Innate Immune Training of Granulopoiesis Promotes Anti-tumor Activity.

Trained innate immunity, induced via modulation of mature myeloid cells or their bone marrow progenitors, mediates sustained increased responsiveness to secondary challenges. Here, we investigated whether anti-tumor immunity can be enhanced through induction of trained immunity. Pre-treatment of mice with ß-glucan, a fungal-derived prototypical agonist of trained immunity, resulted in diminished tumor growth. The anti-tumor effect of ß-glucan-induced trained immunity was associated with transcriptomic and epigenetic rewiring of granulopoiesis and neutrophil reprogramming toward an anti-tumor phenotype; this process required type I interferon signaling irrespective of adaptive immunity in the host. Adoptive transfer of neutrophils from ß-glucan-trained mice to naive recipients suppressed tumor growth in the latter in a ROS-dependent manner. Moreover, the anti-tumor effect of ß-glucan-induced trained granulopoiesis was transmissible by bone marrow transplantation to recipient naive mice. Our findings identify a novel and therapeutically relevant anti-tumor facet of trained immunity involving appropriate rewiring of granulopoiesis.

High diversity in the TCR repertoire of GAD65 autoantigen-specific human CD4+ T cells.

Autoreactive CD4(+) T cells are an essential feature of type 1 diabetes mellitus. We applied single-cell TCR α- and ß-chain sequencing to peripheral blood GAD65-specific CD4(+) T cells, and TCR α-chain next-generation sequencing to bulk memory CD4(+) T cells to provide insight into TCR diversity in autoimmune diabetes mellitus. TCRs obtained for 1650 GAD65-specific CD4(+) T cells isolated from GAD65 proliferation assays and/or GAD65 557I tetramer staining in 6 patients and 10 islet autoantibody-positive children showed large diversity with 1003 different TCRs identified. TRAV and TRBV gene usage was broad, and the TRBV5.1 gene was most prominent within the GAD65 557I tetramer(+) cells. Limited overlap (<5%) was observed between TCRs of GAD65-proliferating and GAD65 557I tetramer(+) CD4(+) T cells. Few TCRs were repeatedly found in GAD65-specific cells at different time points from individual patients, and none was seen in more than one subject. However, single chains were often shared between patients and used in combination with different second chains. Next-generation sequencing revealed a wide frequency range (<0.00001-1.62%) of TCR α-chains corresponding to GAD65-specific T cells. The findings support minor selection of genes and TCRs for GAD65-specific T cells, but fail to provide strong support for TCR-targeted therapies.

Maternal Type 1 Diabetes Reduces Autoantigen-Responsive CD4+ T Cells in Offspring.

Autoimmunity against pancreatic ß-cell autoantigens is a characteristic of childhood type 1 diabetes (T1D). Autoimmunity usually appears in genetically susceptible children with the development of autoantibodies against (pro)insulin in early childhood. The offspring of mothers with T1D are protected from this process. The aim of this study was to determine whether the protection conferred by maternal T1D is associated with improved neonatal tolerance against (pro)insulin. Consistent with improved neonatal tolerance, the offspring of mothers with T1D had reduced cord blood CD4+ T-cell responses to proinsulin and insulin, a reduction in the inflammatory profile of their proinsulin-responsive CD4+ T cells, and improved regulation of CD4+ T cell responses to proinsulin at 9 months of age, as compared with offspring with a father or sibling with T1D. Maternal T1D was also associated with a modest reduction in CpG methylation of the INS gene in cord blood mononuclear cells from offspring with a susceptible INS genotype. Our findings support the concept that a maternal T1D environment improves neonatal immune tolerance against the autoantigen (pro)insulin.

Association of Dendritic Cell Signatures With Autoimmune Inflammation Revealed by Single-Cell Profiling.

OBJECTIVE: To identify single-cell transcriptional signatures of dendritic cells (DCs) that are associated with autoimmunity, and determine whether those DC signatures are correlated with the clinical heterogeneity of autoimmune disease. METHODS: Blood-derived DCs were single-cell sorted from the peripheral blood of patients with rheumatoid arthritis, systemic lupus erythematosus, or type 1 diabetes as well as healthy individuals. DCs were analyzed using single-cell gene expression assays, performed immediately after isolation or after in vitro stimulation of the cells. In addition, protein expression was measured using fluorescence-activated cell sorting. RESULTS: CD1c+ conventional DCs and plasmacytoid DCs from healthy individuals exhibited diverse transcriptional signatures, while the DC transcriptional signatures in patients with autoimmune disease were altered. In particular, distinct DC clusters, characterized by up-regulation of TAP1, IRF7, and IFNAR1, were abundant in patients with systemic autoimmune disease, whereas DCs from patients with type 1 diabetes had decreased expression of the regulatory genes PTPN6, TGFB, and TYROBP. The frequency of CD1c+ conventional DCs that expressed a systemic autoimmune profile directly correlated with the extent of disease activity in patients with rheumatoid arthritis (Spearman's r = 0.60, P = 0.03). CONCLUSION: DC transcriptional signatures are altered in patients with autoimmune disease and are associated with the level of disease activity, suggesting that immune cell transcriptional profiling could improve our ability to detect and understand the heterogeneity of these diseases, and could guide treatment choices in patients with a complex autoimmune disease.

Machine learning based classification of cells into chronological stages using single-cell transcriptomics.

Age-associated deterioration of cellular physiology leads to pathological conditions. The ability to detect premature aging could provide a window for preventive therapies against age-related diseases. However, the techniques for determining cellular age are limited, as they rely on a limited set of histological markers and lack predictive power. Here, we implement GERAS (GEnetic Reference for Age of Single-cell), a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes. GERAS displays greater than 90% accuracy in classifying the chronological stage of zebrafish and human pancreatic cells. The framework demonstrates robustness against biological and technical noise, as evaluated by its performance on independent samplings of single-cells. Additionally, GERAS determines the impact of differences in calorie intake and BMI on the aging of zebrafish and human pancreatic cells, respectively. We further harness the classification ability of GERAS to identify molecular factors that are potentially associated with the aging of beta-cells. We show that one of these factors, junba, is necessary to maintain the proliferative state of juvenile beta-cells. Our results showcase the applicability of a machine learning framework to classify the chronological stage of heterogeneous cell populations, while enabling detection of candidate genes associated with aging.

Hematopoietic stem cells can differentiate into restricted myeloid progenitors before cell division in mice.

Hematopoietic stem cells (HSCs) continuously replenish all blood cell types through a series of differentiation steps and repeated cell divisions that involve the generation of lineage-committed progenitors. However, whether cell division in HSCs precedes differentiation is unclear. To this end, we used an HSC cell-tracing approach and Ki67RFP knock-in mice, in a non-conditioned transplantation model, to assess divisional history, cell cycle progression, and differentiation of adult HSCs. Our results reveal that HSCs are able to differentiate into restricted progenitors, especially common myeloid, megakaryocyte-erythroid and pre-megakaryocyte progenitors, without undergoing cell division and even before entering the S phase of the cell cycle. Additionally, the phenotype of the undivided but differentiated progenitors correlated with the expression of lineage-specific genes and loss of multipotency. Thus HSC fate decisions can be uncoupled from physical cell division. These results facilitate a better understanding of the mechanisms that control fate decisions in hematopoietic cells.

A divergent population of autoantigen-responsive CD4+ T cells in infants prior to ß cell autoimmunity.

Autoimmune diabetes is marked by sensitization to ß cell self-antigens in childhood. We longitudinally followed at-risk children from infancy and performed single-cell gene expression in ß cell antigen-responsive CD4+ T cells through pre- and established autoimmune phases. A striking divergence in the gene signature of ß cell antigen-responsive naïve CD4+ T cells from children who developed ß cell autoimmunity was found in infancy, well before the appearance of ß cell antigen-specific memory T cells or autoantibodies. The signature resembled a pre-T helper 1 (TH1)/TH17/T follicular helper cell response with expression of CCR6, IL21, TBX21, TNF, RORC, EGR2, TGFB1, and ICOS, in the absence of FOXP3, IL17, and other cytokines. The cells transitioned to an IFNG-TH1 memory phenotype with the emergence of autoantibodies. We suggest that the divergent naïve T cell response is a consequence of genetic or environmental priming during unfavorable perinatal exposures and that the signature will guide future efforts to detect and prevent ß cell autoimmunity.

CD8+ T cells specific for the islet autoantigen IGRP are restricted in their T cell receptor chain usage.

CD8+ T cells directed against beta cell autoantigens are considered relevant for the pathogenesis of type 1 diabetes. Using single cell T cell receptor sequencing of CD8+ T cells specific for the IGRP265-273 epitope, we examined whether there was expansion of clonotypes and sharing of T cell receptor chains in autoreactive CD8+ T cell repertoires. HLA-A*0201 positive type 1 diabetes patients (n = 19) and controls (n = 18) were analysed. TCR α- and ß-chain sequences of 418 patient-derived IGRP265-273-multimer+ CD8+ T cells representing 48 clonotypes were obtained. Expanded populations of IGRP265-273-specific CD8+ T cells with dominant clonotypes that had TCR α-chains shared across patients were observed. The SGGSNYKLTF motif corresponding to TRAJ53 was contained in 384 (91.9%) cells, and in 20 (41.7%) patient-derived clonotypes. TRAJ53 together with TRAV29/DV5 was found in 15 (31.3%) clonotypes. Using next generation TCR α-chain sequencing, we found enrichment of one of these TCR α-chains in the memory CD8+ T cells of patients as compared to healthy controls. CD8+ T cell clones bearing the enriched motifs mediated antigen-specific target cell lysis. We provide the first evidence for restriction of T cell receptor motifs in the alpha chain of human CD8+ T cells with specificity to a beta cell antigen.

Measuring T cell receptor and T cell gene expression diversity in antigen-responsive human CD4+ T cells.

T cells have diversity in TCR, epitope recognition, and cytokine production, and can be used for immune monitoring. Furthermore, clonal expansion of TCR families in disease may provide opportunities for TCR-directed therapies. We developed methodology for sequencing expressed genes of TCR alpha and beta chains from single cells and applied this to vaccine (tetanus-toxoid)-responsive CD4(+) T cells. TCR alpha and beta chains were both successfully sequenced in 1309 (43%) of 3038 CD4(+) T cells yielding 677 different receptors. TRAV and TRBV gene usage differed between tetanus-toxoid-responsive and non-responsive cells (p=0.004 and 0.0002), and there was extensive TCR diversity in tetanus-toxoid-responsive cells within individuals. Identical TCRs could be recovered in different samples from the same subject: TCRs identified after booster vaccination were frequent in pre-booster memory T cells (31% of pre-booster TCR), and also identified in pre-booster vaccination naïve cells (6.5%). No TCR was shared between subjects, but tetanus toxoid-responsive cells sharing one of their TCR chains were observed within and between subjects. Coupling single-cell gene expression profiling to TCR sequencing revealed examples of distinct cytokine profiles in cells bearing identical TCR. Novel molecular methodology demonstrates extensive diversity of Ag-responsive CD4(+) T cells within and between individuals.