Genomics Proteomics & BioinformaticsBioinformaticsPeer-reviewed journal article
Single-cell Long Non-coding RNA Landscape of T Cells in Human Cancer Immunity
Haitao LuoiD, Dechao BuiD, Lijuan ShaoiD, Li YangiD, Liang Dan SuniD, Ce WangiD, Jing WangiD, Wei Cheng YangiD, Xiaofei YangiD, Jun DongiD, Yi Chen ZhaoiD, Furong LiiD
Abstract
The development of new therapeutic targets for cancer immunotherapies and the development of new biomarkers require deep understanding of T cells. To date, the complete landscape and systematic characterization of long noncoding RNAs (lncRNAs) in T cells in cancer immunity are lacking. Here, by systematically analyzing full-length single-cell RNA sequencing (scRNA-seq) data of more than 20,000 T cell libraries across three cancer types, we provide the first comprehensive catalog and the functional repertoires of lncRNAs in human T cells. Specifically, we developed a custom pipeline for de novo transcriptome assembly obtaining 9,433 novel lncRNA genes that increased the number of current human lncRNA catalog by 16% and nearly doubled the number of lncRNAs expressed in T cells. We found that a portion of expressed genes in single T cells were lncRNAs which have been overlooked by the majority of previous studies. Based on metacell maps constructed by MetaCell algorithm that partition scRNA-seq datasets into disjointed and homogenous groups of cells (metacells), 154 signature lncRNAs associated with effector, exhausted, and regulatory T cell states are identified, 84 of which are functionally annotated based on co-expression network, indicating that lncRNAs might broadly participate in regulation of T cell functions. Our findings provide a new point of view and resource for investigating the mechanisms of T cell regulation in cancer immunity as well as for novel cancer-immune biomarker development and cancer immunotherapies.
- organism: human
- assay: scRNA-seq
- assay: long-read sequencing
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 28
- FWCI
- 1.98
- Field percentile
- top 13%
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