Panel Optimization for High-Dimensional Immunophenotyping Assays Using Full-Spectrum Flow Cytometry.

ABSTRACT

Technological advancements in fluorescence flow cytometry and an ever-expanding understanding of the complexity of the immune system have led to the development of large flow cytometry panels reaching up to 43 colors at the single-cell level. However, as panel size and complexity increase, so too does the detail involved in designing and optimizing successful high-quality panels fit for downstream high-dimensional data analysis. In contrast to conventional flow cytometers, full-spectrum flow cytometers measure the entire emission spectrum of each fluorophore across all lasers. This allows for fluorophores with very similar emission maxima but unique overall spectral fingerprints to be used in conjunction, enabling relatively straightforward design of larger panels. Although a protocol for best practices in full-spectrum flow cytometry panel design has been published, there is still a knowledge gap in going from the theoretically designed panel to the necessary steps required for panel optimization. Here, we aim to guide users through the theory of optimizing a high-dimensional full-spectrum flow cytometry panel for immunophenotyping using comprehensive step-by-step protocols. These protocols can also be used to troubleshoot panels when issues arise. A practical application of this approach is exemplified with a 24-color panel designed for identification of conventional T-cell subsets in human peripheral blood. © 2021 Malaghan Institute of Medical Research, Cytek Biosciences. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1 Preparation and evaluation of optimal spectral reference controls Support Protocol 1 Antibody titration Support Protocol 2 Changing instrument settings Basic Protocol 2 Unmixing evaluation of fully stained sample Basic Protocol 3 Evaluation of marker resolution Support Protocol 3 Managing heterogeneous autofluorescence Basic Protocol 4 Assessment of data quality using expert gating and dimensionality reduction algorithms.