Single cell analysis of transitional B cells reveals autocrine IFNβ sustains a dynamic type I IFN network in lupus
Bibliographic record
Abstract
Abstract Increased selection of transitional B cells reactive with nucleic acid antigens is a prominent feature of systemic lupus erythematosus (SLE). We found that transitional stage 1 (T1) B cells from both human SLE patients and BXD2 lupus mice intrinsically express significantly higher levels of IFNα and IFNβ compared to normal individuals. IFNβ expression in T1 B cells was significantly correlated with the development of mature autoreactive 9G4+ B cells in SLE patients and La+ B cells in BXD2 mice. IFNβ was also specifically required for optimal B cell survival and TLR7 responses. In single T1 B cells isolated from Ifnb−/− vs. WT B6 bone marrow chimeras, upregulation of Ifnα7 was completely abrogated in single Ifnb−/− T1 B cells, while other genes (Ifna1, Ifna4, Cd86, Tlr7) were significantly but incompletely diminished implicating both autocrine and paracrine IFNβ activity. To resolve these signals in an autoimmune environment, single cell gene expression analysis was carried out on sorted BXD2 T1 B cells. Hierarchical clustering revealed three distinct gene expression patterns. Cluster 1 cells were “IFN producers” characterized by high expression of type I IFN genes, but low co-expression of IFN receptor genes, Ifnar1 and Ifnar2. Cluster 2 “IFN responders” exhibited the highest co-expression of both Ifnar1 and Ifnar2 as well as immunomodulatory IFN response genes (Irg1, IL6, Cd69, Cd86). Cluster 3 had low expression of type I IFN and IFNAR genes. Clusters 1 and 2 exhibited the highest Tlr7 expression, while Cluster 3 had the highest expression of Baffr and Tlr9. The heterogeneity of IFN signatures at the T1 stage suggests that varying interferonogenic phenomena can differentially prime T1 B cells for subsequent responses and cell fates.
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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 | 0.000 |
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".