A straightforward approach for bioorthogonal labeling of proteins and organelles in live mammalian cells, using a short peptide tag
Bibliographic record
Abstract
BACKGROUND: In the high-resolution microscopy era, genetic code expansion (GCE)-based bioorthogonal labeling offers an elegant way for direct labeling of proteins in live cells with fluorescent dyes. This labeling approach is currently not broadly used in live-cell applications, partly because it needs to be adjusted to the specific protein under study. RESULTS: We present a generic, 14-residue long, N-terminal tag for GCE-based labeling of proteins in live mammalian cells. Using this tag, we generated a library of GCE-based organelle markers, demonstrating the applicability of the tag for labeling a plethora of proteins and organelles. Finally, we show that the HA epitope, used as a backbone in our tag, may be substituted with other epitopes and, in some cases, can be completely removed, reducing the tag length to 5 residues. CONCLUSIONS: The GCE-tag presented here offers a powerful, easy-to-implement tool for live-cell labeling of cellular proteins with small and bright probes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".