Abstract 116: Single-synapse Analysis Of Synaptic Remodeling In The Post-stroke Rodent Brain
Bibliographic record
Abstract
Introduction: The mechanisms of functional recovery after stroke are thought to be based on structural and functional changes in brain circuits adjacent to or connected with the stroke site. Deciphering these changes at the synaptic level is key to understanding the re-organization of the synaptic circuitry (i.e. the connectome). Quantitative information about such synapse rearrangements after stroke has been inadequate however, due to the technical limitations of available methodologies. Here we describe the use of array tomography, a new high-resolution proteomic imaging method, to determine the composition of glutamate and GABA synapses in the post-stroke mouse brain. Methods: A cortical lesion was induced in 12-week-old C57BL/6J male mice using the distal middle cerebral artery occlusion model of ischemia. Small tissue sections were removed from the peri-infarct cortex and ribbons of serial ultrathin (70 nm) sections were obtained using an ultramicrotome. Ribbons were stained with antibodies for the synaptic markers SynapsinI, VGlut1, VGlut2, PSD-95, GAD, VGAT. Analysis of the resultant staining pattern was used to identify subtypes of glutamatergic and GABAergic synapses. Results: At 1 week post-stroke, an increase in GABAergic synapses was observed in layer 5 of the peri-infarct cortex. A sub-analysis of the type of inhibitory interneurons (e.g. parvalbumin, somatostatin) expressing these synapses is pending. In addition, a trend for an increase of VGlut1+2 synapses was also observed. However, there were no detectable differences in total synapse number between stroke-injured and naïve animals, thus suggesting that VGluT2 expression may be upregulated in existing glutamatergic VGluT1 synapses after stroke. Further analysis will be extended to cortical layers 2/3 and 4. Conclusion: These results provide new information about the organization of synaptic circuitry and its plasticity after stroke. Furthermore, it demonstrates how array tomography enables a previously unobtainable level of volumetric visualization and quantification of synapses.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".