Current annotation strategies for T cell phenotyping of single-cell RNA-seq data
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 populatio...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-12-01
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| Series: | Frontiers in Immunology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2023.1306169/full |
| _version_ | 1797384270511079424 |
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| author | Kerry A. Mullan Kerry A. Mullan Nicky de Vrij Nicky de Vrij Nicky de Vrij Sebastiaan Valkiers Sebastiaan Valkiers Pieter Meysman Pieter Meysman |
| author_facet | Kerry A. Mullan Kerry A. Mullan Nicky de Vrij Nicky de Vrij Nicky de Vrij Sebastiaan Valkiers Sebastiaan Valkiers Pieter Meysman Pieter Meysman |
| author_sort | Kerry A. Mullan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T21:34:00Z |
| format | Article |
| id | doaj.art-439db203e2954019b8c376aef48d5e0c |
| institution | Directory Open Access Journal |
| issn | 1664-3224 |
| language | English |
| last_indexed | 2024-03-08T21:34:00Z |
| publishDate | 2023-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj.art-439db203e2954019b8c376aef48d5e0c2023-12-21T04:15:00ZengFrontiers Media S.A.Frontiers in Immunology1664-32242023-12-011410.3389/fimmu.2023.13061691306169Current annotation strategies for T cell phenotyping of single-cell RNA-seq dataKerry A. Mullan0Kerry A. Mullan1Nicky de Vrij2Nicky de Vrij3Nicky de Vrij4Sebastiaan Valkiers5Sebastiaan Valkiers6Pieter Meysman7Pieter Meysman8Adrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, BelgiumAntwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS) Consortium, University of Antwerp, Antwerp, BelgiumAdrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, BelgiumAntwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS) Consortium, University of Antwerp, Antwerp, BelgiumClinical Immunology Unit, Department of Clinical Sciences, Institute for Tropical Medicine, Antwerp, BelgiumAdrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, BelgiumAntwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS) Consortium, University of Antwerp, Antwerp, BelgiumAdrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, BelgiumAntwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS) Consortium, University of Antwerp, Antwerp, BelgiumSingle-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.https://www.frontiersin.org/articles/10.3389/fimmu.2023.1306169/fullT cellssingle cellRNA-seqannotationbioinformaticsadaptive immunity |
| spellingShingle | Kerry A. Mullan Kerry A. Mullan Nicky de Vrij Nicky de Vrij Nicky de Vrij Sebastiaan Valkiers Sebastiaan Valkiers Pieter Meysman Pieter Meysman Current annotation strategies for T cell phenotyping of single-cell RNA-seq data Frontiers in Immunology T cells single cell RNA-seq annotation bioinformatics adaptive immunity |
| title | Current annotation strategies for T cell phenotyping of single-cell RNA-seq data |
| title_full | Current annotation strategies for T cell phenotyping of single-cell RNA-seq data |
| title_fullStr | Current annotation strategies for T cell phenotyping of single-cell RNA-seq data |
| title_full_unstemmed | Current annotation strategies for T cell phenotyping of single-cell RNA-seq data |
| title_short | Current annotation strategies for T cell phenotyping of single-cell RNA-seq data |
| title_sort | current annotation strategies for t cell phenotyping of single cell rna seq data |
| topic | T cells single cell RNA-seq annotation bioinformatics adaptive immunity |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2023.1306169/full |
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