A ML-framework for the discovery of next-generation IBD targets using a harmonized single-cell atlas of patient tissue

Abstract Target discovery for IBD has traditionally relied on genetic associations, which lack the cellular resolution needed to identify novel, actionable, cell type-specific disease pathways. Here, we describe an integrated analytical and experimental framework that leverages harmonized single-cell data to systematically discover novel therapeutic strategies for IBD. We used AMICA DBTM, Immunai’s harmonized database of single-cell RNA datasets to construct a harmonized 1 million single-cell atlas of the human intestine. We applied a machine learning framework (Immune Patient Representation, IPR) to identify disease-associated transcriptional programs and cell type-specific gene targets. Candidate targets were prioritized using atlas-derived metrics, refined using custom criteria emphasizing translational actionability, and validated across independent clinical cohorts. Select candidates were evaluated in human primary-cell models reflecting the target’s cell-type context. The IPR framework identified 85 disease-associated transcriptional programs and ranked 400 cell type-specific target genes across immune and stromal lineages. Disease-associated programs were interpreted using a structured AI-assisted reasoning framework for structured biological reasoning, linking them to IBD-relevant pathways and guiding the identification of novel, promising gene targets. Functional validation of two cell-type-specific candidates, PTGIR in myeloid cells and IL6ST in fibroblasts, confirmed the reduction of inflammatory and fibrotic pathways linked to IBD pathology. Multi-omic profiling and projection of in vitro phenotypes to patient datasets demonstrated the reversal of disease-associated programs via mechanisms distinct from those of existing biologics. Our single-cell anchored, machine-learning framework integrates in silico discovery with experimental validation, revealing new cell type-specific therapeutic opportunities and supporting a scalable approach for precision target discovery in IBD and other immune-mediated diseases. Competing Interest Statement All authors declare potential competing interests. Authors affiliated with Immunai and AstraZeneca are employees of their respective organizations and may hold equity or stock options as part of standard compensation. The authors' institutions have filed provisional patent applications related to aspects of the work described in this manuscript. No other financial, professional, or personal conflicts relevant to this manuscript are reported. Footnotes This version of the manuscript has been revised to update the authorship list. - Abbreviations - aTNF - anti–tumor necrosis factor antibody - CD - Crohn’s disease - DEG - differentially expressed gene - IBD - inflammatory bowel disease - IPR - Immune Patient Representation - LAM - lipid-associated macrophage - MNP - mononuclear phagocyte - scRNA-seq - single-cell RNA sequencing - UC - ulcerative colitis - UMAP - uniform manifold approximation and projection