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Single-nuclei RNA-seq on human retinal tissue provides improved transcriptome profiling
Single-cell RNA-seq is a powerful tool in decoding the heterogeneity in complex tissues by generating transcriptomic profiles of the individual cell. Here, we report a single-nuclei RNA-seq (snRNA-seq) transcriptomic study on human retinal tissue, which is composed of multiple cell types with distin...
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/PMC6917696/ https://www.ncbi.nlm.nih.gov/pubmed/31848347 http://dx.doi.org/10.1038/s41467-019-12917-9 |
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author | Liang, Qingnan Dharmat, Rachayata Owen, Leah Shakoor, Akbar Li, Yumei Kim, Sangbae Vitale, Albert Kim, Ivana Morgan, Denise Liang, Shaoheng Wu, Nathaniel Chen, Ken DeAngelis, Margaret M. Chen, Rui |
author_facet | Liang, Qingnan Dharmat, Rachayata Owen, Leah Shakoor, Akbar Li, Yumei Kim, Sangbae Vitale, Albert Kim, Ivana Morgan, Denise Liang, Shaoheng Wu, Nathaniel Chen, Ken DeAngelis, Margaret M. Chen, Rui |
author_sort | Liang, Qingnan |
collection | PubMed |
description | Single-cell RNA-seq is a powerful tool in decoding the heterogeneity in complex tissues by generating transcriptomic profiles of the individual cell. Here, we report a single-nuclei RNA-seq (snRNA-seq) transcriptomic study on human retinal tissue, which is composed of multiple cell types with distinct functions. Six samples from three healthy donors are profiled and high-quality RNA-seq data is obtained for 5873 single nuclei. All major retinal cell types are observed and marker genes for each cell type are identified. The gene expression of the macular and peripheral retina is compared to each other at cell-type level. Furthermore, our dataset shows an improved power for prioritizing genes associated with human retinal diseases compared to both mouse single-cell RNA-seq and human bulk RNA-seq results. In conclusion, we demonstrate that obtaining single cell transcriptomes from human frozen tissues can provide insight missed by either human bulk RNA-seq or animal models. |
format | Online Article Text |
id | pubmed-6917696 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-69176962019-12-19 Single-nuclei RNA-seq on human retinal tissue provides improved transcriptome profiling Liang, Qingnan Dharmat, Rachayata Owen, Leah Shakoor, Akbar Li, Yumei Kim, Sangbae Vitale, Albert Kim, Ivana Morgan, Denise Liang, Shaoheng Wu, Nathaniel Chen, Ken DeAngelis, Margaret M. Chen, Rui Nat Commun Article Single-cell RNA-seq is a powerful tool in decoding the heterogeneity in complex tissues by generating transcriptomic profiles of the individual cell. Here, we report a single-nuclei RNA-seq (snRNA-seq) transcriptomic study on human retinal tissue, which is composed of multiple cell types with distinct functions. Six samples from three healthy donors are profiled and high-quality RNA-seq data is obtained for 5873 single nuclei. All major retinal cell types are observed and marker genes for each cell type are identified. The gene expression of the macular and peripheral retina is compared to each other at cell-type level. Furthermore, our dataset shows an improved power for prioritizing genes associated with human retinal diseases compared to both mouse single-cell RNA-seq and human bulk RNA-seq results. In conclusion, we demonstrate that obtaining single cell transcriptomes from human frozen tissues can provide insight missed by either human bulk RNA-seq or animal models. Nature Publishing Group UK 2019-12-17 /pmc/articles/PMC6917696/ /pubmed/31848347 http://dx.doi.org/10.1038/s41467-019-12917-9 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/. |
spellingShingle | Article Liang, Qingnan Dharmat, Rachayata Owen, Leah Shakoor, Akbar Li, Yumei Kim, Sangbae Vitale, Albert Kim, Ivana Morgan, Denise Liang, Shaoheng Wu, Nathaniel Chen, Ken DeAngelis, Margaret M. Chen, Rui Single-nuclei RNA-seq on human retinal tissue provides improved transcriptome profiling |
title | Single-nuclei RNA-seq on human retinal tissue provides improved transcriptome profiling |
title_full | Single-nuclei RNA-seq on human retinal tissue provides improved transcriptome profiling |
title_fullStr | Single-nuclei RNA-seq on human retinal tissue provides improved transcriptome profiling |
title_full_unstemmed | Single-nuclei RNA-seq on human retinal tissue provides improved transcriptome profiling |
title_short | Single-nuclei RNA-seq on human retinal tissue provides improved transcriptome profiling |
title_sort | single-nuclei rna-seq on human retinal tissue provides improved transcriptome profiling |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6917696/ https://www.ncbi.nlm.nih.gov/pubmed/31848347 http://dx.doi.org/10.1038/s41467-019-12917-9 |
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