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(1) Background: The basophil activation test (BAT) is a functional whole blood-based ex vivo assay to quantify basophil activation after allergen exposure by flow cytometry. One of the most important prerequisites for the use of the BAT in the routine clinical diagnosis of allergies is a reliable, standardized and reproducible data analysis workflow. (2) Methods: We re-analyzed a public mass cytometry dataset from peanut (PN) allergic patients (n = 6) and healthy controls (n = 3) with our binning approach "pattern recognition of immune cells" (PRI). Our approach enabled a comprehensive analysis of the dataset, evaluating 30 markers to achieve optimal basophil identification and activation through multi-parametric analysis and visualization. (3) Results: We found FcεRIα/CD32 (FcγRII) as a new marker couple to identify basophils and kept CD63 as an activation marker to establish a modified BAT in combination with our PRI analysis approach. Based on this, we developed an algorithm for automated raw data processing, which enables direct data analysis and the intuitive visualization of the test results including controls and allergen stimulations. Furthermore, we discovered that the expression pattern of CD32 correlated with FcεRIα, anticorrelated with CD63 and was detectable in both the re-analyzed public dataset and our own flow cytometric results. (4) Conclusions: Our improved BAT, combined with our PRI procedure (bin-BAT), provides a reliable test with a fully reproducible analysis. The advanced bin-BAT enabled the development of an automated workflow with an intuitive visualization to discriminate allergic patients from non-allergic individuals. [ABSTRACT FROM AUTHOR]

(1) Background: The basophil activation test (BAT) is a functional whole blood-based ex vivo assay to quantify basophil activation after allergen exposure by flow cytometry. One of the most important prerequisites for the use of the BAT in the routine clinical diagnosis of allergies is a reliable, standardized and reproducible data analysis workflow. (2) Methods: We re-analyzed a public mass cytometry dataset from peanut (PN) allergic patients (n = 6) and healthy controls (n = 3) with our binning approach “pattern recognition of immune cells” (PRI). Our approach enabled a comprehensive analysis of the dataset, evaluating 30 markers to achieve optimal basophil identification and activation through multi-parametric analysis and visualization. (3) Results: We found FcεRIα/CD32 (FcγRII) as a new marker couple to identify basophils and kept CD63 as an activation marker to establish a modified BAT in combination with our PRI analysis approach. Based on this, we developed an algorithm for automated raw data processing, which enables direct data analysis and the intuitive visualization of the test results including controls and allergen stimulations. Furthermore, we discovered that the expression pattern of CD32 correlated with FcεRIα, anticorrelated with CD63 and was detectable in both the re-analyzed public dataset and our own flow cytometric results. (4) Conclusions: Our improved BAT, combined with our PRI procedure (bin-BAT), provides a reliable test with a fully reproducible analysis. The advanced bin-BAT enabled the development of an automated workflow with an intuitive visualization to discriminate allergic patients from non-allergic individuals.

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. [ABSTRACT FROM AUTHOR]

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.