Multiparameter flow cytometry is a convenient and efficient method for thorough phenotyping of cells, and especially immune cells from various tissues. We have successfully used multiparameter flow cytometry to characterize immune cells from patients with ovarian cancer and leveraged dimensionality reduction and machine learning for optimized visualization and analysis. Herein, we describe our optimized and established protocols for the labeling of cells with fluorophore-conjugated antibody panels, followed by details on data acquisition. Finally, we describe methods for analysis of the flow cytometry data using both FlowJo as well as R package, Cytofkit, for multidimensional data visualization.
Read the full article at: https://link.springer.com/protocol/10.1007/978-1-0716-1956-8_3