Dual-color GRAB sensors for monitoring spatiotemporal serotonin release <i>in vivo</i>
Bibliographic record
Abstract
Abstract The serotonergic system plays important roles in both physiological and pathological processes, and is a widely used therapeutic target for many psychiatric disorders. Although several genetically encoded GFP-based serotonin (5-HT) sensors were recently developed, their sensitivities and spectral profiles are relatively limited. To overcome these limitations, we optimized green fluorescent G-protein-coupled receptor (GPCR)-activation-based 5-HT (GRAB 5-HT ) sensors and developed a new red fluorescent GRAB 5-HT sensor. These sensors have excellent cell surface trafficking, high specificity, sensitivity, and spatiotemporal resolution, making them suitable for monitoring 5-HT dynamics in vivo . Besides recording subcortical 5-HT release in freely moving mice, we observed both uniform and gradient 5-HT release in the mouse dorsal cortex with mesoscopic imaging. Finally, we performed dual-color imaging and observed seizure-induced waves of 5-HT release throughout the cortex following calcium and endocannabinoid waves. In summary, these 5-HT sensors can offer valuable insights regarding the serotonergic system in both physiological and pathological states.
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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 | 0.001 | 0.001 |
| 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".