Spectral Flow Cytometry Methods and Pipelines for Comprehensive Immunoprofiling of Human Peripheral Blood and Bone Marrow

Abstract

Profiling hematopoietic and immune cells provides important information about disease risk, disease status, and therapeutic responses. Spectral flow cytometry enables high-dimensional single-cell evaluation of large cohorts in a high-throughput manner. Here, we designed, optimized, and implemented new methods for deep immunophenotyping of human peripheral blood and bone marrow by spectral flow cytometry. Two blood antibody panels capture 48 cell-surface markers to assess more than 58 cell phenotypes, including subsets of T cells, B cells, monocytes, NK cells, and dendritic cells, and their respective markers of exhaustion, activation, and differentiation in less than 2 ml of blood. A bone marrow antibody panel captures 32 markers for 35 cell phenotypes, including stem/progenitor populations, T cell subsets, dendritic cells, NK cells, and myeloid cells in a single tube. We adapted and developed innovative flow cytometric analysis algorithms, originally developed for single-cell genomics, to improve data integration and visualization. We also highlight technical considerations for users to ensure data fidelity. Our protocol and analysis pipeline accurately identifies rare cell types, discerns differences in cell abundance and phenotype across donors, and shows concordant immune landscape trends in patients with known hematologic malignancy.

Closing Editorial: Immunophenotyping in Autoimmune Diseases and Cancer 3.0

“…The emergence of SFC required the development of new pipelines for the analysis of high-dimensional data generated by SFC; therefore, new workflows handling SFC data integrating previously published R-based packages have been published [ 9 , 10 ]. Spasic et al developed advanced bioinformatic tools and published optimized SFC panels, 2 panels detecting 48 cell surface markers of PBMCs and a bone marrow panel consisting of 32 parameters for the analysis of hematological malignancies [ 11 ]. The concurrent advancement of flow cytometric technology and AI assisted data analysis has revolutionized single-cell immunophenotyping in the 21st century [ 12 ].…”