Monitoring G protein-coupled receptor and β-arrestin trafficking in live cells using enhanced bystander BRET
Bibliographic record
Abstract
Endocytosis and intracellular trafficking of receptors are pivotal to maintain physiological functions and drug action; however, robust quantitative approaches are lacking to study such processes in live cells. Here we present new bioluminescence resonance energy transfer (BRET) sensors to quantitatively monitor G protein-coupled receptors (GPCRs) and β-arrestin trafficking. These sensors are based on bystander BRET and use the naturally interacting chromophores luciferase (RLuc) and green fluorescent protein (rGFP) from Renilla. The versatility and robustness of this approach are exemplified by anchoring rGFP at the plasma membrane or in endosomes to generate high dynamic spectrometric BRET signals on ligand-promoted recruitment or sequestration of RLuc-tagged proteins to, or from, specific cell compartments, as well as sensitive subcellular BRET imaging for protein translocation visualization. These sensors are scalable to high-throughput formats and allow quantitative pharmacological studies of GPCR trafficking in real time, in live cells, revealing ligand-dependent biased trafficking of receptor/β-arrestin complexes.
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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) | 0.001 | 0.000 |
| 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 | 0.002 |
| 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".