Ectopic lymphoid structures form in a wide range of inflammatory conditions, including infection, autoimmune disease, and cancer. In the context of infection, this response can be beneficial for the host: influenza A virus infection–induced pulmonary ectopic germinal centers give rise to more broadly cross-reactive antibody responses, thereby generating cross-strain protection. However, despite the ubiquity of ectopic lymphoid structures and their role in both health and disease, little is known about the mechanisms by which inflammation is able to convert a peripheral tissue into one that resembles a secondary lymphoid organ. Here, we show that type I IFN produced after viral infection can induce CXCL13 expression in a phenotypically distinct population of lung fibroblasts, driving CXCR5-dependent recruitment of B cells and initiating ectopic germinal center formation. This identifies type I IFN as a novel inducer of CXCL13, which, in combination with other stimuli, can promote lung remodeling, converting a nonlymphoid tissue into one permissive to functional tertiary lymphoid structure formation.

T-cell activation relies on the assembly of the immunological synapse, a structure tightly regulated by the actin cytoskeleton. The precise role of the Wiskott-Aldrich syndrome protein, an actin cytoskeleton regulator, in linking immunological synapse structure to downstream signaling remains to be clarified. To address this point, CD4(+) T cells from patients with Wiskott-Aldrich syndrome were stimulated with antigen-presenting cells. The structure and dynamics of the immunological synapse were studied by confocal and video-microscopy. Upon stimulation by antigen-presenting cells, Wiskott-Aldrich syndrome protein-deficient T cells displayed reduced cytokine production and proliferation. Although Wiskott-Aldrich syndrome T cells formed conjugates with antigen-presenting cells at normal frequency and exhibited normal T-cell receptor down-regulation, they emitted actin-rich protrusions away from the immunological synapse area and their microtubule organizing center failed to polarize fully towards the center of the immunological synapse. In parallel, abnormally dispersed phosphotyrosine staining revealed unfocused synaptic signaling in Wiskott-Aldrich syndrome T cells. Time-lapse microscopy confirmed the anomalous morphology of Wiskott-Aldrich syndrome T-cell immunological synapses and showed erratic calcium mobilization at the single-cell level. Taken together, our data show that the Wiskott-Aldrich syndrome protein is required for the assembly of focused immunological synapse structures allowing optimal signal integration and sustained calcium signaling.

An unresolved issue in contemporary biomedicine is the overwhelming number and diversity of complex images that require annotation, analysis and interpretation. Recent advances in Deep Learning have revolutionized the field of computer vision, creating algorithms that compete with human experts in image segmentation tasks. However, these frameworks require large human-annotated datasets for training and the resulting “black box” models are difficult to interpret. In this study, we introduce Kartezio, a modular Cartesian Genetic Programming-based computational strategy that generates fully transparent and easily interpretable image processing pipelines by iteratively assembling and parameterizing computer vision functions. The pipelines thus generated exhibit comparable precision to state-of-the-art Deep Learning approaches on instance segmentation tasks, while requiring drastically smaller training datasets. This Few-Shot Learning method confers tremendous flexibility, speed, and functionality to this approach. We then deploy Kartezio to solve a series of semantic and instance segmentation problems, and demonstrate its utility across diverse images ranging from multiplexed tissue histopathology images to high resolution microscopy images. While the flexibility, robustness and practical utility of Kartezio make this fully explicable evolutionary designer a potential game-changer in the field of biomedical image processing, Kartezio remains complementary and potentially auxiliary to mainstream Deep Learning approaches.

Following infection, inflammatory cues upregulate core transcriptional programs to establish pathogen-specific protection. In viral infections, T follicular helper (TFH) cells express the prototypical T helper 1 transcription factor T-bet. Several studies have demonstrated essential but conflicting roles for T-bet in TFH biology. Understanding the basis of this controversy is crucial, as modulation of T-bet expression instructs TFH differentiation and ultimately protective antibody responses. Comparing influenza and LCMV viral infections, we demonstrate that the role of T-bet is contingent on the environmental setting of TFH differentiation, IL-2 signaling, and T cell competition. Furthermore, we demonstrate that T-bet expression by either TFH or GC B cells independently drives antibody isotype class switching. Specifically, T cell-specific loss of T-bet promotes IgG1, whereas B cell-specific loss of T-bet inhibits IgG2a/c switching. Combined, this work highlights that the context-dependent induction of T-bet instructs the development of protective, neutralizing antibodies following viral infection or vaccination.