TCR repertoire: size, diversity, and function

National Institute On Aging

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Abstract

A diverse TCR repertoire is considered essential for T cell immunity to provide protection of the host against all potential pathogens. However, the size and content of the TCR repertoire and its changes with age remains elusive. Here, we report a longitudinal analysis of TCR repertoire of human CD4+ and CD8+ T cells and nave and memory subsets from 30 healthy adults aged from 25 to 85 at first visit and an average of 9-year follow-up as second visit by RNAseq. Through analysis of combined 1.9 x 108 CD4+ and CD8+ T cells, we identified a total of 1.1 x 106 and 2.8 x 106 unique TCR and TCR sequences, respectively. We predicted that an adult has TCR repertoire richness of 4 x 108. TCR repertoire changes estimated by the actual number of circulating T cells displayed the following key age-associated changes: 1) reduction of TCR and TCR repertoire richness in both CD4+ and CD8+ T cells, with the greatest reduction in nave CD8+ T cells; 2) increased clonal expansion of TCR repertoire in all T cell subsets, with the greatest expansion in memory CD8+ T cells; 3) profoundly reduced content changes of CD4+ and CD8+ TCR repertoire, as retention of TCR and TCR sequences increased between two visits, with memory CD8+ T cells showing the highest; and 4) decreased distinction of TCR and TCR sequences between nave and memory T cells, as well as between CD4+ and CD8+ T cells. These findings predicted the size of TCR repertoire in human and described the precise alterations of TCR repertoire in CD4+ and CD8+ T cells and their subsets with age. Collectively, our results suggested that age-related decline of T cell immunity is more severe in CD8+ T cell mediated cytotoxicity than in CD4+ T helper function. Influenza A virus (IAV) is a major human pathogen that causes seasonal infections worldwide. IAV-specific CD8+ T-cell response provides an essential part of immune protection against IAV infections. In HLA-A2+ humans, the dominant epitope on IAV is matrix protein M1 residues 5866 (GILGFVFTL, referred to as GIL). Here, we investigated characteristics of TCR sequences, tetramer binding features, and signaling strength of GIL-specific CD8+ T cells from healthy old influenza vaccine participants (>70 years old). We identified over one thousand unique ab TCRs of GIL-tetramer positive CD8+ T cells by the scRNAseq method. We then selected 72 GIL-specific TCRs with different usage of VDJb/a and expressed them in a human T cell reporter line (NJ76) which contains Nur77 GFP reporter. Surprisingly, only 30 of the 72 TCRs were positive for GIL-tetramer staining (GIL+%>70%), 32 TCRs are negative (GIL+% <10%), and the rest 10 TCRs possess the GIL+% ranging from 10% to 70%. This suggests that these GIL+ TCRs had different binding avidity. As NJ76 line does not express CD8, we further introduced CD8A and CD8B into NJ67 line using a lentiviral construct. We tested 9 high, middle, and low binding TCRs and found that by adding CD8 the GIL+ cells are increased to 100%, 80%, and 60% of the TCRs. Upon stimulation with GIL peptide-loaded (10-8 M) artificial antigen presenting cells, we observed a range of percentages of GFP positive cells from 2% to 30% after 4 hours stimulation in the native NJ76 line which further increased by 24 times when NJ76 expressing CD8. These results suggest that these GIL+ TCRs had different signaling strengths upon stimulation. Lastly, we trained an ensemble random forest (RF) model on the GIL-specific TCR sequences and found that the ones had high predicted binding scores were generally correlated with the positive percentage of GIL-tetramer staining of these TCR clones. Our findings show that Machine Learning RF model can predict the binding specificity of CD8+ T cells based on their TCR sequences, which will be a valuable tool to understand the antigen-specific composition and possibly function of CD8+ T cells. Overall, our study revealed TCR repertoire size, V gene usage, and signal strength of GIL-specific CD8+ TCRs and indicated the features of GIL-binding TCRs can be identified by our ML model, which paves the way to investigate frequency and quality of antigen specific TCR repertoire size and function in the general T cell population.