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

Area of Science:

• Immunology
• Computational Biology
• Genomics

Background:

• Accurate identification of gamma delta (γδ) T cells within large single-cell RNA sequencing (scRNA-seq) datasets is a significant challenge.
• Existing methods often require supplementary data like single-cell γδ T cell receptor sequencing (sc-γδTCR-seq) or CITE-seq, limiting broader application.
• There is a need for a robust and accessible method to identify γδ T cells directly from standard scRNA-seq data.

Purpose of the Study:

• To develop and validate a novel strategy for identifying human γδ T cells using only scRNA-seq data.
• To establish a reliable method that does not depend on specialized TCR sequencing or CITE-seq.
• To provide a standardized tool for the analysis of γδ T cells in diverse scRNA-seq datasets.

Main Methods:

• Development of a TCR module scoring strategy based on the modular gene expression of constant and variable T cell receptor (TCR) alpha/beta (TRA/TRB) and TCR delta (TRD) genes.
• Evaluation of the method using 5' scRNA-seq datasets with integrated sc-αβTCR-seq and sc-γδTCR-seq data as ground truth.
• Assessment of method performance across various tissue types and distinct γδ T cell subtypes.

Main Results:

• The TCR module scoring strategy demonstrated high sensitivity and accuracy in identifying γδ T cells within scRNA-seq datasets.
• The method's performance remained consistent across datasets originating from different tissues.
• The strategy proved effective for identifying various subtypes of γδ T cells, indicating broad applicability.

Conclusions:

• The proposed TCR module scoring strategy offers a reliable and standardized approach for identifying γδ T cells from 5'-end scRNA-seq data.
• This method overcomes the limitations of requiring additional specialized sequencing techniques.
• It facilitates improved reanalysis and discovery of γδ T cells in existing and future scRNA-seq studies.