Marginal zone B cells have higher basal BCR signaling that correlates with altered mobility and spatial organization of IgM-BCRs
Bibliographic record
Abstract
Abstract Marginal zone (MZ) B cells exist in a partially-activated ‘primed’ state but the molecular basis for this priming is not fully understood. We found that MZ B cells exhibit greater antigen-independent ‘tonic’ BCR signaling than naïve follicular (FO) B cells. Because BCR signaling output is dependent on BCR spatial organization and BCR-BCR interactions, we hypothesized that the increased tonic BCR signaling in MZ B cells is due to altered lateral mobility and nanoscale organization of BCRs. Single-particle tracking showed that surface IgM-BCRs on MZ B cells have higher diffusion coefficients and decreased confinement compared to IgM-BCRs on FO B cells. In contrast, the mobility and confinement of IgD-BCRs was similar on MZ and FO B cells. To assess BCR nanoscale organization, we used dSTORM and a novel graph-based clustering algorithm. This revealed that IgM-BCRs were more dispersed and less clustered on MZ B cells than on FO B cells, whereas IgD-BCR spatial organization was similar on the two cell populations. Importantly, 3-color STED imaging revealed that phospho-CD79 nanoclusters overlapped to a much greater extent with IgM-BCRs than with IgD-BCRs on FO B cells, and that this IgM-pCD79 overlap was even greater in MZ B cells. MZ B cells also exhibited greater pCD79 signaling in response to membrane-bound antigens than FO B cells. Thus, MZ B cells have greater tonic and antigen-dependent BCR signaling, which correlates with altered IgM-BCR mobility and organization
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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".