Application of spatial omics, multi-omics, and artificial intelligence in inflammatory bowel disease: Current applications and emerging opportunities

Table 1 Conceptual transition from conventional clinical and histopathology-based approaches toward next-generation molecular, spatial, and artificial intelligence frameworks in inflammatory bowel disease

Aspect	Traditional approaches	Emerging approaches
Disease definition	Defined by clinical presentation and histopathological features	Defined by molecular, cellular, and spatial disease states within tissue microenvironments[25-27]
Biomarkers	Detected through single serum or faecal biomarkers (e.g., calprotectin)	Integrated multi-layered molecular signatures across histopathology and omics[28-30]
Inflammation assessment	Analysis of endoscopic and histological scoring systems	Quantitative, spatially resolved inflammation mapped at cellular and subcellular resolution[31-33]
Disease prediction	Often, the disease is detected after the clinical onset	AI-enabled integration of multi-modal molecular, spatial, and clinical data to predict disease risk, progression, and outcomes before or early in the disease course[22-24]
Treatment decisions	Treatment choices depend on empirical evidence, incremental approaches, or a trial-and-error method of increasing therapy	Predictive, mechanism-informed therapy guided by molecular and spatial profiling[34-36]
Clinical trials	Mostly rely on animal models and late-stage human testing	Development and advancement of human-relevant non-animal platforms, including intestinal organoids, gut-on-chip systems, and in silico simulations enabling virtual or hybrid clinical trials in IBD[37-39]

IBD: Inflammatory bowel disease; AI: Artificial intelligence.

Table 2 Summary of representative multi-omics, spatial omics, and artificial intelligence studies in inflammatory bowel disease, highlighting datasets, methodologies, and principal findings

Ref.	Technology/modality	Sample type	Cohort size	Analytical approach	Key findings	Clinical/translational relevance
Uniken Venema et al[43]	Epigenomics (DNA methylation)	Blood	240 IBD, 190 controls	Epigenome-wide methylation profiling	Differential methylation at VMP1, ITGB2	Links genetic risk loci to environmental regulation
Chan et al[65]	Proteomics (LC-MS/MS)	Colonic biopsies	10 UC, 10 controls	Gel-free quantitative proteomics	High level of neutrophil-related proteins (calprotectin, lactotransferrin)	Tissue-level inflammatory biomarkers
Quiroga-Centeno et al[84]	Metabolomics (HPLC)	Serum	> 530 IBD	Targeted metabolite profiling	Low levels of tryptophan correlate with disease activity	Potential metabolic biomarker
Aichler and Walch[103]	Spatial transcriptomics, scRNA-seq	Intestinal tissue	Human IBD biopsies	Single-cell, spatial mapping	IDA macrophages, NRG1-dependent epithelial regeneration	Defines pathogenic immune niches
Lins Neto et al[97]	Imaging mass cytometry	Intestinal tissue	Human IBD	Multiplex spatial proteomics	Disease-specific immune-cell neighborhoods	Spatial immune architecture as a biomarker
Mund et al[104]	10 × Visium spatial transcriptomics	Postoperative CD biopsies	43 biopsies	Spatial pathway analysis	TNF, NF-κB, and JAK-STAT are conserved across sites	Shared inflammatory programs
Garrido-Trigo et al[113]	AI (CNN model)	Endoscopic images	Large UC dataset	Deep learning severity scoring	Continuous inflammation scoring (UCEGS)	Objective endoscopic grading
Nair et al[161]	Blood transcriptomics	Blood	IBD patients	Machine learning classifier	Prognostic biomarker for disease course	Patient stratification
Kim et al[132]	Spatial multi-omics (MicroCart)	Murine colitis	Experimental	Integrated host-microbiome mapping	Coordinated immune epithelial microbial niches	Spatial host-microbiome cartography

LC-MS/MS: Liquid chromatography-mass spectrometry/mass spectrometry; HPLC: High performance liquid chromatography; scRNA-seq: Single-cell RNA-sequencing; CNN: Convolutional neural network; AI: Artificial intelligence; MicroCart: Microbiome cartography; CD: Crohn’s disease; IBD: Inflammatory bowel disease; UC: Ulcerative colitis; VMP1: Vacuole membrane protein 1; ITGB2: Integrin subunit β2; IDA: Inflammation-dependent activated macrophages; TNF: Tumor necrosis factor; NF-κB: Nuclear factor kappa-B; JAK-STAT: Janus tyrosine kinase/signal transducer and activator of transcription; UCEGS: Ulcerative colitis endoscopic index of severity.