Spatial Patterning Analysis of Cellular Ensembles (SPACE) discovers complex spatial organization at the cell and tissue levels.

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.

TCR ligand potency differentially impacts PD-1 inhibitory effects on diverse signaling pathways.

Checkpoint blockade revolutionized cancer therapy, but we still lack a quantitative, mechanistic understanding of how inhibitory receptors affect diverse signaling pathways. To address this issue, we developed and applied a fluorescent intracellular live multiplex signal transduction activity reporter (FILMSTAR) system to analyze PD-1-induced suppressive effects. These studies identified pathways triggered solely by TCR or requiring both TCR and CD28 inputs. Using presenting cells differing in PD-L1 and CD80 expression while displaying TCR ligands of distinct potency, we found that PD-1-mediated inhibition primarily targets TCR-linked signals in a manner highly sensitive to peptide ligand quality. These findings help resolve discrepancies in existing data about the site(s) of PD-1 inhibition in T cells while emphasizing the importance of neoantigen potency in controlling the effects of checkpoint therapy.

A CRISPR screen targeting PI3K effectors identifies RASA3 as a negative regulator of LFA-1-mediated adhesion in T cells.

The integrin lymphocyte function-associated antigen 1 (LFA-1) helps to coordinate the migration, adhesion, and activation of T cells through interactions with intercellular adhesion molecule 1 (ICAM-1) and ICAM-2. LFA-1 is activated during the engagement of chemokine receptors and the T cell receptor (TCR) through inside-out signaling, a process that is partially mediated by phosphoinositide 3-kinase (PI3K) and its product phosphatidylinositol 3,4,5-trisphosphate (PIP3). To evaluate potential roles of PI3K in LFA-1 activation, we designed a library of CRISPR/single guide RNAs targeting known and potential PIP3-binding proteins and screened for effects on the ability of primary mouse T cells to bind to ICAM-1. We identified multiple proteins that regulated the binding of LFA-1 to ICAM-1, including the Rap1 and Ras GTPase-activating protein RASA3. We found that RASA3 suppressed LFA-1 activation in T cells, that its expression was rapidly reduced upon T cell activation, and that its activity was inhibited by PI3K. Loss of RASA3 in T cells led to increased Rap1 activation, defective lymph node entry and egress, and impaired responses to T-dependent immunization in mice. Our results reveal a critical role for RASA3 in T cell migration, homeostasis, and function.

The evolution of powerful yet perilous immune systems.

The mammalian immune system packs serious punch against infection but can also cause harm: for example, coronavirus disease 2019 (COVID-19) made headline news of the simultaneous power and peril of human immune responses. In principle, natural selection leads to exquisite adaptation and therefore cytokine responsiveness that optimally balances the benefits of defense against its costs (e.g., immunopathology suffered and resources expended). Here, we illustrate how evolutionary biology can predict such optima and also help to explain when/why individuals exhibit apparently maladaptive immunopathological responses. Ultimately, we argue that the evolutionary legacies of multicellularity and life-history strategy, in addition to our coevolution with symbionts and our demographic history, together explain human susceptibility to overzealous, pathology-inducing cytokine responses. Evolutionary insight thereby complements molecular/cellular mechanistic insights into immunopathology.

Understanding immunity in a tissue-centric context: Combining novel imaging methods and mathematics to extract new insights into function and dysfunction.

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.

Sarcoptic mange severity is associated with reduced genomic variation and evidence of selection in Yellowstone National Park wolves (Canis lupus).

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.

Quorum sensing via dynamic cytokine signaling comprehensively explains divergent patterns of effector choice among helper T cells.

In the animal kingdom, various forms of swarming enable groups of autonomous individuals to transform uncertain information into unified decisions which are probabilistically beneficial. Crossing scales from individual to group decisions requires dynamically accumulating signals among individuals. In striking parallel, the mammalian immune system is also a group of decentralized autonomous units (i.e. cells) which collectively navigate uncertainty with the help of dynamically accumulating signals (i.e. cytokines). Therefore, we apply techniques of understanding swarm behavior to a decision-making problem in the mammalian immune system, namely effector choice among CD4+ T helper (Th) cells. We find that incorporating dynamic cytokine signaling into a simple model of Th differentiation comprehensively explains divergent observations of this process. The plasticity and heterogeneity of individual Th cells, the tunable mixtures of effector types that can be generated in vitro, and the polarized yet updateable group effector commitment often observed in vivo are all explained by the same set of underlying molecular rules. These rules reveal that Th cells harness dynamic cytokine signaling to implement a system of quorum sensing. Quorum sensing, in turn, may confer adaptive advantages on the mammalian immune system, especially during coinfection and during coevolution with manipulative parasites. This highlights a new way of understanding the mammalian immune system as a cellular swarm, and it underscores the power of collectives throughout nature.

Immune Signaling Networks: Sources of Robustness and Constrained Evolvability during Coevolution.

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 signaling molecules 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.

Instructed subsets or agile swarms: how T-helper cells may adaptively counter uncertainty with variability and plasticity.

Over recent years, extensive phenotypic variability and plasticity have been revealed among the T-helper cells of the mammalian adaptive immune system, even within clonal lineages of identical antigen specificity. This challenges the conventional view that T-helper cells assort into functionally distinct subsets following differential instruction by the innate immune system. We argue that the adaptive value of coping with uncertainty can reconcile the 'instructed subset' framework with T-helper variability and plasticity. However, we also suggest that T-helper cells might better be understood as agile swarms engaged in collective decision-making to promote host fitness. With rigorous testing, the 'agile swarms' framework may illuminate how variable and plastic individual T-helper cells interact to create coherent immunity.