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      Phenotypic and transcriptional changes in peripheral blood mononuclear cells during alphavirus encephalitis in mice

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          ABSTRACT

          Sindbis virus (SINV) infection of mice provides a model system for studying the pathogenesis of alphaviruses that infect the central nervous system (CNS) to cause encephalomyelitis. While studies of human viral infections typically focus on accessible cells from the blood, this compartment is rarely evaluated in mice. To bridge this gap, single-cell RNA sequencing (scRNAseq) was combined with flow cytometry to characterize the transcriptional and phenotypic changes of peripheral blood mononuclear cells (PBMCs) from SINV-infected mice. Twenty-one clusters were identified by scRNAseq at 7 days after infection, with a unique cluster and overall increase in naive B cells for infected mice. Uninfected mice had fewer immature T cells and CCR9 + CD4 T cells and a unique immature T cell cluster. Gene expression was most altered in the Ki67 + CD8 T cell cluster, with chemotaxis and proliferation-related genes upregulated. Global analysis indicated metabolic changes in myeloid cells and increased expression of Ccl5 by NK cells. Phenotypes of PBMCs and cells infiltrating the CNS were analyzed by flow cytometry over 14 days after infection. In PBMCs, CD8 and Th1 CD4 T cells increased in representation, while B cells showed a transient decrease at day 5 in total, Ly6a +, and naive cells, and an increase in activated B cells. In the brain, CD8 T cells increased for the first 7 days, while Th1 CD4 T cells and naive and Ly6a + B cells continued to accumulate for 14 days. Therefore, dynamic immune cell changes can be identified in the blood as well as the CNS during viral encephalomyelitis.

          IMPORTANCE

          The outcome of viral encephalomyelitis is dependent on the host immune response, with clearance and resolution of infection mediated by the adaptive immune response. These processes are frequently studied in mouse models of infection, where infected tissues are examined to understand the mechanisms of clearance and recovery. However, studies of human infection typically focus on the analysis of cells from the blood, a compartment rarely examined in mice, rather than inaccessible tissue. To close this gap, we used single-cell RNA sequencing and flow cytometry to profile the transcriptomic and phenotypic changes of peripheral blood mononuclear cells (PBMCs) before and after central nervous system (CNS) infection in mice. Changes to T and B cell gene expression and cell composition occurred in PBMC and during entry into the CNS, with CCL5 being a differentially expressed chemokine. Therefore, dynamic changes occur in the blood as well as the CNS during the response of mice to virus infection, which will inform the analysis of human studies.

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          Most cited references80

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          STAR: ultrafast universal RNA-seq aligner.

          Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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            Massively parallel digital transcriptional profiling of single cells

            Characterizing the transcriptome of individual cells is fundamental to understanding complex biological systems. We describe a droplet-based system that enables 3′ mRNA counting of tens of thousands of single cells per sample. Cell encapsulation, of up to 8 samples at a time, takes place in ∼6 min, with ∼50% cell capture efficiency. To demonstrate the system's technical performance, we collected transcriptome data from ∼250k single cells across 29 samples. We validated the sensitivity of the system and its ability to detect rare populations using cell lines and synthetic RNAs. We profiled 68k peripheral blood mononuclear cells to demonstrate the system's ability to characterize large immune populations. Finally, we used sequence variation in the transcriptome data to determine host and donor chimerism at single-cell resolution from bone marrow mononuclear cells isolated from transplant patients.
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              Spatial reconstruction of single-cell gene expression

              Spatial localization is a key determinant of cellular fate and behavior, but spatial RNA assays traditionally rely on staining for a limited number of RNA species. In contrast, single-cell RNA-seq allows for deep profiling of cellular gene expression, but established methods separate cells from their native spatial context. Here we present Seurat, a computational strategy to infer cellular localization by integrating single-cell RNA-seq data with in situ RNA patterns. We applied Seurat to spatially map 851 single cells from dissociated zebrafish (Danio rerio) embryos, inferring a transcriptome-wide map of spatial patterning. We confirmed Seurat’s accuracy using several experimental approaches, and used it to identify a set of archetypal expression patterns and spatial markers. Additionally, Seurat correctly localizes rare subpopulations, accurately mapping both spatially restricted and scattered groups. Seurat will be applicable to mapping cellular localization within complex patterned tissues in diverse systems.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                mBio
                mBio
                mbio
                mBio
                American Society for Microbiology (1752 N St., N.W., Washington, DC )
                2150-7511
                June 2024
                02 May 2024
                02 May 2024
                : 15
                : 6
                : e00736-24
                Affiliations
                [1 ]W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health; , Baltimore, Maryland, USA
                Duke University School of Medicine; , Durham, North Carolina, USA
                Author notes
                Address correspondence to Diane E. Griffin, dgriffi6@ 123456jhu.edu

                Present address: Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA

                D.E.G. is on an advisory board for GSK and consulted for Merck.

                Author information
                https://orcid.org/0000-0002-8603-4046
                https://orcid.org/0000-0003-1793-1189
                https://orcid.org/0000-0001-6643-2429
                Article
                00736-24 mbio.00736-24
                10.1128/mbio.00736-24
                11237501
                38695564
                20c27b1f-7042-48d2-8ead-bb6ee128e66b
                Copyright © 2024 Nguyen et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.

                History
                : 19 March 2024
                : 28 March 2024
                Page count
                supplementary-material: 0, authors: 5, Figures: 7, References: 80, Pages: 19, Words: 10763
                Categories
                Research Article
                virology, Virology
                Custom metadata
                June 2024

                Life sciences
                viral encephalitis,sindbis virus,peripheral blood mononuclear cells,scrnaseq,inflammation

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