In vivo detection of GPCR-dependent signaling using fiber photometry and FRET-based biosensors
Bibliographic record
Abstract
Genetically encoded fluorescent biosensors allow intracellular signaling dynamics to be tracked in live cells and tissues using optical detection. Many such biosensors are based on the principle of Förster resonance energy transfer (FRET), and we have recently developed a simple approach for in vivo detection of FRET-based biosensor signals using fiber photometry. By combining fiber photometry with FRET-based biosensors, we were able to track GPCR-dependent signaling pathways over time, and in response to drug treatments in freely-moving adult rats. Recording from specific neuronal populations, we can quantify intracellular signaling while simultaneously measuring behavioral responses. Our approach, described in detail here, uses adeno-associated viruses infused intracerebrally in order to express genetically-encoded FRET-based biosensors. After several weeks to allow biosensor expression, fiber photometry is used in order to record drug responses in real time from freely-moving adult rats. This methodology would be compatible with other mammalian species and with many biosensors. Hence, it has wide applicability across a spectrum of neuroscience research, ranging from the study of neural circuits and behavior, to preclinical drug development and screening.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".