state, introduced in 2013 to optimize and standardize image-based profiling. Cell Painting's ability to capture cellular responses to various perturbations has expanded owing to improvements in the protocol, adaptations for different perturbations, and enhanced methodologies for feature extraction, quality control, and batch-effect correction. Cell Painting is a versatile tool that has been used in various applications, alone or with other -omics data, to decipher the mechanism of action of a compound, its toxicity profile, and other biological effects. Future advances will likely involve computational and experimental techniques, new publicly available datasets, and integration with other high-content data types.
现代定量图像分析技术推动了高通量、高内涵成像实验的发展。基于图像的分析利用图像中的丰富信息来识别生物样本之间的异同,而不是像在高内涵筛选中那样仅测量少数几个特征。在此,我们回顾了细胞绘画技术十年来的进展与应用。细胞绘画技术是一种基于显微镜的细胞标记分析方法,于2013年推出,旨在捕捉细胞状态,以优化和标准化基于图像的分析。由于实验方案的改进、针对不同干扰因素的调整以及特征提取、质量控制和批次效应校正等方法的增强,细胞绘画技术捕捉细胞对各种干扰因素反应的能力得到了扩展。细胞绘画技术是一种多功能工具,已被单独或与其他组学数据一起用于各种应用中,以解读化合物的作用机制、其毒性特征及其他生物学效应。未来的进展可能会涉及计算和实验技术、新的公开可用数据集以及与其他高内涵数据类型的整合。