Evaluating the Characteristics of Reporter Ion Signal Acquired in the Orbitrap Analyzer for Isobaric Mass Tag Proteome Quantification Experiments
Bibliographic record
Abstract
Multiplexed quantification with isobaric chemical tags (e.g., TMT, iTRAQ) provides a robust and efficient means to comparatively examine proteome dynamics between several biological states using a mass spectrometer (MS). The quantitative nature of isobaric tags necessitates strict validation of the observed ion signals in the chosen MS detector before differential patterns are extracted between biological states. We present an in-depth analysis of isobaric tag data acquired on current generation Orbitrap MS hardware to illustrate pitfalls in acquisition settings that can negatively impact results. We establish, for the first time, the presence of a notch, a region of no observed values, in the reporter ion distributions from isobaric-labeled peptide mixtures acquired on these instruments. We determine that this notch is present in published data across a wide range of instruments of the same or different type and is isolated to the Orbitrap mass analyzer. We demonstrate that the impact of the notch can be minimized using manipulations of Orbitrap scan parameters and on-column injection amounts. Lastly, using a mixture of synthetic standard peptides we investigated the impact on identification rates and quantification precision. Together, these data highlight an important phenomenon that negatively impacts peptide identification and quantification in the Orbitrap analyzer as well as outlining guidelines to follow to ensure minimization of MS-induced artifacts in isobaric tag experiments resulting from the notch.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".