Deep immunophenotyping reveals circulating activated lymphocytes in individuals at risk for rheumatoid arthritis
Bibliographic record
Abstract
Rheumatoid arthritis (RA) is a systemic autoimmune disease currently with no universally highly effective prevention strategies. Identifying pathogenic immune phenotypes in at-risk populations prior to clinical onset is crucial to establishing effective prevention strategies. Here, we applied multimodal single-cell technologies (mass cytometry and CITE-Seq) to characterize the immunophenotypes in blood from at-risk individuals (ARIs) identified through the presence of serum antibodies against citrullinated protein antigens (ACPAs) and/or first-degree relative (FDR) status, as compared with patients with established RA and people in a healthy control group. We identified significant cell expansions in ARIs compared with controls, including CCR2+CD4+ T cells, T peripheral helper (Tph) cells, type 1 T helper cells, and CXCR5+CD8+ T cells. We also found that CD15+ classical monocytes were specifically expanded in ACPA-negative FDRs, and an activated PAX5lo naive B cell population was expanded in ACPA-positive FDRs. Further, we uncovered the molecular phenotype of the CCR2+CD4+ T cells, expressing high levels of Th17- and Th22-related signature transcripts including CCR6, IL23R, KLRB1, CD96, and IL22. Our integrated study provides a promising approach to identify targets to improve prevention strategy development for RA.
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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 | 0.001 | 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".