Sulfatide Analysis by Mass Spectrometry for Screening of Metachromatic Leukodystrophy in Dried Blood and Urine Samples
Bibliographic record
Abstract
BACKGROUND: Metachromatic leukodystrophy (MLD) is an autosomal recessive disorder caused by deficiency in arylsulfatase A activity, leading to accumulation of sulfatide substrates. Diagnostic and monitoring procedures include demonstration of reduced arylsulfatase A activity in peripheral blood leukocytes or detection of sulfatides in urine. However, the development of a screening test is challenging because of instability of the enzyme in dried blood spots (DBS), the widespread occurrence of pseudodeficiency alleles, and the lack of available urine samples from newborn screening programs. METHODS: We measured individual sulfatide profiles in DBS and dried urine spots (DUS) from MLD patients with LC-MS/MS to identify markers with the discriminatory power to differentiate affected individuals from controls. We also developed a method for converting all sulfatide molecular species into a single species, allowing quantification in positive-ion mode upon derivatization. RESULTS: In DBS from MLD patients, we found up to 23.2-fold and 5.1-fold differences in total sulfatide concentrations for early- and late-onset MLD, respectively, compared with controls and pseudodeficiencies. Corresponding DUS revealed up to 164-fold and 78-fold differences for early- and late-onset MLD patient samples compared with controls. The use of sulfatides converted to a single species simplified the analysis and increased detection sensitivity in positive-ion mode, providing a second option for sulfatide analysis. CONCLUSIONS: This study of sulfatides in DBS and DUS suggests the feasibility of the mass spectrometry method for newborn screening of MLD and sets the stage for a larger-scale newborn screening pilot study.
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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 | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".