SenSkin™: a human skin-specific cellular senescence gene set
Bibliographic record
Abstract
Cellular senescence gene sets have been leveraged to overcome the inadequate sensitivity or specificity of single markers. However, growing evidence of heterogeneity among tissues in senescent cell phenotypes and gene expression profiles has highlighted the need for tissue-specific gene sets. SenSkin™ was curated by an expert review of literature on cellular senescence in the skin and characterized with pathway analysis. To validate SenSkin™, it was evaluated for enrichment with chronological aging in a bulk RNA-sequencing (RNA-seq) dataset and a pseudobulk RNA-seq dataset. Further, changes to SenSkin™ in different skin cell types with photoaging were evaluated in two single-cell RNA-seq datasets. SenSkin™ predominantly included genes related to the senescence-associated secretory phenotype (SASP), which were associated with metabolism and multiple aspects of immune responses. SenSkin™ was more enriched in chronologically aged skin than other commonly used cellular senescence and aging gene sets. In scRNA-seq, SenSkin™ displayed significant upregulation due to photoaging in ten skin cell types. In conclusion, SenSkin™ is a human skin-specific senescence gene set validated in chronological aging and photoaging, which may be more effective at detecting senescent cells in the skin than non-tissue-specific gene sets.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".