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

Hoang Y, Gryzik S, Hoppe I, Rybak A, Schaedlich M, Kadner I, Walther D, Vera J, Radbruch A, Groth D, Baumgart S, Baumgrass R (2022)

Publication Type: Journal article

Publication year: 2022

Journal

Frontiers in Immunology Frontiers Research Foundation / Frontiers Media

Book Volume: 13

DOI: 10.3389/fimmu.2022.849329

Abstract

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.

Authors with CRIS profile

Julio Vera González Professur für Tumorimmunologie (Schwerpunkt Systembiologie)

Involved external institutions

Deutsches Rheuma-Forschungszentrum (DRFZ) DE Germany (DE) Max-Planck-Institut für molekulare Pflanzenphysiologie / Max Planck Institute of Molecular Plant Physiology DE Germany (DE) Universität Potsdam DE Germany (DE)

How to cite

APA:

Hoang, Y., Gryzik, S., Hoppe, I., Rybak, A., Schaedlich, M., Kadner, I.,... Baumgrass, R. (2022). PRI: Re-Analysis of a Public Mass Cytometry Dataset Reveals Patterns of Effective Tumor Treatments. Frontiers in Immunology, 13. https://dx.doi.org/10.3389/fimmu.2022.849329

MLA:

Hoang, Yen, et al. "PRI: Re-Analysis of a Public Mass Cytometry Dataset Reveals Patterns of Effective Tumor Treatments." Frontiers in Immunology 13 (2022).

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