Methylamidation for Sialoglycomics by MALDI-MS: A Facile Derivatization Strategy for Both α2,3- and α2,6-Linked Sialic Acids
Bibliographic record
Abstract
Neutralization of carboxylic acid is an important means to avoid sialic acid dissociation when sialylated glycans are analyzed by matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS). In this paper, we describe a simple and rapid method to modify the sialic acids of sialylated glycans in the presence of methylamine and (7-azabenzotriazol-1-yloxy) trispyrrolidinophosphonium hexafluorophosphate (PyAOP). After methylamidation, sialylated glycans can be analyzed by MALDI-MS without loss of the sialic acid moiety. The electrospray ionization mass spectrometry (ESI-MS) and MALDI-MS analysis of both 3'- and 6'-sialyllactose derivatives indicated that the quantitative conversion of sialic acids was achieved, regardless of their linkage types. This derivatization strategy was further validated with the N-glycans released from three standard glycoproteins (fetuin, human acid glycoprotein, and bovine acid glycoprotein) containing different types of complex glycans. Most importantly, this derivatization method enabled the successful characterization of N-glycans of sera from different species (human, mouse, and rat) by MALDI-MS. Because of the mild reaction conditions, the modification in sialic acid residues can be retained. This improvement makes it possible to detect sialylated glycans containing O-acetylated sialic acid moieties using MALDI-MS in positive-ion mode.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".