The mechanism by which inflammation influences the adaptive response to vaccines is not fully understood. Here, we examine the role of lymph node macrophages (LNMs) in the induction of the cytokine storm triggered by inactivated influenza virus vaccine. Following vaccination, LNMs undergo inflammasome-independent necrosis-like death that is reliant on MyD88 and Toll-like receptor 7 (TLR7) expression and releases pre-stored interleukin-1α (IL-1α). Furthermore, activated medullary macrophages produce interferon-β (IFN-β) that induces the autocrine secretion of IL-1α. We also found that macrophage depletion promotes lymph node-resident dendritic cell (LNDC) relocation and affects the capacity of CD11b LNDCs to capture virus and express co-stimulatory molecules. Inhibition of the IL-1α-induced inflammatory cascade reduced B cell responses, while co-administration of recombinant IL-1α increased the humoral response. Stimulation of the IL-1α inflammatory pathway might therefore represent a strategy to enhance antigen presentation by LNDCs and improve the humoral response against influenza vaccines.
The role of natural killer (NK) cells in the immune response against vaccines is not fully understood. Here, we examine the function of infiltrated NK cells in the initiation of the inflammatory response triggered by inactivated influenza virus vaccine in the draining lymph node (LN). We observed that, following vaccination, NK cells are recruited to the interfollicular and medullary areas of the LN and become activated by type I interferons (IFNs) produced by LN macrophages. The activation of NK cells leads to their early production of IFNg, which in turn regulates the recruitment of IL-6+ CD11b+ dendritic cells. Finally, we demonstrate that the interleukin-6 (IL-6)-mediated inflammation is important for the development of an effective humoral response against influenza virus in the draining LN.
Neutrophils are amongst the first cells to respond to inflammation and infection. Although they play a key role in limiting the dissemination of pathogens, the study of their dynamic behavior in immune organs remains elusive. In this work, we characterized in vivo the dynamic behavior of neutrophils in the mouse popliteal lymph node (PLN) after influenza vaccination with UV-inactivated virus. To achieve this, we used an image-based systems biology approach to detect the motility patterns of neutrophils and to associate them to distinct actions. We described a prominent and rapid recruitment of neutrophils to the PLN following vaccination, which was dependent on the secretion of the chemokine CXCL1 and the alarmin molecule IL-1α. In addition, we observed that the initial recruitment occurred mainly via high endothelial venules located in the paracortical and interfollicular regions of the PLN. The analysis of the spatial-temporal patterns of neutrophil migration demonstrated that, in the initial stage, the majority of neutrophils displayed a patrolling behavior, followed by the formation of swarms in the subcapsular sinus of the PLN, which were associated with macrophages in this compartment. Finally, we observed using multiple imaging techniques, that neutrophils phagocytize and transport influenza virus particles. These processes might have important implications in the capacity of these cells to present viral antigens.
Clustering is a technique to analyze empirical data, with a major application for biomedical research. Essentially, clustering finds groups of related points in a dataset. However, results depend on both metrics for point-to-point similarity and rules for point-to-group association. Non-appropriate metrics and rules can lead to artifacts, especially in case of multiple groups with heterogeneous structure. In this work, we propose a clustering algorithm that evaluates the properties of paths between points (rather than point-to-point similarity) and solves a global optimization problem, finding solutions not obtainable by methods relying on local choices. Moreover, our algorithm is trainable. Hence, it can be adapted and adopted for specific datasets and applications by providing examples of valid and invalid paths to train a path classifier. We demonstrate its applicability to identify heterogeneous groups in challenging synthetic datasets, segment highly nonconvex immune cells in confocal microscopy images, and classify arrhythmic heartbeats in electrocardiographic signals.
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