T cell receptor ligation induces the formation of dynamically regulated signaling assemblies
Bibliographic record
Abstract
Tcell antigen receptor (TCR) ligation initiates tyrosine kinase activation, signaling complex assembly, and immune synapse formation. Here, we studied the kinetics and mechanics of signaling complex formation in live Jurkat leukemic T cells using signaling proteins fluorescently tagged with variants of enhanced GFP (EGFP). Within seconds of contacting coverslips coated with stimulatory antibodies, T cells developed small, dynamically regulated clusters which were enriched in the TCR, phosphotyrosine, ZAP-70, LAT, Grb2, Gads, and SLP-76, excluded the lipid raft marker enhanced yellow fluorescent protein-GPI, and were competent to induce calcium elevations. LAT, Grb2, and Gads were transiently associated with the TCR. Although ZAP-70-containing clusters persisted for more than 20 min, photobleaching studies revealed that ZAP-70 continuously dissociated from and returned to these complexes. Strikingly, SLP-76 translocated to a perinuclear structure after clustering with the TCR. Our results emphasize the dynamically changing composition of signaling complexes and indicate that these complexes can form within seconds of TCR engagement, in the absence of either lipid raft aggregation or the formation of a central TCR-rich cluster.
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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 | 0.000 |
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".