Selective emergence of antibody-secreting cells in the multiple sclerosis brain
Bibliographic record
Abstract
Abstract BackgroundAlthough distinct brain-homing B cells have been identified in multiple sclerosis (MS), it is unknown how these further evolve to contribute to local pathology. In this study, we explored B-cell maturation in the central nervous system (CNS) of MS and control brain donors and determined their association with immunoglobulin (Ig) production, T-cell presence and lesion formation. MethodsEx vivo multicolor flow cytometry was performed on postmortem blood, cerebrospinal fluid (CSF), meninges and white matter from 28 MS and 10 non-demented control donors. MS brain tissue sections were analyzed with immunostainings and laser-capture microdissection to obtain paired lesional rims and perilesional areas. IgG index and CSF oligoclonal bands were measured with nephelometry, isoelectric focusing and immunoblotting. Blood-derived B cells from MS patients and healthy controls were cocultured under T follicular helper-like conditions to evaluate their ASC-differentiating capacity in vitro.ResultsASC versus B-cell ratios were increased in postmortem CNS compartments of MS but not control donors. Local presence of ASCs associated with a mature CD45low phenotype, focal MS lesional activity, lesional Ig gene expression, and CSF IgG levels as well as clonality. In vitro blood B-cell maturation into ASCs did not differ between MS and control donors. Notably, lesional CD4+ memory T cells positively correlated with the presence of ASCs, which was accompanied by local interaction with T cells.Conclusion These findings provide evidence that local B cells in MS preferentially mature into ASCs, which are largely responsible for both intrathecal and local Ig production. This is especially seen in active MS white matter lesions and likely depends on the interaction with CD4+ memory T cells that are locally present.
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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 | 0.000 |
| 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".