Deconstruction of immune-mediated skin diseases defines six inflammatory subtypes
Bibliographic record
Abstract
BACKGROUND: Most chronic skin diseases are classified as immune-mediated disorders, and targeted treatments, such as those blocking the cytokines IL-17, IL-23, IL-4/13, and the JAK-STAT pathway, have revolutionized therapy. However, a substantial proportion of patients fail to achieve remission and remain refractory to these therapies. METHODS: We meta-analyzed heterogeneous cell clusters in different immune-mediated skin diseases to understand the cell states and molecular pathways contributing to pathogenic heterogeneity. RESULTS: Therefore, we re-analyzed single-cell RNA-sequencing of 166 skin tissues, including 605 030 cells, to build a single-cell atlas of immune-mediated skin diseases. The results were validated using an independent patient cohort and in vitro fibroblast experiments. Samples were categorized into six groups, termed inflammatory phenotypes (IPs), each characterized by distinct variations in inflammatory pathways. These IPs offered new insights into inflammatory abnormalities and shared characteristics across different skin conditions. Disease-relevant cell states, cytokines, and genes were systematically mapped to individual IPs. Furthermore, cell-type abundance, cytokine infiltration, and metabolic heterogeneity were found to modulate shifts among Th1, Th2 and Th17/Th22 pathways. IPs were dynamic in individuals and could predict treatment response. Among our cohort, patients with unfavorable therapeutic outcomes exhibited distinct inflammatory patterns, characterized by elevated Th2 in psoriasis and elevated Th17/Th22 in atopic dermatitis. CONCLUSION: This comprehensive atlas and molecular-based stratification of immune-mediated skin diseases provide new insights into the 'pan-inflammation' features of cell states, which could help guide the development of targeted therapies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".