Single-cell RNA sequencing uncovers the excitatory/inhibitory synaptic unbalance in the retrosplenial cortex after peripheral nerve injury
Bibliographic record
Abstract
Abstract Nerve injury in the somatosensory pathway may induce maladaptive changes at the transcriptional or protein level, contributing to the development and maintenance of neuropathic pain. In contrast to the retrosplenial cortex (RSC), which processes nociceptive information and exhibits structural and molecular changes after nerve injury, detailed transcriptional changes in the RSC are not yet known. Here we confirm the involvement of the RSC in regulating pain sensation and observe that the same peripheral stimulation activates more retrosplenial neurons after nerve injury; reducing the activities of CaMKII α + splenial cells relieves peripheral pain hypersensitivity after nerve injury. Using a single-cell RNA sequencing (scRNA-seq) approach, we identified cell-type-specific gene expression changes after nerve injury, and the gene set enrichment analysis results revealed suppressed ion homeostasis in CaMKII α + neurons. Furthermore, examination of the expression of genes encoding ligand-gated ion channels showed a decrease in Gabar1a but an increase in Gria1 in CaMKII α + neurons; consistently, we confirmed the unbalanced excitatory/inhibitory synaptic transmission by using the electrophysiological recording approach. Moreover, micro-infusion of 1-Naphthyl acetyl spermine in the RSC to reduce excitatory synaptic transmission alleviated peripheral pain hypersensitivity. Our data confirm the involvement of the RSC in pain regulation and provide information on cell type-dependent transcriptomic changes after nerve injury, which will contribute to the understanding of the mechanisms mediating neuropathic pain.
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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".