Single-cell RNA sequencing analysis of T helper cell differentiation and heterogeneity
Language English Country Netherlands Media print-electronic
Document type Journal Article, Research Support, Non-U.S. Gov't
PubMed
35779629
DOI
10.1016/j.bbamcr.2022.119321
PII: S0167-4889(22)00113-6
Knihovny.cz E-resources
- Keywords
- Activation, Cell cycle regression, Correction for batch effect, Data analysis, Differential expression, Differentiation, Gene expression profiling, Plasticity, Signature genes, Single-cell RNA sequencing, T helper cells,
- MeSH
- Lymphocyte Activation * MeSH
- Cell Differentiation genetics MeSH
- Th17 Cells MeSH
- Humans MeSH
- Membrane Glycoproteins metabolism MeSH
- Sequence Analysis, RNA MeSH
- Th2 Cells * metabolism MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Names of Substances
- Membrane Glycoproteins MeSH
- SPINT2 protein, human MeSH Browser
Single-cell transcriptomics has emerged as a powerful tool to investigate cells' biological landscape and focus on the expression profile of individual cells. Major advantage of this approach is an analysis of highly complex and heterogeneous cell populations, such as a specific subpopulation of T helper cells that are known to differentiate into distinct subpopulations. The need for distinguishing the specific expression profile is even more important considering the T cell plasticity. However, importantly, the universal pipelines for single-cell analysis are usually not sufficient for every cell type. Here, the aims are to analyze the diversity of T cell phenotypes employing classical in vitro cytokine-mediated differentiation of human T cells isolated from human peripheral blood by single-cell transcriptomic approach with support of labelled antibodies and a comprehensive bioinformatics analysis using combination of Seurat, Nebulosa, GGplot and others. The results showed high expression similarities between Th1 and Th17 phenotype and very distinct Th2 expression profile. In a case of Th2 highly specific marker genes SPINT2, TRIB3 and CST7 were expressed. Overall, our results demonstrate how donor difference, Th plasticity and cell cycle influence the expression profiles of distinct T cell populations. The results could help to better understand the importance of each step of the analysis when working with T cell single-cell data and observe the results in a more practical way by using our analyzed datasets.
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