Researchers identify a potential new target for head and neckcancer immunotherapy ucsandiego PNASNexus
We performed stratification using the standards of High , Medium , and Low expression levels for the variables of interest. For two variables, such as B cell High/TERT High , we took the intersection of samples that satisfied both criteria. For immune cells , levels were quantified using the geometric mean of log2TPM of defined cell markers: CD19 and MS4A1 for B cells; CD3D, CD3E, CD3G, CD4 for CD4 T cells; CD3D, CD3E, CD3G, CD8A and CD8B for CD8 T cells.
-seq alignment files were reprocessed using Sailfish software version 0.7.4 (
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Neutrophil conversion to a tumor-killing phenotype underpins effective microbial therapyAbstract. The inflammatory microenvironment of solid tumors creates a pro-tumorigenic milieu that resembles chronic inflammation akin to a subverted wound healing response. Here we investigated the effect of converting the tumor microenvironment from a chronically inflamed state to one of acute microbial inflammation by injecting microbial bioparticles directly into tumors. Intratumoral microbial bioparticle injection led to rapid and dramatic changes in the tumor immune composition, the most striking of which was a substantial increase in the presence of activated neutrophils. In situ photoconversion and intravital microscopy indicated that tumor neutrophils transiently switched from sessile producers of vascular endothelial growth factor to highly motile neutrophils that clustered to make neutrophil-rich domains in the tumor. The neutrophil clusters remodeled tumor tissue and repressed tumor growth. Single cell transcriptional analysis of microbe-stimulated neutrophils showed a profound shift in gene expression towards heightened activation and anti-microbial effector function. Microbe-activated neutrophils also upregulated chemokines known to regulate neutrophil and CD8+ T cell recruitment. Microbial therapy also boosted CD8+ T cell function and enhanced the therapeutic benefit of checkpoint inhibitor therapy in tumor-bearing mice and provided protection in a model of tumor recurrence. These data indicate that one of the major effector mechanisms of microbial therapy is the conversion of tumor neutrophils from a wound healing to an acutely activated cytotoxic phenotype, highlighting a rationale for broader deployment of microbial therapy in the treatment of solid cancers.
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There's a Dead Cells: The Rogue-Lite Board game on the wayA campaign to crowdfund a new rogue-lite board game inspired by Dead Cells will shortly launch on Kickstarter. Dead Cel…
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Single-cell transcriptomic atlas-guided development of CAR-T cells for the treatment of acute myeloid leukemia - Nature BiotechnologyNew targets for CAR-Tcelltherapy against acutemyeloidleukemia through AI-assisted analysis NatureBiotech
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Scientists discover a new way to prevent secondary breast cancerScientists have discovered why breast cancer cells that have spread to the lungs may 'wake up' following years of sleep, forming incurable secondary tumors.
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How does temperature affect host responses to SARS-CoV-2 and IAV infection in human nasal epithelial cells?How does temperature affect host responses to SARS-CoV-2 and IAV infection in human nasal epithelial cells? biorxivpreprint JohnsHopkinsSPH HopkinsMedicine SARSCoV2 COVID19 coronavirus covid influenza virus
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scEvoNet: a gradient boosting-based method for prediction of cell state evolution - BMC BioinformaticsBackground Exploring the function or the developmental history of cells in various organisms provides insights into a given cell type's core molecular characteristics and putative evolutionary mechanisms. Numerous computational methods now exist for analyzing single-cell data and identifying cell states. These methods mostly rely on the expression of genes considered as markers for a given cell state. Yet, there is a lack of scRNA-seq computational tools to study the evolution of cell states, particularly how cell states change their molecular profiles. This can include novel gene activation or the novel deployment of programs already existing in other cell types, known as co-option. Results Here we present scEvoNet, a Python tool for predicting cell type evolution in cross-species or cancer-related scRNA-seq datasets. ScEvoNet builds the confusion matrix of cell states and a bipartite network connecting genes and cell states. It allows a user to obtain a set of genes shared by the characteristic signature of two cell states even between distantly-related datasets. These genes can be used as indicators of either evolutionary divergence or co-option occurring during organism or tumor evolution. Our results on cancer and developmental datasets indicate that scEvoNet is a helpful tool for the initial screening of such genes as well as for measuring cell state similarities. Conclusion The scEvoNet package is implemented in Python and is freely available from https://github.com/monsoro/scEvoNet . Utilizing this framework and exploring the continuum of transcriptome states between developmental stages and species will help explain cell state dynamics.
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