The most significant genetic risk factor for developing late-onset Alzheimer’s disease (AD) is the ε4 allele of apolipoprotein E (APOE4). APOE genotype and biological sex are key modulators of microglial and astroglial function, which exert multiple effects on AD pathogenesis. Here, we show astroglial interactions with amyloid plaques in the EFAD transgenic mouse model of AD. Using confocal microscopy, we observed significantly lower levels of astrocytic plaque coverage and plaque compaction (beneficial effects of glial barrier formation) with APOE4 genotype and female sex. Conversely, neurite damage and astrocyte activation in the plaque environment were significantly higher in APOE4 carriers and female mice. Astrocyte coverage of plaques was highest in APOE3 males and poorest in APOE4 females. Collectively, our findings provide new insights into the roles of astroglia and highlight the importance of addressing independent and interactive effects of APOE genotype and biological sex in understanding processes contributing to AD pathogenesis.

The most significant genetic risk factor for developing late-onset Alzheimer’s disease (AD) is the ε4 allele of apolipoprotein E (APOE4). APOE genotype and biological sex are key modulators of microglial and astroglial function, which exert multiple effects on AD pathogenesis. Here we show astroglial interactions with amyloid plaques in the EFAD transgenic mouse model of AD. Using confocal microscopy, we observed significantly lower levels of astrocytic plaque coverage and plaque compaction (beneficial effects of glial barrier formation) with APOE4 genotype and female sex. Conversely, neurite damage and astrocyte activation in the plaque environment were significantly higher in APOE4 carriers and female mice. Astrocyte coverage of plaques was highest in APOE3 males and poorest in APOE4 females. Collectively, our findings provide new insights into the roles of astroglia and highlight the importance of addressing independent and interactive effects of APOE genotype and biological sex in understanding processes contributing to AD pathogenesis.

Analyzing Alzheimers disease (AD) pathology within anatomical subregions is a significant challenge, often carried out by pathologists using a standardized, semi-quantitative approach. To augment traditional methods, a high-throughput, high-resolution pipeline was created to classify the distribution of AD pathology within hippocampal subregions. USC ADRC post-mortem tissue sections from 51 patients were stained with 4G8 for amyloid, Gallyas for neurofibrillary tangles (NFTs) and Iba1 for microglia. Machine learning (ML) techniques were utilized to identify and classify amyloid pathology (dense, diffuse and APP (amyloid precursor protein)), NFTs, neuritic plaques and microglia. These classifications were overlaid within manually segmented regions (aligned with the Allen Human Brain Atlas) to create detailed pathology maps. Cases were separated into low, intermediate, or high AD stages. Further data extraction enabled quantification of plaque size and pathology density alongside ApoE genotype, sex, and cognitive status. Our findings revealed that the increase in pathology burden across AD stages was driven mainly by diffuse amyloid. The pre and para-subiculum had the highest levels of diffuse amyloid while NFTs were highest in the A36 region in high AD cases. Moreover, different pathology types had distinct trajectories across disease stages. In a subset of AD cases, microglia were elevated in intermediate and high compared to low AD. Microglia also correlated with amyloid pathology in the Dentate Gyrus. The size of dense plaques, which may represent microglial function, was lower in ApoE4 carriers. In addition, individuals with memory impairment had higher levels of both dense and diffuse amyloid. Taken together, our findings integrating ML classification approaches with anatomical segmentation maps provide new insights on the complexity of disease pathology in AD progression. Specifically, we identified diffuse amyloid pathology as being a major driver of AD in our cohort, regions of interest and microglial responses that might advance AD diagnosis and treatment.