Graham D Thomas1, Anouk A J Hamers2, Catherine Nakao2, Paola Marcovecchio2, Angela M Taylor2, Chantel McSkimming2, Anh Tram Nguyen2, Coleen A McNamara2, Catherine C Hedrick1. 1. From the Division of Inflammation Biology, La Jolla Institute for Allergy and Immunology, CA (G.D.T., A.A.J.H., C.N., P.M., C.C.H.); and Division of Cardiology and Robert M. Berne Cardiovascular Center, University of Virginia, Charlottesville (A.M.T., C.M., A.T.N., C.A.M.). gthomas@coipharma.com hedrick@lji.org. 2. From the Division of Inflammation Biology, La Jolla Institute for Allergy and Immunology, CA (G.D.T., A.A.J.H., C.N., P.M., C.C.H.); and Division of Cardiology and Robert M. Berne Cardiovascular Center, University of Virginia, Charlottesville (A.M.T., C.M., A.T.N., C.A.M.).
Abstract
OBJECTIVE: Human monocyte subsets are defined as classical (CD14++CD16-), intermediate (CD14++CD16+), and nonclassical (CD14+CD16+). Alterations in monocyte subset frequencies are associated with clinical outcomes, including cardiovascular disease, in which circulating intermediate monocytes independently predict cardiovascular events. However, delineating mechanisms of monocyte function is hampered by inconsistent results among studies. APPROACH AND RESULTS: We use cytometry by time-of-flight mass cytometry to profile human monocytes using a panel of 36 cell surface markers. Using the dimensionality reduction approach visual interactive stochastic neighbor embedding (viSNE), we define monocytes by incorporating all cell surface markers simultaneously. Using viSNE, we find that although classical monocytes are defined with high purity using CD14 and CD16, intermediate and nonclassical monocytes defined using CD14 and CD16 alone are frequently contaminated, with average intermediate and nonclassical monocyte purity of ≈86.0% and 87.2%, respectively. To improve the monocyte purity, we devised a new gating scheme that takes advantage of the shared coexpression of cell surface markers on each subset. In addition to CD14 and CD16, CCR2, CD36, HLA-DR, and CD11c are the most informative markers that discriminate among the 3 monocyte populations. Using these additional markers as filters, our revised gating scheme increases the purity of both intermediate and nonclassical monocyte subsets to 98.8% and 99.1%, respectively. We demonstrate the use of this new gating scheme using conventional flow cytometry of peripheral blood mononuclear cells from subjects with cardiovascular disease. CONCLUSIONS: Using cytometry by time-of-flight mass cytometry, we have identified a small panel of surface markers that can significantly improve monocyte subset identification and purity in flow cytometry. Such a revised gating scheme will be useful for clinical studies of monocyte function in human cardiovascular disease.
OBJECTIVE:Human monocyte subsets are defined as classical (CD14++CD16-), intermediate (CD14++CD16+), and nonclassical (CD14+CD16+). Alterations in monocyte subset frequencies are associated with clinical outcomes, including cardiovascular disease, in which circulating intermediate monocytes independently predict cardiovascular events. However, delineating mechanisms of monocyte function is hampered by inconsistent results among studies. APPROACH AND RESULTS: We use cytometry by time-of-flight mass cytometry to profile human monocytes using a panel of 36 cell surface markers. Using the dimensionality reduction approach visual interactive stochastic neighbor embedding (viSNE), we define monocytes by incorporating all cell surface markers simultaneously. Using viSNE, we find that although classical monocytes are defined with high purity using CD14 and CD16, intermediate and nonclassical monocytes defined using CD14 and CD16 alone are frequently contaminated, with average intermediate and nonclassical monocyte purity of ≈86.0% and 87.2%, respectively. To improve the monocyte purity, we devised a new gating scheme that takes advantage of the shared coexpression of cell surface markers on each subset. In addition to CD14 and CD16, CCR2, CD36, HLA-DR, and CD11c are the most informative markers that discriminate among the 3 monocyte populations. Using these additional markers as filters, our revised gating scheme increases the purity of both intermediate and nonclassical monocyte subsets to 98.8% and 99.1%, respectively. We demonstrate the use of this new gating scheme using conventional flow cytometry of peripheral blood mononuclear cells from subjects with cardiovascular disease. CONCLUSIONS: Using cytometry by time-of-flight mass cytometry, we have identified a small panel of surface markers that can significantly improve monocyte subset identification and purity in flow cytometry. Such a revised gating scheme will be useful for clinical studies of monocyte function in humancardiovascular disease.
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