DMS as an orthogonal separation to LC/ESI/MS/MS for quantifying isomeric cerebrosides in plasma and cerebrospinal fluid
Bibliographic record
Abstract
Cerebrosides, including glucosylceramides (GlcCers) and galactosylceramides (GalCers), are important membrane components of animal cells with deficiencies resulting in devastating lysosomal storage disorders. Their quantification is essential for disease diagnosis and a better understanding of disease mechanisms. The simultaneous quantification of GlcCer and GalCer isomers is, however, particularly challenging due to their virtually identical structures. To address this challenge, we developed a new LC/MS-based method using differential ion mobility spectrometry (DMS) capable of rapidly and reproducibly separating and quantifying isomeric cerebrosides in a single run. We show that this LC/ESI/DMS/MS/MS method exhibits robust quantitative performance within an analyte concentration range of 2.8-355 nM. We further report the simultaneous quantification of nine GlcCers (16:0, 18:0, 20:0, 22:0, 23:0, 24:1, 24:0, 25:0, and 26:0) and five GalCers (16:0, 22:0, 23:0, 24:1, and 24:0) molecular species in human plasma, as well as six GalCers (18:0, 22:0, 23:0, 24:1, 24:0 and 25:0) and two GlcCers (24:1 and 24:0) in human cerebrospinal fluid. Our method expands the potential of DMS technology in the field of glycosphingolipid analysis for both biomarker discovery and drug screening by enabling the unambiguous assignment and quantification of cerebroside lipid species in biological samples.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".