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CPHNet: a novel pipeline for anti-HAPE drug screening via deep learning-based Cell Painting scoring
De-Zhi Sun1, Xi-Ru Yang2,1, Cong-Shu Huang3
1Department of Pharmaceutical Sciences, Beijing Institute of Radiation Medicine, No. 27, Taiping Road, Haidian District, Beijing, 100850, China.
Background:
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.
Methods:
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.
Results:
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).
Conclusion:
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.
