Visualization of real-time receptor endocytosis in dopamine neurons enabled by NTSR1-Venus knock-in mice
Bibliographic record
Abstract
Dopamine (DA) neurons are primarily concentrated in substantia nigra (SN) and ventral tegmental area (VTA). A subset of these neurons expresses the neurotensin receptor NTSR1 and its putative ligand neurotensin (Nts). NTSR1, a G protein-coupled receptor (GPCR), which classically activates Gαq/calcium signaling, is a potential route for modulating DA activity. Drug development efforts have been hampered by the receptor’s complex pharmacology and a lack of understanding about its endogenous location and signaling responses. Therefore, we have generated NTSR1-Venus knock-in (KI) mice to study NTSR1 receptors in their physiological context. In primary hippocampal neurons, we show that these animals express functional receptors that respond to agonists by increasing intracellular calcium release and trafficking to endosomes. Moreover, systemic agonist administration attenuates locomotion in KIs as it does in control animals. Mapping receptor protein expression at regional and cellular levels, located NTSR1-Venus on the soma and dendrites of dopaminergic SN/VTA neurons. Direct monitoring of receptor endocytosis, as a proxy for activation, enabled profiling of NTSR1 agonists in neurons, as well as acute SN/VTA containing brain slices. Taken together, NTSR1-Venus animals express traceable receptors that will improve understanding of NTSR1 and DA activities and more broadly how GPCRs act in vivo.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".