Liu Yang, Zhijie Li
Classical accounts of retinal aging emphasize oxidative, mitochondrial, inflammatory, vascular, proteostatic, and retinal pigment epithelium (RPE)-centered mechanisms. Omics studies now confirm and refine this non-uniformity, showing that aging-associated signals vary by cell type, anatomical niche, molecular layer, and functional reserve. Direct evidence from single-cell and single-nucleus transcriptomics is strong for lineage- and subtype-specific aging programs in neural retina, RPE, glia, vascular cells, and choroidal immune populations. Spatial omics can generate and test localization hypotheses across anatomically defined foveolar/foveal, parafoveal, perifoveal, peripheral, vascular, glial, optic nerve head, and RPE-Bruch's membrane-choroid niches, but direct spatial-omics evidence for normal human retinal aging remains sparse. Proteomic, metabolic, lipidomic, epigenomic, and imaging mass spectrometry data help determine whether transcriptional aging states translate into altered protein turnover, metabolic reserve, lipid handling, chromatin state, and inflammatory memory. AI-derived retinal age clocks provide population-scale imaging phenotypes, but color fundus photography alone is poorly depth-resolved; biological interpretation requires optical coherence tomography (OCT), visual function, biomarkers, destructive cell-type-resolved or spatially resolved tissue data, and longitudinal imaging or functional data. We integrate these findings into a multi-scale model in which normal retinal aging may lower resilience thresholds in defined cells and niches, thereby contributing to susceptibility to age-related macular degeneration, glaucoma, diabetic retinopathy, and inherited retinal degeneration. We also outline minimum standards for retinal aging omics, including anatomically specified sampling, donor metadata, evidence-source class interpretation, spatial validation, perturbation or rescue testing, and measurable endpoints of retinal resilience and visual function.