Unveiling the molecular complexity of proliferative diabetic retinopathy through scRNA-seq, AlphaFold 2, and machine learning
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Background: Proliferative diabetic retinopathy (PDR), a major cause of blindness, is characterized by complex pathogenesis. This study integrates single-cell RNA sequencing (scRNA-seq), Non-negative Matrix Factorization (NMF), machine learning, and AlphaFold 2 methods to explore the molecular level of PDR. Methods: We analyzed scRNA-seq data from PDR patients and healthy controls to identify distinct cellular subtypes and gene expression patterns. NMF was used to define specific transcriptional programs in PDR. The oxidative stress-related genes (ORGs) identified within Meta-Program 1 were utilized to construct a predictive model using twelve machine learning algorithms. Furthermore, we employed AlphaFold 2 for the prediction of protein structures, complementing this with molecular docking to validate the structural foundation of potential therapeutic targets. We also analyzed protein−protein interaction (PPI) networks and the interplay among key ORGs. Results: Our scRNA-seq analysis revealed five major cell types and 14 subcell types in PDR patients, with significant differences in gene expression compared to those in controls. We identified three key meta-programs underscoring the role of microglia in the pathogenesis of PDR. Three critical ORGs (ALKBH1, PSIP1, and ATP13A2) were identified, with the best-performing predictive model demonstrating high accuracy (AUC of 0.989 in the training cohort and 0.833 in the validation cohort). Moreover, AlphaFold 2 predictions combined with molecular docking revealed that resveratrol has a strong affinity for ALKBH1, indicating its potential as a targeted therapeutic agent. PPI network analysis, revealed a complex network of interactions among the hub ORGs and other genes, suggesting a collective role in PDR pathogenesis. Conclusion: This study provides insights into the cellular and molecular aspects of PDR, identifying potential biomarkers and therapeutic targets using advanced technological approaches.
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Only two of seven omics-based DR studies employed untargeted approaches.
“All included studies adopted retrospective and single-center designs, with only two (2/7) employing untargeted approaches [54, 55], while the remaining primarily utilized targeted omics profiling.”
From Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics (2026) · cites this paper as [55]
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- Authors
- Wang, J., Sun, H., Mou, L., Lu, Y., Wu, Z., Pu, Z., & Yang, M.-M.
- Affiliations
- Jinan University (CN)
- Shenzhen University (CN)
- Corresponding author
- Pu, Zuhui
- Venue
- Frontiers in Endocrinology
- Publisher
- Frontiers Media SA
- Published
- 2024-05-10
- Volume
- 15
- Pages
- 1382896-1382896
- ISSN
- 1664-2392 (ISSN-L)
- PMC
- PMC11116564
- OpenAlex
- W4396809490
- Cited by
- 13
- References
- 37
- Citations by year
- 2026: 5 · 2025: 6 · 2024: 2
- FWCI
- 5.17
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Endocrinology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Natural Science Foundation of Shenzhen City
- Topics
- Health Sciences › Medicine › Ophthalmology › Retinal Diseases and Treatments
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Keywords
- proliferative diabetic retinopathy, oxidative stress, microglia, ALKBH1, PSIP1, ATP13A2, resveratrol, protein-protein interaction network, cell subtypes, prediction model, therapeutic target, biomarkers, protein structure prediction
- MeSH terms
- Machine Learning, RNA-Seq, Single-Cell Gene Expression Analysis, Diabetic Retinopathy, Female, Humans, Male, Case-Control Studies, Sequence Analysis, RNA, Oxidative Stress, Single-Cell Analysis, Protein Interaction Maps, Molecular Docking Simulation
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