A New Enhanced, Rapid and Precise Sample Preparation Protocol for Label-free Protein Quantification
Bibliographic record
Abstract
Label-free quantification using liquid chromatography-mass spectrometry (LC-MS) has now become a widely accepted analytical approach for the comparison of differential protein expression levels across multiple samples. One major concern of current label-free strategies is the technical variability introduced at multiple points during sample preparation. Typical workflows require cell lysis with buffers containing detergents, overnight proteolysis, removal of potential interferences such as salts and detergents by solid phase extraction (SPE) and subsequent solvent evaporation and reconstitution prior to analysis. Each of these stages is likely to introduce sample variability. Here we present a new strategy, which incorporates an acid-cleavable detergent in the lysis buffer, one-hour digestion with a temperature stable, immobilized enzyme and no requirement for SPE clean-up. The entire sample preparation stage takes less than three hours from cell pellet to autosampler vial and sample handling is kept to an absolute minimum. Our data demonstrate a significant reduction in the technical variability of sample preparation compared to a typical protocol along with a dramatic time saving with no cost in terms of qualitative peptide identifications.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".