The GluN2A Subunit Regulates Neuronal NMDA receptor-Induced Microglia-Neuron Physical Interactions
Bibliographic record
Abstract
Microglia are known to engage in physical interactions with neurons. However, our understanding of the detailed mechanistic regulation of microglia-neuron interactions is incomplete. Here, using high resolution two photon imaging, we investigated the regulation of NMDA receptor-induced microglia-neuron physical interactions. We found that the GluN2A inhibitor NVPAAM007, but not the GluN2B inhibitor ifenprodil, blocked the occurrence of these interactions. Consistent with the well-known developmental regulation of the GluN2A subunit, these interactions are absent in neonatal tissues. Furthermore, consistent with a preferential synaptic localization of GluN2A subunits, there is a differential sensitivity of their occurrence between denser (stratum radiatum) and less dense (stratum pyramidale) synaptic sub-regions of the CA1. Finally, consistent with differentially expressed GluN2A subunits in the CA1 and DG areas of the hippocampus, these interactions could not be elicited in the DG despite robust microglial chemotactic capabilities. Together, these results enhance our understanding of the mechanistic regulation of NMDA receptor-dependent microglia-neuronal physical interactions phenomena by the GluN2A subunit that may be relevant in the mammalian brain during heightened glutamatergic neurotransmission such as epilepsy and ischemic stroke.
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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) | 0.001 | 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".