Interpretable Aging Signatures in Human Retinal Cell Types Revealed by Single-Cell RNA Sequencing and Sparse Logistic Regression

Purpose: To characterize cell-type-specific transcriptional changes during human retinal aging and develop machine learning (ML) model for cellular age discrimination in a Chinese cohort. Design: Cross-sectional, laboratory-based observational study. Participants: Eighteen unfrozen retinas from 12 Chinese donors (9 young, 34-55 y; 9 old, 68-92 y). Methods: Single-cell RNA sequencing (10x, v3.1) generated 223 612 cells, batch-corrected with single-cell variational inference; age-related signatures were defined by intersecting single-cell and pseudobulk differentially expressed genes (DEGs), then cell-type-specific panels were rank-ordered with L1-regularized logistic regression plus recursive feature elimination and interpreted through hallmark-pathway enrichment and transcription factor (TF) regulon mapping. Main Outcome Measures: Age-related cellular composition shifts; cell-type-specific DEGs; ML classifier accuracy and feature rankings; TF regulon activity changes. Results: Eleven major retinal cell populations were identified. Aging showed declining rod-to-cone ratios, reduced bipolar cell (BC) proportions among interneurons, and increased astrocyte abundance. Müller glial cells exhibited the most pronounced transcriptional changes, followed by BCs and rods. Machine learning classifiers achieved 80% to 96% accuracy across cell types (microglia 96%, horizontal cells [HCs] 93%, BCs 91%, cones 90%, rods 89%). Shared aging signatures included mitochondrial dysfunction and inflammatory activation. Cell-specific vulnerabilities emerged: mitochondria-centric stress in rods/BCs, proteostasis-retinoid metabolism in cones, and structural-RNA maintenance in HCs. Conclusions: This study provides the first ML derived, cell-type-specific aging signatures for human retina in a Chinese cohort, revealing both conserved molecular hallmarks and distinctive cellular vulnerabilities that inform targeted therapeutic strategies for retinal aging. Financial Disclosure(s): The author has no/the authors have no proprietary or commercial interest in any materials discussed in this article.