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Turtle, C. J.

Publications and source records attributed to Turtle, C. J..

2 recordsLinked to original sources

Endothelial-leukocyte interaction in CAR T cell neurotoxicity

CAR-T cells treat cancer, but also cause systemic cytokine release and immune effector cell associated neurotoxicity syndrome (ICANS). In an immunocompetent mouse model, we show by in vivo two-photon imaging that CD19-CAR T treatment causes brain capillary plugging by circulating CAR-T cells and other CD45+ leukocytes, as well as cortical hypoxia. This is accompanied by increased endothelial ICAM-1 and VCAM-1 expression in the brain capillary-venule transition zone, where most of the capillary stalls occur. In the mouse model, circulating CAR-T cells strongly upregulate integrin 4{beta}1 affinity to VCAM-1, but not affinity of integrin L{beta}2 to ICAM-1. Blockade of integrin 4 but not integrin L improves locomotion behavior. In vitro, human brain microendothelial cells upregulate ICAM-1 more than VCAM-1 in response to TNF, IFN-{gamma}, and IL-1{beta}. In a 3D brain human microvessel model, treatment with TNF and IFN-{gamma} is sufficient to induce adhesion of CAR T cells under flow conditions, which is blocked synergistically by antibodies against integrins 4 and L. Finally, patients with the highest levels of TNF and IFN-{gamma} also have the highest blood levels of soluble ICAM-1 and VCAM-1, which in turn correlate with ICANS. Integrin 4 but not L increases in CAR-T cells after they are infused into patients. Combined data from patients, mouse models and in vitro microvessels indicate differential regulation of interactions of ICAM-1 and VCAM-1 with their respective leukocyte integrins. Overall, our study supports the hypothesis that cytokine-driven upregulation of endothelial-leukocyte adhesion is sufficient to induce acute, reversible neurotoxicity. One Sentence SummaryDuring CAR T cell therapy, cytokine release induces white blood cell stalling in brain capillaries by upregulating ICAM-1/VCAM-1-integrin interactions.

immunology↗

Multi-view gene panel characterization for spatially resolved omics

Spatially resolved transcriptomics has transformed our ability to study complex tissues at the cellular and subcellular resolution. However, targeted spatial technologies require pre-selected gene panels, which are typically curated based on existing biological knowledge and prior research hypotheses. While current methods often prioritize capturing cell type information, we argue that an effective gene panel should also capture cell type diversity, cell states, pathway-level information, and minimize redundancy. To address these broader requirements, we developed a gene panel characterization platform that characterizes panels across multiple perspectives, thus allowing us to compare panels comprehensively. Notably, computationally constructed gene panels performed competitively in capturing major cell types when compared to our in-house manually curated panel. However, refined manual curation offered distinct advantages, particularly in capturing minor and rare cell types and exhibited lower information redundancy comparatively. Building on this framework, we integrated these metrics into a deep learning platform, panelScope, leveraging them as a loss function to design holistic gene panels. Using an acute myeloid leukemia (AML) dataset with 42 well-defined cell types and the 5K Xenium panel from 10X Genomics, we demonstrate the utility of our framework in comprehensively characterizing gene panels, enabling the design of tailored panels that address diverse research needs.

bioinformatics↗