Faculty Opinions recommendation of New insights into the DT40 B cell receptor cluster using a proteomic proximity labeling assay.
Bibliographic record
Abstract
Background: B cell receptor (BCR) clusters modulate BCR signaling in B-lymphocytes.Results: We used a quantitative proteomic proximity assay to analyze the BCR cluster in DT40 cells.Conclusion: Our proximity labeling assay identified novel components of the BCR cluster linked to integrin signaling.Significance: We provide new insights into BCR assembly and identify new and unexpected targets for further functional analysis.In the vertebrate immune system, each B-lymphocyte expresses a surface IgM-class B cell receptor (BCR).When cross-linked by antigen or anti-IgM antibody, the BCR accumulates with other proteins into distinct surface clusters that activate cell signaling, division, or apoptosis.However, the molecular composition of these clusters is not well defined.Here we describe a quantitative assay we call selective proteomic proximity labeling using tyramide (SPPLAT).It allows proteins in the immediate vicinity of a target to be selectively biotinylated, and hence isolated for mass spectrometry analysis.Using the chicken B cell line DT40 as a model, we use SPPLAT to provide the first proteomic analysis of any BCR cluster using proximity labeling.We detect known components of the BCR cluster, including integrins, together with proteins not previously thought to be BCR-associated.In particular, we identify the chicken B-lymphocyte allotypic marker chB6.We show that chB6 moves to within about 30 -40 nm of the BCR following BCR cross-linking, and we show that cross-linking chB6 activates cell binding to integrin substrates laminin and gelatin.Our work provides new insights into the nature and composition of the BCR cluster, and confirms SPPLAT as a useful research tool in molecular and cellular proteomics.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.076 | 0.054 |
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".