Concerted changes in the pediatric single-cell intestinal ecosystem before and after anti-TNF blockade

Abstract

Crohn'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-naive 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-naive 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-naive 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-naive cohort, high-resolution principled scRNA-seq data analysis, and clinical outcomes to understand which baseline cell states may predict inflammatory disease trajectory.

Single cell genomics as a transformative approach for aquaculture research and innovation

“…This approach is commonly used and avoids under clustering very diverse cell lineages (e.g., haematopoietic, including immune cells) or over clustering homogeneous populations, but is time consuming. Alternatively, other systematic approaches have been developed to deal with this issue 184,185 …”

Single-cell profiling of environmental enteropathy reveals signatures of epithelial remodeling and immune activation

“…1C). To identify cellular subsets, we applied an existing pipeline for iterative clustering of cell subsets to the Zambian and U.S. datasets (19). Next, we integrated these data with the South African data to account for potential batch effects (20).…”

“…To identify cell subsets, we adopted an existing pipeline for automated iterative clustering of single-cell data that has been shown to identify batch effects without collapsing distinct rare cell types (19).…”

Single-cell profiling of environmental enteropathy reveals signatures of epithelial remodeling and immune activation in severe disease

“…1c). In order to rigorously identify cellular subsets across these biological covariates, we applied an existing pipeline for iterative clustering of cell subsets to the Zambian and U.S. datasets (which were available before the South African data and processed in a different laboratory) (28,29) annotating the resulting clusters by known lineage markers and, in rare cases, merging together subsets across batches that co-expressed known lineage markers (see supplementary methods). Next, we integrated this data with the South African data, using the Seurat V3 method to correct for potential batch effects between data collected in different laboratories while allowing for the possibility that new cell subsets could exist in the South African data (30).…”

“…QC was performed to remove low quality cell barcodes and doublet cells. To identify cell subsets, we adopted an existing pipeline for automated iterative clustering of single-cell data (pipeline described in detail in supplementary methods) that has been shown to identify batch effects without collapsing distinct rare cell types (28,29,97).…”