From Cells to Peptides: “One-Stop” Integrated Proteomic Processing Using Amphipols
Bibliographic record
Abstract
In proteomics, detergents and chaotropes are indispensable for proteome analysis, not only for protein extraction, but also for protein digestion. To increase the protein extraction efficiency, detergents are usually added in the lysis buffer to extract membrane proteins out of membrane structure and to maintain protein in solutions. In general, these detergents need to be removed prior to protein digestion, usually by precipitation or ultrafiltration. Digestion often takes place in the presence of chaotropic reagents, such as urea, which often need to be removed prior to mass spectrometry. The addition and removal of detergents and chaotropes require multiple steps that are time-consuming and can cause sample losses. Amphipols (APols) are a different class of detergents that have physical and solubilization properties that are distinct from conventional detergents. They have primarily been used in protein structure analysis for membrane protein trapping and stabilization. Here, we demonstrate a simple and rapid protocol for total and membrane proteome preparation using APols. We demonstrate that APols added for cell lysis help maintain the proteome in solution, are compatible with protein digestion using trypsin, and can readily be removed prior to mass spectrometry by a one-step acidification and centrifugation. This protocol is much faster, can be performed in a single tube, and can readily replace the conventional detergent/chaotrope approaches for total and membrane proteome 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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.007 |
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".