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Exploring Glucocorticoid Receptor Agonists Mechanism of Action Through Mass Cytometry and Radial Visualizations

Abraham, Yann, Gerrits, Bertran, Rebhan, Michael, Ludwig, Marie-Gabrielle and Gubser Keller, Caroline (2017) Exploring Glucocorticoid Receptor Agonists Mechanism of Action Through Mass Cytometry and Radial Visualizations. Cytometry. Part B : clinical cytometry., 92 (1). pp. 42-56. ISSN 1552-4957; 1552-4949


Recent advances in combining flow cytometry and mass spectrometry have led to the development of mass cytometry, allowing for the interrogation of complex cell populations on an unprecedented scale. The volumes and high dimensionality of mass cytometry data pose significant challenges in terms of analysis and visualization. We implement a method called Radviz, where multidimensional single cell data can be visualized as a projection that maintains the original dimensions and data complexity whilst facilitating analysis and visualization. This enables identification of changes in populations, focusing the analysis on the most relevant aspect of large multidimensional datasets. To highlight the potential of Radviz, we profiled peripheral mononuclear blood cells (PBMCs) from three healthy donors and showed donor-specific differences in the number and composition of cell populations. In a second study, we explored the anti-inflammatory effects of two glucocorticoid receptor (GR) ligands (cpd6 and cpd11) compared to dexamethasone (Dex) on human primary macrophages. Standard analysis at the population level showed that cpd6 and cpd11 have an overall anti-inflammatory profile similar to that of Dex. CyTOF profiling and Radviz-driven analysis at the single cell level confirmed this observation, and identified a concentration-dependent effect of cpd6 that was not detected at the population level. Altogether, Radviz combines the strengths of a projection method, reducing the dimensionality of datasets, with that of a scatter plot, where the identity of each point can be inferred from the distance to the axis. This enables the visual exploration, analysis, and interpretation of complex, high dimensional data.

Item Type: Article
Date Deposited: 13 Jun 2017 00:45
Last Modified: 13 Jun 2017 00:45


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