Robustness and repeatability of GlycoWorks RapiFluor-MS IgG <i>N</i>-glycan profiling in a long-term high-throughput glycomic study
Bibliographic record
Abstract
Protein glycosylation is the attachment of a carbohydrate moiety to a protein backbone affecting both structure and function of the protein. Abnormal glycosylation is associated with various diseases, and some of the changes in glycosylation are detectable even before symptom development. As such, glycans have emerged as compelling new biomarker candidates. A wide range of analytical methods exist for small-scale glycan analyses. However, there is a growing need for highly robust and reproducible high-throughput techniques that allow for large-scale glycoprofiling. Here, we describe the evaluation of robustness and repeatability of immunoglobulin G (IgG) N-glycan analysis using the GlycoWorks RapiFluor-MS N-Glycan Kit followed by hydrophilic interaction ultra-high-performance liquid chromatography (HILIC-UHPLC) from 335 technical replicates of human plasma randomly distributed across 67 96-well plates. The data was collected over a 5-month period using multiple UHPLC systems and chromatographic columns. Following relative IgG N-glycan quantification in acquired chromatograms, data analysis showed that the most abundant peaks that together made up for three-fourths of the detected IgG N-glycome all had coefficients of variation (CVs) lower than 2%. The highest CVs ranging from 16 to 29% accompanied low abundance glycan peaks with the individual relative peak area below 1% that together made up for <2% of the detected IgG N-glycome. These results show that the tested method is very robust and repeatable, making it suitable for the IgG N-glycan analysis of a large number of samples in a high-throughput manner over a longer period of time.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".