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      Mouse Spinal Cord Vascular Transcriptome Analysis Identifies CD9 and MYLIP as Injury-Induced Players

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          Abstract

          Traumatic spinal cord injury (SCI) initiates a cascade of cellular events, culminating in irreversible tissue loss and neuroinflammation. After the trauma, the blood vessels are destroyed. The blood-spinal cord barrier (BSCB), a physical barrier between the blood and spinal cord parenchyma, is disrupted, facilitating the infiltration of immune cells, and contributing to a toxic spinal microenvironment, affecting axonal regeneration. Understanding how the vascular constituents of the BSCB respond to injury is crucial to prevent BSCB impairment and to improve spinal cord repair. Here, we focus our attention on the vascular transcriptome at 3- and 7-days post-injury (dpi), during which BSCB is abnormally leaky, to identify potential molecular players that are injury-specific. Using the mouse contusion model, we identified Cd9 and Mylip genes as differentially expressed at 3 and 7 dpi. CD9 and MYLIP expression were injury-induced on vascular cells, endothelial cells and pericytes, at the injury epicentre at 7 dpi, with a spatial expression predominantly at the caudal region of the lesion. These results establish CD9 and MYLIP as two new potential players after SCI, and future studies targeting their expression might bring promising results for spinal cord repair.

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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            clusterProfiler: an R package for comparing biological themes among gene clusters.

            Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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              limma powers differential expression analyses for RNA-sequencing and microarray studies

              limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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                Author and article information

                Contributors
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                Journal
                IJMCFK
                International Journal of Molecular Sciences
                IJMS
                MDPI AG
                1422-0067
                April 2023
                March 29 2023
                : 24
                : 7
                : 6433
                Article
                10.3390/ijms24076433
                10094762
                37047406
                22ba646f-14ef-4dd0-a701-f067ea6fef9c
                © 2023

                https://creativecommons.org/licenses/by/4.0/

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