Gao Xiangrui, Zhang Fan, Guo Xueyu, Yao Mengcheng, Wang Xiaoxiao, Chen Dong, Zhang Genwei, Wang Xiaodong, Lai Lipeng
XtalPi Innovation Center, 706 Block B, Dongsheng Building, Haidian District, Beijing, China.
Sci Rep. 2025 Jan 8;15(1):1265. doi: 10.1038/s41598-025-85608-9.
High-content analysis (HCA) holds enormous potential for drug discovery and research, but widely used methods can be cumbersome and yield inaccurate results. Noisy and redundant signals in cell images impede accurate deep learning-based image analysis. To address these issues, we introduce X-Profiler, a novel HCA method that combines cellular experiments, image processing, and deep learning modeling. X-Profiler combines the convolutional neural network and Transformer to encode high-content images, effectively filtering out noisy signals and precisely characterizing cell phenotypes. In comparative tests on drug-induced cardiotoxicity, mitochondrial toxicity classification, and compound classification, X-Profiler outperformed both DeepProfiler and CellProfiler, as two highly recognized and representative methods in this field. Our results demonstrate the utility and versatility of X-Profiler, and we anticipate its wide application in HCA for advancing drug development and disease research.
高内涵分析（HCA）在药物发现和研究中具有巨大潜力，但广泛使用的方法可能繁琐且结果不准确。细胞图像中的噪声和冗余信号阻碍了基于深度学习的准确图像分析。为了解决这些问题，我们引入了X-Profiler，这是一种新颖的HCA方法，它结合了细胞实验、图像处理和深度学习建模。X-Profiler结合了卷积神经网络和Transformer对高内涵图像进行编码，有效滤除噪声信号并精确表征细胞表型。在药物诱导的心脏毒性、线粒体毒性分类和化合物分类的对比测试中，X-Profiler优于DeepProfiler和CellProfiler这两种该领域高度认可且具有代表性的方法。我们的结果证明了X-Profiler的实用性和多功能性，并且我们预计它将在HCA中广泛应用以推动药物开发和疾病研究。
