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GepLiver: an integrative liver expression atlas spanning developmental stages and liver disease phases

Chronic liver diseases usually developed through stepwise pathological transitions under the persistent risk factors. The molecular changes during liver transitions are pivotal to improve liver diagnostics and therapeutics yet still remain elusive. Cumulative large-scale liver transcriptomic studies...

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Autores principales: Li, Ziteng, Zhang, Hena, Li, Qin, Feng, Wanjing, Jia, Xiya, Zhou, Runye, Huang, Yi, Li, Yan, Hu, Zhixiang, Hu, Xichun, Zhu, Xiaodong, Huang, Shenglin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10257690/
https://www.ncbi.nlm.nih.gov/pubmed/37301898
http://dx.doi.org/10.1038/s41597-023-02257-1
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author Li, Ziteng
Zhang, Hena
Li, Qin
Feng, Wanjing
Jia, Xiya
Zhou, Runye
Huang, Yi
Li, Yan
Hu, Zhixiang
Hu, Xichun
Zhu, Xiaodong
Huang, Shenglin
author_facet Li, Ziteng
Zhang, Hena
Li, Qin
Feng, Wanjing
Jia, Xiya
Zhou, Runye
Huang, Yi
Li, Yan
Hu, Zhixiang
Hu, Xichun
Zhu, Xiaodong
Huang, Shenglin
author_sort Li, Ziteng
collection PubMed
description Chronic liver diseases usually developed through stepwise pathological transitions under the persistent risk factors. The molecular changes during liver transitions are pivotal to improve liver diagnostics and therapeutics yet still remain elusive. Cumulative large-scale liver transcriptomic studies have been revealing molecular landscape of various liver conditions at bulk and single-cell resolution, however, neither single experiment nor databases enabled thorough investigations of transcriptomic dynamics along the progression of liver diseases. Here we establish GepLiver, a longitudinal and multidimensional liver expression atlas integrating expression profiles of 2469 human bulk tissues, 492 mouse samples, 409,775 single cells from 347 human samples and 27 liver cell lines spanning 16 liver phenotypes with uniformed processing and annotating methods. Using GepLiver, we have demonstrated dynamic changes of gene expression, cell abundance and crosstalk harboring meaningful biological associations. GepLiver can be applied to explore the evolving expression patterns and transcriptomic features for genes and cell types respectively among liver phenotypes, assisting the investigation of liver transcriptomic dynamics and informing biomarkers and targets for liver diseases.
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spelling pubmed-102576902023-06-12 GepLiver: an integrative liver expression atlas spanning developmental stages and liver disease phases Li, Ziteng Zhang, Hena Li, Qin Feng, Wanjing Jia, Xiya Zhou, Runye Huang, Yi Li, Yan Hu, Zhixiang Hu, Xichun Zhu, Xiaodong Huang, Shenglin Sci Data Data Descriptor Chronic liver diseases usually developed through stepwise pathological transitions under the persistent risk factors. The molecular changes during liver transitions are pivotal to improve liver diagnostics and therapeutics yet still remain elusive. Cumulative large-scale liver transcriptomic studies have been revealing molecular landscape of various liver conditions at bulk and single-cell resolution, however, neither single experiment nor databases enabled thorough investigations of transcriptomic dynamics along the progression of liver diseases. Here we establish GepLiver, a longitudinal and multidimensional liver expression atlas integrating expression profiles of 2469 human bulk tissues, 492 mouse samples, 409,775 single cells from 347 human samples and 27 liver cell lines spanning 16 liver phenotypes with uniformed processing and annotating methods. Using GepLiver, we have demonstrated dynamic changes of gene expression, cell abundance and crosstalk harboring meaningful biological associations. GepLiver can be applied to explore the evolving expression patterns and transcriptomic features for genes and cell types respectively among liver phenotypes, assisting the investigation of liver transcriptomic dynamics and informing biomarkers and targets for liver diseases. Nature Publishing Group UK 2023-06-10 /pmc/articles/PMC10257690/ /pubmed/37301898 http://dx.doi.org/10.1038/s41597-023-02257-1 Text en © The Author(s) 2023 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 Data Descriptor
Li, Ziteng
Zhang, Hena
Li, Qin
Feng, Wanjing
Jia, Xiya
Zhou, Runye
Huang, Yi
Li, Yan
Hu, Zhixiang
Hu, Xichun
Zhu, Xiaodong
Huang, Shenglin
GepLiver: an integrative liver expression atlas spanning developmental stages and liver disease phases
title GepLiver: an integrative liver expression atlas spanning developmental stages and liver disease phases
title_full GepLiver: an integrative liver expression atlas spanning developmental stages and liver disease phases
title_fullStr GepLiver: an integrative liver expression atlas spanning developmental stages and liver disease phases
title_full_unstemmed GepLiver: an integrative liver expression atlas spanning developmental stages and liver disease phases
title_short GepLiver: an integrative liver expression atlas spanning developmental stages and liver disease phases
title_sort gepliver: an integrative liver expression atlas spanning developmental stages and liver disease phases
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10257690/
https://www.ncbi.nlm.nih.gov/pubmed/37301898
http://dx.doi.org/10.1038/s41597-023-02257-1
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