Micro scale chromatography of human plasma proteins for nano LC-ESI-MS/MS
Bibliographic record
Abstract
Organic precipitation of proteins with acetonitrile demonstrated complete protein recovery and improved chromatography of human plasma proteins. The separation of 25 μL of human plasma into 22 fractions on a QA SAX resin facilitated more effective protein discovery despite the limited sample size. Micro chromatography of plasma proteins over quaternary amine (QA) strong anion exchange (SAX) resins performed best, followed by diethylaminoethyl (DEAE), heparin (HEP), carboxymethyl cellulose (CMC), and propyl sulfate (PS) resins. Two independent statistical methods, Monte Carlo comparison with random MS/MS spectra and the rigorous X!TANDEM goodness of fit algorithm protein p-values corrected to false discovery rate q -values ( q ≤ 0.01) agreed on at least 12,000 plasma proteins, each represented by at least three fully tryptic corrected peptide observations. There was qualitative agreement on 9393 protein/gene symbols between the linear quadrupole versus orbital ion trap but also quantitative agreement with a highly significant linear regression relationship between log observation frequency (F value 4,173, p-value 2.2e-16) . The use of a QA resin showed nearly perfect replication of all the proteins that were also found using DEAE-, HEP-, CMC-, and P S-based chromatographic methods combined and together estimated the size of the size of the plasma proteome as ≥12,000 gene symbols. • Precipitation of plasma required for excellent protein chromatography. • Comparison of plasma proteins for one-step versus step-wise chromatography. • Quaternary amine (ammonia) chromatography of plasma protein shows best results. • Combination of chromatographic methods yields large plasma proteome from X!TANDEM.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".