From Bivariate to Multivariate Analysis of Cytometric Data: Overview of Computational Methods and Their Application
Simone Lucchesi1, Simone Furini2, Donata Medaglini1
1Laboratory of Molecular Microbiology and Biotechnology (LA.M.M.B.), Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy.
Insights
Automated tools enhance multiparametric flow cytometry (MFC) data analysis for immune response studies. These computational methods overcome manual gating limitations, improving cell population characterization in complex datasets.
Area of Science:
- Immunology
- Computational Biology
- Biotechnology
Background:
- Multiparametric flow cytometry (MFC) enables detailed analysis of immune cell phenotypes and functions.
- Analyzing high-dimensional MFC data manually is challenging due to the vast number of parameters.
- Automated tools are increasingly developed for MFC data analysis, particularly for vaccination studies.
Purpose of the Study:
- To provide an overview of automated analysis pipelines for multiparametric flow cytometry data.
- To highlight the benefits of computational tools in overcoming manual gating limitations.
- To showcase applications of automated tools in analyzing cell populations from vaccination studies.
Main Methods:
- Review of automated analysis pipelines, from pre-processing to population analysis.
- Discussion of algorithms for unbiased, data-driven examination of complex datasets.
- Presentation of case studies from vaccination research.
Main Results:
- Automated tools offer improved data quality and unbiased analysis of complex datasets.
- Computational approaches reduce subjectivity and operator bias inherent in manual gating.
- Successful applications demonstrate enhanced characterization of cell populations in vaccination studies.
Conclusions:
- Bridging the gap between algorithm developers and biomedical users is crucial for adopting automated MFC analysis.
- Automated analysis tools significantly improve the efficiency and accuracy of characterizing immune cell populations.
- Wider adoption of these tools will advance the understanding of immune responses, especially post-vaccination.
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.
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