# A shotgun proteomic dataset of human mucosal-associated invariant T cells

## TL;DR

This paper presents a detailed proteomic dataset comparing human MAIT cells and conventional T cells to better understand their unique functions and potential roles in disease.

## Contribution

The study provides a high-resolution, label-free quantitative proteomic dataset of human MAIT cells compared to conventional T cells.

## Key findings

- The dataset identified 4,442 proteins in MAIT and conventional T cells with a 1% false discovery rate.
- 3,680 proteins were assessed for differential abundance between MAIT cells and other T cell populations.
- The dataset can serve as a reference for studying MAIT cell behavior and discovering therapeutic targets.

## Abstract

Mucosal-associated invariant T (MAIT) cells represent a unique unconventional T cell population important in eliciting immunomodulatory responses in a range of diseases, including infectious diseases, autoimmunity and cancer. This innate-like T cell subset predominantly express CD8 in humans. Unlike conventional CD8+ T cells, which recognize peptide antigen presented by polymorphic major histocompatibility complex (MHC) molecules, MAIT cells are restricted by MR1, a non-polymorphic antigen-presenting molecule widely expressed in multiple tissues. Thus, identification of proteomic signature of MAIT cells in relation to conventional T cells is pivotal in understanding it's specific functional characteristics. The high-resolution dataset presents here comprehensively describes and compare the whole cell proteomes of MAIT (TCRVα7.2+CD161+) and conventional/non-MAIT T cells (TCR Vα7.2−CD161−) in humans. The dataset was generated using the proteomic samples prepared from matched T cell subsets sorted from peripheral blood mononuclear cells (PBMC) of three healthy volunteers. Peptides obtained from trypsin-digested cell lysates were analysed using Data-Dependent Mass Spectrometry (DDA-MS). Label-free quantitation of DDA-MS data using MaxQuant and MaxLFQ software identified 4,442 proteins at a 1 % false discovery rate. Of them, 3680 proteins that were detected with single UniProt accession and a minimum of 2 unique or razor peptides were assessed to identify differentially abundant proteins between MAIT cells and conventional T cells, including total T cells and CD8+ T cells. The dataset comprises high-quality label-free quantitative proteomic data that can be used to compare the expression pattern of whole cell proteomes between the above-mentioned T cell populations. Further, this can be used as a reference proteome of human MAIT cells for the in-depth understanding of the MAIT cell behaviour among T cells and to discover potential therapeutic targets to modulate MAIT cell function.