Comparative spatial transcriptomics of hair follicle-T cell interactions in mouse, dog and human reveals conserved drivers of primary cicatricial alopecia
Bibliographic record
Abstract
Abstract Primary cicatricial alopecias (PCA) encompass several autoimmune disorders characterized by scarring hair loss. Many of these conditions are lymphocytic and are thought to be driven by T cell populations. Here, we sought to characterize potential T cell-hair follicle communication pathways in the microanatomical niche using spatial transcriptomics across 3 mammalian species including a novel mouse model, spontaneous disease in companion dogs and human archival diagnostic biopsies. Flow cytometry of mouse model skin confirmed loss of CD34+ bulge cells and keratinocytes, and bulk microarray and histology revealed expression of collagens and development of fibrosis. In vivo ear imaging in mice engrafted with Kikume photoconvertible OT1 CD8+ T cells confirmed long-lived RFP+ T cells in skin arrest near hair follicles and recruit other GFP+ T cells. OT1 T cells expressed CD69, CD103, CD122 and CD62L, which is a binding partner of CD34. Digital spatial profiling using CD3, CD8 and CD45 cell masking identified CXCR3 ligands and IFN response genes in hair follicles, and “metabolic” pathways in T cells, which were also recapitulated in dog and human biopsies. Bulk human RNA as well as spatial analysis of perifollicular T cells confirmed enrichment of CD69 and SELL/CD62L . Different pathways predominated in other CD3+ regions of interest in CD4+ driven conditions including mucocutaneous lupus erythematosus and subacute cutaneous lupus erythematosus. Last, we identify novel drug-targetable pathways, namely CFD and S100A8/9 , that could be further explored to disrupt processes in these conditions through veterinary and human trials.
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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".