Sun De-Zhi, Yang Xi-Ru, Huang Cong-Shu, Bai Zhi-Jie, Shen Pan, Ni Zhe-Xin, Huang-Fu Chao-Ji, Hu Yang-Yi, Wang Ning-Ning, Tang Xiang-Lin, Li Yong-Fang, Gao Yue, Zhou Wei
Department of Pharmaceutical Sciences, Beijing Institute of Radiation Medicine, No. 27, Taiping Road, Haidian District, Beijing, 100850, China.
Department of Pharmacy, Medical College of Qinghai University, Xining, Qinghai, 810001, China.
Respir Res. 2025 Mar 8;26(1):91. doi: 10.1186/s12931-025-03173-1.
High altitude pulmonary edema (HAPE) poses a significant medical challenge to individuals ascending rapidly to high altitudes. Hypoxia-induced cellular morphological changes in the alveolar-capillary barrier such as mitochondrial structural alterations and cytoskeletal reorganization, play a crucial role in the pathogenesis of HAPE. These morphological changes are critical in understanding the cellular response to hypoxia and represent potential therapeutic targets. However, there is still a lack of effective and valid drug discovery strategies for anti-HAPE treatments based on these cellular morphological features. This study aims to develop a pipeline that focuses on morphological alterations in Cell Painting images to identify potential therapeutic agents for HAPE interventions.
We generated over 100,000 full-field Cell Painting images of human alveolar adenocarcinoma basal epithelial cells (A549s) and human pulmonary microvascular endothelial cells (HPMECs) under different hypoxic conditions (1%~5% of oxygen content). These images were then submitted to our newly developed segmentation network (SegNet), which exhibited superior performance than traditional methods, to proceed to subcellular structure detection and segmentation. Subsequently, we created a hypoxia scoring network (HypoNet) using over 200,000 images of subcellular structures from A549s and HPMECs, demonstrating outstanding capacity in identifying cellular hypoxia status.
We proposed a deep neural network-based drug screening pipeline (CPHNet), which facilitated the identification of two promising natural products, ferulic acid (FA) and resveratrol (RES). Both compounds demonstrated satisfactory anti-HAPE effects in a 3D-alveolus chip model (ex vivo) and a mouse model (in vivo).
This work provides a brand-new and effective pipeline for screening anti-HAPE agents by integrating artificial intelligence (AI) tools and Cell Painting, offering a novel perspective for AI-driven phenotypic drug discovery.
高原肺水肿（HAPE）对迅速登上高原的个体构成了重大医学挑战。缺氧诱导的肺泡-毛细血管屏障细胞形态变化，如线粒体结构改变和细胞骨架重组，在HAPE发病机制中起关键作用。这些形态变化对于理解细胞对缺氧的反应至关重要，并且代表了潜在的治疗靶点。然而，基于这些细胞形态特征的抗HAPE治疗仍缺乏有效且可靠的药物发现策略。本研究旨在开发一种流程，重点关注细胞绘画图像中的形态改变，以识别用于HAPE干预的潜在治疗药物。
我们生成了超过100,000张人肺泡腺癌基底上皮细胞（A549s）和人肺微血管内皮细胞（HPMECs）在不同缺氧条件（氧含量1%~5%）下的全场细胞绘画图像。然后将这些图像提交给我们新开发的分割网络（SegNet），该网络表现出比传统方法更优的性能，以进行亚细胞结构检测和分割。随后，我们使用来自A549s和HPMECs的超过200,000张亚细胞结构图像创建了一个缺氧评分网络（HypoNet），该网络在识别细胞缺氧状态方面表现出卓越能力。
我们提出了一种基于深度神经网络的药物筛选流程（CPHNet），该流程有助于识别两种有前景的天然产物，阿魏酸（FA）和白藜芦醇（RES）。这两种化合物在3D肺泡芯片模型（体外）和小鼠模型（体内）中均表现出令人满意的抗HAPE效果。
这项工作通过整合人工智能（AI）工具和细胞绘画，为筛选抗HAPE药物提供了一种全新且有效的流程，为人工智能驱动的表型药物发现提供了新的视角。
