Enhanced Protein Detection Using a Trapping Mode on a Hybrid Quadrupole Linear Ion Trap (Q-Trap)
Bibliographic record
Abstract
A novel method to improve the detection of protein ions using a linear ion trap mass spectrometer is presented. A scan function combining charge separation with segmented transmission of multiply charged ions was developed to enhance the sensitivity and resolution of the linear ion trap for the nanoLC-MS analysis of intact proteins. The analytical benefits of the present method are particularly apparent in protein analyses, where the increased proportion of multiply charged ions can exacerbate space-charge effects and compromise the dynamic range of the linear ion trap instrument. The enhanced ion storage and charge separation capabilities of our targeted and enhanced multiply charged scan mode provided a 4-fold increase in signal-to-noise and 5-fold increase in resolution, thus enabling the detection of closely related protein isoforms. The application of this method is demonstrated for low femtomole detection of protein standards and nuclear extracts enriched in histone proteins. The enhanced resolution of this scan mode also enabled us to monitor subtle changes in the methylation of a subpopulation of histone H3 that occurs in chicken DT40 cells lacking specific methyltransferase activity. The extent of the fold change and PTM site localization was performed using predictive software tools and targeted multiple reaction monitoring analysis of histone peptides. Monomethylation of Lys 79 in histone H3 (H3K79me1) was down regulated by 240-fold in methyltransferase deficient cells.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".