T-Cell Receptor Repertoire Analysis with Computational Tools-An Immunologist's Perspective.

Mahima Arunkumar,Christina E Zielinski

doi:10.3390/cells10123582

Abstract

Over the last few years, there has been a rapid expansion in the application of information technology to biological data. Particularly the field of immunology has seen great strides in recent years. The development of next-generation sequencing (NGS) and single-cell technologies also brought forth a revolution in the characterization of immune repertoires. T-cell receptor (TCR) repertoires carry comprehensive information on the history of an individual’s antigen exposure. They serve as correlates of host protection and tolerance, as well as biomarkers of immunological perturbation by natural infections, vaccines or immunotherapies. Their interrogation yields large amounts of data. This requires a suite of highly sophisticated bioinformatics tools to leverage the meaning and complexity of the large datasets. Many different tools and methods, specifically designed for various aspects of immunological research, have recently emerged. Thus, researchers are now confronted with the issue of having to choose the right kind of approach to analyze, visualize and ultimately solve their task at hand. In order to help immunologists to choose from the vastness of available tools for their data analysis, this review addresses and compares commonly used bioinformatics tools for TCR repertoire analysis and illustrates the advantages and limitations of these tools from an immunologist’s perspective.

Highlights

Long-lasting T- and B-cell responses are the hallmark of immunological memory.Quantitative shifts in T cells with distinct T-cell receptors occur as a result of proliferation and clonal expansion in response to cognate antigens, which can originate from a plethora of microbial pathogens and autoantigens
The results indicated that memory CD4+ T cells are generated in the human fetal intestine, and this was an unexpected, considering that the fetus is thought to be protected from exposure to foreign antigens [48,49,50]
CoNGA is a graph-based approach which identifies correlations between gene-expression data and T-cell receptor (TCR) sequences through statistical analysis of gene expression and TCR similarity graphs. It can be useful when studying the complex relationships between TCR sequences and T-cell phenotypes in large heterogeneous singlecell datasets [42,43,51,52,53,54,55]

Summary

Introduction

Long-lasting T- and B-cell responses are the hallmark of immunological memory. Quantitative shifts in T cells with distinct T-cell receptors occur as a result of proliferation and clonal expansion in response to cognate antigens, which can originate from a plethora of microbial pathogens and autoantigens. In T-cell repertoires, diversity takes the clonal composition into account, the number of unique TCR sequences (richness) and the relative abundance of these sequences (evenness). The Morisita–Horn index is a statistical measure of dispersion of individuals in a population It accounts for both the number and abundance of shared TCRs between two repertoires, and its score ranges between 0, meaning no overlap, and 1, meaning all clones overlap at similar frequencies. The Yu–Clayton index is one of the few similarity indices that can detect and compare the presence and abundance of same TCRs among samples [22] It has, only rarely been used so far in the field of immunology, as compared to the Morisita–Horn index or the Jaccard index. Re-sampling allows us to make unbiased estimates, as it is drawn from unbiased samples

Scirpy

Immunarch

Immcantation Framework

VDJtools

Discussion