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Title
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Current annotation strategies for T cell phenotyping of single-cell RNA-seq data
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Author
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Abstract
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| 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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Language
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English
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Source (journal)
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Frontiers in immunology. - Place of publication unknown
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Publication
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Place of publication unknown
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2023
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ISSN
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1664-3224
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DOI
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10.3389/FIMMU.2023.1306169
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Volume/pages
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14
(2023)
, p. 1-10
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Article Reference
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1306169
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ISI
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001135190900001
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Pubmed ID
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38187377
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Medium
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E-only publicatie
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Full text (Publisher's DOI)
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Full text (open access)
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