Distinguishing activated T regulatory cell and T conventional cells by single-cell technologies

Research output: Contribution to journalResearch articleContributedpeer-review

Contributors

Abstract

Resting conventional T cells (Tconv) can be distinguished from T regulatory cells (Treg) by the canonical markers FOXP3, CD25 and CD127. However, the expression of these proteins alters after T-cell activation leading to overlap between Tconv and Treg. The objective of this study was to distinguish resting and antigen-responsive T effector (Tconv) and Treg using single-cell technologies. CD4+ Treg and Tconv cells were stimulated with antigen and responsive and non-responsive populations processed for targeted and non-targeted single-cell RNAseq. Machine learning was used to generate a limited set of genes that could distinguish responding and non-responding Treg and Tconv cells and which was used for single-cell multiplex qPCR and to design a flow cytometry panel. Targeted scRNAseq clearly distinguished the four-cell populations. A minimal set of 27 genes was identified by machine learning algorithms to provide discrimination of the four populations at >95% accuracy. In all, 15 of the genes were validated to be differentially expressed by single-cell multiplex qPCR. Discrimination of responding Treg from responding Tconv could be achieved by a flow cytometry strategy that included staining for CD25, CD127, FOXP3, IKZF2, ITGA4, and the novel marker TRIM which was strongly expressed in Tconv and weakly expressed in both responding and non-responding Treg. A minimal set of genes was identified that discriminates responding and non-responding CD4+ Treg and Tconv cells and, which have identified TRIM as a marker to distinguish Treg by flow cytometry.

Details

Original languageEnglish
Pages (from-to)121-137
Number of pages17
Journal Immunology : an official journal of the British Society for Immunology ; the journal of cells, molecules, systems and technologies
Volume166
Issue number1
Early online date23 Feb 2022
Publication statusPublished - May 2022
Peer-reviewedYes

External IDs

PubMed 35196398
Mendeley 1e2e4d42-8db1-376c-8cb9-5f526f631875

Keywords

ASJC Scopus subject areas

Keywords

  • activation, CD4 cell, T cell, transcriptomics, Treg, Flow Cytometry, Forkhead Transcription Factors/metabolism, Lymphocyte Activation, Lymphocyte Count, T-Lymphocytes, Regulatory, Biomarkers/metabolism

Library keywords