Article ; Online: Current annotation strategies for T cell phenotyping of single-cell RNA-seq data.
2023 Volume 14, Page(s) 1306169
Abstract: 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, ... ...
| Abstract | 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. |
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| MeSH term(s) | T-Lymphocytes ; Single-Cell Gene Expression Analysis ; Data Analysis ; Sequence Analysis, RNA ; Receptors, Antigen, T-Cell/genetics |
| Chemical Substances | Receptors, Antigen, T-Cell |
| Language | English |
| Publishing date | 2023-12-21 |
| Publishing country | Switzerland |
| Document type | Journal Article ; Review ; Research Support, Non-U.S. Gov't |
| ZDB-ID | 2606827-8 |
| ISSN | 1664-3224 ; 1664-3224 |
| ISSN (online) | 1664-3224 |
| ISSN | 1664-3224 |
| DOI | 10.3389/fimmu.2023.1306169 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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