Human Serum Proteins Fractionated by Preparative Partition Chromatography Prior to LC-ESI-MS/MS
Bibliographic record
Abstract
Many proteomics studies are limited to the identification of only the most abundant proteins in a sample due to the high sample complexity in most proteomes. We have here addressed this problem by prefractionation of human blood samples using microchromatography. We show that our approach resulted in high-stringency tryptic peptides identified by LC-ESI-MS/MS. Serum proteins were fractionated by batch and stepwise preparative chromatography using various types of chromatography resins (propyl sulfate, quaternary amine, diethylaminoethanol, cibachron blue, phenol Sepharose, carboxy methyl sepharose, hydroxyl apatite, heparin, concanavalin A and protein G) that were compared. The efficacy of sample fractionation was determined by protein assays, electrophoresis, and mass spectrometry. Tryptic peptides were separated by C18 liquid chromatography with electrospray ionization via metal needle at 2 microL/min with ion trap tandem mass spectrometry. The MS/MS spectra were correlated to some 4396 distinct sequences of the human forward RefSeq by X!TANDEM. Of these, 61% have been detected by other algorithms, but 3219 (73%) were never previously reported from blood by X!TANDEM. The use of a simple apparatus for making gravity microchromatography columns that permits the rapid side-by-side fractionation of many serum samples is described. Disposable microcolumns rapidly prepared blood samples for LC ESI-MS/MS that detected both tissue and cell leakage proteins known to exist in the approximately 1 ng/mL range and some circulating receptor sequences. Our results demonstrate that the depletion of albumin or IgG was not necessary prior to LC-MS/MS and that multiple forms of protein chromatography will be useful for complete identification of blood proteins.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".