Distinct dynamics of antigen-specific induction and differentiation of different CD11c+Tbet+ B-cell subsets
Bibliographic record
Abstract
Background CD11c + Tbet + B cells are enriched in autoimmunity and chronic infections and also expand on immune challenge in healthy individuals. CD11c + Tbet + B cells remain an enigmatic B-cell population because of their intrinsic heterogeneity. Objectives We investigated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antigen-specific development and differentiation properties of 3 separate CD11c + B-cell subsets—age-associated B cells (ABCs), double-negative 2 (DN2) B cells, and activated naive B cells—and compared them to their canonical CD11c − counterparts. Methods Dynamics of the response of the 3 CD11c + B-cell subsets were assessed at SARS-CoV-2 vaccination in healthy donors by spectral flow cytometry. Distinct CD11c + B-cell subsets were functionally characterized by optimized in vitro cultures. Results In contrast to a durable expansion of antigen-specific CD11c − memory B cells over time, both ABCs and DN2 cells were strongly expanded shortly after second vaccination and subsequently contracted. Functional characterization of antibody-secreting cell differentiation dynamics revealed that CD11c + Tbet + B cells were primed for antibody-secreting cell differentiation compared to relevant canonical CD11c − counterparts. Conclusion Overall, CD11c + Tbet + B cells encompass heterogeneous subpopulations, of which primarily ABCs as well as DN2 B cells respond early to immune challenge and display a pre–antibody-secreting cell phenotype.
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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) | 0.002 | 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".