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A central question in immunology is what features allow the immune system to respond in a timely manner to a variety of pathogens encountered at unanticipated times and diverse body sites. Two decades of advanced and static dynamic imaging methods have now revealed several major principles facilitating host defense. Suborgan spatial prepositioning of distinct cells promotes time‐efficient interactions upon pathogen sensing. Such pre‐organization also provides an effective barrier to movement of pathogens from parenchymal tissues into the blood circulation. Various molecular mechanisms maintain effective intercellular communication among otherwise rapidly moving cells. These and related discoveries have benefited from recent increases in the number of parameters that can be measured simultaneously in a single tissue section and the extension of such multiplex analyses to 3D tissue volumes. The application of new computational methods to such imaging data has provided a quantitative, in vivo context for cell trafficking and signaling pathways traditionally explored in vitro or with dissociated cell preparations. Here, we summarize our efforts to devise and employ diverse imaging tools to probe immune system organization and function, concluding with a commentary on future developments, which we believe will reveal even more about how the immune system operates in health and disease. [ABSTRACT FROM AUTHOR]

Population genetic theory posits that molecular variation buffers against disease risk. Although this "monoculture effect" is well supported in agricultural settings, its applicability to wildlife populations remains in question. In the present study, we examined the genomics underlying individual‐level disease severity and population‐level consequences of sarcoptic mange infection in a wild population of canids. Using gray wolves (Canis lupus) reintroduced to Yellowstone National Park (YNP) as our focal system, we leveraged 25 years of observational data and biobanked blood and tissue to genotype 76,859 loci in over 400 wolves. At the individual level, we reported an inverse relationship between host genomic variation and infection severity. We additionally identified 410 loci significantly associated with mange severity, with annotations related to inflammation, immunity, and skin barrier integrity and disorders. We contextualized results within environmental, demographic, and behavioral variables, and confirmed that genetic variation was predictive of infection severity. At the population level, we reported decreased genome‐wide variation since the initial gray wolf reintroduction event and identified evidence of selection acting against alleles associated with mange infection severity. We concluded that genomic variation plays an important role in disease severity in YNP wolves. This role scales from individual to population levels, and includes patterns of genome‐wide variation in support of the monoculture effect and specific loci associated with the complex mange phenotype. Results yielded system‐specific insights, while also highlighting the relevance of genomic analyses to wildlife disease ecology, evolution, and conservation. [ABSTRACT FROM AUTHOR]

Defense against infection incurs costs as well as benefits that are expected to shape the evolution of optimal defense strategies. In particular, many theoretical studies have investigated contexts favoring constitutive versus inducible defenses. However, even when one immune strategy is theoretically optimal, it may be evolutionarily unachievable. This is because evolution proceeds via mutational changes to the protein interaction networks underlying immune responses, not by changes to an immune strategy directly. Here, we use a theoretical simulation model to examine how underlying network architectures constrain the evolution of immune strategies, and how these network architectures account for desirable immune properties such as inducibility and robustness. We focus on immune signaling because signalingmolecules are common targets of parasitic interference but are rarely studied in this context. We find that in the presence of a coevolving parasite that disrupts immune signaling, hosts evolve constitutive defenses even when inducible defenses are theoretically optimal. This occurs for two reasons. First, there are relatively few network architectures that produce immunity that is both inducible and also robust against targeted disruption. Second, evolution toward these few robust inducible network architectures often requires intermediate steps that are vulnerable to targeted disruption. The few networks that are both robust and inducible consist of many parallel pathways of immune signaling with few connections among them. In the context of relevant empirical literature, we discuss whether this is indeed the most evolutionarily accessible robust inducible network architecture in nature, and when it can evolve. [ABSTRACT FROM AUTHOR]

Stem-like progenitors are a critical subset of cytotoxic T cells that self-renew and give rise to expanded populations of effector cells critical for successful checkpoint blockade immunotherapy. Emerging evidence suggests that the tumor-draining lymph nodes can support the continuous generation of these stem-like cells that replenish the tumor sites and act as a critical source of expanded effector populations, underlining the importance of understanding what factors promote and maintain activated T cells in the stem-like state. Using advanced 3D multiplex immunofluorescence imaging, here we identified antigen-presentation niches in tumor-draining lymph nodes that support the expansion, maintenance, and affinity evolution of a unique population of TCF-1+PD-1+SLAMF6 hi stem-like CD8+ T cells. Our results show that contrary to the prevailing view that persistent TCR signaling drives terminal effector differentiation, prolonged antigen engagement well beyond the initial priming phase sustained the proliferation and self-renewal of these stem-like T cells in vivo . The inhibitory PD-1 pathway plays a central role in this process by mediating the fine-tuning of TCR and co-stimulatory signal input that enables selective expansion of high affinity TCR stem-like clones, enabling them to act as a renewable source of high affinity effector cells. PD-1 checkpoint blockade disrupts this fine tuning of input signaling, leading to terminal differentiation to the effector state or death of the most avid anti-tumor stem-like cells. Our results thus reveal an unexpected relationship between TCR ligand affinity recognition, a key negative feedback regulatory loop, and T cell stemness programming. Furthermore, these findings raise questions about whether anti-PD-1 checkpoint blockade during cancer immunotherapy provides a short-term anti-tumor effect that comes at the cost of diminishing efficacy due to progressive loss of these critical high affinity stem-like precursors.

Spatial patterns of cells and other biological elements drive both physiologic and pathologic processes within tissues. While many imaging and transcriptomic methods document tissue organization, discerning these patterns is challenging, especially when they involve multiple elements in complex arrangements. To address this challenge, we present Spatial Patterning Analysis of Cellular Ensembles (SPACE), an R package for analysis of high-plex spatial data. SPACE is compatible with any data collection modality that records values (i.e., categorical cell/structure types or quantitative expression levels) at fixed spatial coordinates (i.e., 2d pixels or 3d voxels). SPACE detects not only broad patterns of co-occurrence but also context-dependent associations, quantitative gradients and orientations, and other organizational complexities. Via a robust information theoretic framework, SPACE explores all possible ensembles of tissue elements - single elements, pairs, triplets, and so on - and ranks the most strongly patterned ensembles. For single images, rankings reflect patterns that differ from random assortment. For sets of images, rankings reflect patterns that differ across sample groups (e.g., genotypes, treatments, timepoints, etc.). Further tools then thoroughly characterize the nature of each pattern for intuitive interpretation. We validate SPACE and demonstrate its advantages using murine lymph node images for which ground truth has been defined. We then use SPACE to detect new patterns across varied datasets, including tumors and tuberculosis granulomas., Competing Interests: Competing Interest Statement: The authors have no competing interests.