Use of a Small Reporter Molecule To Determine Cell-Surface Proteins by Capillary Electrophoresis and Laser-Induced Fluorescence: Use of 5-SAENTA-x8f for Quantitation of the Human Equilibrative Nucleoside Transporter 1 Protein
Bibliographic record
Abstract
The human equilibrative nucleoside transporter 1 protein (hENT1) is a major mediator of cellular entry of nucleosides and anticancer nucleoside drugs; its assay is important in understanding and diagnosing chemotherapy resistance. Here we present a novel assay for quantifying hENT1 using capillary electrophoresis with laser-induced fluorescence detection (CE-LIF). A cellular population is treated with 5'-S-(2-aminoethyl)-N6-(4-nitrobenzyl)-5'-thioadenosine-x8-fluorescein (5-SAENTA-x8f), which binds with high affinity and specificity to the hENT1 protein. The cells are washed to remove excess reagent, lysed, and centrifuged, and the supernatant is analyzed by CE-LIF with the use of an internal standard. Accuracy was evaluated by comparing the capillary electrophoresis results with those obtained by flow cytometry; the results were highly correlated, r = 0.96. The relative standard deviation of the hENT1 assay was 10%, determined from nine independent assays of the same cell line, which is 3 times superior to results obtained in a flow cytometry assay. The detection limit for 5-SAENTA-x8f was 4300 molecules injected into the capillary.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".