IBDome: An Integrated Molecular, Histopathological, and Clinical Atlas of Inflammatory Bowel Diseases

Multiomic and multimodal datasets with detailed clinical annotations offer significant potential to advance understanding of inflammatory bowel diseases (IBDs), refine diagnostics, and enable personalized therapeutic strategies.

In this multicohort study, an extensive multiomic and multimodal analysis of 1002 clinically annotated patients with IBD and non-IBD controls was performed, 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.

Transcriptomic profiles of normal and inflamed tissues revealed distinct site-specific inflammatory signatures in Crohn's disease and ulcerative colitis. Leveraging serum proteomics, an inflammatory protein severity signature that reflects underlying intestinal molecular inflammation was developed. Furthermore, foundation model-based deep learning accurately predicted histologic disease activity scores and enabled Crohn's disease vs ulcerative colitis classification from images of H&E-stained intestinal tissue sections, offering a robust tool for clinical evaluation.

This integrative, publicly available multiomics resource for IBD research highlights the potential of combining multiomics and advanced computational approaches to improve understanding and management of IBD.