Reconstitution models to evaluate natural killer T cell function in tumor control
Bibliographic record
Abstract
Natural killer T (NKT) cells are glycolipid-reactive T lymphocytes that function in immunosurveillance and immune regulation. However, reduced tumor control in NKT cell-deficient Jα18(-/-) mice may be confounded by an overall reduction in T-cell receptor (TCR) repertoire diversity in these animals. Mechanistic studies are also hindered by a lack of tools to target molecules specifically in NKT cells. To address these issues, we developed protocols to expand functional NKT cells and stably reconstitute them in Jα18(-/-) mice. In vivo delivery of α-galactosylceramide (α-GalCer)-loaded dendritic cells expanded NKT cells in wild-type mice without skewing CD4 or TCR Vβ expression profiles. Expanded NKT cells exhibited enhanced cytokine responses upon re-stimulation with glycolipid or CD3 ligation. Adoptive transfer of recently expanded wild-type or interferon (IFN)-γ(-/-) NKT cells protected recipient Jα18(-/-) mice from B16 melanoma metastasis without the need for additional glycolipid stimulation. However, NKT cell reconstitution in recipient Jα18(-/-) mice was short lived. Long-term reconstitution was only achieved when expanded NKT cells were transferred into sublethally irradiated recipients. Thirty days after transfer, NKT cell numbers, phenotype and α-GalCer-induced cytokine responses were equivalent to naive wild-type mice. Jα18(-/-) recipients reconstituted with wild-type or IFN-γ(-/-) NKT cells were both protected from B16 melanoma metastasis following α-GalCer treatment, and NK cell transactivation was intact in mice reconstituted with IFN-γ(-/-) NKT cells. These studies validate the use of reconstitution protocols to investigate the mechanisms of NKT cell immune function, demonstrating that NKT cell-derived IFN-γ and the altered TCR repertoire in Jα18(-/-) mice do not impact NKT cell-mediated antitumor responses.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".