A Treatment-Naïve Cellular Atlas of Pediatric Crohn’s Disease Predicts Disease Severity and Therapeutic Response

AbstractCrohn’s disease is an inflammatory bowel disease (IBD) which most often presents with patchy lesions in the terminal ileum and colon and requires complex clinical care. Recent advances in the targeting of cytokines and leukocyte migration have greatly advanced treatment options, but most patients still relapse and inevitably progress. Although single-cell approaches are transforming our ability to understand the barrier tissue biology of inflammatory disease, comprehensive single-cell RNA-sequencing (scRNA-seq) atlases of IBD to date have largely sampled pre-treated patients with established disease. This has limited our understanding of which cell types, subsets, and states at diagnosis are predictive of disease severity and response to treatment. Here, through a combined clinical, flow cytometric, and scRNA-seq study, we profile diagnostic human biopsies from the terminal ileum of treatment-naïve pediatric patients with Crohn’s disease (pediCD; n=14) and from non-inflamed pediatric controls with functional gastrointestinal disorders (FGID; n=13). To fully resolve and annotate epithelial, stromal, and immune cell states among the 201,883 single-cell transcriptomes, we develop and deploy a principled and unbiased tiered clustering approach, ARBOL, yielding 138 FGID and 305 pediCD end cell clusters. Notably, through both flow cytometry and scRNA-seq, we observe that at the level of broad cell types, treatment-naïve pediCD is not readily distinguishable from FGID in cellular composition. However, by integrating high-resolution scRNA-seq analysis, we identify significant differences in cell states that arise during pediCD relative to FGID. Furthermore, by closely linking our scRNA-seq analysis with clinical meta-data, we resolve a vector of lymphoid, myeloid, and epithelial cell states in treatment-naïve samples which can distinguish patients with less severe disease (those not on anti-TNF therapies (NOA)), from those with more severe disease at presentation who require anti-TNF therapies. Moreover, this vector was also able to distinguish those patients that achieve a full response (FR) to anti-TNF blockade from those more treatment-resistant patients who only achieve a partial response (PR). Our study jointly leverages a treatment-naïve cohort, high-resolution principled scRNA-seq data analysis, and clinical outcomes to understand which baseline cell states may predict inflammatory disease trajectory.