Limits for resolving tandem mass tag reporter ions with identical integer mass using phase constrained spectrum deconvolution
Bibliographic record
Abstract
ABSTRACT A popular method for peptide quantification relies on isobaric labeling such as tandem mass tags (TMT) which enables multiplexed proteome analyses. Quantification is achieved by reporter ions generated by fragmentation in a tandem mass spectrometer. However, with higher degrees of multiplexing, the smaller mass differences between the reporter ions increase the mass resolving power requirements. This contrasts with faster peptide sequencing capabilities enabled by lowered mass resolution on Orbitrap instruments. It is therefore important to determine the mass resolution limits for highly multiplexed quantification when maximizing proteome depth. Here we defined the lower boundaries for resolving TMT reporter ions with 0.0063 Da mass differences using an ultra-high-field Orbitrap mass spectrometer. We found the optimal method depends on the relative ratio between closely spaced reporter ions and that 64 ms transient acquisition time provided sufficient resolving power for separating TMT reporter ions with absolute ratio changes up to 16-fold. Furthermore, a 32 ms transient processed with phase-constrained spectrum deconvolution provides >50% more identifications with >99% quantified, but with a slight loss in quantification precision and accuracy. These findings should guide decisions on what Orbitrap resolution settings to use in future proteomics experiments relying on TMT reporter ion quantification with identical integer masses.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".