A versatile GPCR toolkit to track in vivo neuromodulation: not a one-size-fits-all sensor
Bibliographic record
Abstract
Measuring the real-time dynamics of neuromodulator release in the brain with subcellular resolution is a long-sought goal in neuroscience, due to the immense implications for basic science and medicine. The past 3 years have brought this goal within reach, with the appearance of a new class of genetically encoded fluorescent sensors for neuromodulators [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ] (Fig. 1 ) constructed using G-protein-coupled-receptors (GPCR) [ 12 ]. GPCR sensor design takes advantage of the fact that most neuromodulators harbor GPCRs as their native receptors, and builds on protein engineering expertise acquired through work on genetically encoded calcium sensors [ 13 , 14 , 15 , 16 ]. High-throughput screening techniques are used to incorporate circularly permuted fluorescent proteins (cpFP) within GPCRs of interest, enabling the optical visualization of neuromodulator dynamics [ 17 ]. Given the diversity of naturally existing GPCR scaffolds, there is a large realm of opportunities to generate new GPCR-sensors with tailored properties adapted for each neuromodulator. The dLight1 family exemplifies this possibility, providing a panoply of eight sensors engineered using DRD1, DRD2, and DRD4 receptor subtypes, each with different properties [ 18 ]. The rapid developments in GPCR sensor engineering are now allowing an ever-growing ability to tailor sensor use to specific experimental applications, but may create a dilemma for end-users pondering which sensor is best suited for their work or how to interpret results. Fig. 1: Choosing a neuromodulator GPCR sensor, a balancing act (see main text for details). (1) K d for 2AG (reported K d for AEA: 500 nM [ 10 ]). (2) Effect size ( d 1, d 2): magnitude of change in neuromodulator levels between groups, estimated e.g. by calculating Cohen’s d : delta of the means of the groups, normalized to the pooled standard deviation [ 19 ]. (3) ”High” and “low” affinity denominations are relative (here chosen based on DA/NE systems) and may be shifted for other neuromodulators. (4) Expected future developments. 5HT serotonin, Ach acetylcholine, Ado adenosine, CNO clozapine-N-oxide, C-term C-terminal, DA dopamine, DRD1/DRD2 DA receptor 1 and 2, dFFmax dynamic range (maximal dFF), eCB endocannabinoid, FP fluorescent protein, FSCV fast-scan cyclic voltammetry, GRP gastrin-like peptide, K d apparent affinity, ND not determined, NE noradrenaline, τ-on/τ-off on- and off-sensor kinetics (half-rise, decay times). Full size image
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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 teacher head, 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".