PRI: Re-Analysis of a Public Mass Cytometry Dataset Reveals Patterns of Effective Tumor Treatments.

Autor:	Hoang Y; German Rheumatism Research Center (DRFZ), A Leibniz Institute, Berlin, Germany., Gryzik S; German Rheumatism Research Center (DRFZ), A Leibniz Institute, Berlin, Germany., Hoppe I; German Rheumatism Research Center (DRFZ), A Leibniz Institute, Berlin, Germany., Rybak A; German Rheumatism Research Center (DRFZ), A Leibniz Institute, Berlin, Germany.; Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany., Schädlich M; German Rheumatism Research Center (DRFZ), A Leibniz Institute, Berlin, Germany.; Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany., Kadner I; German Rheumatism Research Center (DRFZ), A Leibniz Institute, Berlin, Germany.; Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany., Walther D; Bioinformatics, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany., Vera J; Laboratory of Systems Tumor Immunology, Friedrich-Alexander University of Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany., Radbruch A; German Rheumatism Research Center (DRFZ), A Leibniz Institute, Berlin, Germany.; Department of Rheumatology and Clinical Immunology, Charité, Campus Berlin Mitte, Berlin, Germany., Groth D; Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany., Baumgart S; German Rheumatism Research Center (DRFZ), A Leibniz Institute, Berlin, Germany.; Institute of Immunology, Core Facility Cytometry, University Hospital Jena, Jena, Germany., Baumgrass R; German Rheumatism Research Center (DRFZ), A Leibniz Institute, Berlin, Germany.; Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany.
Jazyk:	angličtina
Zdroj:	Frontiers in immunology [Front Immunol] 2022 May 03; Vol. 13, pp. 849329. Date of Electronic Publication: 2022 May 03 (Print Publication: 2022).
DOI:	10.3389/fimmu.2022.849329
Abstrakt:	Recently, mass cytometry has enabled quantification of up to 50 parameters for millions of cells per sample. It remains a challenge to analyze such high-dimensional data to exploit the richness of the inherent information, even though many valuable new analysis tools have already been developed. We propose a novel algorithm "pattern recognition of immune cells (PRI)" to tackle these high-dimensional protein combinations in the data. PRI is a tool for the analysis and visualization of cytometry data based on a three or more-parametric binning approach, feature engineering of bin properties of multivariate cell data, and a pseudo-multiparametric visualization. Using a publicly available mass cytometry dataset, we proved that reproducible feature engineering and intuitive understanding of the generated bin plots are helpful hallmarks for re-analysis with PRI. In the CD4 + T cell population analyzed, PRI revealed two bin-plot patterns (CD90/CD44/CD86 and CD90/CD44/CD27) and 20 bin plot features for threshold-independent classification of mice concerning ineffective and effective tumor treatment. In addition, PRI mapped cell subsets regarding co-expression of the proliferation marker Ki67 with two major transcription factors and further delineated a specific Th1 cell subset. All these results demonstrate the added insights that can be obtained using the non-cluster-based tool PRI for re-analyses of high-dimensional cytometric data. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2022 Hoang, Gryzik, Hoppe, Rybak, Schädlich, Kadner, Walther, Vera, Radbruch, Groth, Baumgart and Baumgrass.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu