Nielsen, Ole Haagen

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 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. References: 1.Kong L, Pokatayev V, Lefkovith A, Carter GT, Creasey EA, Krishna C, Subramanian S, Kochar B, Ashenberg O, Lau H, Ananthakrishnan AN, Graham DB, Deguine J, Xavier RJ. The landscape of immune dysregulation in Crohn’s disease revealed through single-cell transcriptomic profiling in the ileum and colon. Immunity. 2023 Feb 14;56(2):444-458.e5. doi: 10.1016/j.immuni.2023.01.002. Epub 2023 Jan 30. Erratum in: Immunity. 2023 Dec 12;56(12):2855. doi: 10.1016/j.immuni.2023.10.017. PMID: 36720220; PMCID: PMC9957882. 2.Smillie CS, Biton M, Ordovas-Montanes J, Sullivan KM, Burgin G, Graham DB, Herbst RH, Rogel N, Slyper M, Waldman J, Sud M, Andrews E, Velonias G, Haber AL, Jagadeesh K, Vickovic S, Yao J, Stevens C, Dionne D, Nguyen LT, Villani AC, Hofree M, Creasey EA, Huang H, Rozenblatt-Rosen O, Garber JJ, Khalili H, Desch AN, Daly MJ, Ananthakrishnan AN, Shalek AK, Xavier RJ, Regev A. Intra- and Inter-cellular Rewiring of the Human Colon during Ulcerative Colitis. Cell. 2019 Jul 25;178(3):714-730.e22. doi: 10.1016/j.cell.2019.06.029. PMID: 31348891; PMCID: PMC6662628. 3.Dann E, Henderson NC, Teichmann SA, Morgan MD, Marioni JC. Differential abundance testing on single-cell data using k-nearest neighbor graphs. Nat Biotechnol. 2022 Feb;40(2):245-253. doi: 10.1038/s41587-021-01033-z. Epub 2021 Sep 30. PMID: 34594043; PMCID: PMC7617075. 4.Pavlović M, Scheffer L, Motwani K, Kanduri C, Kompova R, Vazov N, Waagan K, Bernal FLM, Costa AA, Corrie B, Akbar R, Al Hajj GS, Balaban G, Brusko TM, Chernigovskaya M, Christley S, Cowell LG, Frank R, Grytten I, Gundersen S, Haff IH, Hovig E, Hsieh PH, Klambauer G, Kuijjer ML, Lund-Andersen C, Martini A, Minotto T, Pensar J, Rand K, Riccardi E, Robert PA, Rocha A, Slabodkin A, Snapkov I, Sollid LM, Titov D, Weber CR, Widrich M, Yaari G, Greiff V, Sandve GK. The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires. 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

ImportanceImmune checkpoint inhibitors (ICIs) improve outcomes in a wide range of cancers; however, serious adverse effects, including cardiovascular adverse effects (CVAEs), can occur.ObjectiveTo determine the incidence of CVAEs and analyze data on the management of myocarditis in patients exposed to ICIs.Data SourcesPubMed, Embase, and Cochrane Central Register of Controlled Trials from inception were searched on April 4, 2023.Study SelectionTwo separate studies were performed. Key inclusion criteria for study 1 were phases 1 to 4 trials involving adults with malignant neoplasms treated with an ICI and toxicity data; for study 2, publications (case reports and retrospective analyses) on clinical manifestations and treatment of patients with ICI-induced CVAEs. Studies with dose escalation or fewer than 11 patients in each group and all case reports, retrospective analyses, letters, reviews, and editorials were excluded from study 1. Studies not published in English were excluded from study 2.Data Extraction and SynthesisThe PRISMA guidelines and Cochrane Handbook for Systematic Reviews were followed. Data were extracted independently by 2 researchers. A meta-analysis of the incidence of CVAEs in clinical trials and a systematic review of the evidence for the management of myocarditis were performed. Data were pooled using a random-effects model.Main Outcomes and MeasuresIn study 1, the primary outcome was incidence CVAEs in clinical trials with ICIs and ICI combination therapies. Study 2 examined evidence supporting specific management strategies that may decrease the mortality rate of myocarditis. The primary outcomes were planned before data collection began.ResultsIn study 1, a total of 83 315 unique participants in 589 unique trials were included in the meta-analysis. Incidence of CVAEs induced by anti–programmed cell death 1 and/or programmed cell death ligand 1 was 0.80% (95% CI, 0%-1.66%) in clinical trials, with no differences between the compounds, except for cemiplimab, which was associated with a higher risk of CVAEs. Incidence of CVAEs following ipilimumab treatment was 1.07% (95% CI, 0%-2.58%). The incidence of myocarditis was significantly higher following treatment with dual ICIs. However, CVAE incidence was not higher with dual ICIs, ICI combination with chemotherapy, or tyrosine kinase inhibitors. Evidence from randomized clinical trials on recommended monitoring and treatment strategies for ICI-induced myocarditis was lacking. Study 2 showed that myocarditis-associated mortality occurred in 83 of 220 patients (37.7%). Prospective data from 40 patients with myocarditis indicated that systematic screening for respiratory muscle involvement, coupled with active ventilation, prompt use of abatacept, and the addition of ruxolitinib, may decrease the mortality rate.Conclusions and RelevanceImmune checkpoint inhibitor–induced CVAEs and/or myocarditis were recorded in 1.07% of patients in clinical trials. The CVAE mortality risk remains high, justifying the need for monitoring and management strategies for which evidence from randomized clinical trials is absent. Early recognition, ICI therapy cessation, prompt initiation of corticosteroid therapy, and escalation of therapy are all crucial elements for achieving optimal outcomes. Prospective clinical trials or at least prospective registration of treatments and outcomes are highly warranted.

Microscopic colitis is an inflammatory bowel disease divided into two subtypes: collagenous colitis and lymphocytic colitis. With an increasing incidence of microscopic colitis exceeding those of ulcerative and Crohn’s disease among elderly people in some countries, microscopic colitis is a debilitating life experience. Therefore, physicians should be familiar with its clinical features and management strategies because the disease deserves the same attention as the classical inflammatory bowel diseases. Here, state-of-the-art knowledge of microscopic colitis is provided from a global perspective with reference to etiopathology and how to establish the diagnosis with the overall aim to create awareness and improve rational management in clinical practice. The immune system and a dysregulated immune response seem to play a key role combined with risk factors (e.g. cigarette smoking) in genetically predisposed individuals. The symptoms are characterized by recurrent or chronic nonbloody, watery diarrhea, urgency, weight loss, and a female preponderance. As biomarkers are absent, the diagnosis relies on colonoscopy with a histological assessment of biopsy specimens from all parts of the colon. Although the disease is not associated with a risk of colorectal cancer, a recent nationwide, population-based cohort study found an increased risk of lymphoma and lung cancer. Budesonide is the first-line therapy for management, whereas immunomodulatory drugs (including biologics) and drugs with antidiarrheal properties may be indicated in those failing, dependent, or intolerant to budesonide. In microscopic colitis induced by checkpoint inhibitors, a drug class used increasingly for a wide range of malignancies, a more aggressive therapeutic approach with biologics introduced early seems reasonable. However, particular attention needs to be drawn to the existence of incomplete forms of microscopic colitis with the risk of being overlooked in routine clinical settings.