DOP35 Spatial transcriptomics reveals cellular niches associated with histological inflammation in postoperative Crohn’s Disease
Bibliographic record
Abstract
Abstract Background Postoperative Crohn’s disease (CD) recurs in ~70% of patients ≤1 year following ileocolonic resection (ICR), as observed endoscopically. Current understanding of molecular determinants underlying histological intestinal inflammation in patients with postoperative CD recurrence is minimal. To address this issue, we used advanced spatial transcriptomic analyses on intestinal biopsy samples with matched histological and endoscopic assessments from neoterminal ileum, anastomosis, and colon of patients with CD who had undergone colonoscopy following ICR. Methods Formalin-fixed paraffin-embedded (FFPE) tissue biopsies from neoterminal ileum, anastomosis, and colon were prospectively collected from a 15-patient cross-sectional cohort with CD who underwent routine endoscopic assessment following ICR. Spatial transcriptomics was performed on samples using 10X Genomics Visium CytAssist Spatial Gene Expression for FFPE V2.0. Annotation of tissue regions of histologically active inflammation and classification of histologically inactive (Robarts Histopathology Index ≤3 with subscores of 0 for neutrophils in the lamina propria and epithelium) or active disease were performed by an expert gastrointestinal pathologist. R programming language was used for data analysis (Figure). Results Fourteen of the 43 (32.6%) FFPE biopsies had histological evidence of inflammation. Spatial transcriptomics clustering revealed a distinct transcriptional signature by the segregation of 4 clusters ("inflamed clusters") composed mostly (≥69%) of cells from samples defined by histologically active disease. Tissue regions identified as inflamed by expert pathologist annotation contributed to >46% of inflamed clusters’ composition and <15% of remaining clusters. Cell type decomposition and cellular niche analysis revealed immune (e.g., myeloid, T cells, and B cells) and epithelial cells in close proximity sharing a similar microenvironment in 3 inflamed clusters while the 4th cluster was composed of >90% epithelial cells. Differentially expressed genes from the inflamed clusters were similarly expressed across intestinal segments including inflamed anastomotic biopsies and were enriched for signalling pathways involving immune cells recruitment/activation, inflammatory signalling, extracellular matrix organisation, and oxidative stress response. Conclusion This is the first study to explore spatial transcriptomic signatures associated with histological inflammation in patients with postoperative CD recurrence. Our analysis revealed a spatial signature of cellular niches and signalling pathways arising upon histological inflammation, providing insight into therapeutic targets and biomarkers across anatomic locations and the anastomosis.
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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) | 0.002 | 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".