journal contribution
posted on 2024-03-25, 14:20 authored by Milos Spasic, Esther R. Ogayo, Adrienne M. Parsons, Elizabeth A. Mittendorf, Peter van Galen, Sandra S. McAllister Figure S2. Unmixing Accuracy Assessment. NxN permutations showing density plots of the same marker on the x-axis and every other fluorochrome plotted on the y-axis. Plots were manually examined for accuracy of unmixing. (A) T/B panel; (B) M/N/D panel; (E, F) BMC panel. The same PBMC donor sample was used for (A and B) and data represent cells gated as singlets, non-RBC, and live cells. (C) Density plot of CD159c-BYG575 vs CD19-BYG710 after automated unmixing. (D) Density plot of CD159c-BYG575 vs CD19-BYG710 after applying additional manual compensation of -2.79 using the SpectroFlo compensation tool. (F) NxN plots after removing antibody aggregates in NovaFluor Blue 610-70S – CD19. (G) Antibody aggregates are manually removed through the use of a NOT gate in the aberrant population.
Funding
HHS | National Institutes of Health (NIH)
American Association for Cancer Research (AACR)
HHS | NIH | National Cancer Institute (NCI)
HMS | Ludwig Center at Harvard (Ludwig Center)
Starr Foundation (TSF)
Glenn Foundation for Medical Research (GFMR)
American Federation for Aging Research (AFAR)
Bertarelli Rare Cancers Fund
BWH | Brigham Research Institute (BRI)
Dana-Farber/Harvard Cancer Center (DF/HCC)
U.S. Department of Defense (DOD)
History
ARTICLE 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, natural killer (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.
This study introduces optimized methods and analysis algorithms that enhance capabilities in comprehensive immunophenotyping of human blood and bone marrow using spectral flow cytometry. This approach facilitates detection of rare cell types, enables measurement of cell variations across donors, and provides proof-of-concept in identifying known hematologic malignancies. By unlocking complexities of hematopoietic and immune landscapes at the single-cell level, this advancement holds potential for understanding disease states and therapeutic responses.
