Reshef, Laurie Rumker.Ĭenter for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA These authors contributed equally: Yakir A. Simultaneous epitope and transcriptome measurement in single cells. TH1-biased immunity induced by exposure to Antarctic winter. Age related human T cell subset evolution and senescence. Benchmarking atlas-level data integration in single-cell genomics. Single-cell transcriptomics identifies an effectorness gradient shaping the response of CD4 + T cells to cytokines. Lymphocyte innateness defined by transcriptional states reflects a balance between proliferation and effector functions. Sepsis induces telomere shortening: a potential mechanism responsible for delayed pathophysiological events in sepsis survivors? Mol. Multiple roles for IL-12 in a model of acute septic peritonitis. Serial increase of IL-12 response and human leukocyte antigen-DR expression in severe sepsis survivors. Histone deacetylation inhibitors as therapy concept in sepsis. Suppression of the RAC1/MLK3/p38 signaling pathway by β-elemene alleviates sepsis-associated encephalopathy in mice. Lipopolysaccharide induces Rac1-dependent reactive oxygen species formation and coordinates tumor necrosis factor-α secretion through IKK regulation of NF-κB. Defining inflammatory cell states in rheumatoid arthritis joint synovial tissues by integrating single-cell transcriptomics and mass cytometry. Neural crest cell-autonomous roles of fibronectin in cardiovascular development. Notch signaling in postnatal joint chondrocytes, but not subchondral osteoblasts, is required for articular cartilage and joint maintenance. Maximizing statistical power to detect clinically associated cell states with scPOST. Mixed-effects association of single cells identifies an expanded effector CD4 + T cell subset in rheumatoid arthritis. Jointly defining cell types from multiple single-cell datasets using LIGER. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Fast, sensitive and accurate integration of single-cell data with Harmony. Notch signalling drives synovial fibroblast identity and arthritis pathology. An immune-cell signature of bacterial sepsis. Multimodally profiling memory T cells from a tuberculosis cohort identifies cell state associations with demographics, environment and disease. Quantifying the effect of experimental perturbations at single-cell resolution. From Louvain to Leiden: guaranteeing well-connected communities. Current best practices in single-cell RNA-seq analysis: a tutorial. Tutorial: guidelines for the computational analysis of single-cell RNA sequencing data. Single-cell sequencing techniques from individual to multiomics analyses. When applied to published datasets, CNA captures a Notch activation signature in rheumatoid arthritis, identifies monocyte populations expanded in sepsis and identifies a novel T cell population associated with progression to active tuberculosis. Simulations show that CNA enables more sensitive and accurate identification of disease-associated cell states than a cluster-based approach. CNA performs statistical testing for associations between any sample-level attribute and the abundances of these co-varying neighborhood groups. CNA characterizes dominant axes of variation across samples by identifying groups of small regions in transcriptional space-termed neighborhoods-that co-vary in abundance across samples, suggesting shared function or regulation. Here we present co-varying neighborhood analysis (CNA), an unbiased method to identify associated cell populations with greater flexibility than cluster-based approaches. Current statistical approaches typically map cells to clusters and then assess differences in cluster abundance. As single-cell datasets grow in sample size, there is a critical need to characterize cell states that vary across samples and associate with sample attributes, such as clinical phenotypes.
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