Spatial transcriptomics reveals unique inflammatory signatures across all anatomic locations in postoperative Crohn’s disease
Bibliographic record
Abstract
BACKGROUND AND AIMS: Most patients with Crohn's disease (CD) who have undergone ileocolonic resection experience recurrent inflammation within 1 year after surgery. We examined the molecular basis underlying gastrointestinal inflammation in postoperative CD across 3 common anatomic locations of recurrence. METHODS: To characterize spatial transcriptomic signatures, this study utilized biopsies from the colon, neo-terminal ileum, and anastomosis of patients with postoperative CD in the PREDICT-OR study. Sample analyses were performed with 10X Genomics Visium CytAssist system V2.0, and data analyses with R. RESULTS: Histologically inflamed biopsies from all locations shared transcriptional signatures across 3 cellular niches (myeloid, B, T cells) and a specialized epithelial cell type expressing inflammation-associated genes. Differentially expressed genes overexpressed inflammatory pathway activity across the 3 locations, whereas hypoxic pathways were less apparent. In addition to genes for known treatment targets, epidermal growth factor receptor and mitogen-activated protein kinase pathways were upregulated. Cellular niches shaped inflammatory microenvironments through endoplasmic reticulum stress and extracellular matrix remodeling signaling. CONCLUSIONS: Application of spatial transcriptomics revealed a common disease signature for postoperative CD across the colon, neo-terminal ileum, and anastomosis. Inflamed biopsies from all locations demonstrated similar immune cell and inflammatory gene expression patterns as opposed to hypoxic pathways, and unique inflammatory pathways were revealed.
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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 | 0.000 |
| 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".