Fardeen, Touran

Single-cell studies have defined immune states in inflammatory bowel disease (IBD), but how adaptive receptor histories organize circulating immunity remains unclear. We generated a single-cell transcriptomic atlas of peripheral blood from 249 participants with Crohn's disease, ulcerative colitis, or non-IBD control status, including 182 with productive TCR and BCR recovery. Expanded TCR clonotypes marked inflammatory-memory and cytotoxic states, while distinct but similar paired TCRs shared inflammatory programs across participants. BCR lineage maturation linked IgA-associated mucosal and plasma B cell programs to somatic mutation and class switching, distinguishing maturation-associated biology from clonal expansion. Helper, regulatory, and cytotoxic T-cell programs covaried with B-cell states, and inferred interactions nominated reciprocal antigen-presentation and helper pathways. Repertoire-based machine learning distinguished diagnosis, inflammation, and contemporaneous six-month treatment-response status. Together, this atlas connects receptor architecture to coordinated systemic immune remodeling, establishes a foundation for repertoire-informed patient stratification, and prioritizes candidate mechanisms of IBD pathogenesis.

Abstract Background and Aims Fibroblast–neutrophil interactions are implicated in ulcerative colitis (UC) treatment resistance, but whether fibroblasts modify neutrophil inflammatory activity remains unclear. We investigated responses to fibroblast exposure, stromal depletion, and α5β1-directed treatment. Methods We integrated human and mouse colonic single-cell RNA sequencing (scRNA-seq), spatial protein profiling (CODEX), fibroblast–neutrophil coculture, and dextran sulfate sodium (DSS) mouse colitis with fibroblast activation protein (FAP) ablation or α5β1-directed treatment. Readouts included cellular representation and protein fluorescence via flow cytometry, neutrophil extracellular trap (NET)-associated elastase activity, RNA programs and epithelial responses using scRNA-seq. Results Inflamed UC tissue contained higher fractions of inflammatory fibroblasts and oncostatin M (OSM)- and CXCR4-associated neutrophils. Inflammatory fibroblasts had higher FAP–α5β1 module scores, and proximity to FAP-high fibroblasts was associated with higher neutrophil OSM and CXCR4 fluorescence. UC fibroblasts increased neutrophil OSM, CXCR4 and myeloperoxidase (MPO) fluorescence and NET-associated elastase activity relative to control fibroblasts, while α5β1-directed treatment attenuated these responses. Both mouse FAP ablation and α5β1 blockade in DSS colitis reduced histologic inflammation and overall neutrophil frequency. Ablation broadly reduced recovered neutrophil representation, whereas blockade increased OSM-positive and PADI4-positive percentages within neutrophils despite lower MPO fluorescence within these subsets. Both interventions were associated with lower epithelial chemokine scores but distinct absorptive and mucus/secretory responses. Conclusions Fibroblast exposure modifies neutrophil inflammatory phenotype and effector-associated activity, identifying candidate therapeutic pathways in UC. Evaluating these responses alongside neutrophil representation and tissue inflammation could inform pharmacodynamic assessment of stromal-directed therapies. Graphical Abstract What You Need to Know Background and Context Fibroblast–neutrophil interactions are associated with treatment resistance in ulcerative colitis. Whether fibroblast exposure modifies neutrophil inflammatory phenotype and effector activity, beyond its established recruitment role, remains unclear. New Findings UC fibroblasts increased neutrophil inflammatory proteins and NET-associated elastase activity; α5β1-directed treatment attenuated these responses. FAP ablation and α5β1-directed treatment reduced colitis and overall neutrophil frequency. α5β1-directed treatment lowered MPO within OSM-positive and PADI4-positive neutrophils despite higher marker-positive percentages. Epithelial single-cell RNA sequencing revealed reduced chemokine programs with both interventions but distinct barrier-associated and regenerative expression patterns. Limitations Cellular drug targets remain unresolved. Spatial associations and elastase activity do not establish direct contact, NET formation, or clinical efficacy. Clinical Research Relevance Fibroblast-associated pathways offer candidate therapeutic opportunities to modify neutrophil inflammatory phenotype and effector activity in UC. Coculture responses were pharmacologically modifiable, and stromal interventions reduced experimental colitis. Measuring neutrophil frequency, subset proteins, and effector activity alongside tissue inflammation could guide pharmacodynamic assessment. Clinical efficacy and treatment-selection utility require validation. Basic Research Relevance Human coculture provides experimental evidence that fibroblast exposure modifies neutrophil inflammatory proteins and NET-associated elastase activity. FAP+ stromal ablation and α5β1-directed treatment produced distinct patterns of neutrophil representation and subset proteins in colitis. Epithelial single-cell RNA sequencing revealed reduced chemokine programs with both interventions but differing barrier-associated and regenerative expression patterns. Together, these findings motivate mechanistic studies of how stromal recruitment cues, soluble signals, and matrix-associated interactions regulate neutrophil responses, and whether these changes contribute to reduced epithelial inflammation and tissue repair. Lay Summary Fibroblasts, support cells in the intestine, increased inflammatory activity in immune cells called neutrophils. Blocking a pathway involved in cell attachment reduced these responses in cultures. Targeting fibroblast-associated pathways also reduced experimental colitis, with changes in intestinal lining gene activity related to inflammation, barrier function, and repair. These findings suggest potential approaches to treating ulcerative colitis.

Abstract Background Inflammatory bowel disease (IBD), including ulcerative colitis (UC) and Crohn’s disease (CD), is characterized by immune dysregulation1,2. Blood-based biomarkers in diagnosing patients with IBD are lacking. We tested the hypothesis that circulating immune cell types and immune repertoire could distinguish patients with IBD from controls. Methods We performed deep immunophenotyping with scRNA-seq with T-cell receptor (TCR) and B-cell receptor (BCR) sequencing on peripheral blood mononuclear cells (PBMCs) from patients with IBD and healthy controls (87 UC, 127 CD, 35 controls). We applied machine learning to quantify cell abundance (miloR3), construct diagnostic classifiers (immuneML4), and infer cell-cell ligand-receptor interactions (MultiNicheNet5) between patients with IBD and controls. Results A scRNA-seq atlas of over 1.1 million PBMCs consisting of 53 distinct cell types was generated (Fig1A). In classifying UC vs controls, support vector machine model had a balanced accuracy of 0.90, consisting of increased IgA and IgG plasma B cells, switched memory B cells, TRegs, and decreased CD16 monocytes, cDC1, non-switched B cells, and MAIT (Fig.1B). In classifying CD vs controls, K-nearest neighbors model had a balanced accuracy of 0.87, consisting of increased IgA plasma B cells, non-switched B cells, CD4 Th22, TReg cytotoxic, CD4 Th1/Th17, and pDC and decreased CD16 monocytes, gdT, CD4 Th17, CD4 Th2, and MAIT cells.Fig.1C). Inflammation was associated with increased IgG plasma B in patients with both UC and CD. Using 4-mer amino acid motif of the BCR complementary-determining region 3 (CDR3) logistic regression model, immuneML had a balanced accuracy of 0.82 (0.75-0.92) in classifying CD vs controls (Fig.1D with models showing top 25 BCR predictive features). Using 4-mer amino acid motif of the BCR CDR3 logistic regression model, immuneML had a balanced accuracy of 0.90 (0.70-1.00) in classifying UC vs controls (Fig.1E with models showing top 25 BCR predictive features). Using 3-mer amino acid motif of the TCR CDR3 logistic regression model, immuneML had a balanced accuracy of 0.76 (0.72-081) in classifying CD vs UC. (Fig.1F with models showing top 25 TCR predictive features). In UC vs controls, MultiNicheNet demonstrated increased signaling in ADAM10-NOTCH2 between TRegs and naïve B cells and IL15-IL2RA between CD14 monocytes and CD4 Th17 cells. In CD vs controls, there was increased signaling in IL15-IL2RA between CD14 monocytes and CD4 Th17 cells and ADAM10-TSPAN5 between CD14 monocytes and TRegs (Fig.1G). Conclusion Applying machine learning models to scRNA-seq and immune repertoire datasets enable blood-based deep immunophenotyping with diagnostic potential and reveals novel insights into cell signaling in IBD. 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Nat Mach Intell. 2021 Nov;3(11):936-944. doi: 10.1038/s42256-021-00413-z. Epub 2021 Nov 16. PMID: 37396030; PMCID: PMC10312379. 5.Browaeys, R., Gilis, J., Sang-Aram, C., De Bleser, P., Hoste, L., Tavernier, S., Lambrechts, D., Seurinck, R. and Saeys, Y., 2023. MultiNicheNet: a flexible framework for differential cell-cell communication analysis from multi-sample multi-condition single-cell transcriptomics data. BioRxiv, pp.2023-06. Conflict of interest: Dr. Gubatan, John Mark: No conflict of interest Jiayu, Ye: No conflict of interest Christin, Lund-Andersen: No conflict of interest Boye, Theresa: No conflict of interest Hoang, Jacqueline: No conflict of interest Sojwal, Raoul: No conflict of interest Fardeen, Touran: No conflict of interest Tran, Tracy: No conflict of interest Sokzini, Peacha: No conflict of interest Koh, Ally: No conflict of interest Gibson, Marielle: No conflict of interest Sinha, Sidhartha: No conflict of interest Rogalla, Stephan: No conflict of interest Rosen, Michael: No conflict of interest Nielsen, Ole Haagen: No conflict of interest Sandve, Geir Kjetil: No conflict of interest