Enhancing tandem MS sensitivity and peptide identification via ion pre-accumulation in an Orbitrap mass spectrometer
Bibliographic record
Abstract
Abstract High-throughput mass spectrometry-based proteomics has gained increasing interest for both academic and industrial applications. As implementation of faster gradients has facilitated higher sample throughput, mass spectrometers must adapt to shorter analysis times by enhancing scanning speed and sensitivity. For Orbitrap™ mass spectrometers, faster scan rates are constrained by the need for sufficient ion accumulation time, particularly given limitations on duty cycle at high repetition rate, and transient length, which determines analyzer sensitivity and resolving power. In this context, implementing alternative ion scheduling and better ion signal-processing strategies are needed to unleash the speed of these instruments. Here, we introduce a new scanning strategy termed pre-accumulation, which enables the storage of ions in the bent flatapole in parallel to the operation of the C-trap/IRM, leading to a significant improvement in ion beam utilization and enabling for the first time scanning speeds of >70 Hz on hybrid Orbitrap instruments. The combination of pre-accumulation and increased scan speeds notably enhances peptide and protein group identifications for short LC gradients and improves sensitivity for high-throughput applications. These benefits were further amplified when coupled with the full mass range phase-constrained spectrum deconvolution method (ΦSDM), especially for fast, lower-resolving Orbitrap measurements used with short LC gradients. Overall, we demonstrate that pre-accumulation of ions in the bent flatapole offers distinct advantages, particularly for conditions with reduced signal input. Since no hardware changes are required, this approach is highly attractive for Orbitrap mass spectrometers operated with fast MS/MS acquisition methods.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".