Frontiers in Immunology (May 2022)

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

  • Yen Hoang,
  • Stefanie Gryzik,
  • Ines Hoppe,
  • Alexander Rybak,
  • Alexander Rybak,
  • Martin Schädlich,
  • Martin Schädlich,
  • Isabelle Kadner,
  • Isabelle Kadner,
  • Dirk Walther,
  • Julio Vera,
  • Andreas Radbruch,
  • Andreas Radbruch,
  • Detlef Groth,
  • Sabine Baumgart,
  • Sabine Baumgart,
  • Ria Baumgrass,
  • Ria Baumgrass

DOI
https://doi.org/10.3389/fimmu.2022.849329
Journal volume & issue
Vol. 13

Abstract

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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.

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