Spatiotemporal Modeling of the Key Migratory Events During the Initiation of Adaptive Immunity

Initiation of adaptive immunity involves distinct migratory cell populations coming together in a highly dynamic and spatially organized process. However, we lack a detailed spatiotemporal map of these events due to our inability to track the fate of cells between anatomically distinct locations or functionally identify cell populations as migratory. We used photo-convertible transgenic mice (Kaede) to spatiotemporally track the fate and composition of the cell populations that leave the site of priming and enter the draining lymph node to initiate immunity. We show that following skin priming, the lymph node migratory population is principally composed of cells recruited to the site of priming, with a minor contribution from tissue resident cells. In combination with the YAe/Eα system, we also show that the majority of cells presenting antigen are CD103+CD11b+ dendritic cells that were recruited to the site of priming during the inflammatory response. This population has previously only been described in relation to mucosal tissues. Comprehensive phenotypic profiling of the cells migrating from the skin to the draining lymph node by mass cytometry revealed that in addition to dendritic cells, the migratory population also included CD4+ and CD8+ T cells, B cells, and neutrophils. Taking our complex spatiotemporal data set, we then generated a model of cell migration that quantifies and describes the dynamics of arrival, departure, and residence times of cells at the site of priming and in the draining lymph node throughout the time-course of the initiation of adaptive immunity. In addition, we have identified the mean migration time of migratory dendritic cells as they travel from the site of priming to the draining lymph node. These findings represent an unprecedented, detailed and quantitative map of cell dynamics and phenotypes during immunization, identifying where, when and which cells to target for immunomodulation in autoimmunity and vaccination strategies

RA-MAP, molecular immunological landscapes in early rheumatoid arthritis and healthy vaccine recipients

Rheumatoid arthritis (RA) is a chronic inflammatory disorder with poorly defined aetiology characterised by synovial inflammation with variable disease severity and drug responsiveness. To investigate the peripheral blood immune cell landscape of early, drug naive RA, we performed comprehensive clinical and molecular profiling of 267 RA patients and 52 healthy vaccine recipients for up to 18 months to establish a high quality sample biobank including plasma, serum, peripheral blood cells, urine, genomic DNA, RNA from whole blood, lymphocyte and monocyte subsets. We have performed extensive multi-omic immune phenotyping, including genomic, metabolomic, proteomic, transcriptomic and autoantibody profiling. We anticipate that these detailed clinical and molecular data will serve as a fundamental resource offering insights into immune-mediated disease pathogenesis, progression and therapeutic response, ultimately contributing to the development and application of targeted therapies for RA.</p

Tumor necrosis factor alpha (TNF-α) autoregulates its expression and induces adhesion molecule expression in asthma

Subjects with mild asthma underwent repeated low-dose allergen exposure and bronchial biopsies were examined for the expression of TNF-α and adhesion molecules. Bronchial biopsies from moderately severe asthmatics were then tested in an explant culture system to assess the effect of Der p and CDP-870, a TNF-α blocking pegylated-antibody Fab, on expression of TNF-α and adhesion molecules. Low-dose allergen challenge significantly upregulated sub-mucosal mast cells, TNF-α+ cells, and VCAM. When bronchial explants were exposed to Der p and CDP 870 for 24 h, CDP 870 caused a significant reduction in TNF-α release both at baseline and following stimulation with Der p allergen. The bronchial biopsies showed significant upregulation of TNF-α positive cells and ICAM-1 following exposure to Der p (p = 0.03) and this was reduced in the presence of CDP-870. So, allergen exposure up-regulates TNF-α expression in asthma and down-stream targets, including adhesion molecules that contribute to airway inflammation

Altered Epithelial Gene Expression in Peripheral Airways of Severe Asthma

<div><p>Management of severe asthma remains a challenge despite treatment with glucocorticosteroid therapy. The majority of studies investigating disease mechanisms in treatment-resistant severe asthma have previously focused on the large central airways, with very few utilizing transcriptomic approaches. The small peripheral airways, which comprise the majority of the airway surface area, remain an unexplored area in severe asthma and were targeted for global epithelial gene expression profiling in this study. Differences between central and peripheral airways were evaluated using transcriptomic analysis (Affymetrix HG U133 plus 2.0 GeneChips) of epithelial brushings obtained from severe asthma patients (N = 17) and healthy volunteers (N = 23). Results were validated in an independent cohort (N = 10) by real-time quantitative PCR. The IL-13 disease signature that is associated with an asthmatic phenotype was upregulated in severe asthmatics compared to healthy controls but was predominantly evident within the peripheral airways, as were genes related to mast cell presence. The gene expression response associated with glucocorticosteroid therapy (i.e. <i>FKBP5</i>) was also upregulated in severe asthmatics compared to healthy controls but, in contrast, was more pronounced in central airways. Moreover, an altered epithelial repair response (e.g. <i>FGFBP1</i>) was evident across both airway sites reflecting a significant aspect of disease in severe asthma unadressed by current therapies. A transcriptomic approach to understand epithelial activation in severe asthma has thus highlighted the need for better-targeted therapy to the peripheral airways in severe asthma, where the IL-13 disease signature persists despite treatment with currently available therapy.</p></div

Peripheral airways versus central airways, in healthy volunteers and in severe asthmatics.

<p><b>A.</b><i>Greater expression</i>, genes with a greater expression in peripheral airways compared to central airways; <i>Lesser expression</i>, genes with a lesser expression in peripheral airways compared to central airways. <b>B.</b> Heatmap depicting unsupervised hierarchical clustering (Pearson complete) of modules identified using SSIM. Expression values of genes within each module were averaged and scaled to indicate the number of standard deviations above (red) or below (blue) the mean, denoted as row Z-score. <i>PA</i>, peripheral airways; <i>SA</i>, severe asthmatics; <i>ns</i>, not significant. <b>C.</b> Fold changes for mast cell proteases obtained by differential gene expression analysis. <b>D.</b> RT-qPCR validation of differential gene expression analysis of mast cell proteases. Changes in gene expression (mean and standard error of mean) are shown relative to <i>GAPDH</i>. <i>HC</i>, central airways in health; <i>HP</i>, peripheral airways in health; <i>SC</i>, central airways in severe asthma; <i>SP</i>, peripheral airways in severe asthma; <i>*</i>, p-value < 0.05.</p

Severe asthma versus health, in central airways and in peripheral airways.

<p><b>A.</b><i>Greater expression</i>, genes with a greater expression in severe asthmatics when compared to healthy volunteers; <i>Lesser expression</i>, genes with a lesser expression in severe asthmatics when compared to healthy controls. <b>B.</b> Fold changes for IL-13 disease signature and steroid response obtained by differential gene expression analysis. <b>C.</b> RT-qPCR validation of microarray findings. Changes in gene expression (mean and standard error of mean) are shown relative to <i>GAPDH</i>. <i>HC</i>, central airways in health; <i>HP</i>, peripheral airways in health; <i>SC</i>, central airways in severe asthma; <i>SP</i>, peripheral airways in severe asthma; <i>*</i>, p-value < 0.05.</p

Interaction analysis to evaluate the effects of disease and lung airway region on gene expression.

<p>Log₂ intensities are plotted and significance of interaction is depicted by: <i>DR</i>, interaction between disease and lung airway region; <i>D</i>, main effect of disease; <i>R</i>, main effect of region; p-values <i>*</i> < 0.05, <i>**</i> < 0.01, *** <0.001, **** <0.0001; <i>ns</i>, not significant.</p

Clinical characteristics of subjects who participated in the microarray and RT-qPCR study.

<p>Clinical characteristics of subjects who participated in the microarray and RT-qPCR study.</p