Enhanced Sensitivity in Proteomics Experiments Using FAIMS Coupled with a Hybrid Linear Ion Trap/Orbitrap Mass Spectrometer
Bibliographic record
Abstract
We describe the use and application of high-field asymmetric waveform ion mobility spectrometry combined with nanoscale liquid chromatography mass spectrometry (nanoLC-FAIMS-MS) to improve the sensitivity and dynamic range of proteomics analyses on a hybrid LTQ-Orbitrap mass spectrometer. The ability of FAIMS to enrich multiply protonated peptides against background ions confers a marked advantage in proteomics analyses by decreasing the limits of detection to facilitate the identification of low-abundance peptide ions. These multiply charged ions are recorded into separate acquisition channels to enhance the overall population of detectable peptide ions from a single analysis. NanoLC-FAIMS-MS experiments performed on peptides spiked into complex proteins digests provided more than 10-fold improvement in limits of detection compared to conventional nanoelectrospray mass spectrometry. This enhancement of sensitivity is reflected by a 55% increase in the number of assigned MS/MS spectra contributing to an overall improvement in protein identification and sequence coverage. The application of FAIMS in label-free quantitative proteomics is demonstrated for the identification of differentially abundant proteins from human U937 monocytic cells exposed to phorbol ester.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".