Quantifying changes in pain sensitivity using reproducible transcutaneous optogenetic stimulation in behaving mice
Bibliographic record
Abstract
ABSTRACT Optogenetics provides an unprecedented opportunity to delineate how different somatosensory afferents contribute to sensation, including pain. Afferents expressing channelrhodopsin-2 (ChR2) can be selectively activated by transcutaneous photostimuli applied to behaving mice. Despite care taken to precisely target expression of ChR2 to specific cell types, imprecise photostimulation has hindered quantitative optogenetic-based behavioral testing. Here, we used a robot to reproducibly apply transcutaneous optogenetic stimuli to the hind paw of mice while measuring nocifensive withdrawal. Different photostimulus waveforms (pulses and ramps) and response metrics (threshold and latency) were compared in mice expressing ChR2 in all afferents (Advillin-ChR2) or preferentially in nociceptors (Na V 1.8-ChR2). CFA-induced inflammation caused withdrawal from ramped photostimuli to become faster relative to baseline and to vehicle-injected controls whereas pulsed photostimuli revealed a modest increase in threshold. Analgesia caused by Na V 1.7 and 1.8 channel blockade was evident in both testing protocols. Overall, ramp-based testing was more effective and more efficient (i.e. required less time and total stimulation) than pulse-based testing. Electrophysiological measurements confirmed that inflammation increases nociceptor excitability without affecting phototransduction, and suggests that withdrawal latency depends on the number of nociceptors activated rather than how strongly each nociceptor is activated. Consistent with changes described in nociceptor somata, the behavioral consequences of peripherally blocking different voltage-gated sodium (Na V ) channels showed that nociceptor axons normally rely on Na V 1.8 but upregulate Na V 1.7 after inflammation, with important clinical implications for drug efficacy. Collectively, these results demonstrate the utility of optogenetic pain testing when reproducibly delivered and strategically designed photostimuli are used.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".