Human microglia differentially respond to β‐amyloid, tau, and combined Alzheimer's disease pathologies in vivo
Bibliographic record
Abstract
INTRODUCTION: Recent studies have identified important species-dependent differences in the response of microglia to β-amyloid (Aβ) pathology. Yet, whether human microglia also interact differently with the pathognomonic combination of amyloid and tau pathologies that occur in Alzheimer's disease (AD) remains unclear. METHODS: We generated a xenotolerant mouse model of AD that develops both plaque and tangle pathologies, transplanted stem cell-derived microglial progenitors and examined the interactions between human microglia and AD pathologies with scRNA sequencing, immunohistochemistry, and in vitro modeling. RESULTS: The combined amyloid and tau pathologies induced robust type-I interferon and proinflammatory cytokine responses, as well as an increased adoption of a distinct "rod" morphology in human microglia. The rod morphology could be induced with type-I interferon treatment in vitro. DISCUSSION: We provide new insights into human microglial responses to combined AD pathologies and a novel platform to investigate and manipulate human microglia in vivo. HIGHLIGHTS: Amyloid pathology promotes the rapid development of neurofibrillary tangles and neuronal loss in a novel chimeric model of AD. Combined Alzheimer's disease pathologies lead to an expansion of disease-associated microglia (DAM) and exacerbate Interferon-responsive and cytokine/chemokine-enriched states in xenotransplanted human microglia. The combination of amyloid and tau promotes the development of a distinctive rod microglial phenotype that closely correlates with tau pathology and neurodegeneration. Rod morphology and transcriptional changes can be modeled in vitro by treatment of induced pluripotent stem cells (iPSC) -microglia with type-I interferons.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".