Innate memory of human Vγ2Vδ2 T cells: Distinct proliferative, migratory and tumorcidal capabilities of γδ memory subsets (52.17)
Bibliographic record
Abstract
Abstract Human Vγ2Vδ2 T cells are unconventional T cells that monitor isoprenoid metabolism by using their antigen receptors to recognize foreign and self prenyl pyrophosphates. Although important for immunity to infections and for tumor immunotherapy, little is known about the development of γδ memory. Elucidation of γδ differentiation pathways will help to develop ways to boost their immunity. Here we report that there are many unique features compared with αβ T cells. Unlike αβ T cells, at birth half of Vγ2Vδ2 cells have memory phenotypes and ~25-40% express inflammatory chemokine receptors. In adults, 98.4% of Vγ2Vδ2 T cells are memory cells that can be separated into CD28+CD27+ TEarly (central); CD28+CD27- TEarly 27-; CD28-CD27+ TIntermediate (effector); and CD28-CD27- CD45RA+ TLate RA (effector) subsets. The memory subsets display distinct functional properties with differential expression of effector molecules, NK receptors, and chemokine receptors. Intermediate and late effector memory cells express higher levels of cytotoxic effector molecules, innate CXCR1, CXCR2, and CX3CR1 chemokine receptors, KIR, CD16, CD56, and CD57 NK receptors, and lower levels of the β7 integrin, and proliferate poorly compared with early memory cells that express CXCR6, CCR1, and CCR2. Thus, different inflammatory chemokines attract each Vγ2Vδ2 memory subset. The rapid conversion of Vγ2Vδ2 cells from naive to memory allows them to mount memory responses to primary bacterial and protozoan infections.
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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) | 0.003 | 0.001 |
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".