Qualitative and Quantitative Assessment on the Use of Magnetic Nanoparticles for Glycopeptide Enrichment
Bibliographic record
Abstract
Glycoproteomics represent the field of study of the dynamic changes occurring among glycoconjugates within the cellular compartments. Changes in glycosylation have been linked to various diseases, including metastatic carcinomas in which the 9 carbon sialic acid moiety has been shown to play a prominent role. The common method used to study these aberrant changes most often includes a mass spectrometer at some stage in the workflow. However, serum samples contain many proteins which inhibit the analysis of these glycosylation changes, and ergo, enrichment steps are employed as a measure to help alleviate this ailment. Routinely, this is accomplished using lectins, either alone or in combination, to retrieve proteins with specific sugar linkages within the serum sample. This methodology, although known to be very specific, requires many washing steps, making it a cumbersome addition to a high throughput workflow. Presented here is an alternative protocol using custom-made amine functionalized magnetic nanoparticles (MNP) which are nearly 4× smaller than those used before for similar purposes. The developed protocol is based on both hydrophilic interaction and weak anion exchange principles, allowing it to target glycopeptides but, more specifically, those which contain sialylation. For quantification purposes, tandem mass tags from Thermo Scientific were utilized to compare the enrichment efficiencies between the magnetic nanoparticle method and a commercially available glycopeptide enrichment kit offered through EMD Millipore. The MNP method is fast (~10 min) and simple and can quantitatively and qualitatively enrich sialylated glycopeptides more than the commercially available kit.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".