Ultrastructural evidence of microglial heterogeneity in Alzheimer’s disease amyloid pathology
Bibliographic record
Abstract
Alzheimer’s disease (AD) is the most common neurodegenerative disease, characterized by the deposition of extracellular fibrillar amyloid β (fΑβ) and the intracellular accumulation of neurofibrillary tangles. As AD progresses, Aβ drives a robust and prolonged inflammatory response via its recognition by microglia, the brain’s immune cells. Microglial reactivity to fAβ plaques may impair their normal surveillance duties, facilitating synaptic loss and neuronal death, as well as cognitive decline in AD. In the current study, we performed correlative light, transmission, and scanning electron microscopy to provide insights into microglial structural and functional heterogeneity. We analyzed microglial cell bodies and processes in areas containing fAβ plaques and neuronal dystrophy, dystrophy only, or appearing healthy, among the hippocampus CA1 of 14-month-old APPSwe-PS1Δe9 mice versus wild-type littermates. Our quantitative analysis revealed that microglial cell bodies in the AD model mice were larger and displayed ultrastructural signs of cellular stress, especially nearby plaques. Microglial cell bodies and processes were overall less phagocytic in AD model mice. However, they contained increased fibrillar materials and non-empty inclusions proximal to plaques. Microglial cell bodies and processes in AD model mice also displayed reduced association with extracellular space pockets that contained debris. In addition, microglial processes in healthy subregions of AD model mice encircled synaptic elements more often compared with plaque-associated processes. These observations in mice were qualitatively replicated in post-mortem hippocampal samples from two patients with AD (Braak stage 5). Together, our findings identify at the ultrastructural level distinct microglial transformations common to mouse and human in association with amyloid pathology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".