Regulatory T cells exhibit distinct lymph node homing and trafficking kinetics as compared to conventional CD4+ T cells
Bibliographic record
Abstract
Abstract Regulatory T cells (Tregs) express the canonical marker Foxp3 and are critical for suppressing the immune response in homeostatic and inflammatory conditions. Despite how widely studied CD4+Foxp3+ Tregs are in mice, their physiologic trafficking and scanning behavior of antigen-presenting cells in secondary lymphoid organs (SLOs) have yet to be quantitatively analyzed in the intravital setting. We have previously described the dynamics of T cell trafficking through SLOs and scanning of dendritic cells (DCs), revealing that CD4+ T cells and CD8+ T cells exhibit different transit kinetics through lymph nodes (LNs). On average, CD4+ T cells spend less time scanning for self-peptide/MHC (self-pMHC), dwell shorter in LNs, and egress faster compared to CD8+ T cells. Here we use flow cytometry and 2-photon laser scanning microscopy (2-PLSM) to quantitate the homeostatic trafficking behavior and surveillance strategy of Tregs in LNs and to compare their behavior to conventional T cells (Tconvs). Our data reveal significant differences in Treg versus Tconv homing and transit through peripheral and mesenteric LNs. We find that Tregs with an effector phenotype are preferentially retained in LNs, and complementary transwell assays demonstrate that Tregs have blunted response to CCL21 compared to Tconvs. Finally, we use intravital 2-PLSM to quantify the contribution of self-pMHC recognition to Treg kinetics and scanning of DCs in peripheral and mesenteric LNs, taking advantage of a novel gut SLO stabilization device allowing up to 10 hours of continuous imaging. These results add quantitative data to the emerging paradigm that self-pMHC interactions dynamically control homeostatic Treg retainment and localized suppressive function in LNs.
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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) | 0.002 | 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".