Early and progressive retinal microglial changes in APPNL-F/NL-F mouse model of Alzheimer’s disease revealed by an automated image analysis software

Alzheimer’s disease (AD) is characterized by the accumulation of misfolded proteins that trigger neuroinflammation and neuronal loss. The retina, as an extension of the central nervous system, mirrors these pathological processes and represents a potential biomarker. Microglial activation, a key component of neuroinflammation, can be morphologically assessed through automated image analysis. This study performed a quantitative and morphological analysis of retinal microglia in the APP NL-F/NL-F mouse model of AD across aging (6–20 months) and comparing them with age-matched C57BL/6 J controls using an automated image analysis software. A cross-sectional design was applied to 72 mice (36 APP NL-F/NL-F and 36 WT). Retinas samples were processed by Iba-1 immunohistochemistry. Quantified parameters included cell number, soma size, arborization area, skeletonization, fluorescence intensity, and Feret’s Diameter Ratio across OS, OPL, IPL, and NFL/GCL layers. Image analysis was performed using a custom automated system, called MorphoSomas, specifically developed for the comprehensive morphological assessment of microglia. Age-dependent changes were observed in both groups. WT mice showed a later and more gradual activation pattern, whereas APP NL-F/NL-F mice exhibited early activation from 6 months, characterized by increased cell number and soma size, followed by reductions in arborization and skeletonization, indicating progressive activation. The automated system allowed precise and reproducible assessment, highlighting significant differences between groups and retinal layers. In conclusion, retinal microglia in APP NL-F/NL-F mice exhibit early and biphasic activation followed by signs of dysfunction, reflecting AD neuropathology. Automated analysis enhances objectivity and efficiency in morphological studies. These findings support the retina as a promising, non-invasive biomarker for early AD detection.