Differential Localization of Effector and Memory CD8 T Cell Subsets in Lymphoid Organs during Acute Viral Infection
Bibliographic record
Abstract
It is unclear where within tissues subsets of effector and memory CD8 T cells persist during viral infection and whether their localization affects function and long-term survival. Following lymphocytic choriomeningitis virus infection, we found most killer cell lectin-like receptor G1 (KLRG1)(lo)IL-7R(hi) effector and memory cells, which are long-lived and high proliferative capacity, in the T cell zone of the spleen. In contrast, KLRG1(hi)IL-7R(lo) cells, which appear terminally differentiated and have shorter life spans, were exclusively localized to the red pulp. KLRG1(lo)IL-7R(hi) T cells homed to the T cell zone using pertussis toxin-sensitive chemokine receptors and appeared to contact gp38(+) stromal cells, which produce the chemokines CCL19 and CCL21 and the T cell survival cytokine IL-7. The transcription factors T-bet and B lymphocyte-induced maturation protein-1 controlled effector CD8 T cell splenic migration. Effector CD8 T cells overexpressing T-bet homed to the red pulp, whereas those lacking B lymphocyte-induced maturation protein-1 homed to the T cell zone. Upon memory formation, CD62L(+) memory T cells were predominantly found in the T cell zone, whereas CD62L(-) cells were found in the red pulp. Thus, effector and memory CD8 T cell subset localization within tissues is linked to their differentiation states, and this may identify anatomical niches that regulate their longevity and homeostasis.
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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".