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

## TL;DR

This study creates a detailed atlas of inflammatory bowel diseases by combining molecular, histopathological, and clinical data from over 1,000 patients.

## Contribution

The study introduces IBDome, an integrated atlas combining multi-omics, histopathology, and clinical data to better understand and manage IBD.

## Key findings

- Transcriptomic analysis identified site-specific inflammatory signatures in Crohn’s disease and ulcerative colitis.
- A serum protein signature was developed to reflect intestinal molecular inflammation.
- Deep learning models accurately predicted histologic disease activity from tissue images.

## Abstract

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. 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. 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 from images of H&Estained intestinal tissue sections, offering a robust tool for clinical evaluation. Our integrative analysis highlights the potential of combining multi-omics and advanced computational approaches to improve our understanding and management of IBD.