TY  - JOUR
A1  - Gryzik, Stefanie
A1  - Hoang, Yen
A1  - Lischke, Timo
A1  - Mohr, Elodie
A1  - Venzke, Melanie
A1  - Kadner, Isabelle
A1  - Pötzsch, Josephine
A1  - Groth, Detlef
A1  - Radbruch, Andreas
A1  - Hutloff, Andreas
A1  - Baumgrass, Ria
T1  - Identification of a super-functional Tfh-like subpopulation in murine lupus by pattern perception
JF  - eLife
N2  - 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.
Y1  - 2020
U6  - https://doi.org/10.7554/eLife.53226
SN  - 2050-084X
VL  - 9
PB  - eLife Sciences Publications
CY  - Cambridge
ER  - 
TY  - JOUR
A1  - Hoang, Yen
A1  - Gryzik, Stefanie
A1  - Hoppe, Ines
A1  - Rybak, Alexander
A1  - Schädlich, Martin
A1  - Kadner, Isabelle
A1  - Walther, Dirk
A1  - Vera, Julio
A1  - Radbruch, Andreas
A1  - Groth, Detlef
A1  - Baumgart, Sabine
A1  - Baumgrass, Ria
T1  - PRI: Re-analysis of a public mass cytometry dataset reveals patterns of effective tumor treatments
JF  - Frontiers in immunology
N2  - 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.
KW  - multi-parametric analysis
KW  - re-analysis
KW  - combinatorial protein
KW  - expression
KW  - high-dimensional cytometry data
KW  - mass cytometry data
KW  - pattern perception
Y1  - 2022
U6  - https://doi.org/10.3389/fimmu.2022.849329
SN  - 1664-3224
VL  - 13
PB  - Frontiers Media
CY  - Lausanne
ER  -