Abstract
Flow cytometry has been used for the last two decades to identify which immune cell subsets diapedese from the periphery into the brain parenchyma following injuries, including ischemic and hemorrhagic stroke. Recent developments have moved the analysis of high-parameter flow cytometry data sets from the traditional analysis method of manual gating to using unbiased analyses to improve scientific rigor. This chapter gives a step-by-step guide on using modern computational approaches to analyze complex flow cytometry data sets in FlowJo™ Software v10. The section will describe pre-processing and outline the steps needed to perform unsupervised clustering of your data set in addition to using nonlinear dimensionality reduction for visualizing your analysis. While these methods can identify long-term neuroinflammatory responses after stroke, the methods could be applied to a variety of flow cytometry data sets.
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Acknowledgments
Establishment of these protocols is supported by grants from the National Institutes of Health NINDS NS088555 to AMS, NINDS 5T32NS077889 to TAU, and from the American Heart Association to AMS (19EIA34760279).
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Ujas, T.A., Obregon-Perko, V., Stowe, A.M. (2023). A Guide on Analyzing Flow Cytometry Data Using Clustering Methods and Nonlinear Dimensionality Reduction (tSNE or UMAP). In: Karamyan, V.T., Stowe, A.M. (eds) Neural Repair. Methods in Molecular Biology, vol 2616. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2926-0_18
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DOI: https://doi.org/10.1007/978-1-0716-2926-0_18
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