Conventional Dendritic Cells Are Required for the Activation of Helper-Dependent CD8 T Cell Responses to a Model Antigen After Cutaneous Vaccination with Lentiviral Vectors
Bibliographic record
Abstract
Cutaneous vaccination with lentiviral vectors generates systemic CD8 T cell responses that have the potential to eradicate tumors for cancer immunotherapy. However, although s.c. immunization with <1 million lentiviral particles clearly primes cytotoxic T cells, vaccination with much higher doses has routinely been used to define the mechanisms of T cell activation by lentiviral vectors. In particular, experiments to test presentation of lentiviral Ags by dendritic cells (DC) require injection of high viral titers, which may result in aberrant transduction of different DC populations. We exploited inducible murine models of DC depletion to investigate which DC prime the lentiviral response after s.c. immunization with low doses of lentiviral particles. In this article, we demonstrate that conventional DC are required to present Ag to CD8 T cells in draining lymph nodes. Langerhans cells are not required to activate the effector response, and neither Langerhans cells nor plasmacytoid DC are sufficient to prime Ag-specific T cells. Immunization drives the generation of endogenous long-lived memory T cells that can be reactivated to kill Ag-specific targets in the absence of inflammatory challenge. Furthermore, lentiviral vaccination activates expansion of endogenous CD4 Th cells, which are required for the generation of effector CD8 T cells that produce IFN-γ and kill Ag-specific targets. Collectively, we demonstrate that after cutaneous immunization with lentiviral particles, CD4-licensed lymph node conventional DC present Ag to CD8 T cells, resulting in the generation of protective endogenous antitumor immunity that may be effective for cancer immunotherapy.
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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 | 0.001 |
| 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".