Automating Single Subunit Counting of Membrane Proteins in Mammalian Cells
Bibliographic record
Abstract
Elucidating subunit stoichiometry of neurotransmitter receptors is preferably carried out in a mammalian expression system where the rules of native protein assembly are strictly obeyed. Although successful in Xenopus oocytes, single subunit counting, manually counting photobleaching steps of GFP-tagged subunits, has been hindered in mammalian cells by high background fluorescence, poor control of expression, and low GFP maturation efficiency. Here, we present a fully automated single-molecule fluorescence counting method that separates tagged proteins on the plasma membrane from background fluorescence and contaminant proteins in the cytosol or the endoplasmic reticulum and determines the protein stoichiometry. Lower GFP maturation rates observed in cells cultured at 37 °C were partly offset using a monomeric version of superfolder GFP. We were able to correctly identify the stoichiometry of GluK2 and α1 glycine receptors. Our approach permits the elucidation of stoichiometry for a wide variety of plasma membrane proteins in mammalian cells with any commercially available TIRF microscope.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".