Quantitative Comparison of 3D Microglial Morphology in Control and 5xFAD Alzheimer’s Model Mice

Abstract

Microglia, the brain’s resident immune cells, play a critical role in Alzheimer’s disease (AD) progression through phagocytosis of amyloid-beta (Aβ) proteins. In the hippocampus, a region heavily impacted by AD, microglia undergo morphological changes as they attempt to engulf Aβ plaques, contributing to chronic neuroinflammation. Current literature lacks a quantitative understanding of how microglial morphology differs in AD. This study addresses this gap by comparing microglia in control and 5xFAD Alzheimer’s model mice.

We utilize data from NeuroMorpho.Org, an open-source repository of digitally reconstructed neurons. Using the Python NeuroM package, a library for extracting morphometric features from SWC files, we quantified hippocampal microglia and conducted statistical and computational analyses to identify distinguishing features between the groups. Principal Component Analysis revealed that soma and structural complexity metrics contributed most to group variance. Welch’s two-sample t-test confirmed all features differed significantly (p < 0.0001). Supervised machine learning models, specifically logistic regression, random forest, and gradient boosting, identified soma-based metrics, average segment length, and average section length as top predictors of group classification. These results support the hypothesis that AD alters microglial morphology in measurable ways.

Preliminary unsupervised machine learning with K-means clustering is ongoing to explore natural data groupings. Future directions include incorporating age-based categorization and applying SHapley Additive exPlanations (SHAP) to further interpret machine learning model feature importance. By identifying distinguishing features in disease and healthy states, this research contributes to early diagnosis and understanding of AD pathology, supporting the United Nations Sustainable Development Goal #3: Good Health and Well-being.