Improvement in Peptide Detection for Proteomics Analyses Using NanoLC−MS and High-Field Asymmetry Waveform Ion Mobility Mass Spectrometry
Why this work is in the frame
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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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it