⚡ Quick Summary
The study introduces CPHNet, a novel pipeline for screening anti-HAPE drugs using deep learning and Cell Painting techniques. This innovative approach successfully identified two natural products, ferulic acid and resveratrol, as promising candidates for HAPE treatment.
🔍 Key Details
- 📊 Dataset: Over 100,000 Cell Painting images of A549 and HPMEC cells
- 🧩 Features used: Morphological alterations under hypoxic conditions
- ⚙️ Technology: Deep learning segmentation network (SegNet) and hypoxia scoring network (HypoNet)
- 🏆 Performance: CPHNet identified effective compounds with significant anti-HAPE effects
🔑 Key Takeaways
- 🌄 HAPE is a serious condition affecting individuals at high altitudes.
- 🔬 Cell Painting provides a unique method to visualize cellular responses to hypoxia.
- 🤖 AI integration enhances drug discovery processes by analyzing complex data.
- 🌱 Ferulic acid and resveratrol show potential as anti-HAPE agents.
- 🧪 3D-alveolus chip models and mouse models were used for testing efficacy.
- 💡 This study paves the way for future AI-driven phenotypic drug discovery.
- 📈 Over 200,000 images were utilized to train the hypoxia scoring network.
- 🌍 Research conducted by a team of experts in respiratory research.
📚 Background
High altitude pulmonary edema (HAPE) is a critical health issue for those rapidly ascending to high altitudes. The condition arises from hypoxia-induced changes in the alveolar-capillary barrier, leading to significant cellular morphological alterations. Understanding these changes is essential for developing effective therapeutic strategies, yet traditional drug discovery methods have struggled to address this challenge adequately.
🗒️ Study
The researchers aimed to create a comprehensive pipeline for identifying anti-HAPE agents by focusing on the morphological changes observed in cells under hypoxic conditions. They generated a vast dataset of over 100,000 Cell Painting images from human alveolar adenocarcinoma basal epithelial cells (A549s) and human pulmonary microvascular endothelial cells (HPMECs) exposed to varying oxygen levels (1% to 5%).
📈 Results
The study successfully developed CPHNet, a deep neural network-based drug screening pipeline. This innovative approach led to the identification of two natural products, ferulic acid and resveratrol, both of which exhibited promising anti-HAPE effects in both ex vivo and in vivo models. The results underscore the effectiveness of integrating AI tools with Cell Painting methodologies for drug discovery.
🌍 Impact and Implications
The findings from this study represent a significant advancement in the search for effective treatments for HAPE. By leveraging artificial intelligence and advanced imaging techniques, researchers can now explore new therapeutic avenues that were previously overlooked. This approach not only enhances our understanding of cellular responses to hypoxia but also opens doors for future research in drug discovery across various medical fields.
🔮 Conclusion
The development of CPHNet marks a promising step forward in the fight against HAPE. By combining deep learning with innovative imaging techniques, this study highlights the potential for AI-driven solutions in drug discovery. As we continue to explore these technologies, we can anticipate more breakthroughs that will improve patient outcomes and expand our therapeutic options.
💬 Your comments
What are your thoughts on the integration of AI in drug discovery? Do you believe this approach could lead to more effective treatments for conditions like HAPE? Let’s engage in a discussion! 💬 Share your insights in the comments below or connect with us on social media:
CPHNet: a novel pipeline for anti-HAPE drug screening via deep learning-based Cell Painting scoring.
Abstract
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.
Author: [‘Sun DZ’, ‘Yang XR’, ‘Huang CS’, ‘Bai ZJ’, ‘Shen P’, ‘Ni ZX’, ‘Huang-Fu CJ’, ‘Hu YY’, ‘Wang NN’, ‘Tang XL’, ‘Li YF’, ‘Gao Y’, ‘Zhou W’]
Journal: Respir Res
Citation: Sun DZ, et al. CPHNet: a novel pipeline for anti-HAPE drug screening via deep learning-based Cell Painting scoring. CPHNet: a novel pipeline for anti-HAPE drug screening via deep learning-based Cell Painting scoring. 2025; 26:91. doi: 10.1186/s12931-025-03173-1