Single-cell RNA sequencing of circulating immune cells supports inhibition of TNFAIP3 and NFKBIA translation as psoriatic arthritis biomarkers
Bibliographic record
Abstract
Objective To identify biomarkers that distinguish psoriatic arthritis (PsA) from cutaneous psoriasis without arthritis (PsC) and healthy controls (HC) using single cell RNA sequencing (scRNA-seq). Method Peripheral blood mononuclear cell samples from three patients with PsA fulfilling CASPAR criteria, three patients with PsC and two HC were profiled using scRNA-seq. Differentially expressed genes (DEGs) identified through scRNA-seq were validated on classical monocytes, and CD4+ and CD8+ T cell subsets derived from an independent cohort of patients using the NanoString nCounter® platform. Protein expression was measured in CD4+ and CD8+ T cells by immunoblotting. Results A total of 18 immune cell population clusters were identified. Across 18 cell clusters, we identified 234 DEGs. NFKBIA and TNFAIP3 were overexpressed in PsA vs HC and PsC patients. Immunoblotting of the proteins encoded in these genes (IκBα and A20, respectively) showed higher levels in PsA CD4+ T cells compared to HC. Conversely, lower levels were observed in PsA CD8+ T cell lysates compared to HC for both proteins. Conclusion These results suggest that translation of TNFAIP3 and NFKBIA may be inhibited in PsA CD8+ T cells. This study provides insight into the cellular heterogeneity of PsA, showing that non-cell type specific expression of genes associated with the disease can be dysregulated through different mechanisms in distinct cell types.
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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 | 0.000 | 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 | 0.001 |
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".