Improved Precision of iTRAQ and TMT Quantification by an Axial Extraction Field in an Orbitrap HCD Cell
Bibliographic record
Abstract
Improving analytical precision is a major goal in quantitative differential proteomics as high precision ensures low numbers of outliers, a source of false positives with regard to quantification. In addition, higher precision increases statistical power, i.e., the probability to detect significant differences. With chemical labeling using isobaric tags for relative and absolute quantitation (iTRAQ) or tandem mass tag (TMT) reagents, quantification is based on the extraction of reporter ions from tandem mass spectrometry (MS/MS) spectra. We compared the performance of two versions of the LTQ Orbitrap higher energy collisional dissociation (HCD) cell with and without an axial electric field with regard to reporter ion quantification. The HCD cell with the axial electric field was designed to push fragment ions into the C-trap and this version is mounted in current Orbitrap XL ETD and Orbitrap Velos instruments. Our goal was to evaluate whether the purported improvement in ion transmission had a measurable impact on the precision of MS/MS based quantification using peptide labeling with isobaric tags. We show that the axial electric field led to an increased percentage of HCD spectra in which the complete set of reporter ions was detected and, even more important, to a reduction in overall variance, i.e., improved analytical precision of the acquired data. Notably, adequate precision of HCD-based quantification was maintained even for low precursor ion intensities of a complex biological sample. These findings may help researchers in their design of quantitative proteomics studies using isobaric tags and establish HCD-based quantification on the LTQ Orbitrap as a highly precise approach in quantitative proteomics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".