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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".