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CTLA-4 gene constructs were designed to express CTLA-4 exclusively in the endoplasmic reticulum (ER). Four different CTLA-4 gene constructs were transfected into HEK 293 (human embryonic kidney) and A20 (Balb/c mouse B lymphoma) cells. All constructs contained an ER retention signal and coded for CTLA-4 expression in the ER. One of the constructs, which contained the membrane part of CTLA-4, coded for an expression both on the cell surface and in the ER. Three of the expressed CTLA-4 types (including the ER-membrane-expressed form) caused a reduced surface expression of B7 in the A20 cells. Only constructs which allow dimerization of CTLA-4 showed this effect. It is assumed that intracellular CTLA-4 bound B7 and inhibited therefore the transport of B7 to the surface. The binding obviously caused also an enhanced degradation of the complexes because both proteins showed a low concentration in the transfected cell lines. CTLA-4-transfected and B7-reduced A20 cells showed a diminished costimulating activity upon T cells. This was demonstrated by a reduced proliferation of T cells from ovalbumin-immunized Balb/c mice, incubated with ovalbumin peptide-primed CTLA-4-transfected A20 cells.
PRI: Re-analysis of a public mass cytometry dataset reveals patterns of effective tumor treatments
(2022)
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.