Live-Cell Nucleocytoplasmic Protein Shuttle Assay Utilizing Laser Confocal Microscopy and FRAP
Bibliographic record
Abstract
In eukaryotes, protein trafficking to and from the nucleus, or shuttling, has been demonstrated to be an important function for proteins that have vital roles in one or both subcellular compartments. Current techniques of detecting protein nuclear shuttling are extremely labor intensive and only statically visualize evidence of shuttling. Fluorescence recovery after photobleaching (FRAP), or fluorescence microphotolysis, has proven to be an effective method of analyzing protein dynamics in live cells, especially when coupled to GFP technology. Here, we describe a relatively simple in vivo protein nuclear shuttling assay that utilizes red fluorescent and green fluorescent protein fusions as substrates for FRAP using a laser confocal microscope. This technique is less time consuming than established shuttle assays, is internally controlled, and visualizes nucleocytoplasmic shuttling in living cells of the same species and cell type. This technique can be potentially used to detect the ability of any nuclear protein to shuttle from the nucleus to any other subcellular compartment for any eukaryotic species in which GFP or dsRed1 fusion protein can be expressed.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".