@article{HoangGryzikHoppeetal.2022,
  author    = {Hoang, Yen and Gryzik, Stefanie and Hoppe, Ines and Rybak, Alexander and Sch{\"a}dlich, Martin and Kadner, Isabelle and Walther, Dirk and Vera, Julio and Radbruch, Andreas and Groth, Detlef and Baumgart, Sabine and Baumgrass, Ria},
  title     = {PRI: Re-analysis of a public mass cytometry dataset reveals patterns of effective tumor treatments},
  series = {Frontiers in immunology},
  volume    = {13},
  journal   = {Frontiers in immunology},
  publisher = {Frontiers Media},
  address   = {Lausanne},
  issn      = {1664-3224},
  doi       = {10.3389/fimmu.2022.849329},
  pages     = {9},
  year      = {2022},
  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.},
  language  = {en}
}
@article{GryzikHoangLischkeetal.2020,
  author    = {Gryzik, Stefanie and Hoang, Yen and Lischke, Timo and Mohr, Elodie and Venzke, Melanie and Kadner, Isabelle and P{\"o}tzsch, Josephine and Groth, Detlef and Radbruch, Andreas and Hutloff, Andreas and Baumgrass, Ria},
  title     = {Identification of a super-functional Tfh-like subpopulation in murine lupus by pattern perception},
  series = {eLife},
  volume    = {9},
  journal   = {eLife},
  publisher = {eLife Sciences Publications},
  address   = {Cambridge},
  issn      = {2050-084X},
  doi       = {10.7554/eLife.53226},
  pages     = {21},
  year      = {2020},
  abstract  = {Dysregulated cytokine expression by T cells plays a pivotal role in the pathogenesis of autoimmune diseases. However, the identification of the corresponding pathogenic subpopulations is a challenge, since a distinction between physiological variation and a new quality in the expression of protein markers requires combinatorial evaluation. Here, we were able to identify a super-functional follicular helper T cell (Tfh)-like subpopulation in lupus-prone NZBxW mice with our binning approach "pattern recognition of immune cells (PRI)". PRI uncovered a subpopulation of IL-21(+) IFN-gamma(high) PD-1(low) CD40L(high) CXCR5(-) Bcl-6(-) T cells specifically expanded in diseased mice. In addition, these cells express high levels of TNF-alpha and IL-2, and provide B cell help for IgG production in an IL-21 and CD40L dependent manner. This super-functional T cell subset might be a superior driver of autoimmune processes due to a polyfunctional and high cytokine expression combined with Tfh-like properties.},
  language  = {en}
}