Current annotation strategies for T cell phenotyping of single-cell RNA-seq data

KA Mullan, N de Vrij, S Valkiers, P Meysman

Research output: Contribution to journalReviewpeer-review


Single-cell RNA sequencing (scRNA-seq) has become a popular technique for interrogating the diversity and dynamic nature of cellular gene expression and has numerous advantages in immunology. For example, scRNA-seq, in contrast to bulk RNA sequencing, can discern cellular subtypes within a population, which is important for heterogenous populations such as T cells. Moreover, recent advancements in the technology allow the parallel capturing of the highly diverse T-cell receptor (TCR) sequence with the gene expression. However, the field of single-cell RNA sequencing data analysis is still hampered by a lack of gold-standard cell phenotype annotation. This problem is particularly evident in the case of T cells due to the heterogeneity in both their gene expression and their TCR. While current cell phenotype annotation tools can differentiate major cell populations from each other, labelling T-cell subtypes remains problematic. In this review, we identify the common automated strategy for annotating T cells and their subpopulations, and also describe what crucial information is still missing from these tools.
Original languageEnglish
Article number1306169
JournalFrontiers in Immunology
Number of pages10
Publication statusPublished - 2023


  • RNA-seq
  • T cells
  • T-cell receptor
  • Adaptive immunity
  • Annotation
  • Bioinformatics
  • Single cell


Dive into the research topics of 'Current annotation strategies for T cell phenotyping of single-cell RNA-seq data'. Together they form a unique fingerprint.

Cite this