A Novel Ultrahigh-Resolution Y-Injection Multireflecting Time-of-Flight Mass Spectrometer for Bottom-Up Proteomics
Bibliographic record
Abstract
The first results of using a new type of ultrahigh-resolution mass analyzer based on a planar multipass time-of-flight mass spectrometer with periodic reflecting lenses (Y-MRT MS) for bottom-up whole-proteome analysis are presented. The instrument achieves a resolving power in a range of 600,000–800,000 for peptide ions across the whole m / z range, with a high repetition rate of 300 Hz (averaged to 0.5–4 Hz for enhanced dynamic range). In preliminary experiments for human cell lines, MCF-7 and HeLa, single-shot 30 min gradient HPLC separations of 1 μg proteolytic digests yielded, on average, over 4000 protein groups in MS/MS-free proteome analyses using the DirectMS1 method. Combining three technical runs increased these numbers to 4500 protein groups at 1% FDR. Peptide ion mass measurements demonstrated an accuracy of 70–130 ppb across the whole m / z range, with a dynamic range exceeding 10 4 . In DIA mode (SWATH-DIA, 20 Th window, 30 min gradient), 4350 protein IDs were obtained at 1% FDR on average in single-shot LC-MS/MS runs. These results highlight the Y-MRT mass analyzer's potential for bottom-up proteomics. Further improvements in proteome coverage and analysis time are anticipated with optimized HPLC configurations and the integration of gas-phase ion mobility separation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.004 | 0.003 |
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".