Landscape of transcript isoforms in single T cells infiltrating in non-small-cell lung cancer
Single-cell RNA sequencing (scRNA-seq) has enabled high-resolution characterization of molecular signatures of tumor-infiltrating lymphocytes. However, analyses at the transcript isoform level are rarely reported. As alternative splicing is critical to T-cell differentiation and activation, here, we...
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Published in: | Journal of genetics and genomics Vol. 47; no. 7; pp. 373 - 388 |
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Main Authors: | , , , |
Format: | Journal Article |
Language: | English |
Published: |
China
Elsevier Ltd
20-07-2020
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Subjects: | |
Online Access: | Get full text |
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Summary: | Single-cell RNA sequencing (scRNA-seq) has enabled high-resolution characterization of molecular signatures of tumor-infiltrating lymphocytes. However, analyses at the transcript isoform level are rarely reported. As alternative splicing is critical to T-cell differentiation and activation, here, we proposed a computational method named IDEA (Isoform Detection, Enrichment, and functional Annotation) to comprehensively detect and annotate differentially used isoforms across cell subtypes. We applied IDEA on a scRNA-seq data set of 12,346 T cells from non-small-cell lung cancer (NSCLC). We found that most genes tend to dominantly express one isoform in single T cells, enabling typing T cells based on the isotypes, given a gene. Isotype analysis suggested that tumor-infiltrating T cells significantly preferred specific isotypes for 245 genes in CD8+ T cells and 456 genes in CD4+ T cells. Functional annotation suggests that the preferred isoforms involved in coding/noncoding switches, transcription start site changes, gains/losses of domains, and subcellular translocation. Clonal analysis revealed that isoform switching occurred during T-cell activation/differentiation. Our analysis provides precise characterization of the molecular events in tumor-infiltrating T cells and sheds new light on the regulatory mechanisms of tumor-infiltrating T cells. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 1673-8527 |
DOI: | 10.1016/j.jgg.2020.06.006 |