Parallelized Acquisition of Orbitrap and Astral Analyzers Enables High-Throughput Quantitative Analysis
Bibliographic record
Abstract
Abstract The growing trend towards high-throughput proteomics demands rapid liquid chromatography-mass spectrometry (LC-MS) cycles that limit the available time to gather the large numbers of MS/MS fragmentation spectra required for identification. Orbitrap analyzers scale performance with acquisition time, and necessarily sacrifice sensitivity and resolving power to deliver higher acquisition rates. We developed a new mass spectrometer that combines a mass resolving quadrupole, the Orbitrap and the novel Asymmetric Track Lossless (Astral) analyzer. The new hybrid instrument enables faster acquisition of high-resolution accurate mass (HRAM) MS/MS spectra compared to state-of-the-art mass spectrometers. Accordingly, new proteomics methods were developed that leverage the strengths of each HRAM analyzer, whereby the Orbitrap analyzer performs full scans with high dynamic range and resolution, synchronized with Astral analyzer’s acquisition of fast and sensitive HRAM MS/MS scans. Substantial improvements are demonstrated over previous methods using current state-of-the-art mass spectrometers. Abstract Figure
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.025 |
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".