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

Plattner, Christina; Sturm, Gregor; Kühl, Anja A.; Atreya, Raja; Carollo, Sandro; Rieder, Dietmar; Gronauer, Raphael; Günther, Michael; Ormanns, Steffen; Manzl, Claudia; Wirtz, Stefan; Meneghetti, Asier Rabasco; Hegazy, Ahmed N.; Patankar, Jay V.; Carrero, Zunamys I.; Grabherr, Felix; Meyer, Moritz; Adolph, Timon E.; Tilg, Herbert; Neurath, Markus F.; Kather, Jakob Nikolas; Becker, Christoph; Siegmund, Britta; Trajanoski, Zlatko

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

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Item Type:	Article
Title:	IBDome: an integrated molecular, histopathological, and clinical atlas of inflammatory bowel diseases
Creators Name:	Plattner, Christina, Sturm, Gregor, Kühl, Anja A., Atreya, Raja, Carollo, Sandro, Rieder, Dietmar, Gronauer, Raphael, Günther, Michael, Ormanns, Steffen, Manzl, Claudia, Wirtz, Stefan, Meneghetti, Asier Rabasco, Hegazy, Ahmed N., Patankar, Jay V., Carrero, Zunamys I., Grabherr, Felix, Meyer, Moritz, Adolph, Timon E., Tilg, Herbert, Neurath, Markus F., Kather, Jakob Nikolas, Becker, Christoph, Siegmund, Britta and Trajanoski, Zlatko
Abstract:	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.
Keywords:	Crohn's Disease, Ulcerative Colitis, Multi-Omics Database, Artificial Intelligence
Source:	Gastroenterology
ISSN:	0016-5085
Publisher:	Elsevier / AGA Institute
Date:	10 June 2026
Additional Information:	Leif S. Ludwig and Ashley D. Sanders are members of the TRR241 IBDome Consortium.
Official Publication:	https://doi.org/10.1053/j.gastro.2026.05.023
PubMed:	View item in PubMed
Related to:	URL URL Type https://ibdome.org External Dataset https://doi.org/10.5281/zenodo.18153919 External Dataset https://www.ebi.ac.uk/biostudies/bioimages/studies/S-BIAD1753 External Dataset https://github.com/orgs/ibdome/repositories Software

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