Even in conventional flow cytometry, technologies are used to increase the number of markers included in one panel ( 3). Up to date, panels of over 40 colors have been developed ( 1, 2), with larger panels expected to emerge in the near future. Not being limited to the number of channels of the instrument, spectral flow cytometry enables multicolor panels with many more parameters than ever deemed possible in conventional flow cytometry. Over the years, the number of variables measured in flow cytometry experiments has increased, especially with the recent development of spectral flow cytometry. Application of our workflow will aid spectral flow cytometry users to obtain valid and reproducible results. This methods article provides an R-based pipeline based on previously published packages, that are readily available to use. Using healthy control data as example, we will go through the concepts of quality control, data cleaning, transformation, correcting for batch effects, subsampling, clustering and data integration. Moreover, we will describe a workflow to properly prepare spectral flow cytometry data for high dimensional analysis and tools for integrating new data at later time points. In this article, we will give insight into the pitfalls of handling spectral flow cytometry datasets. However, preparing spectral flow cytometry data for high-dimensional analysis can be challenging, because of several technical aspects. To fully explore the resulting high-dimensional single cell datasets, high-dimensional analysis is needed, as opposed to the common practice of manual gating in conventional flow cytometry. Spectral flow cytometry is an upcoming technique that allows for extensive multicolor panels, enabling simultaneous investigation of a large number of cellular parameters in a single experiment. 3Department of Clinical Immunology and Rheumatology, Maasstad Hospital, Rotterdam, Netherlands.2Department of Immunology, Erasmus University Medical Center, Rotterdam, Netherlands.1Department of Rheumatology, Erasmus University Medical Center, Rotterdam, Netherlands. Hannah den Braanker 1,2,3†, Margot Bongenaar 1,2† and Erik Lubberts 1,2*
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