Protocol for generating high-quality, 45-color spectral flow cytometry data for unsupervised clustering to investigate aging in human PBMCs.

Spectral flow cytometry enables the use of high-parameter panels. Using a Sony ID7000-optimized 45-color panel, we present a protocol for acquiring and preprocessing high-quality, high-parameter spectral flow cytometry data. We describe steps for batch-learning-assisted comparative cluster identification to study age-induced differences in human peripheral mononuclear cells. We detail spectral flow cytometry procedures, including sample staining, spectral reference acquisition, and unmixing. We also provide data preprocessing steps, including biexponential axis optimization, which is important for accurate dimensionality reduction and cluster identification.