Inhibitory Synapse Dynamics: Coordinated Presynaptic and Postsynaptic Mobility and the Major Contribution of Recycled Vesicles to New Synapse Formation
Bibliographic record
Abstract
Dynamics of GABAergic synaptic components have been studied previously over milliseconds to minutes, revealing mobility of postsynaptic scaffolds and receptors. Here we image inhibitory synapses containing fluorescently tagged postsynaptic scaffold Gephyrin, together with presynaptic vesicular GABA transporter (VGAT) or postsynaptic GABA(A) receptor γ2 subunit (GABA(A)Rγ2), over seconds to days in cultured rat hippocampal neurons, revealing modes of inhibitory synapse formation and remodeling. Entire synapses were mobile, translocating rapidly within a confined region and exhibiting greater nonstochastic motion over multihour periods. Presynaptic and postsynaptic components moved in unison, maintaining close apposition while translocating distances of several micrometers. An observed flux in the density of synaptic puncta partially resulted from the apparent merging and splitting of preexisting clusters. De novo formation of inhibitory synapses was observed, marked by the appearance of stably apposed Gephyrin and VGAT clusters at sites previously lacking either component. Coclustering of GABA(A)Rγ2 supports the identification of such new clusters as synapses. Nascent synapse formation occurred by gradual accumulation of components over several hours, with VGAT clustering preceding that of Gephyrin and GABA(A)Rγ2. Comparing VGAT labeling by active uptake of a luminal domain antibody with post hoc immunocytochemistry indicated that recycling vesicles from preexisting boutons significantly contribute to vesicle pools at the majority of new inhibitory synapses. Although new synapses formed primarily on dendrite shafts, some also formed on dendritic protrusions, without apparent interconversion. Altogether, the long-term imaging of GABAergic presynaptic and postsynaptic components reveals complex dynamics and perpetual remodeling with implications for mechanisms of assembly and synaptic integration.
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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".