Gel-Eluted Liquid Fraction Entrapment Electrophoresis: An Electrophoretic Method for Broad Molecular Weight Range Proteome Separation
Bibliographic record
Abstract
Although well-established as a technique for protein purification, the application of continuous elution tube gel electrophoresis to proteome fractionation remains problematic. Difficulties associated with sample collection, particularly at the high mass range or at low sample loadings, continue to plague the technique. Furthermore, an upper mass limit is imposed as slow-moving higher molecular weight proteins are progressively diluted during the collection phase. In short, with current technology, effective separation over a broad mass range has not been achieved. In this work, we present improved techniques for continuous elution tube gel electrophoresis to accommodate broad mass range separation of proteins. Our device enables rapid partitioning of a proteome into discrete mass range fractions in the solution phase. High recovery is achieved at submicrogram to milligram sample loadings. We demonstrate comprehensive, reproducible separations of protein mixtures, as well as separation of a proteome in as fast as 1 h, over mass ranges from below 10 to 250 kDa. Finally, we identified proteins from a prefractionated standard protein mixture using liquid chromatography tandem mass spectrometric (LC-MS/MS) analysis.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".