4.5 Article

Single-cell RNA sequencing analysis of T helper cell differentiation and heterogeneity

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ELSEVIER
DOI: 10.1016/j.bbamcr.2022.119321

Keywords

Thelpercells; Activation; Differentiation; Plasticity; Single-cellRNAsequencing; Geneexpressionprofiling; Signaturegenes; Differentialexpression; Cellcycleregression; Correctionforbatcheffect; Dataanalysis

Funding

  1. Institute of Biophysics of the Czech Academy of Sciences
  2. European Structural and Investment Funds, Operational Program Research, Development and Education - Preclinical Progression of New Organic Compounds with Targeted Biological Activity (Preclinprogress) [CZ.02.1.01/0.0/0.0/16_025/0007381]
  3. Ministry of Health of the Czech Republic [NU20-08-00314]
  4. MEYS CR [LM2018132]

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Single-cell transcriptomics is a powerful tool for investigating the biological characteristics of cells. In this study, human T cells were analyzed using single-cell transcriptomic approach with support of labelled antibodies and comprehensive bioinformatics analysis. The results revealed differences in expression profiles among different T cell subpopulations, providing insights into the importance of each step of the analysis and aiding in a more practical understanding of the results.
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 similar-ities 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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