ECCO Fellowship Generation of an IBD single-cell multiome ATLAS for deeper understanding of IBD sub-phenotypes

Abstract

Abstract Background and Aims Inflammatory Bowel Disease (IBD) is one of the most prevalent gastrointestinal diseases worldwide (1, 2, 3). Disabling symptoms affect the quality of life of IBD patients, and incurred disability and healthcare costs result in a high socio-economic burden (1, 4, 5). Despite new biologicals and small-molecule drugs, patients deal with high rates of non-response, requiring therapy switching without evidence-based guidance on optimal therapy choices for each specific disease condition. Methods In an effort to better understand the disease pathomechanisms underlying IBD, an essential role has been played by single-cell RNA sequencing (scRNAseq). Two scRNAseq studies, one in Crohn’s disease (CD) (Martin e.a., Cell 2019), and one in ulcerative colitis (UC) (Smillie e.a., Cell 2019), characterized the molecular and functional heterogeneity of phenotypically similar IBD cases, and gave us the first clues on the mechanism underlying non-response to anti-TNFa. However, the statistical power of these studies is limited due to their small cohort sizes (6, 7, 8). To acquire further insights into IBD, and to definitively establish the mechanism underlying anti-TNFa non-response without having to generate costly new single-cell experiments, we propose harmonizing smaller IBD scRNAseq studies and performing large-scale meta-analyses. Specifically, our goal is to start with meta-analyzing four IBD scRNAseq datasets (two CD, each ~100 ind, and two UC, each ~30 ind) and to perform a powerful meta-analysis. To easily add more datasets we will create a pipeline for integrating scRNAseq data. Anticipated Impact The large meta-analysis will allow us to get more insight into the mechanisms underlying disease subphenotypes, and to get insight into the effect of commonly used IBD therapies (anti-TNFa, thiopurines) on the gut mucosal composition and inflammatory pathways. The datasets and related results will be made publicly available through a single-cell IBD ATLAS web interface, fulfilling the need for an easily queryable large IBD scRNAseq dataset.