Single cell BCR and transcriptome analysis after respiratory virus infection reveals spatiotemporal dynamics of antigen-specific B cell responses
Bibliographic record
Abstract
SUMMARY B cell responses are a critical component of anti-viral immunity. However, a comprehensive picture of antigen-specific B cell responses, differentiation, clonal proliferation and dynamics in different organs after infection is lacking. Here, we combined single-cell RNA sequencing with single-cell B cell receptor (BCR) characterization of antigen-specific cells in the draining lymph nodes, spleen and lungs after influenza infection. We identify several novel B cell subpopulations forming after infection and find organ-specific differences that persist over the course of the response. We discover important transcriptional differences between memory cells in lungs and lymphoid organs and describe organ-restricted clonal expansion. Strikingly, by combining BCR mutational analysis, monoclonal antibody expression and affinity measurements we find no differences between germinal center (GC)-derived memory and plasmacells, at odds with an affinity-based selection model. By linking antigen-recognition with transcriptional programming, clonal-proliferation and differentiation, these finding provide important advances in our understanding of antiviral B cell immunity.
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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".