From Bivariate to Multivariate Analysis of Cytometric Data: Overview of Computational Methods and Their Application in Vaccination Studies

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.

Shelter from the cytokine storm: pitfalls and prospects in the development of SARS-CoV-2 vaccines for an elderly population

“…Currently, computational models have been applied to immunology data, for example, for the analysis of a high-dimensional dataset in vaccination studies [107,108], but these models are limited to particular aspects [109,110]. There is the potential for these models to become more sophisticated and to predict how responses to pathogens and vaccines are affected by pre-disposing factors [111,112].…”

The prospects of single‐cell analysis in autoimmunity

“…The interest in trajectory inference analysis is therefore growing, also due to the measurement of cellular processes at the single‐cell level (eg cell cycle, cell differentiation and cell activation). Through such measurements and marker expression profiles, advanced tools can model trajectories of cell development and the transition between different cell states 137 . The application Wanderlust is an example used for trajectory tracking of cell development and has a wide use.…”

Omics and Bioinformatics Approaches to Identify Novel Antigens for Vaccine Investigation and Development

“…In their review, Lucchesi et al presented an overview of computational tools available for the analysis of data from flow cytometry in vaccination studies [ 15 ]. Flow cytometry, and in particular multiparametric flow cytometry, are adequate for the in-depth analysis of immune responses to vaccination.…”

“…Flow cytometry, and in particular multiparametric flow cytometry, are adequate for the in-depth analysis of immune responses to vaccination. The authors reviewed the usefulness and applicability of several bioinformatics tools, from data pre-processing to automated data analysis and the interpretation of results [ 15 ]. The authors also reported the increasing number of publications using the automated analysis of multidimensional cytometry data, based on searches on the Web of Science, and identified a rising trend starting in 2008, with a stronger increase in the period 2014–2019 [ 15 ].…”

“…The automated analysis of data from cytometry has already proven to offer reliable and reproducible results. The integration of these data, from a systems biology perspective, with data from Omics approaches (genomics, transcriptomics, proteomics, metabolomics) and also with clinical data, will certainly lead to a better understanding of the responses of the immune system to antigenic challenge, as is the case of vaccination [ 15 ].…”