Publication details
Single-cell RNA sequencing analysis of T helper cell differentiation and heterogeneity
| Authors | |
|---|---|
| Year of publication | 2022 |
| Type | Article in Periodical |
| Magazine / Source | Biochimica et Biophysica Acta (BBA) - Molecular Cell Research |
| MU Faculty or unit | |
| Citation | |
| web | https://doi.org/10.1016/j.bbamcr.2022.119321 |
| Doi | http://dx.doi.org/10.1016/j.bbamcr.2022.119321 |
| Keywords | Thelpercells; Activation; Differentiation; Plasticity; Single-cellRNAsequencing; Geneexpressionprofiling; Signaturegenes; Differentialexpression; Cellcycleregression; Correctionforbatcheffect; Dataanalysis |
| Description | 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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