Improvement in Peptide Detection for Proteomics Analyses Using NanoLC−MS and High-Field Asymmetry Waveform Ion Mobility Mass Spectrometry
Bibliographic record
Abstract
Sensitive and selective detection of multiply charged peptide ions from complex tryptic digests was achieved using high-field asymmetric waveform ion mobility spectrometry (FAIMS) combined with nanoscale liquid chromatography-mass spectrometry (nanoLC-FAIMS-MS). The combination of FAIMS provided a marked advantage over conventional nanoLC-MS experiments by reducing the extent of chemical noise associated with singly charged ions and enhancing the overall population of detectable tryptic peptides. Such advantages were evidenced by a 6-12-fold improvement in signal-to-noise ratio measurements for a wide range of multiply charged peptide ions. An increase of 20% in the number of detected peptides compared to conventional nanoelectrospray was achieved by transmitting ions of different mobilities at high electric field vs low field while simultaneously recording each ion population in separate mass spectrometry acquisition channels. This method provided excellent reproducibility across replicate nanoLC-FAIMS-MS runs with more than 90% of all detected peptide ions showing less than 30% variation in intensity. The application of this technique in the context of proteomics research is demonstrated for the identification of trace-level proteins showing differential expression in U937 monocyte cell extracts following incubation with phorbol ester.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".