Cellular landscape of the esophageal epithelium in systemic sclerosis
Bibliographic record
Abstract
Abstract Systemic sclerosis (SSc) is a rare autoimmune disease characterized by vasculopathy and progressive fibrosis of the skin and internal organs. Individuals with SSc often suffer from chronic acid reflux and dysphagia due to loss of esophageal motility, but this pathogenesis is poorly understood. Recently, studies have suggested that esophageal epithelial cells (EECs) may play a central role in the pathogenesis of SSc esophageal dysmotility. In this study, we performed a thorough transcriptomic investigation of the SSc esophageal epithelium in humans to determine whether distinct changes in EECs contribute to esophageal impairment in SSc. We performed single-cell RNA sequencing of paired proximal and distal esophageal mucosa biopsies from 10 individuals with SSc, 4 comparator individuals with gastroesophageal reflux disease (GERD), and 6 healthy controls (HCs), yielding 230,720 EECs across 40 samples. SSc and GERD samples had significantly fewer terminally differentiated, superficial cells than HCs, and differential gene expression between conditions was primarily limited to superficial EECs. Gene dysregulation in SSc was highly correlated with GERD, but was relatively greater in the proximal region, including unique dysregulation of immune mediators that correlated with esophageal dysmotility. This work sheds light on the cellular roots of esophageal dysfunction in SSc and serves as an atlas to guide future efforts to identify actionable targets in the esophagus in SSc.
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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".