Enhanced detergent extraction for analysis of membrane proteomes by two-dimensional gel electrophoresis
Bibliographic record
Abstract
BACKGROUND: The analysis of hydrophobic membrane proteins by two-dimensional gel electrophoresis has long been hampered by the concept of inherent difficulty due to solubility issues. We have optimized extraction protocols by varying the detergent composition of the solubilization buffer with a variety of commercially available non-ionic and zwitterionic detergents and detergent-like phospholipids. RESULTS: After initial analyses by one-dimensional SDS-PAGE, quantitative two-dimensional analyses of human erythrocyte membranes, mouse liver membranes, and mouse brain membranes, extracted with buffers that included the zwitterionic detergent MEGA 10 (decanoyl-N-methylglucamide) and the zwitterionic lipid LPC (1-lauroyl lysophosphatidylcholine), showed selective improvement over extraction with the common 2-DE detergent CHAPS (3 [(3-cholamidopropyl)dimethylammonio]-1-propanesulfonate). Mixtures of the three detergents showed additive improvements in spot number, density, and resolution. Substantial improvements in the analysis of a brain membrane proteome were observed. CONCLUSION: This study demonstrates that an optimized detergent mix, coupled with rigorous sample handling and electrophoretic protocols, enables simple and effective analysis of membrane proteomes using two-dimensional electrophoresis.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
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".