HABA-based ionic liquid matrices for UV-MALDI-MS analysis of heparin and heparan sulfate oligosaccharides
Bibliographic record
Abstract
Polysulfated carbohydrates such as heparin (HP) and heparan sulfate (HS) are not easily amenable to usual ultraviolet matrix-assisted laser desorption/ionization-mass spectrometry (UV-MALDI)-MS analysis due to the thermal lability of their O- and N-SO(3) moieties, and their poor ionization efficiency with common crystalline matrices. Recently, ionic liquid matrices showed considerable advantages over conventional matrices for MALDI-MS of acidic compounds. Two new ionic liquid matrices (ILMs) based on the combination of 2-(4-hydroxyphenylazo)benzoic acid (HABA) with 1,1,3,3-tetramethylguanidine and spermine were evaluated in the study herein. Both ILMs were successfully applied to the analysis of synthetic heparin oligosaccharides of well-characterized structures as well as to heparan sulfate-derived oligosaccharides from enzymatic depolymerization. HABA-based ILMs showed improved signal-to-noise ratio as well as a decrease of fragmentation/desulfation processes and cation exchange. Sulfated oligosaccharides were detected with higher sensitivity than usual crystalline matrices, and their intact fully O- and N-sulfated species [M-Na](-) were easily observed on mass spectra. MALDI-MS characterization of challenging analytes such as heparin octasaccharide carrying 8-O and 4 N-sulfo groups, and heparin octadecasulfated dodecasaccharide was successfully achieved.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".