NSOM/QD-based nanoscale immunofluorescence imaging of antigen-specific T-cell receptor responses during an in vivo clonal Vγ2Vδ2 T-cell expansion
Bibliographic record
Abstract
Nanoscale imaging of an in vivo antigen-specific T-cell immune response has not been reported. Here, the combined near-field scanning optical microscopy- and fluorescent quantum dot-based nanotechnology was used to perform immunofluorescence imaging of antigen-specific T-cell receptor (TCR) response in an in vivo model of clonal T-cell expansion. The near-field scanning optical microscopy/quantum dot system provided a best-optical-resolution (<50 nm) nano-scale imaging of Vgamma2Vdelta2 TCR on the membrane of nonstimulated Vgamma2Vdelta2 T cells. Before Ag-induced clonal expansion, these nonstimulating Vgamma2Vdelta2 TCRs appeared to be distributed differently from their alphabeta TCR counterparts on the cell surface. Surprisingly, Vgamma2Vdelta2 TCR nanoclusters not only were formed but also sustained on the membrane during an in vivo clonal expansion of Vgamma2Vdelta2 T cells after phosphoantigen treatment or phosphoantigen plus mycobacterial infection. The TCR nanoclusters could array to form nanodomains or microdomains on the membrane of clonally expanded Vgamma2Vdelta2 T cells. Interestingly, expanded Vgamma2Vdelta2 T cells bearing TCR nanoclusters or nanodomains were able to rerecognize phosphoantigen and to exert better effector function. These studies provided nanoscale insight into the in vivo T-cell immune response.
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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".