Microglia-astrocyte crosstalk in the amyloid plaque niche of an Alzheimer’s disease mouse model, as revealed by spatial transcriptomics
Bibliographic record
Abstract
The amyloid plaque niche is a pivotal hallmark of Alzheimer's disease (AD). Here, we employ two high-resolution spatial transcriptomics (ST) platforms, CosMx and Spatial Enhanced Resolution Omics-sequencing (Stereo-seq), to characterize the transcriptomic alterations, cellular compositions, and signaling perturbations in the amyloid plaque niche in an AD mouse model. We discover heterogeneity in the cellular composition of plaque niches, marked by an increase in microglial accumulation. We profile the transcriptomic alterations of glial cells in the vicinity of plaques and conclude that the microglial response to plaques is consistent across different brain regions, while the astrocytic response is more heterogeneous. Meanwhile, as the microglial density of plaque niches increases, astrocytes acquire a more neurotoxic phenotype and play a key role in inducing GABAergic signaling and decreasing glutamatergic signaling in hippocampal neurons. We thus show that the accumulation of microglia around hippocampal plaques disrupts astrocytic signaling, in turn inducing an imbalance in neuronal synaptic signaling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".