IBDome: an integrated molecular, histopathological, and clinical atlas of inflammatory bowel diseases

BACKGROUND & AIMS: Multi-omic and multimodal datasets with detailed clinical annotations offer significant potential to advance our understanding of inflammatory bowel diseases (IBD), refine diagnostics, and enable personalized therapeutic strategies. METHODS: In this multi-cohort study, we performed an extensive multi-omic and multimodal analysis of 1,002 clinically annotated patients with IBD and non-IBD controls, incorporating whole-exome and RNA sequencing of normal and inflamed gut tissues, serum proteomics, and histopathological assessments from images of H&E-stained tissue sections. RESULTS: Transcriptomic profiles of normal and inflamed tissues revealed distinct site-specific inflammatory signatures in Crohn’s disease (CD) and ulcerative colitis (UC). Leveraging serum proteomics, we developed an inflammatory protein severity signature that reflects underlying intestinal molecular inflammation. Furthermore, foundation model-based deep learning accurately predicted histologic disease activity scores and enabled CD versus UC classification from images of H&E-stained intestinal tissue sections, offering a robust tool for clinical evaluation. CONCLUSIONS: Our integrative, publicly available multi-omics resource for IBD research highlights the potential of combining multi-omics and advanced computational approaches to improve our understanding and management of IBD.