Dendritic cells (DC) are professional antigen-presenting cells that orchestrate immune responses. The human DC population comprises two main functionally specialized lineages, whose origins and differentiation pathways remain incompletely defined. Here, we combine two high-dimensional technologies-single-cell messenger RNA sequencing (scmRNAseq) and cytometry by time-of-flight (CyTOF)-to identify human blood CD123CD33CD45RA DC precursors (pre-DC). Pre-DC share surface markers with plasmacytoid DC (pDC) but have distinct functional properties that were previously attributed to pDC. Tracing the differentiation of DC from the bone marrow to the peripheral blood revealed that the pre-DC compartment contains distinct lineage-committed subpopulations, including one early uncommitted CD123 pre-DC subset and two CD45RACD123 lineage-committed subsets exhibiting functional differences. The discovery of multiple committed pre-DC populations opens promising new avenues for the therapeutic exploitation of DC subset-specific targeting.

Cytokine dysregulation is a central driver of chronic inflammatory diseases such as multiple sclerosis (MS). Here, we sought to determine the characteristic cellular and cytokine polarization profile in patients with relapsing-remitting multiple sclerosis (RRMS) by high-dimensional single-cell mass cytometry (CyTOF). Using a combination of neural network-based representation learning algorithms, we identified an expanded T helper cell subset in patients with MS, characterized by the expression of granulocyte-macrophage colony-stimulating factor and the C-X-C chemokine receptor type 4. This cellular signature, which includes expression of very late antigen 4 in peripheral blood, was also enriched in the central nervous system of patients with relapsing-remitting multiple sclerosis. In independent validation cohorts, we confirmed that this cell population is increased in patients with MS compared with other inflammatory and non-inflammatory conditions. Lastly, we also found the population to be reduced under effective disease-modifying therapy, suggesting that the identified T cell profile represents a specific therapeutic target in MS.

OBJECTIVES:Biologics treatment with antitumour necrosis factor alpha (TNFα) is efficacious in patients with juvenile idiopathic arthritis (JIA). Despite displaying clinical inactivity during treatment, many patients will flare on cessation of therapy. The inability to definitively discriminate patients who will relapse or continue to remain in remission after therapy withdrawal is currently a major unmet medical need. CD4 T cells have been implicated in active disease, yet how they contribute to disease persistence despite treatment is unknown. METHODS:We interrogated the circulatory reservoir of CD4 immune subsets at the single-cell resolution with mass cytometry (cytometry by time of flight) of patients with JIA (n=20) who displayed continuous clinical inactivity for at least 6 months with anti-TNFα and were subsequently withdrawn from therapy for 8 months, and scored as relapse or remission. These patients were examined prior to therapy withdrawal for putative subsets that could discriminate relapse from remission. We verified on a separate JIA cohort (n=16) the dysregulation of these circulatory subsets 8 months into therapy withdrawal. The immunological transcriptomic signature of CD4 memory in relapse/remission patients was examined with NanoString. RESULTS:An inflammatory memory subset of CD3CD4CD45RATNFα T cells deficient in immune checkpoints (PD1CD152) was present in relapse patients prior to therapy withdrawal. Transcriptomic profiling reveals divergence between relapse and remission patients in disease-centric pathways involving (1) T-cell receptor activation, (2) apoptosis, (3) TNFα, (4) nuclear factor-kappa B and (5) mitogen-activated protein kinase signalling. CONCLUSIONS:A unique discriminatory immunomic and transcriptomic signature is associated with relapse patients and may explain how relapse occurs.

BACKGROUND & AIMS:Studies are needed to determine the mechanisms of mucosal dysregulation in patients with inflammatory bowel diseases (IBDs) and differences in inflammatory responses of patients with ulcerative colitis (UC) vs Crohn's disease (CD). We used mass cytometry (CyTOF) to characterize and compare immune cell populations in the mucosa and blood from patients with IBD and without IBD (controls) at single-cell resolution. METHODS:We performed CyTOF analysis of colonic mucosa samples (n = 87) and peripheral blood mononuclear cells (n = 85) from patients with active or inactive UC or CD and controls. We also performed single-cell RNA sequencing, flow cytometry, and RNA in situ hybridization analyses to validate key findings. We used random forest modeling to identify differences in signatures across subject groups. RESULTS:Compared with controls, colonic mucosa samples from patients with IBD had increased abundances of HLA-DR+CD38+ T cells, including T-regulatory cells that produce inflammatory cytokines; CXCR3+ plasmablasts; and IL1B+ macrophages and monocytes. Colonic mucosa samples from patients with UC were characterized by expansion of IL17A+ CD161+ effector memory T cells and IL17A+ T-regulatory cells; expansion of HLA-DR+CD56+ granulocytes; and reductions in type 3 innate lymphoid cells. Mucosal samples from patients with active CD were characterized by IL1B+HLA-DR+CD38+ T cells, IL1B+TNF+IFNG naïve B cells, IL1B+ dendritic cells (DCs), and IL1B+ plasmacytoid DCs. Peripheral blood mononuclear cells from patients with active CD differed from those of active UC in that the peripheral blood mononuclear cells from patients with CD had increased IL1B+ T-regulatory cells, IL1B+ DCs and IL1B+ plasmacytoid DCs, IL1B+ monocytes, and fewer group 1 innate lymphoid cells. Random forest modeling differentiated active UC from active CD in colonic mucosa and blood samples; top discriminating features included many of the cellular populations identified above. CONCLUSIONS:We used single-cell technologies to identify immune cell populations specific to mucosa and blood samples from patients with active or inactive CD and UC and controls. This information might be used to develop therapies that target specific cell populations in patients with different types of IBD.