Graded FGF activity patterns distinct cell types within the apical sensory organ of the sea anemone Nematostella vectensis.

Bilaterian animals have evolved complex sensory organs comprised of distinct cell types that function coordinately to sense the environment. Each sensory unit has a defined architecture built from component cell types, including sensory cells, non-sensory support cells, and dedicated sensory neurons. Whether this characteristic cellular composition is present in the sensory organs of non-bilaterian animals is unknown. Here, we interrogate the cell type composition and gene regulatory networks controlling development of the larval apical sensory organ in the sea anemone Nematostella vectensis. Using single cell RNA sequencing and imaging approaches, we reveal two unique cell types in the Nematostella apical sensory organ, GABAergic sensory cells and a putative non-sensory support cell population. Further, we identify the paired-like (PRD) homeodomain gene prd146 as a specific sensory cell marker and show that Prd146+ sensory cells become post-mitotic after gastrulation. Genetic loss of function approaches show that Prd146 is essential for apical sensory organ development. Using a candidate gene knockdown approach, we place prd146 downstream of FGF signaling in the apical sensory organ gene regulatory network. Further, we demonstrate that an aboral FGF activity gradient coordinately regulates the specification of both sensory and support cells. Collectively, these experiments define the genetic basis for apical sensory organ development in a non-bilaterian animal and reveal an unanticipated degree of complexity in a prototypic sensory structure.

IL-8 Instructs Macrophage Identity in Lateral Ventricle Contacting Glioblastoma.

Adult IDH-wildtype glioblastoma (GBM) is a highly aggressive brain tumor with no established immunotherapy or targeted therapy. Recently, CD32+ HLA-DRhi macrophages were shown 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. 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. Finally, 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. 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.

Velociraptor: Cross-Platform Quantitative Search Using Hallmark Cell Features.

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.