Remote Optogenetic Activation and Sensitization of Pain Pathways in Freely Moving Mice
Bibliographic record
Abstract
We report a novel model in which remote activation of peripheral nociceptive pathways in transgenic mice is achieved optogenetically, without any external noxious stimulus or injury. Taking advantage of a binary genetic approach, we selectively targeted Nav1.8(+) sensory neurons for conditional expression of channelrhodopsin-2 (ChR2) channels. Acute blue light illumination of the skin produced robust nocifensive behaviors, evoked by the remote stimulation of both peptidergic and nonpeptidergic nociceptive fibers as indicated by c-Fos labeling in laminae I and II of the dorsal horn of the spinal cord. A non-nociceptive component also contributes to the observed behaviors, as shown by c-Fos expression in lamina III of the dorsal horn and the expression of ChR2-EYFP in a subpopulation of large-diameter Nav1.8(+) dorsal root ganglion neurons. Selective activation of Nav1.8(+) afferents in vivo induced central sensitization and conditioned place aversion, thus providing a novel paradigm to investigate plasticity in the pain circuitry. Long-term potentiation was similarly evoked by light activation of the same afferents in isolated spinal cord preparations. These findings demonstrate, for the first time, the optical control of nociception and central sensitization in behaving mammals and enables selective activation of the same class of afferents in both in vivo and ex vivo preparations. Our results provide a proof-of-concept demonstration that optical dissection of the contribution of specific classes of afferents to central sensitization is possible. The high spatiotemporal precision offered by this non-invasive model will facilitate drug development and target validation for pain therapeutics.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".