Analytical performance of a 25-marker spectral cytometry immune monitoring assay in peripheral blood

Herein, we describe the development and analytical performance characteristics of a spectral flow cytometry assay for longitudinal immune monitoring biomarker applications in human whole blood and/or peripheral blood mononuclear cells (PBMCs). This 25 immune biomarker panel and robust gating strategy, developed in normal healthy volunteers, resolves memory, polarization, and activation markers on T, B, and NK cells as well as myeloid subpopulations including monocytes, dendritic cells, neutrophils, basophils, and myeloid-derived suppressor cells (MDSCs). Three associated fluorescence-minus-multiple (FMM) designs are proposed as gating controls. To our knowledge, this is the first report to investigate intra-run precision, post collection whole blood stability, and the impact of PBMC processing in the context of spectral cytometry. We achieved high intra-run sample precision (<20% CV) for >95% of all gated immune cell populations analyzed across biospecimen types. Additionally, we explored the application of FlowSOM analysis and resulting impact on assay precision metrics. We observed biomarker stability in blood out to 24 h (86%), 48 h (83%), and 72 h (76%) while highlighting select markers (i.e., CXCR3) and rare cell subsets (i.e., naive Tregs, plasmacytoid DCs, MDSCs) affected by storage and/or PBMC manipulation. Interrogation of sample type is imperative to coherent application of immune monitoring assays. Overall based on analytical performance, this 25-biomarker spectral cytometry assay is sufficiently robust for implementation in translational research settings.

Automated summary

This study presented a spectral flow cytometry assay for longitudinal immune monitoring biomarkers in human blood, demonstrating high precision and stability. With a panel of 25 immune biomarkers, the assay achieved robust analytical performance and explored the impact of sample types and storage conditions on biomarker stability. The method showed promise for translational research applications.