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Paired TCR and transcriptome analysis of single T cells characterizes clonally expanded human conventional and unconventional CD8+ T cells

ABSTRACT: Purpose: Single cell RNAseq has revolutionized our understanding of the heterogeneity of immune system. The goal of this study is to characterize functions of Mycobacterium tuberculosis (Mtb) specific CD8+ T cell clones using single cell RNAseq Methods: PBMCs from Mtb exposed patients were stimulated ex vivo with Mtb lysate for 12 hours. Activated CD8+ T cells were selected and single cell sorted based on the upregulation of activation markers, including CD154 and CD137. Results: Using an optimized Smart-seq2 protocol, we could successfully combine TCR sequencing with transcriptome sequencing on single T cell. TCR sequence analysis allows us to preferentially identify and select expanded conventional CD8+ T cells as well as invariant natural killer T (iNKT) cells and mucosal-associated invariant T (MAIT) cells. In summary, we analyzed 921 single cells with 2,500-3000 detected genes for each cell. The iNKT and MAIT cells have a nearly superimposable transcriptional pattern, indicating that they are variations of the same cell type, and differ considerably from conventional CD8+ T cells. While there are no distinct gene expression differences between clonally expanded versus unique iNKT or MAIT cells, highly expanded conventional CD8+ T cells downregulate the interleukin-2 (IL-2) receptor alpha (IL2RA, or CD25) protein and show signs of senescence. This suggests inherent limits to clonal expansion that act to diversify the T cell response repertoire. Conclusions: we combine targeted TCR sequencing with a sensitive RNA-seq protocol that yields both paired TCR sequences and extensive transcriptional information on single T cells. This enables the selection of rare, clonal and specific T cell subtypes, enabling analysis of their transcriptomes. Our targeted approach allowed us to efficiently and economically select clonal cells with a sensitive clone size dynamic range. And by using a short ex vivo stimulation (12~24 hours) to identify pathogen-responsive T cells, we could circumvent long-term ex vivo culture artifacts. By leveraging single cell analysis of clonal T cells in additional contexts we may improve our understanding of immune system responses to diseases such as cancer, additional infections, as well as aid in vaccine development.

ORGANISM(S): Homo sapiens

PROVIDER: GSE107646 | GEO | 2019/03/23

REPOSITORIES: GEO

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Project description:A recently identified unconventional T cell population known as mucosal-associated invariant T (MAIT) cells are characterized by the expression of semi-invariant T cell receptor (TCR) with a canonical TRAV1-2/TRAJ33 (Vα7.2/Jα33). These evolutionary conserved, innate-like T cells recognize vitamin B metabolites, derived from some bacteria and fungi. Due to their presence not only in the T cell repertoire of mucosal surfaces but also in peripheral blood and liver, and their significant involvement in a wide range of diseases, in-depth characterization of human MAIT cells is a timely requirement. Studies that examined the transcriptome, immunoproteome, and whole-cell proteome characterized the role of cytotoxic molecules and cytokines in effector functions of MAIT cells and their relationship with some other immune cell subsets. As MAIT cells are classified under the CD3+ T cell compartment and the majority express surface receptor CD8, identifying their proteomic relationship with CD3+ and CD8+ T cells is pivotal. Thus, a high-resolution dataset was generated using the cell populations sorted from peripheral blood mononuclear cells of three healthy volunteers to describe the whole cell proteomes of MAIT, CD3+, and CD8+ T cells. Trypsin-digested peptide samples obtained from the methanol co-precipitation method were analyzed using an Orbitrap FusionTM TribridTM mass spectrometer (Thermo Fisher Scientific, USA) inline coupled to nanoACQUITY ultra-performance liquid chromatography system (Waters, USA) to acquire data-dependent shotgun proteomic data (DDA-MS) for label-free quantification. Analysis of raw DDA-MS data using MaxQuant software and maxLFQ identified and quantified 4,442 protein groups at a 1% false discovery rate. Further analysis identified 3,680 proteins which were detected with a single UniProt accession and a minimum of 2 unique or razor peptides. Thus this proteomic dataset can be used as a reference proteome for future studies on human MAIT cells.