Investigating Acquisition Performance on the Orbitrap Fusion When Using Tandem MS/MS/MS Scanning with Isobaric Tags
Bibliographic record
Abstract
Methods for isobaric-tagged peptide analysis (e.g., TMT, iTRAQ), such as the synchronous precursor selection (SPS) tandem MS/MS/MS (MS 3 ) approach, enable maintenance of reporter ion accuracy and precision by reducing the ratio compression caused by coisolated precursor ions. However, the decreased throughput of the MS 3 approach necessitates careful optimization of acquisition strategies and methods to ensure maximal proteome coverage. We present a systematic analysis of acquisition parameters used to analyze isobaric-tagged peptide samples on current generation Orbitrap mass spectrometer (MS) hardware. In contrast with previously reported works, we demonstrate the limited utility of acquiring reporter ion data in the ion trap analyzer; ion trap acquisition had only a minimal increase in identification depth and reduced quantification precision. We establish that despite the significantly increased scan rate afforded through the use of higher energy collisional dissociation (HCD) in MS 3 -based ion trap isobaric tag analyses, the reduced quantification precision and reporter ion yields negate the potential benefits in proteome coverage. Lastly, using optimized parameter sets, we further demonstrate the limited utility of the ion trap detector versus the Orbitrap for reporter ion detection in an in-depth analysis of a complex proteome sample. Together, these data will serve as a valuable resource to researchers undertaking analysis on current generation Orbitrap instrumentation with isobaric tags.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".