Resident memory T cells in the lymph node balance localized and systemic protection
Bibliographic record
Abstract
Abstract The endogenous mechanisms that protect against pathogen infection in the lymph node (LN) and LN metastasis during early tumor development remain poorly understood. Resident memory T cells (Trm), which do not circulate, provide highly localized protection to both pathogens and cancer, and correlate with better clinical outcomes. Here we track LN Trm development using models of cutaneous viral infection to determine the kinetics and requirements for their differentiation. We show that after viral skin infection, Trm form in the draining LN concurrent with their establishment at the initial site of infection in skin. LN Trm formed specifically in LNs draining infected skin expressing cognate antigen and did not form in contralateral controls. These data supported a hypothesis that T cell egress out of infected skin through lymphatic vessels was necessary for LN Trm formation. Consistent with this hypothesis, LN Trm were absent from mice lacking dermal lymphatic vessels indicating that either fluid transport or cellular transport from the skin to the LN was necessary for the establishment of LN Trm. To decouple LN priming from T cell egress we resected infected skin days 5 and 20 post infection. Ear resection at 5 days post infection impaired Trm formation but circulating memory T cell responses were unaffected. We demonstrated that LN Trm are poised for cell killing as evidenced by constitutive expression of granzymes, and were able to protect against viral rechallenge. These findings indicate that LN Trm are poised to provide rapid, localized protection specific to the draining LN. These data may suggest that LN’s harbor populations capable of controlling regional cancer metastasis and pathogenic infections. Supported by grants from NIH (R01CA238163)
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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 | 0.001 |
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".