American Association for Cancer Research
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Figure S7 from Spectral Flow Cytometry Methods and Pipelines for Comprehensive Immunoprofiling of Human Peripheral Blood and Bone Marrow

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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 S7. UMAP Marker Positioning. (A-E) UMAPs showing marker expression patterns in PBMC and BMC panels. Marker expression intensity is indicated by the scale bar to the right of each plot, where red is high, and blue is low. Data are derived from concatenated events from all 3 donors in the PBMC panels (A-D), and concatenated events from all 3 donors in the BMC panel (E). Insert in bottom right of each figure shows canonically gated major cell populations. Populations 10, 11, and 12 share CD16 expression, and as such occupy similar locations in UMAP space.


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)



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.