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Exploring the cellular landscape of circular RNAs using full-length single-cell RNA sequencing
Previous studies have demonstrated the highly specific expression of circular RNAs (circRNAs) in different tissues and organisms, but the cellular architecture of circRNA has never been fully characterized. Here, we present a collection of 171 full-length single-cell RNA-seq datasets to explore the...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9187688/ https://www.ncbi.nlm.nih.gov/pubmed/35688820 http://dx.doi.org/10.1038/s41467-022-30963-8 |
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author | Wu, Wanying Zhang, Jinyang Cao, Xiaofei Cai, Zhengyi Zhao, Fangqing |
author_facet | Wu, Wanying Zhang, Jinyang Cao, Xiaofei Cai, Zhengyi Zhao, Fangqing |
author_sort | Wu, Wanying |
collection | PubMed |
description | Previous studies have demonstrated the highly specific expression of circular RNAs (circRNAs) in different tissues and organisms, but the cellular architecture of circRNA has never been fully characterized. Here, we present a collection of 171 full-length single-cell RNA-seq datasets to explore the cellular landscape of circRNAs in human and mouse tissues. Through large-scale integrative analysis, we identify a total of 139,643 human and 214,747 mouse circRNAs in these scRNA-seq libraries. We validate the detected circRNAs with the integration of 11 bulk RNA-seq based resources, where 216,602 high-confidence circRNAs are uniquely detected in the single-cell cohort. We reveal the cell-type-specific expression pattern of circRNAs in brain samples, developing embryos, and breast tumors. We identify the uniquely expressed circRNAs in different cell types and validate their performance in tumor-infiltrating immune cell composition deconvolution. This study expands our knowledge of circRNA expression to the single-cell level and provides a useful resource for exploring circRNAs at this unprecedented resolution. |
format | Online Article Text |
id | pubmed-9187688 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-91876882022-06-12 Exploring the cellular landscape of circular RNAs using full-length single-cell RNA sequencing Wu, Wanying Zhang, Jinyang Cao, Xiaofei Cai, Zhengyi Zhao, Fangqing Nat Commun Article Previous studies have demonstrated the highly specific expression of circular RNAs (circRNAs) in different tissues and organisms, but the cellular architecture of circRNA has never been fully characterized. Here, we present a collection of 171 full-length single-cell RNA-seq datasets to explore the cellular landscape of circRNAs in human and mouse tissues. Through large-scale integrative analysis, we identify a total of 139,643 human and 214,747 mouse circRNAs in these scRNA-seq libraries. We validate the detected circRNAs with the integration of 11 bulk RNA-seq based resources, where 216,602 high-confidence circRNAs are uniquely detected in the single-cell cohort. We reveal the cell-type-specific expression pattern of circRNAs in brain samples, developing embryos, and breast tumors. We identify the uniquely expressed circRNAs in different cell types and validate their performance in tumor-infiltrating immune cell composition deconvolution. This study expands our knowledge of circRNA expression to the single-cell level and provides a useful resource for exploring circRNAs at this unprecedented resolution. Nature Publishing Group UK 2022-06-10 /pmc/articles/PMC9187688/ /pubmed/35688820 http://dx.doi.org/10.1038/s41467-022-30963-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Wu, Wanying Zhang, Jinyang Cao, Xiaofei Cai, Zhengyi Zhao, Fangqing Exploring the cellular landscape of circular RNAs using full-length single-cell RNA sequencing |
title | Exploring the cellular landscape of circular RNAs using full-length single-cell RNA sequencing |
title_full | Exploring the cellular landscape of circular RNAs using full-length single-cell RNA sequencing |
title_fullStr | Exploring the cellular landscape of circular RNAs using full-length single-cell RNA sequencing |
title_full_unstemmed | Exploring the cellular landscape of circular RNAs using full-length single-cell RNA sequencing |
title_short | Exploring the cellular landscape of circular RNAs using full-length single-cell RNA sequencing |
title_sort | exploring the cellular landscape of circular rnas using full-length single-cell rna sequencing |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9187688/ https://www.ncbi.nlm.nih.gov/pubmed/35688820 http://dx.doi.org/10.1038/s41467-022-30963-8 |
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