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Front Immunol. 2019 Jul 3;10:1515. doi: 10.3389/fimmu.2019.01515. eCollection 2019.

Meeting the Challenges of High-Dimensional Single-Cell Data Analysis in Immunology.

Palit S1,2,3, Heuser C1,2,3, de Almeida GP1,2,3, Theis FJ4,5, Zielinski CE1,2,3.

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TranslaTUM, Technical University of Munich, Munich, Germany.
Institute of Virology, Technical University of Munich, Munich, Germany.
Partner Site Munich, German Center for Infection Research, Munich, Germany.
Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.
Department of Mathematics, Technical University of Munich, Munich, Germany.


Recent advances in cytometry have radically altered the fate of single-cell proteomics by allowing a more accurate understanding of complex biological systems. Mass cytometry (CyTOF) provides simultaneous single-cell measurements that are crucial to understand cellular heterogeneity and identify novel cellular subsets. High-dimensional CyTOF data were traditionally analyzed by gating on bivariate dot plots, which are not only laborious given the quadratic increase of complexity with dimension but are also biased through manual gating. This review aims to discuss the impact of new analysis techniques for in-depths insights into the dynamics of immune regulation obtained from static snapshot data and to provide tools to immunologists to address the high dimensionality of their single-cell data.


CyTOF; high-dimensional data analysis; single-cell genomics; single-cell profiling; systems immunology; trajectory inference; visualization

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