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Deep single-cell RNA sequencing data of individual T cells from treatment-naïve colorectal cancer patients
T cells, as a crucial compartment of the tumour microenvironment, play vital roles in cancer immunotherapy. However, the basic properties of tumour-infiltrating T cells (TILs) such as the functional state, migratory capability and clonal expansion remain elusive. Here, using Smart-seq2 protocol, we...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6656756/ https://www.ncbi.nlm.nih.gov/pubmed/31341169 http://dx.doi.org/10.1038/s41597-019-0131-5 |
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author | Zhang, Yuanyuan Zheng, Liangtao Zhang, Lei Hu, Xueda Ren, Xianwen Zhang, Zemin |
author_facet | Zhang, Yuanyuan Zheng, Liangtao Zhang, Lei Hu, Xueda Ren, Xianwen Zhang, Zemin |
author_sort | Zhang, Yuanyuan |
collection | PubMed |
description | T cells, as a crucial compartment of the tumour microenvironment, play vital roles in cancer immunotherapy. However, the basic properties of tumour-infiltrating T cells (TILs) such as the functional state, migratory capability and clonal expansion remain elusive. Here, using Smart-seq2 protocol, we have generated a RNA sequencing dataset of 11,138 T cells isolated from peripheral blood, adjacent normal and tumour tissues of 12 colorectal cancer (CRC) patients, including 4 with microsatellite instability (MSI). The dataset contained an expression profile of 10,805 T cells, as well as the full-length T cell receptor (TCR) sequences of 9,878 cells after quality control. To facilitate data mining of our T cell dataset, we developed a web-based application to deliver systematic interrogations and customizable functionalities (http://crctcell.cancer-pku.cn/). Functioning with our dataset, the web tool enables the characterization of TILs based on both transcriptome and assembled TCR sequences at the single cell level, which will help unleash the potential value of our CRC T cell data resource. |
format | Online Article Text |
id | pubmed-6656756 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-66567562019-07-29 Deep single-cell RNA sequencing data of individual T cells from treatment-naïve colorectal cancer patients Zhang, Yuanyuan Zheng, Liangtao Zhang, Lei Hu, Xueda Ren, Xianwen Zhang, Zemin Sci Data Data Descriptor T cells, as a crucial compartment of the tumour microenvironment, play vital roles in cancer immunotherapy. However, the basic properties of tumour-infiltrating T cells (TILs) such as the functional state, migratory capability and clonal expansion remain elusive. Here, using Smart-seq2 protocol, we have generated a RNA sequencing dataset of 11,138 T cells isolated from peripheral blood, adjacent normal and tumour tissues of 12 colorectal cancer (CRC) patients, including 4 with microsatellite instability (MSI). The dataset contained an expression profile of 10,805 T cells, as well as the full-length T cell receptor (TCR) sequences of 9,878 cells after quality control. To facilitate data mining of our T cell dataset, we developed a web-based application to deliver systematic interrogations and customizable functionalities (http://crctcell.cancer-pku.cn/). Functioning with our dataset, the web tool enables the characterization of TILs based on both transcriptome and assembled TCR sequences at the single cell level, which will help unleash the potential value of our CRC T cell data resource. Nature Publishing Group UK 2019-07-24 /pmc/articles/PMC6656756/ /pubmed/31341169 http://dx.doi.org/10.1038/s41597-019-0131-5 Text en © The Author(s) 2019 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/. The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article. |
spellingShingle | Data Descriptor Zhang, Yuanyuan Zheng, Liangtao Zhang, Lei Hu, Xueda Ren, Xianwen Zhang, Zemin Deep single-cell RNA sequencing data of individual T cells from treatment-naïve colorectal cancer patients |
title | Deep single-cell RNA sequencing data of individual T cells from treatment-naïve colorectal cancer patients |
title_full | Deep single-cell RNA sequencing data of individual T cells from treatment-naïve colorectal cancer patients |
title_fullStr | Deep single-cell RNA sequencing data of individual T cells from treatment-naïve colorectal cancer patients |
title_full_unstemmed | Deep single-cell RNA sequencing data of individual T cells from treatment-naïve colorectal cancer patients |
title_short | Deep single-cell RNA sequencing data of individual T cells from treatment-naïve colorectal cancer patients |
title_sort | deep single-cell rna sequencing data of individual t cells from treatment-naïve colorectal cancer patients |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6656756/ https://www.ncbi.nlm.nih.gov/pubmed/31341169 http://dx.doi.org/10.1038/s41597-019-0131-5 |
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