Labeling and imaging of GLUT4 in live L6 cells with quantum dots
Bibliographic record
Abstract
GLUT4 is sequestered in intracellular storage compartments in a basal state and is rapidly translocated to the cell surface in response to insulin stimulation. Regulation of GLUT4 distribution is key for maintaining whole-body glucose homeostasis. To investigate the complicated intracellular movement of GLUT4 vesicles and their interactions with organelles in detail, new probes suitable for long-term tracking of cellular events are required. In this study, we introduce for the first time quantum dots (QDs) as a superior probe into the research of the mechanisms of GLUT4 translocation. QDs are light-emitting semiconductor nanoparticles with unique optical and spectroscopic properties, such as broad absorption, narrow and tunable emission, resistance to photobleaching, strong luminescence, and long luminescent lifetimes. Owing to their remarkable photophysical properties and relatively small size, QDs are emerging as an alternative to conventional dyes for fluorescence-based applications. We have developed a procedure for labeling and imaging GLUT4 in live cells with streptavidin-conjugated quantum dot (QD-SA) and demonstrated that QDs contained in cytoplasm have no obvious negative influence on L6 cells. This study provides a sensitive, nontoxic, long-term imaging platform for observing the dynamics and regulated characteristics of GLUT4 transport.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".