Intact, the skin typically serves as an effective barrier to the external world; however, once pathogens have breached this barrier via a wound, such as a tick bite, the surrounding tissues must recruit immune cells from the blood to neutralize the pathogen. With innate and adaptive immune systems being similar between the guinea pig and human systems, the ability of guinea pigs to show clinical signs of many infectious diseases, and the large size of guinea pigs relative to a murine model, the guinea pig is a valuable model for studying tick-borne and other pathogens that invade the skin. Here, we report a novel assay for assessing guinea pig leukocyte infiltration in the skin. Briefly, we developed an optimized six-color/eight-parameter polychromatic flow cytometric panel that combines enzymatic and mechanical dissociation of skin tissue with fluorescent antibody staining to allow for the immunophenotyping of guinea pig leukocytes that have migrated into the skin, resulting in inflammation. We designed this assay using a guinea pig model for tick-borne rickettsiosis to further investigate host–pathogen interactions in the skin, with preliminary data demonstrating immunophenotyping at skin lesions from infected ticks. We anticipate that future applications will include hypothesis testing to define the primary immune cell infiltrates responding to exposure to virulent, avirulent tick-borne rickettsiae, and tick-borne rickettsiae of unknown virulence. Other relevant applications include skin lesions resulting from other vector-borne pathogens, Staphylococcus aureus infection, and Buruli ulcer caused by Mycobacterium ulcerans.

Adult IDH-wildtype glioblastoma (GBM) is a highly aggressive brain tumor with no established immunotherapy or targeted therapy. Recently, CD32 ⁺ HLA-DR ^hi macrophages were discovered to have displaced resident microglia in GBM tumors that contact the lateral ventricle stem cell niche. Since these lateral ventricle contacting GBM tumors have especially poor outcomes, identifying the origin and role of these CD32 ⁺ macrophages is likely critical to developing successful GBM immunotherapies. Here, we identify these CD32 ⁺ cells as M_IL-8 macrophages and establish that IL-8 is sufficient and necessary for tumor cells to instruct healthy macrophages into CD32 ⁺ M_IL-8 M2 macrophages. Using a set of 73 GBM tumors, IL-8 protein is shown to be present in GBM tumor cells in vivo and especially common in tumors contacting the lateral ventricle. Finally, in ex vivo experiments with conditioned medium from primary human tumor cells, inhibitory antibodies to IL-8 blocked the generation of CD32 ⁺ M_IL-8 cells. These results provide a mechanistic origin for CD32 ⁺ macrophages that predominate in the microenvironment of the most aggressive GBM tumors. IL-8 and CD32 ⁺ macrophages should now be explored as targets in combination with GBM immunotherapies, especially for patients whose tumors present with radiographic contact with the ventricular subventricular zone stem cell niche.

A key challenge for single cell discovery analysis is to identify new cell types, describe them quantitatively, and seek these novel cells in new studies often using a different platform. Over the last decade, tools were developed to address identification and quantitative description of cells in human tissues and tumors. However, automated validation of populations at the single cell level has struggled due to the cytometry field's reliance on hierarchical, ordered use of features and on platform-specific rules for data processing and analysis. Here we present Velociraptor, a workflow that implements Marker Enrichment Modeling in three cross-platform modules: 1) identification of cells specific to disease states, 2) description of hallmark features for each cell and population, and 3) searching for cells matching one or more hallmark feature sets in a new dataset. A key advance is that Velociraptor registers cells between datasets, including between flow cytometry and quantitative imaging using different, overlapping feature sets. Four datasets were used to challenge Velociraptor and reveal new biological insights. Working at the individual sample level, Velociraptor tracked the abundance of clinically significant glioblastoma brain tumor cell subsets and characterized the cells that predominate in recurrent tumors as a close match for rare, negative prognostic cells originally observed in matched pre-treatment tumors. In patients with inborn errors of immunity, Velociraptor identified genotype-specific cells associated with GATA2 haploinsufficiency. Finally, in cross-platform analysis of immune cells in multiplex imaging of breast cancer, Velociraptor sought and correctly identified memory T cell subsets in tumors. Different phenotypic descriptions generated by algorithms or humans were shown to be effective as search inputs, indicating that cell identity need not be described in terms of per-feature cutoffs or strict hierarchical analyses. Velociraptor thus identifies cells based on hallmark feature sets, such as protein expression signatures, and works effectively with data from multiple sources, including suspension flow cytometry, imaging, and search text based on known or theoretical cell features.

Spotted Fever Rickettsiosis (SFR) is caused by spotted fever group Rickettsia spp. (SFGR), and is associated with symptoms common to other illnesses, making it challenging to diagnose before detecting SFGR-specific antibodies. The guinea pig is a valuable biomedical model for studying Spotted Fever Rickettsiosis (SFR); its immune system is more like the human immune system than that of the murine model, and guinea pigs develop characteristic clinical signs. Thus, we have a compelling interest in developing, expanding, and optimizing tools for use in our guinea pig-Amblyomma-Rickettsia system for understanding host-tick-pathogen interactions. With the design and optimization of the three multiplex TaqMan^® qPCR assays described here, we can detect the two SFGR, their respective primary Amblyomma sp. vectors, and the guinea pig model as part of controlled experimental studies using tick-transmission of SFGR to guinea pigs. We developed qPCR assays that reliably detect each specific target down to 10 copies by producing plasmid standards for each assay target, optimizing the individual primer-probe sets, and optimizing the final multiplex reactions in a methodical, stepwise fashion. We anticipate that these assays, currently designed for in vivo studies, will serve as a foundation for optimal SFGR detection in other systems, including fieldwork.

Mutations in the gene PSTPIP1 may cause several different autoinflammatory syndromes, but the mechanisms by which distinct PSTPIP1 mutations lead to these differing phenotypes are not fully understood. The two best characterized autoinflammatory conditions resulting from PSTPIP1 mutation are pyogenic arthritis, pyoderma gangrenosum, and acne (PAPA) syndrome and PSTPIP1-associated myeloid-related proteinemia inflammatory (PAMI) syndrome. Here, we report a novel gain-of-function PSTPIP1 mutation (p.N236K) causing PAMI syndrome in a patient with systemic autoinflammation and severe neutropenia. This mutant form of PSTPIP1 shows increased binding to pyrin and leads to heightened inflammasome formation, relative to WT PSTPIP1. We also identify a transcriptional signature in blood from PAMI patients suggestive of enhanced T cell activation and altered neutrophil survival and/or function. Further research on PSTPIP1-related autoinflammatory conditions is needed to more deeply understand the genetic and immunological drivers of disease and contribute to improving patient outcomes.