Utility of Bulk T-Cell Receptor Repertoire Sequencing Analysis in Understanding Immune Responses to COVID-19

Abstract

Measuring immunity to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 19 (COVID-19), can rely on antibodies, reactive T cells and other factors, with T-cell-mediated responses appearing to have greater sensitivity and longevity. Because each T cell carries an essentially unique nucleic acid sequence for its T-cell receptor (TCR), we can interrogate sequence data derived from DNA or RNA to assess aspects of the immune response. This review deals with the utility of bulk, rather than single-cell, sequencing of TCR repertoires, considering the importance of study design, in terms of cohort selection, laboratory methods and analysis. The advances in understanding SARS-CoV-2 immunity that have resulted from bulk TCR repertoire sequencing are also be discussed. The complexity of sequencing data obtained by bulk repertoire sequencing makes analysis challenging, but simple descriptive analyses, clonal analysis, searches for specific sequences associated with immune responses to SARS-CoV-2, motif-based analyses, and machine learning approaches have all been applied. TCR repertoire sequencing has demonstrated early expansion followed by contraction of SARS-CoV-2-specific clonotypes, during active infection. Maintenance of TCR repertoire diversity, including the maintenance of diversity of anti-SARS-CoV-2 response, predicts a favourable outcome. TCR repertoire narrowing in severe COVID-19 is most likely a consequence of COVID-19-associated lymphopenia. It has been possible to follow clonotypic sequences longitudinally, which has been particularly valuable for clonotypes known to be associated with SARS-CoV-2 peptide/MHC tetramer binding or with SARS-CoV-2 peptide-induced cytokine responses. Closely related clonotypes to these previously identified sequences have been shown to respond with similar kinetics during infection. A possible superantigen-like effect of the SARS-CoV-2 spike protein has been identified, by means of observing V-segment skewing in patients with severe COVID-19, together with structural modelling. Such a superantigen-like activity, which is apparently absent from other coronaviruses, may be the basis of multisystem inflammatory syndrome and cytokine storms in COVID-19. Bulk TCR repertoire sequencing has proven to be a useful and cost-effective approach to understanding interactions between SARS-CoV-2 and the human host, with the potential to inform the design of therapeutics and vaccines, as well as to provide invaluable pathogenetic and epidemiological insights.

Keywords: COVID-19; SARS-CoV-2; T-cell receptor repertoire; antibody; coronavirus; diversity; immune response; immunological memory; immunoreceptor; machine learning.

Figure 1

V(D)J recombination determines T-cell receptor specificity. The TCR specificity of αβ T cells is determined by the unique V(D)J recombination events that occur during the development of each T cell. During this process, V, D and J gene segments are randomly selected and are spliced together on the β chain, while the α-chain rearrangement of the V–J gene segments occurs in a similar process. During this process, the random addition or deletion of nucleotides can occur at segment junctions. The complementarity-determining region 3 (CDR3) encoded by sequences located in the V(D)J junction has the greatest diversity and is what determines the antigen specificity of each TCR. TCRβ: T-cell receptor beta; CDR3β: the gene sequence encoding the complementarity-determining region 3 of the TCR beta chain.

Figure 2

Overview of machine learning approaches. (A) Training data and training labels are used to train the model by obtaining its optimal parameters. The model with initial parameters makes predictions for the training data. These predictions are compared to the training labels and the error between them is calculated. The model parameters are updated to correct for the error. These steps continue until the error cannot be made smaller and the model is trained. (B) A trained model should be tested with a separate set of data and labels called the testing data. Positive or negative predictions are made for the testing data using the trained model with its optimal parameters. Each prediction is compared to the positive or negative testing label and categorised as a true positive (TP), false positive (FP), true negative (TN) or false negative (FN). The model’s sensitivity can be estimated as TP/(TP + FN), specificity as TN/(TN + FP) and accuracy as (TP + TN)/(TP + FP + TN + FN).

Figure 3

Schematic overview of insights into TCR repertoire, observed over time after infection, obtained by bulk TCR repertoire sequencing. MIS: Multisystem Inflammatory Syndrome.