Oligomeric Size of the M2 Muscarinic Receptor in Live Cells as Determined by Quantitative Fluorescence Resonance Energy Transfer
Bibliographic record
Abstract
Fluorescence resonance energy transfer (FRET), measured by fluorescence intensity-based microscopy and fluorescence lifetime imaging, has been used to estimate the size of oligomers formed by the M(2) muscarinic cholinergic receptor. The approach is based on the relationship between the apparent FRET efficiency within an oligomer of specified size (n) and the pairwise FRET efficiency between a single donor and a single acceptor (E). The M(2) receptor was fused at the N terminus to enhanced green or yellow fluorescent protein and expressed in Chinese hamster ovary cells. Emission spectra were analyzed by spectral deconvolution, and apparent efficiencies were estimated by donor-dequenching and acceptor-sensitized emission at different ratios of enhanced yellow fluorescent protein-M(2) receptor to enhanced green fluorescent protein-M(2) receptor. The data were interpreted in terms of a model that considers all combinations of donor and acceptor within a specified oligomer to obtain fitted values of E as follows: n = 2, 0.495 +/- 0.019; n = 4, 0.202 +/- 0.010; n = 6, 0.128 +/- 0.006; n = 8, 0.093 +/- 0.005. The pairwise FRET efficiency determined independently by fluorescence lifetime imaging was 0.20-0.24, identifying the M(2) receptor as a tetramer. The strategy described here yields an explicit estimate of oligomeric size on the basis of fluorescence properties alone. Its broader application could resolve the general question of whether G protein-coupled receptors exist as dimers or larger oligomers. The size of an oligomer has functional implications, and such information can be expected to contribute to an understanding of the signaling process.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".