Intrapatient comparison of atopic dermatitis skin transcriptome shows differences between tape‐strips and biopsies
Bibliographic record
Abstract
BACKGROUND: Our knowledge of etiopathogenesis of atopic dermatitis (AD) is largely derived from skin biopsies, which are associated with pain, scarring and infection. In contrast, tape-stripping is a minimally invasive, nonscarring technique to collect skin samples. METHODS: To construct a global AD skin transcriptomic profile comparing tape-strips to whole-skin biopsies, we performed RNA-seq on tape-strips and biopsies taken from the lesional skin of 20 moderate-to-severe AD patients and the skin of 20 controls. Differentially expressed genes (DEGs) were defined by fold-change (FCH) ≥2.0 and false discovery rate <0.05. RESULTS: We detected 4104 (2513 Up; 1591 Down) and 1273 (546 Up; 727 Down) DEGs in AD versus controls, in tape-strips and biopsies, respectively. Although both techniques captured dysregulation of key immune genes, tape-strips showed higher FCHs for innate immunity (IL-1B, IL-8), dendritic cell (ITGAX/CD11C, FCER1A), Th2 (IL-13, CCL17, TNFRSF4/OX40), and Th17 (CCL20, CXCL1) products, while biopsies showed higher upregulation of Th22 associated genes (IL-22, S100As) and dermal cytokines (IFN-γ, CCL26). Itch-related genes (IL-31, TRPV3) were preferentially captured by tape-strips. Epidermal barrier abnormalities were detected in both techniques, with terminal differentiation defects (FLG2, PSORS1C2) better represented by tape-strips and epidermal hyperplasia changes (KRT16, MKI67) better detected by biopsies. CONCLUSIONS: Tape-strips and biopsies capture overlapping but distinct features of the AD molecular signature, suggesting their respective utility for monitoring specific AD-related immune, itch, and barrier abnormalities in clinical trials and longitudinal studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".