Tissue microarray methodology identifies complement pathway activation and dysregulation in progressive multiple sclerosis
Bibliographic record
Abstract
The complement pathway has potential contributions to both white (WM) and grey matter (GM) pathology in Multiple Sclerosis (MS). A quantitative assessment of complement involvement is lacking. Here we describe the use of Tissue MicroArray (TMA) methodology in conjunction with immunohistochemistry to investigate the localization of complement pathway proteins in progressive MS cortical GM and subcortical WM. Antibodies targeting complement proteins C1q, C3b, regulatory proteins C1 inhibitor (C1INH, complement receptor 1 (CR1), clusterin, factor H (FH) and the C5a anaphylatoxin receptor (C5aR) were utilised alongside standard markers of tissue pathology. All stained slides were digitised for quantitative analysis. We found that numbers of cells immunolabelled for HLA-DR, GFAP, C5aR, C1q and C3b were increased in WM lesions (WML) and GM lesions (GML) compared to normal appearing WM (NAWM) and GM (NAGM), respectively. The complement regulators C1INH, CR1, FH and clusterin were more abundant in WM lesions, while the number of C1q+ neurons were increased and the number of C1INH+, clusterin+, FH+ and CR1+ neurons decreased in GM lesions. The number of complement component positive cells (C1q, C3b) correlated with complement regulator expression in WM, but there was no statistical association between complement activation and regulator expression in the GM. We conclude that TMA methodology and quantitative analysis provides evidence of complement dysregulation in MS GML, including an association of the numerical density of C1q+ cells with tissue lesions. Our work confirms that complement activation and dysregulation occur in all cases of progressive MS and suggest that complement may provide potential biomarkers of the 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 | 0.000 | 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) | 0.002 | 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".