Beta Amyloid Plaques Histology for Mouse & Rat Models of Alzheimer's Disease

At 6 months-of-age, initial amyloid plaque deposition becomes detectable, with plaques emerging in specific brain regions, such as the cerebral cortex. At this stage, plaque burden remains relatively low and regionally restricted, representing an early phase of amyloid-β pathology prior to widespread cortical and subcortical involvement.

Between 6 and 9 months-of-age, there is a pronounced increase in both the number and size of amyloid plaques. Amyloidosis becomes prominent across multiple brain regions, including areas that previously exhibited minimal pathology, such as the entorhinal cortex and hippocampus. Plaques display greater morphological heterogeneity, with both dense-core and diffuse types emerging, reflecting the progressive maturation of amyloid-β pathology. This stage marks a transition from early, regionally-restricted deposition to widespread cortical and subcortical involvement, accompanied by increased microglial and astrocytic responses in the surrounding tissue.

From 9 to 12 months-of-age, amyloid accumulation continues, though the rate of increase is less pronounced than in earlier stages. Brain regions that were relatively spared at younger ages, such as the midbrain, now exhibit higher plaque densities. In the regions analyzed, plaque density showed an upward trend; however, the increases did not reach statistical significance, suggesting that amyloid deposition is approaching a plateau in these areas. This stage reflects the maturation and stabilization of amyloid-β pathology, with ongoing local microglial and astrocytic responses in the vicinity of existing plaques.

In contrast, in wild-type (WT) animals, no amyloid plaque deposition is detected, and neuroinflammatory markers remain at baseline levels. Microglia show predominantly ramified morphologies and astrocytes show minimal reactivity, establishing a baseline reference for pathology-driven changes observed in the transgenic mice.

The image depicts Iba-1–labeled microglia in a 9-month-old mouse. In this model, microgliosis closely follows the spatiotemporal progression of OC-positive amyloid pathology and can be quantitatively assessed through measurements of Iba-1 staining density.

To derive a more sensitive metric of disease state, we applied our microglial morphology analysis pipeline to quantify the density of activated microglia. Detected microglial contours are color-coded based on morphological classification, with ramified microglia shown in green and activated microglia in purple. Only microglia with nuclei fully in-plane were included in the the detection step and analysis.

Quantification of activated microglial density revealed a similar pattern of progressive microgliosis, but with substantially greater statistical sensitivity than measurements based solely on Iba-1 staining density. Notably, the 6- to 9-month comparison reached statistical significance in the hippocampus and showed increased significance in the anterior cortex. In a preclinical therapeutic efficacy setting, this increased sensitivity could enable detection of smaller treatment effects without increasing cohort size.

To learn more about our microglia morphology analysis pipeline, please see our Innovation Presentation on microglia morphology.

Similarly, we quantified the astrogliosis by measuring the stain density of GFAP, as shown on this slide of from 9 month-old mouse. Astrogliosis also followed the temporal trend of the amyloid plaque density.

We applied our pipeline for astrocyte detection and morphology analysis, this time measuring the mean hypertrophy score of astrocytes in each ROI. The contour of in-plane astrocytes is shown colored by their morphology: green for normal astrocytes and orange for reactive astrocytes.

Similar to the microglia analysis, the mean hypertrophy score showed a similar increase with age, but with higher statistical significance than GFAP stain density alone. For example, the early changes in the piriform cortex at 6 months-of-age became significant as compared to control.

For more information about our astrocyte morphology analysis, please see our Innovation Presentation on astrocyte morphology.

We next characterized the plaque-associated microenvironment by quantifying glial cell density as a function of distance from individual amyloid plaques. Spatial profiling was performed in 10 µm concentric increments extending outward from the plaque boundary. For clarity of visualization, only the outer boundary of the 20 µm periplaque microenvironment is displayed.

This analysis revealed a pronounced enrichment of activated microglia in direct contact with amyloid plaques. The accompanying heatmap illustrates activated microglial density as a function of distance from the plaque across experimental groups, with the color scale ranging from black (low density) to beige (high density). Activated microglia density was very high in direct plaque contact and low elsewhere, highlighting the highly localized nature of plaque-associated microglia.

In contrast, reactive astrocytes exhibited a more distal spatial distribution relative to amyloid plaques. Astrocytic density peaked within the 0–10 µm periplaque zone and remained elevated up to approximately 50 µm from the plaque boundary, indicating a broader response compared to activated microglia. Spatial distribution patterns were consistent across age groups; therefore, subsequent analyses were focused on the 9-month-old cohort as a representative stage of established pathology.

To obtain a better understanding of the microenvironment spatiotemporal dynamics, we measured the plaque size and used it as a proxy for plaque age. The image shows plaques colored by size for visualization in a 12 month-old mouse: small plaques with an equivalent radius of less than 10 μm, medium-sized plaques with an equivalent radius between 10 μm and 20 μm, and large plaques with an equivalent radius of more than 20 μm. The equivalent radius of a plaque is defined as the radius of a plaque of the same area but perfectly circular. The histogram below shows the full distribution of plaque size across the different age groups.

We then quantified the glial cell density as a function of distance from the plaque and plaque size.

The heatmap below now shows the activated microglia density as a function of distance to the plaque and plaque size, or plaque equivalent radius. We can see that, once again, the microglia are highly enriched in direct contact with the plaques. The density rapidly increases in small plaques and plateau in medium-sized plaques.

Now looking at the astrocytes, we see a very different dynamic. First, the reactive astrocytes are detected in slightly larger plaques than microglia. In addition, their numbers in contact with the plaque increase in the medium-sized plaques, but then decreased in the largest plaques.

Because astrocytes and microglia have different behaviors in larger plaques, which are predominantly found in older groups, we hypothesized that a metric based on this phenomenon could help differentiate the 9 and 12 month-old groups.

We measured the ratio of microglia-to-astrocyte in the plaque proximity, and found that this ratio was higher in the 12 month-old group as compared to the other disease groups. In our study, this metric was the only one that showed a statistically significant difference between the 9 and 12 month-old groups.

While the other metrics, such as the amyloid-β and GFAP stain density, generally trended higher in the 12 month-old group, none showed a statistically significant change between the two older groups.

This approach illustrates how quantifying astrocyte-microglia interactions can provide an informative metric of disease progression.

In conclusion, we have shown how our fully automated platform can quantify glial cell phenotypes and their spatial relationship to the plaque environment. Using this approach in the APP/PS1 mouse model of AD, we quantified different metrics, such as the activated microglia density, the astrocytes hypertrophy score, and the microglia-to-astrocyte ratio in the proximity of the plaque.

These complementary metrics demonstrated higher sensitivity than simple stain density measurements, revealing subtle spatial and morphological features of microglia and astrocytes that could be missed using standard approaches.

In a preclinical therapeutic efficacy study, this enhanced sensitivity would allow detection of smaller treatment effects without increasing cohort size. In addition, this comprehensive suite of metrics could be particularly insightful for evaluating therapeutics targeting microglial plaque engagement, microglia-astrocyte interactions, particular glial cell phenotypes, or other related mechanisms.

Please feel free to further explore the microscopy images in the viewer.

We would be happy to discuss this amyloid mouse model, its characterization, and our amyloid plaque microenvironment and neuroinflammation analyses if you would like to Contact Us.