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A Multi-Omics Analysis of Mucosal-Associated-Invariant T cells reveals key drivers of distinct modes of activation

ABSTRACT: A Multi-Omics Analysis of Mucosal-Associated-Invariant T cells reveals key drivers of distinct modes of activation

PROVIDER: PRJNA665228 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR12698605_1.fastq.gz	Fastqsanger.gz
	SRR12698605_2.fastq.gz	Fastqsanger.gz
	SRR12698606_1.fastq.gz	Fastqsanger.gz
	SRR12698606_2.fastq.gz	Fastqsanger.gz
	SRR12698607_1.fastq.gz	Fastqsanger.gz

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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.

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A Multi-Omics Analysis of Mucosal-Associated-Invariant T cells reveals key drivers of distinct modes of activation

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