Profiling of Glycosphingolipids with SCDase Digestion and HPLC-FLD-MS
Bibliographic record
Abstract
Lipid components of cells and tissues feature a large diversity of structures that present a challenging problem for molecular analysis. Glycolipids from mammalian cells contain glycosphingolipids (GSLs) as their major glycolipid component, and these structures vary in the identity of the glycan headgroup as well as the structure of the fatty acid and sphingosine (Sph) tails. Analysis of intact GSLs is challenging due to the low abundance of these species. Here, we develop a new strategy for the analysis of lyso-GSL (l-GSL), GSL that retain linkage of the glycan headgroup with the Sph base. The analysis begins with digestion of a GSL sample with sphingolipid ceramide N-deacylase (SCDase), followed by labelling with an amine-reactive fluorophore. The sample was then analyzed by HPLC-FLD-MS and quantitated by addition of an external standard. This method was compared analysis of GSL glycans after cleavage by an Endoglycoceramidase (EGCase) enzyme and labeling with a fluorophore (2-anthranilic acid, 2AA). The two methods are complementary, with EGCase providing improved signal (due to fewer species) and SCDase providing analysis of lyso-GSL. Importantly the SCDase method provides Sph composition of GSL species. We demonstrate the method on cultured human cells (Jurkat T cells) and tissue homogenate (porcine brain).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".