Absolute Quantitation of Glycoforms of Two Human IgG Subclasses Using Synthetic Fc Peptides and Glycopeptides
Bibliographic record
Abstract
Immunoglobulins, such as immunoglobulin G (IgG), are of prime importance in the immune system. Polyclonal human IgG comprises four subclasses, of which IgG1 and IgG2 are the most abundant in healthy individuals. In an effort to develop an absolute MALDI-ToF-MS quantitative method for these subclasses and their Fc N-glycoforms, (glyco)peptides were synthesized using a solid-phase approach and used as internal standards. Tryptic digest glycopeptides from monoclonal IgG1 and IgG2 samples were first quantified using EEQYN(GlcNAc)STYR and EEQFN(GlcNAc)STFR standards, respectively. For IgG1, a similar glycopeptide where tyrosine (Y) was isotopically labelled was used to quantify monoclonal IgG1 that had been treated with the enzyme Endo-F2, i.e., yielding tryptic glycopeptide EEQYN(GlcNAc)STYR. The next step was to quantify single subclasses within polyclonal human IgG samples. Although ion abundances in the MALDI spectra often showed higher signals for IgG2 than IgG1, depending on the spotting solvent used, determination of amounts using the newly developed quantitative method allowed to obtain accurate concentrations where IgG1 species were predominant. It was observed that simultaneous analysis of IgG1 and IgG2 yielded non-quantitative results and that more success was obtained when subclasses were quantified one by one. More experiments served to assess the respective extraction and ionization efficiencies of EEQYNSTYR/EEQFNSTFR and EEQYN(GlcNAc)STYR/EEQFN(GlcNAc)STFR mixtures under different solvent and concentration conditions. Graphical Abstract ᅟ.
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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 | 0.000 | 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 | 0.000 |
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".