ACCELERATED DERMAL FIBROBLAST AGING IN CUTANEOUS LUPUS ERYTHEMATOSUS LESIONS
Bibliographic record
Abstract
PV066 / #446 Poster Topic: AS07 - Cutaneous Lupus Background/Purpose The pathogenesis of cutaneous lupus erythematosus (CLE) is not fully understood. While much attention has been given to the inflammatory cell infiltrate, much less is known about changes to stromal and other structural cells. Aged (or senescent) cells can secrete proinflammatory chemokines and cytokines and may contribute to the inflammatory milieu. We aimed to identify whether non-immune cells expressed gene signatures of premature cellular aging in skin samples from patients with SLE. Methods Lesional and non-lesional skin biopsies were obtained from patients with SLE and healthy controls (HC). Samples were enzymatically digested and 3’ scRNA-seq performed using the Chromium 10x Genomics platform. Six aged gene signatures (senCID) from Tao et al.[1] were validated in the GSE130973 dataset of young and old HC skin. Each SID is typically enriched in genes of specific function (SID1: cell proliferation, SID2: lipid and nucleotide synthesis, SID3: mitochondria/redox reactions, SID4: unfolded protein response, SID5: protein ubiquitination, SID6: vesicle loading). Results Of the 6 gene signatures, SID1-5 scores were increased in the fibroblast (FB) and endothelial cell populations in older HC skin compared to younger skin. We then applied these signatures to 6 samples from 3 patients with SLE (3 lesional and 3 non-lesional) and 3 HC. Overall, the gene scores of each SID demonstrated a bimodal pattern in SLE. For most SIDs, the lower gene scores were in T lymphocytes. As FBs may drive persistence of inflammation in other conditions such as rheumatoid arthritis, we focused our analysis on aged FB in CLE skin. While there was no difference in the SID1 score in FBs between groups, SID2 and SID3 were increased in lesional FBs compared to both non-lesional or HC FBs. SID4-6 demonstrated a stepwise increase from HC, to non-lesional and lesional FBs. The expression of SID2-6 also varied between fibroblast subsets. Mechano-sensing FBs (expressing CDH19 ) showed little difference in SID2-6 scores between sample types. In contrast, both immunofibroblast subsets ( TNFSF13B+/CCL19+ and TNFSF13B+/CCL19-) appeared to demonstrate the greatest difference in SID2-6 scores between sample types. Expression of SID4 and SID6 was also markedly increased in lesional CXCL1+ and pro-fibrotic (DPP4+) FB subsets. Conclusions The SID gene modules 1-5 are increased in healthy aged FBs. Distinct FB subsets from cutaneous lesions in SLE show signs of increased cellular aging. These cells express proinflammatory genes and may offer novel therapeutic targets to reduce the persistence of cutaneous inflammation. [1] References: [1.] Tao W. Cell Metabolism 2024;36(5):1126-43.e5.
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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 | 0.001 |
| 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) | 0.004 | 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".