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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\u2009121 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.<\/jats:p>","DOI":"10.1093\/hmg\/ddag069","type":"journal-article","created":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T11:40:48Z","timestamp":1784029248000},"source":"Crossref","is-referenced-by-count":0,"title":["Single-cell transcriptomic integrated with machine learning reveals human retinal cell-specific biomarkers in diabetic retinopathy"],"prefix":"10.1093","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7695-0964","authenticated-orcid":false,"given":"Sen","family":"Lin","sequence":"first","affiliation":[{"name":"Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute, University of Nottingham Ningbo China , Ningbo 315100 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