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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]

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.