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

Joglekar, A.; Joseph, A.; Honsa, P.; Ruppova, K.; Pizzarella, V.; Honan, A.; Mediratta, D.; Vollmer, E.; Geller, E.; Valny, M.; Macuchova, E.; Zheng, S.; Greenberg, A.; Taus, P.; Kline-Schoder, A.; Konickova, R.; Cerna, L.; Sharim, H.; Ness, L.; Camilli, G.; Chouri, E.; Kaymak, I.; D'Rozario, J.; Castiblanco, D.; Oliveira, J.; Prandi, F.; Popov, N.; Moldoveanu, A. L.; Oliphant, C.; Escudero-Ibarz, L.; Uhlitz, F.; Freinkman, E.; Sponarova, J.; Vijay, P.; Joyce, C.; Leonardi, I.; Nayar, S.; Platt, A.; Ort, T.; De Baets, G.; Corridoni, D.; Wroblewska, A.; Rahman, A.

doi:10.64898/2026.02.06.699999

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bioRxiv · 10.64898/2026.02.06.699999

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

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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, Immunais 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 targets 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.

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Joglekar, A., Joseph, A., Honsa, P., Ruppova, K., Pizzarella, V., Honan, A., Mediratta, D., Vollmer, E., Geller, E., Valny, M., Macuchova, E., Zheng, S., Greenberg, A., Taus, P., Kline-Schoder, A., Konickova, R., Cerna, L., Sharim, H., Ness, L., Camilli, G., Chouri, E., Kaymak, I., D'Rozario, J., Castiblanco, D., Oliveira, J., Prandi, F., Popov, N., Moldoveanu, A. L., Oliphant, C., Escudero-Ibarz, L., Uhlitz, F., Freinkman, E., Sponarova, J., Vijay, P., Joyce, C., Leonardi, I., Nayar, S., Platt, A., Ort, T., De Baets, G., Corridoni, D., Wroblewska, A., Rahman, A.. 2026-02-09. A ML-framework for the discovery of next-generation IBD targets using a harmonized single-cell atlas of patient tissue. https://doi.org/10.64898/2026.02.06.699999

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