Simple Capillary Electrophoresis–Mass Spectrometry Method for Complex Glycan Analysis Using a Flow-Through Microvial Interface
Bibliographic record
Abstract
A flow-through microvial is used to interface capillary electrophoresis and mass spectrometry (CE-MS) to develop a method for simultaneous profiling both neutral and sialylated glycans without derivatization or labeling. The CE separation was performed at near-zero electroosmotic flow in a capillary with neutral, hydrophilic coating, using 50 mM ammonium acetate in 20% methanol (pH 3.1) as the background electrolyte. The method was optimized with reversed CE polarity and negative ion ESI-MS. Enzymatically released N-glycans from human immunoglobulin G (IgG) were used as the test sample. The approach was also used to study the more complex N-glycans from recombinant human erythropoietin (rHuEPO) expressed in Chinese hamster ovary (CHO) cells. Glycoscreening of rHuEPO was performed using a triple quadrupole MS and an ultrahigh resolution TOF-MS. The high sensitivity and high mass accuracy of the TOF-MS revealed the presence of more than 70 glycans. Three mono- and di-sialylated tetra-antennary N-glycans and one mono-sialylated tri-antennary N-glycan of rHuEPO are reported for the first time. Further glycan heterogeneity was identified of the highly sialylated N-glycans of rHuEPO by extensive acetylation, Neu5Ac/Neu5Gc variation and the presence of N-acetyl-lactosamine repeats. For comparative purposes, porous graphitic carbon-based LC-MS/MS was also used to glycoprofile rHuEPO. This work demonstrates the potential of CE-MS to provide a comprehensive glycosylation profile with detailed features of the secondary glycan modifications. The CE-MS based method eliminates the need to label the N-glycans, as well as the requirement to desialylate before analysis, and could complement other established techniques for glycan characterization of therapeutic glycoproteins.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".