Comprehensive Analysis of Low Molecular Weight Serum Proteome Enrichment for Mass Spectrometric Studies
Bibliographic record
Abstract
RATIONALE: The low molecular weight (LMW) proteins present in circulating body fluids, such as serum and plasma, hold biological significance as possible biomarkers. A major obstacle in mass spectrometry-based proteomics of serum is the presence of abundant high molecular weight proteins which mask the identification and quantitation of lower molecular weight proteins. Traditional methods involve the use of affinity resins to remove high molecular weight proteins, such as albumin and immunoglobulin G, with concomitant loss of lower molecular weight proteins. Considering the importance of depleting high molecular proteins, this paper compares an affinity resin, a gel-filter, and an acetonitrile (ACN) precipitation method to achieve successful removal of high molecular weight proteins and recovery of lower molecular weight proteins. METHODS: Serum enrichment was carried out by multiple methods such as with the commercially available serum protein mini kit, ACN precipitation, and a gel filter method. Mass spectrometric runs were carried out on an AB SCIEX ESI QTOF 5600 mass spectrometer. Mass spectrometry analysis of the enriched serum obtained by ACN precipitation and gel filter method was performed for global proteome profiling. Quantitative mass spectrometry using isobaric tags for relative and absolute quantitation (iTRAQ) for ACN-precipitated enriched serum was also carried out. RESULTS: The gel filter method, though allowing for the resolution and identification of LMW proteins, was better suited for global proteome analysis and not preferred for quantitative proteomic experiments. In contrast, enrichment by the ACN precipitation method allowed for the reproducible identification and quantitation of LMW proteins having molecular weight ≥4 kDa. CONCLUSIONS: Using only chilled ACN and centrifugation, most of the highly abundant proteins were successfully removed from the serum, while recovering a significant portion of the LMW proteome. A more rapid protocol, which is compatible with iTRAQ labeling, to achieve improved results has been elucidated, thus allowing for better screening and identification of potential biomarkers.
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 | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".