Abstract
Flow and mass cytometry are used to quantify the expression of multiple extracellular or intracellular molecules on single cells, allowing the phenotypic and functional characterization of complex cell populations. Multiparametric flow cytometry is particularly suitable for deep analysis of immune responses after vaccination, as it allows to measure the frequency, the phenotype, and the functional features of antigen-specific cells. When many parameters are investigated simultaneously, it is not feasible to analyze all the possible bi-dimensional combinations of marker expression with classical manual analysis and the adoption of advanced automated tools to process and analyze high-dimensional data sets becomes necessary. In recent years, the development of many tools for the automated analysis of multiparametric cytometry data has been reported, with an increasing record of publications starting from 2014. However, the use of these tools has been preferentially restricted to bioinformaticians, while few of them are routinely employed by the biomedical community. Filling the gap between algorithms developers and final users is fundamental for exploiting the advantages of computational tools in the analysis of cytometry data. The potentialities of automated analyses range from the improvement of the data quality in the pre-processing steps up to the unbiased, data-driven examination of complex datasets using a variety of algorithms based on different approaches. In this review, an overview of the automated analysis pipeline is provided, spanning from the pre-processing phase to the automated population analysis. Analysis based on computational tools might overcame both the subjectivity of manual gating and the operator-biased exploration of expected populations. Examples of applications of automated tools that have successfully improved the characterization of different cell populations in vaccination studies are also presented.
Highlights
Flow cytometry allows to simultaneously quantify expression of extracellular and intracellular molecules targeted by dyes or monoclonal antibodies, as well as to measure multiple characteristics of a single cell such as size and granularity
The analysis shows that vi-SNE and ACCENSE, implementations of t-distributed stochastic neighbor embedding (t-SNE), are the tools most cited for dimensionality reduction analysis, while Phenograph, SPADE, Citrus, FlowSOM, and X-shift for clustering approaches (Figure 3b)
Automated analysis of cytometric data has widely been demonstrated to efficiently achieve reproducible results compared to manual analysis, with the important advantages of eliminating the bias toward expected populations, the subjectivity in manual drawing of gates and in marker selection, and most importantly the possibility to identify unexpected cell populations
Summary
Flow cytometry allows to simultaneously quantify expression of extracellular and intracellular molecules targeted by dyes or monoclonal antibodies, as well as to measure multiple characteristics of a single cell such as size and granularity. Operator subjectivity occurs at the level of choosing the hierarchy in which parameter combinations have to be considered, as well as in the shape and boundary of each gate specified in the analysis To overcome these limitations, novel computational techniques have been developed in recent years, and computational flow cytometry has become a novel discipline useful for providing a set of tools to analyze, visualize, and interpret large amounts of cell data in a more automated and unbiased way. Since flow cytometry is a powerful technology for studying multiple immune function in response to vaccination, ranging from the phenotypic and functional characterization of cellular immune responses to antibody detection and functional assessment, the use of computational tools represents a powerful strategy for the interpretation of large datasets that can be instrumental to profile the vaccine immune response For these reasons, immunologists should be aware of the potentiality of automated tools, which should not remain exclusive to computer-science experts. An overview of the impact of automated analysis in the knowledge of biological processes, especially in the vaccine field, is presented
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