Human γδ T cell identification from single-cell RNA sequencing datasets by modular TCR expression

J Leukoc Biol. 2023 Nov 24;114(6):630-638. doi: 10.1093/jleuko/qiad069.

Abstract

Accurately identifying γδ T cells in large single-cell RNA sequencing (scRNA-seq) datasets without additional single-cell γδ T cell receptor sequencing (sc-γδTCR-seq) or CITE-seq (cellular indexing of transcriptomes and epitopes sequencing) data remains challenging. In this study, we developed a TCR module scoring strategy for human γδ T cell identification (i.e. based on modular gene expression of constant and variable TRA/TRB and TRD genes). We evaluated our method using 5' scRNA-seq datasets comprising both sc-αβTCR-seq and sc-γδTCR-seq as references and demonstrated that it can identify γδ T cells in scRNA-seq datasets with high sensitivity and accuracy. We observed a stable performance of this strategy across datasets from different tissues and different subtypes of γδ T cells. Thus, we propose this analysis method, based on TCR gene module scores, as a standardized tool for identifying and reanalyzing γδ T cells from 5'-end scRNA-seq datasets.

Keywords: TCR genes; TCR module scores; scRNA-seq; scTCR-seq; γδ t cells.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Humans
  • Intraepithelial Lymphocytes*
  • Receptors, Antigen, T-Cell, gamma-delta* / genetics
  • Receptors, Antigen, T-Cell, gamma-delta* / metabolism
  • Sequence Analysis, RNA
  • Single-Cell Analysis / methods
  • Transcriptome

Substances

  • Receptors, Antigen, T-Cell, gamma-delta