Distinct behavioral responses evoked by selective optogenetic stimulation of the major TRPV1+ and MrgD+ subsets of C-fibers
Bibliographic record
Abstract
Primary C-fiber nociceptors are broadly divided into peptidergic and nonpeptidergic afferents. TRPV1 is a thermosensitive cation channel mainly localized in peptidergic nociceptors, whereas MrgD is a sensory G protein-coupled receptor expressed in most nonpeptidergic nociceptive afferents. TRPV1 and MrgD fibers have been reported to be primarily involved in thermal and mechanical nociception, respectively. Yet, their functional assessment in somatosensory transmission relied on ablation strategies that do not account for compensatory mechanisms. To achieve selective activation of these 2 major subsets of C-fibers in vivo in adult mice, we used optogenetics to specifically deliver the excitatory opsin channelrhodopsin-2 (ChR2) to TRPV1 or MrgD primary sensory neurons, as confirmed by histology and electrophysiology. This approach allowed, for the first time, the characterization of behavioral responses triggered by direct noninvasive activation of peptidergic TRPV1 or nonpeptidergic MrgD fibers in freely moving mice. Transdermal blue light stimulation of the hind paws of transgenic mice expressing ChR2 in TRPV1 neurons generated nocifensive behaviors consisting mainly of paw withdrawal and paw licking, whereas paw lifting occurrence was limited. Conversely, optical activation of cutaneous MrgD afferents produced mostly withdrawal and lifting. Of interest, in a conditioned place avoidance assay, blue light induced aversion in TRPV1-ChR2 mice, but not in MrgD-ChR2 mice. In short, we present novel somatosensory transgenic models in which control of specific subsets of peripheral unmyelinated nociceptors with distinct functions can be achieved with high spatiotemporal precision. These new tools will be instrumental in further clarifying the contribution of genetically identified C-fiber subtypes to chronic 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) | 0.001 | 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 | 0.001 |
| 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".