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- Title
PRI: Re-Analysis of a Public Mass Cytometry Dataset Reveals Patterns of Effective Tumor Treatments.
- Authors
Hoang, Yen; Gryzik, Stefanie; Hoppe, Ines; Rybak, Alexander; Schädlich, Martin; Kadner, Isabelle; Walther, Dirk; Vera, Julio; Radbruch, Andreas; Groth, Detlef; Baumgart, Sabine; Baumgrass, Ria
- 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.
- Subjects
CYTOMETRY; TUMOR treatment; TH1 cells; IMMUNE recognition; CELL populations
- Publication
Frontiers in Immunology, 2022, Vol 13, p1
- ISSN
1664-3224
- Publication type
Article
- DOI
10.3389/fimmu.2022.849329