Hyaluronan binding identifies the most proliferative activated and memory T cells
Bibliographic record
Abstract
CD44 is expressed on T cells where its ability to bind hyaluronan is tightly regulated. Here, we investigated when T cells bind hyaluronan during an immune response. We found that naïve, murine T cells do not bind fluoresceinated hyaluronan but are induced to bind upon antigen-induced T-cell activation in vitro and in vivo. Hyaluronan binding occurred on proliferating T cells and the percentage of hyaluronan-binding cells correlated with the strength of the activation stimulus. A small percentage of hyaluronan-binding cells persisted after in vitro activation and had a memory phenotype (CD122(+) CD44(hi)). This hyaluronan-binding population increased after culture with IL-7 or IL-15 and proliferated more rapidly than nonbinding cells. In vivo, approximately 20-30% of antigen-specific OT-I CD8(+) memory T cells in the spleen and BM bound hyaluronan. Hyaluronan binding identified memory cells that proliferated faster in IL-7 and IL-15, and enriched for CD62L(+) central memory cells. In vivo homeostatic proliferation induced hyaluronan binding on a small percentage of the most rapidly dividing cells after several cell divisions. This study demonstrates that hyaluronan binding is induced upon antigen-induced T-cell activation and occurs on a percentage of the most proliferative activated and memory T cells.
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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".