A high-resolution mass spectrometry based proteomic dataset of human regulatory T cells

Weerakoon, Harshi, Miles, John J., Lepletier, Ailin, and Hill, Michelle M. (2022) A high-resolution mass spectrometry based proteomic dataset of human regulatory T cells. Data in Brief, 40. 107687.

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Abstract

Regulatory T cells (Tregs) play a core role in maintaining immune tolerance, homeostasis, and host health. High-resolution analysis of the Treg proteome is required to identify enriched biological processes and pathways distinct to this important immune cell lineage. We present a comprehensive proteomic dataset of Tregs paired with conventional CD4+ (Conv CD4+) T cells in healthy individuals. Tregs and Conv CD4+ T cells were sorted to high purity using dual magnetic bead-based and flow cytometry-based methodologies. Proteins were trypsin-digested and analysed using label-free data-dependent acquisition mass spectrometry (DDA-MS) followed by label free quantitation (LFQ) proteomics analysis using MaxQuant software. Approximately 4,000 T cell proteins were identified with a 1% false discovery rate, of which approximately 2,800 proteins were consistently identified and quantified in all the samples. Finally, flow cytometry with a monoclonal antibody was used to validate the elevated abundance of the protein phosphatase CD148 in Tregs. This proteomic dataset serves as a reference point for future mechanistic and clinical T cell immunology and identifies receptors, processes, and pathways distinct to Tregs. Collectively, these data will lead to a better understanding of Treg immunophysiology and potentially reveal novel leads for therapeutics seeking Treg regulation.

Item ID: 76531
Item Type: Article (Research - C1)
ISSN: 2352-3409
Keywords: Conventional T cell, LC-MS/MS, Proteomics, Regulatory T cell, Tandem mass spectrometry
Copyright Information: © 2021 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Date Deposited: 17 May 2023 00:00
FoR Codes: 31 BIOLOGICAL SCIENCES > 3101 Biochemistry and cell biology > 310109 Proteomics and intermolecular interactions (excl. medical proteomics) @ 100%
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