Activation of Neuronal NMDA Receptors Triggers Transient ATP-Mediated Microglial Process Outgrowth
Bibliographic record
Abstract
Microglia are morphologically dynamic cells that rapidly extend their processes in response to various stimuli including extracellular ATP. In this study, we tested the hypothesis that stimulation of neuronal NMDARs trigger ATP release leading to communication with microglia. We used acute mouse hippocampal brain slices and two-photon laser scanning microscopy to study microglial dynamics and developed a novel protocol for fixation and immunolabeling of microglia processes. Similar to direct topical ATP application in vivo, short multiple applications of NMDA triggered transient microglia process outgrowth that was reversible and repeatable indicating that this was not due to excitotoxic damage. Stimulation of NMDAR was required as NMDAR antagonists, but not blockers of AMPA/kainate receptors or voltage-gated sodium channels, prevented microglial outgrowth. We report that ATP release, secondary to NMDAR activation, was the key mediator of this neuron-microglia communication as both blocking purinergic receptors and inhibiting hydrolysis of ATP to prevent locally generated gradients abolished outgrowth. Pharmacological and genetic analyses showed that the NMDA-triggered microglia process extension was independent of Pannexin 1, the ATP releasing channels, ATP release from astrocytes via connexins, and nitric oxide generation. Finally, using whole-cell patch clamping we demonstrate that activation of dendritic NMDAR on single neurons is sufficient to trigger microglia process outgrowth. Our results suggest that dendritic neuronal NMDAR activation triggers ATP release via a Pannexin 1-independent manner that induces outgrowth of microglia processes. This represents a novel uncharacterized form of neuron-microglial communication mediated by ATP.
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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".