Academic Journal
Single-cell transcriptomic integrated with machine learning reveals human retinal cell-specific biomarkers in diabetic retinopathy
| Τίτλος: | Single-cell transcriptomic integrated with machine learning reveals human retinal cell-specific biomarkers in diabetic retinopathy |
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| Συγγραφείς: | Lin, Sen, Yang, Luning, Tao, Yiwen, Pan, Qi, Cai, Tengda, Ye, Yunyan, Liu, Jianhui, Zhou, Yang, Shao, Yongqing, Yi, Quanyong, Lu, Zen H., Chen, Lie, McKay, Gareth, Rankin, Richard, Li, Fan, Meng, Weihua |
| Πηγή: | Lin, S, Yang, L, Tao, Y, Pan, Q, Cai, T, Ye, Y, Liu, J, Zhou, Y, Shao, Y, Yi, Q, Lu, Z H, Chen, L, McKay, G, Rankin, R, Li, F & Meng, W 2026, 'Single-cell transcriptomic integrated with machine learning reveals human retinal cell-specific biomarkers in diabetic retinopathy', Human Molecular Genetics, vol. 35, no. 16. https://doi.org/10.1093/hmg/ddag069 |
| Έτος έκδοσης: | 2026 |
| Συλλογή: | Queen's University Belfast: Research Portal |
| Θεματικοί όροι: | Humans, Diabetic Retinopathy/genetics, Machine Learning, Biomarkers/metabolism, Single-Cell Analysis/methods, Single-Cell Gene Expression Analysis, Retina/metabolism, Transcriptome/genetics, Female, Male, Gene Expression Profiling, Middle Aged, /dk/atira/pure/sustainabledevelopmentgoals/good_health_and_well_being, name=SDG 3 - Good Health and Well-being |
| Περιγραφή: | BACKGROUND: Diabetic retinopathy (DR) is a leading cause of vision impairment worldwide, yet the cell-type-specific molecular alterations associated with disease progression remain incompletely understood. This study aimed to characterize transcriptional changes across retinal cell types in diabetes and DR and identify candidate disease-associated biomarkers using single-cell transcriptomics and machine-learning approaches. METHODS: We generated a single-cell RNA sequencing (scRNA-seq) atlas comprising 297 121 high-quality retinal cells from 20 eyes of 13 Chinese donors, including non-diabetic controls (NON), diabetes without retinopathy (DM), and DR samples. Following quality control, batch correction, clustering, and cell-type annotation, differential expression analyses were performed across disease states within each retinal cell type. Candidate biomarkers were further prioritized using a machine-learning framework incorporating L1-regularized logistic regression, recursive feature elimination with cross-validation, and stability selection. RESULTS: We identified 10 major retinal cell populations and characterized extensive cell-type-specific transcriptional alterations associated with diabetes and DR. Pathway enrichment analyses consistently highlighted immune activation, oxidative stress, neurodegeneration, and synaptic dysfunction across multiple retinal cell types. A total of 707 cell-type-specific candidate marker genes were identified, providing a comprehensive resource for investigating disease-associated molecular mechanisms and potential therapeutic targets. CONCLUSIONS: This study establishes a single-cell transcriptomic atlas of the Chinese diabetic retina and reveals cell-type-specific molecular signatures associated with DR progression. These findings provide biological insights into retinal disease mechanisms and nominate candidate biomarkers for future functional and translational studies. |
| Τύπος εγγράφου: | article in journal/newspaper |
| Γλώσσα: | English |
| ISSN: | 0964-6906 1460-2083 |
| Relation: | info:eu-repo/semantics/altIdentifier/pmid/42531047; info:eu-repo/semantics/altIdentifier/pissn/0964-6906; info:eu-repo/semantics/altIdentifier/eissn/1460-2083 |
| DOI: | 10.1093/hmg/ddag069 |
| Διαθεσιμότητα: | https://pure.qub.ac.uk/en/publications/f1a7339b-7a8f-4c60-b9be-a78409592603 https://doi.org/10.1093/hmg/ddag069 |
| Rights: | info:eu-repo/semantics/openAccess |
| Αριθμός Καταχώρησης: | edsbas.302A8617 |
| Βάση Δεδομένων: | BASE |
| ISSN: | 09646906 14602083 |
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| DOI: | 10.1093/hmg/ddag069 |