Back

Multimodal optical imaging reveals spatial metabolic heterogeneity in the aging retina

Jang, H.; Wu, S.; Gao, F.; Skowronska-Krawczyk, D.; Shi, L.

2026-08-21 bioengineering
10.64898/2026.08.17.745175 bioRxiv
Show abstract

Understanding how aging reshapes retinal metabolism requires methods that can resolve molecular and structural changes across the retinas highly organized cellular layers. Here, we applied a nonlinear multimodal imaging platform that integrates fluorescence lifetime imaging microscopy (FLIM), second-harmonic generation (SHG), hyperspectral stimulated Raman scattering (HS-SRS), and deuterium oxide-based stimulated Raman scattering (DO-SRS) to map age-associated metabolic and compositional alterations in young and aged mouse retinas. FLIM analysis of the outer nuclear layer (ONL) revealed increased free NADH and NADPH fractions in aged retinas, consistent with reduced oxidative phosphorylation and enhanced lipid anabolic activity. SHG imaging of the sclera showed pronounced age-related remodeling of collagen organization, including increased fiber density, elevated anisotropy, and the emergence of densely crosslinked bundles in the central sclera. DO-SRS further demonstrated elevated lipid turnover in rod photoreceptor outer segments and the retinal pigment epithelium (RPE) with aging which was confirmed by lipidomic analysis. Complementary HS-SRS analysis revealed reduced triacylglycerol and cholesterol content together with localized sphingosine accumulation in the RPE. Together, these findings provide a spatially resolved view of metabolic remodeling in the aging retina and establish multimodal optical imaging as a powerful framework for studying alterations associated with age-related retinal disease.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.