Skin-Resident <i>γδ</i> T Cells Exhibit Site-Specific Morphology and Activation States
Bibliographic record
Abstract
Skin-resident γδ T cells play an important role in maintaining the immune barrier at the epithelial surface. Their roles in wound healing, regulation of immune response to injury, and reepithelialization have been characterized extensively in the mouse, though their function in human skin remains largely unknown. Human skin-resident γδ T cells sparsely populate the skin and are often small and rounded in appearance. Those in the mouse ear and back, which line the dermal barrier, are highly arborized cells with many processes extending from the cell body. To date, these cells have been studied primarily in the mouse ear and back; however, it is important to further identify and characterize γδ T cells in other body sites to better understand their function and study their contribution to injury and disease. We developed a novel method to visualize these cells in the skin (whole-mount and cryosections) that when combined with flow cytometry allowed us to assess differences in skin-resident γδ T cell numbers, morphology, and activation state in the ear, back, and footpad (chosen for their importance in immunological and pain research). In comparing cell length, number of dendritic processes, and expression of the activation marker CD69, we found that γδ T cell morphology and activation states vary significantly among the three tissue environments. Specifically, γδ T cells in the footpad are smaller, have fewer processes, and show the highest levels of activation compared to back- and ear-resident cells. Our observations suggest that our understanding of skin-resident γδ T cell functionality, drawn from the experiments performed in the ear and back tissue, may not be applicable to all skin environments. The footpad-resident cells also more closely resemble γδ T cells in human skin, suggesting that cells in this tissue environment may serve as a better translational model when studying γδ T cell function/activity.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".