Proteogenomic immune signatures delineate the landscape of pediatric acquired demyelinating syndromes
Bibliographic record
Abstract
Abstract Approximately 20–30% of children presenting with acquired inflammatory demyelinating syndromes (ADS) have multiple sclerosis (MS). Another 30% harbor serum antibodies against myelin oligodendrocyte glycoprotein and are referred to as having MOG-associated disease (MOGAD). While MS and MOGAD can have similar features, differences in response to immune therapies point to distinct underlying immune mechanisms. To assess potentially distinct immune mechanisms underlying MS and MOGAD, we applied proteogenomics to high quality cryopreserved peripheral blood mononuclear cells collected from patients with ADS prior to institution of immune therapy, as well as from healthy controls. CITE-Seq profiling was applied to a total of 92,716 single cells with equal contribution from 24 children (6 healthy donors; 6 with ADS but neither MS or MOGAD; 6 with MOGAD; and 6 with MS, ascertained with long-term follow-up). Analysis revealed a pan-ADS enrichment of atypical (CD11c+) B cells compared to healthy controls. Children with MS were distinguished from children with MOGAD by MS-specific enrichments of checkpoint-molecule (TIGIT and CD137)-expressing CD8 memory T cells, STAT4+ Th1 CD4 memory T cells and CD56dim/CD16+ NK cells. Overall, our study identifies distinct features of circulating cellular immune profiles that may serve to distinguish children with MS and MOGAD, and provides novel insights into early immune mechanisms that may be involved in each of these conditions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".