Dynamic Micropatterning Reveals Spatial Dynamics of B Cell Receptor Signaling and Immune Synapse Formation
Bibliographic record
Abstract
B cell activation by foreign antigens, recognized by the B cell receptor (BCR), is the key prerequisite for cell differentiation for antibody production. B cells typically encounter antigens bound to the surface of antigen presenting cells (APCs), leading to the formation of the immunological synapse (IS). To gain a deeper understanding of lymphocyte activation, we developed a dynamic micropatterning technique that enables the modeling of IS formation with exceptional spatial and temporal control. Using this method, we can image B cells before and after BCR engagement, in both fixed and live samples. We compared the activation of different BCR proximal signaling proteins in activatory and non-activatory areas of the synapse and found that the activated signaling proteins exhibited distinct spatial distributions. While pCD79A was strongly localized in the antigen-tethered area, surprisingly, pPLCγ2 was enriched in regions lacking BCR ligands. We also visualized the formation of the IS in living cells using enhanced-resolution microscopy in 3D. We identified different cell behaviors during this process, including the repurposing of pre-existing actin-based protrusions as ready-made building blocks for the IS — a feature uniquely detectable with this highly controllable system. Extending our approach to include a co-stimulatory B cell ligand, ICAM-1, and T cell system using CD3 and CD28 antibodies as ligands, we demonstrate the broader applicability of this method. Overall, our results highlight the power of dynamic micropatterning in elucidating the rapid and dynamic earliest steps of the IS formation with high spatial and temporal precision.
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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 | 0.001 |
| 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".