Literature DB >> 26860370

Linking the T cell receptor to the single cell transcriptome in antigen-specific human T cells.

Auda A Eltahla1, Simone Rizzetto1, Mehdi R Pirozyan1, Brigid D Betz-Stablein1, Vanessa Venturi2, Katherine Kedzierska3, Andrew R Lloyd1, Rowena A Bull1, Fabio Luciani1.   

Abstract

Heterogeneity of T cells is a hallmark of a successful adaptive immune response, harnessing the vast diversity of antigen-specific T cells into a coordinated evolution of effector and memory outcomes. The T cell receptor (TCR) repertoire is highly diverse to account for the highly heterogeneous antigenic world. During the response to a virus multiple individual clones of antigen specific CD8+ (Ag-specific) T cells can be identified against a single epitope and multiple epitopes are recognised. Advances in single-cell technologies have provided the potential to study Ag-specific T cell heterogeneity at both surface phenotype and transcriptome levels, thereby allowing investigation of the diversity within the same apparent sub-population. We propose a new method (VDJPuzzle) to reconstruct the native TCRαβ from single cell RNA-seq data of Ag-specific T cells and then to link these with the gene expression profile of individual cells. We applied this method using rare Ag-specific T cells isolated from peripheral blood of a subject who cleared hepatitis C virus infection. We successfully reconstructed productive TCRαβ in 56 of a total of 63 cells (89%), with double α and double β in 18, and 7% respectively, and double TCRαβ in 2 cells. The method was validated via standard single cell PCR sequencing of the TCR. We demonstrate that single-cell transcriptome analysis can successfully distinguish Ag-specific T cell populations sorted directly from resting memory cells in peripheral blood and sorted after ex vivo stimulation. This approach allows a detailed analysis of the TCR diversity and its relationship with the transcriptional profile of different clones.

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Year:  2016        PMID: 26860370     DOI: 10.1038/icb.2016.16

Source DB:  PubMed          Journal:  Immunol Cell Biol        ISSN: 0818-9641            Impact factor:   5.126


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