A multi-omic pipeline identifies complement as a driver of age-dependent progression in a model of multiple sclerosis
Bibliographic record
Abstract
Age is the strongest predictor of multiple sclerosis (MS) progression, but the reasons for this are poorly understood. We recently described an experimental autoimmune encephalomyelitis (EAE) model that replicates aspects of age-dependent MS progression, including leptomeningeal inflammation and subpial hippocampal pathology. Here, we sought to develop an experimental and computational pipeline to identify and therapeutically target neuroimmune pathways that moderate disease progression in EAE mice. To this end, we performed single-cell RNA sequencing (scRNA-Seq) of leptomeninges from young or old mice at EAE initiation, peak, and recovery (for young mice) vs chronic (for old mice) disease phases. In parallel, we developed a novel approach to terminally collect up to 30 μL of pure cerebrospinal fluid (CSF) from individual young vs old mice across disease stages. Using data-independent acquisition LC-MS/MS we analyzed the global proteome of individual mice and resolved >2300 proteins, which varied systematically in abundance between young and old mice throughout EAE. Integrating scRNA-Seq data with complementary CSF proteome and immunofluorescence imaging, we identified production of complement C3 mRNA in the leptomeninges, C3 protein accumulation in the CSF and C3 activation in the hippocampus as a prominent marker of aged EAE disease. Using an adeno-associated viral (AAV) approach to overexpress the C3 inhibitor Crry at sites of C3 activation in the EAE hippocampus, we found that inhibition of C3 activation in old but not young mice resulted in milder disease. These data suggest that C3 activation in EAE is a mechanism driving age-divergent disease worsening in mice. Using imaging mass cytometry and downstream analysis pipeline, C3 activation products were also found on oligodendroglia in the hippocampus of a subset of progressive MS patient brains that showed evidence of leptomeningeal inflammation and hippocampal demyelination. Taken together, our data identifies complement as a driver of age-dependent progression in EAE that is relevant to the human disease.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".