Human molecular genetics · 2026
Single-cell transcriptomic integrated with machine learning reveals human retinal cell-specific biomarkers in diabetic retinopathy.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
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
Аннотация
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.
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.