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Traumatic brain injury (TBI) results in a cascade of cellular responses, which produce neuroinflammation, partly due to microglial activation. Transforming from surveying to primed phenotypes, microglia undergo considerable molecular changes. However, specific microglial profiles in rat remain elusive due to tedious methodology and limited availability of reagents. Here, we present a flow cytometry-based analysis of rat microglia 24 h after TBI using the controlled cortical impact model, validated with a bioinformatics approach. Isolated microglia are analyzed for morphological changes and their expression of activation markers using flow cytometry, traditional gating-based analysis methods and support the data by employing bioinformatics statistical tools. We use CD45, CD11b/c, and p2y12 receptor to identify microglia and evaluate their activation state using CD32, CD86, RT1B, CD200R, and CD163. The results from logic-gated flow cytometry analysis was validated with bioinformatics-based analysis and machine learning algorithms to detect quantitative changes in morphology and marker expression in microglia due to activation following TBI.
Pubmed ID: 32686718
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Multi paradigm numerical computing environment and fourth generation programming language developed by MathWorks. Allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages, including C, C++, Java, Fortran and Python. Used to explore and visualize ideas and collaborate across disciplines including signal and image processing, communications, control systems, and computational finance.
View all literature mentionsSoftware for single-cell flow cytometry analysis. Its functions include management, display, manipulation, analysis and publication of the data stream produced by flow and mass cytometers.
View all literature mentionsSoftware package for family-based genomewide association (GWA) analysis, with the ability to infer missing genotypes using the Elston-Stewart algorithm. When SNPs from an association panel are less complete (i.e., having more missing genotypes) than markers from a linkage panel, many of the missing genotypes can be determined. GHOST can handle large pedigrees -- when pedigrees are small, Merlin is also recommended for this analysis. (entry from Genetic Analysis Software)
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