Syk Inhibition Induces Platelet Dependent Peri-islet Hemorrhage in the Rat Pancreas.

Spleen tyrosine kinase (Syk) is a nonreceptor tyrosine kinase that is an important signaling enzyme downstream of immunoreceptors containing an intracellular immunoreceptor tyrosine activating motif (ITAM). These receptors encompass a wide variety of biological functions involved in autoimmune disease pathogenesis. There has been considerable interest in the development of inhibitors of the Syk pathway for the treatment of rheumatoid arthritis and systemic lupus erythematosus. We report that Syk inhibition mechanistically caused peri-islet hemorrhages and fibrin deposition in the rat pancreas and that this finding is due to a homeostatic functional defect in platelets. In more limited studies, similar lesions could not be induced in mice, dogs, and cynomolgus monkeys at similar or higher plasma drug concentrations. Irradiation-induced thrombocytopenia caused a phenotypically similar peri-islet pancreas lesion and the formation of this lesion could be prevented by platelet transfusion. In addition, Syk inhibitor-induced lesions were prevented by the coadministration of prednisone. A relatively greater sensitivity of rat platelets to Syk inhibition was supported by functional analyses demonstrating rat-specific differences in response to convulxin, a glycoprotein VI agonist that signals through Syk. These data demonstrate that the Syk pathway is critical in platelet-endothelial cell homeostasis in the peri-islet pancreatic microvasculature in rats.

Uncover spatially informed variations for single-cell spatial transcriptomics with STew.

Motivation: The recent spatial transcriptomics (ST) technologies have enabled characterization of gene expression patterns and spatial information, advancing our understanding of cell lineages within diseased tissues. Several analytical approaches have been proposed for ST data, but effectively utilizing spatial information to unveil the shared variation with gene expression remains a challenge. Results: We introduce STew, a Spatial Transcriptomic multi-viEW representation learning method, to jointly analyze spatial information and gene expression in a scalable manner, followed by a data-driven statistical framework to measure the goodness of model fit. Through benchmarking using human dorsolateral prefrontal cortex and mouse main olfactory bulb data with true manual annotations, STew achieved superior performance in both clustering accuracy and continuity of identified spatial domains compared with other methods. STew is also robust to generate consistent results insensitive to model parameters, including sparsity constraints. We next applied STew to various ST data acquired from 10× Visium, Slide-seqV2, and 10× Xenium, encompassing single-cell and multi-cellular resolution ST technologies, which revealed spatially informed cell type clusters and biologically meaningful axes. In particular, we identified a proinflammatory fibroblast spatial niche using ST data from psoriatic skins. Moreover, STew scales almost linearly with the number of spatial locations, guaranteeing its applicability to datasets with thousands of spatial locations to capture disease-relevant niches in complex tissues. Availability and implementation: Source code and the R software tool STew are available from github.com/fanzhanglab/STew.

Single-cell computational machine learning approaches to immune-mediated inflammatory disease: New tools uncover novel fibroblast and macrophage interactions driving pathogenesis.

Recent advances in single-cell sequencing technologies call for greater computational scalability and sensitivity to analytically decompose diseased tissues and expose meaningful biological relevance in individual cells with high resolution. And while fibroblasts, one of the most abundant cell types in tissues, were long thought to display relative homogeneity, recent analytical and technical advances in single-cell sequencing have exposed wide variation and sub-phenotypes of fibroblasts of potential and apparent clinical significance to inflammatory diseases. Alongside anticipated improvements in single cell spatial sequencing resolution, new computational biology techniques have formed the technical backbone when exploring fibroblast heterogeneity. More robust models are required, however. This review will summarize the key advancements in computational techniques that are being deployed to categorize fibroblast heterogeneity and their interaction with the myeloid compartments in specific biological and clinical contexts. First, typical machine-learning-aided methods such as dimensionality reduction, clustering, and trajectory inference, have exposed the role of fibroblast subpopulations in inflammatory disease pathologies. Second, these techniques, coupled with single-cell predicted computational methods have raised novel interactomes between fibroblasts and macrophages of potential clinical significance to many immune-mediated inflammatory diseases such as rheumatoid arthritis, ulcerative colitis, lupus, systemic sclerosis, and others. Third, recently developed scalable integrative methods have the potential to map cross-cell-type spatial interactions at the single-cell level while cross-tissue analysis with these models reveals shared biological mechanisms between disease contexts. Finally, these advanced computational omics approaches have the potential to be leveraged toward therapeutic strategies that target fibroblast-macrophage interactions in a wide variety of inflammatory diseases.