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A multi-omics dataset of human transcriptome and proteome stable reference
The development of high-throughput omics technology has greatly promoted the development of biomedicine. However, the poor reproducibility of omics techniques limits their application. It is necessary to use standard reference materials of complex RNAs or proteins to test and calibrate the accuracy...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10344951/ https://www.ncbi.nlm.nih.gov/pubmed/37443183 http://dx.doi.org/10.1038/s41597-023-02359-w |
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author | Lu, Shaohua Lu, Hong Zheng, Tingkai Yuan, Huiming Du, Hongli Gao, Youhe Liu, Yongtao Pan, Xuanzhen Zhang, Wenlu Fu, Shuying Sun, Zhenghua Jin, Jingjie He, Qing-Yu Chen, Yang Zhang, Gong |
author_facet | Lu, Shaohua Lu, Hong Zheng, Tingkai Yuan, Huiming Du, Hongli Gao, Youhe Liu, Yongtao Pan, Xuanzhen Zhang, Wenlu Fu, Shuying Sun, Zhenghua Jin, Jingjie He, Qing-Yu Chen, Yang Zhang, Gong |
author_sort | Lu, Shaohua |
collection | PubMed |
description | The development of high-throughput omics technology has greatly promoted the development of biomedicine. However, the poor reproducibility of omics techniques limits their application. It is necessary to use standard reference materials of complex RNAs or proteins to test and calibrate the accuracy and reproducibility of omics workflows. The transcriptome and proteome of most cell lines shift during culturing, which limits their applicability as standard samples. In this study, we demonstrated that the human hepatocellular cell line MHCC97H has a very stable transcriptome (r = 0.983~0.997) and proteome (r = 0.966~0.988 for data-dependent acquisition, r = 0.970~0.994 for data-independent acquisition) after 9 subculturing generations, which allows this steady standard sample to be consistently produced on an industrial scale in long term. Moreover, this stability was maintained across labs and platforms. In sum, our study provides omics standard reference material and reference datasets for transcriptomic and proteomics research. This helps to further standardize the workflow and data quality of omics techniques and thus promotes the application of omics technology in precision medicine. |
format | Online Article Text |
id | pubmed-10344951 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-103449512023-07-15 A multi-omics dataset of human transcriptome and proteome stable reference Lu, Shaohua Lu, Hong Zheng, Tingkai Yuan, Huiming Du, Hongli Gao, Youhe Liu, Yongtao Pan, Xuanzhen Zhang, Wenlu Fu, Shuying Sun, Zhenghua Jin, Jingjie He, Qing-Yu Chen, Yang Zhang, Gong Sci Data Data Descriptor The development of high-throughput omics technology has greatly promoted the development of biomedicine. However, the poor reproducibility of omics techniques limits their application. It is necessary to use standard reference materials of complex RNAs or proteins to test and calibrate the accuracy and reproducibility of omics workflows. The transcriptome and proteome of most cell lines shift during culturing, which limits their applicability as standard samples. In this study, we demonstrated that the human hepatocellular cell line MHCC97H has a very stable transcriptome (r = 0.983~0.997) and proteome (r = 0.966~0.988 for data-dependent acquisition, r = 0.970~0.994 for data-independent acquisition) after 9 subculturing generations, which allows this steady standard sample to be consistently produced on an industrial scale in long term. Moreover, this stability was maintained across labs and platforms. In sum, our study provides omics standard reference material and reference datasets for transcriptomic and proteomics research. This helps to further standardize the workflow and data quality of omics techniques and thus promotes the application of omics technology in precision medicine. Nature Publishing Group UK 2023-07-13 /pmc/articles/PMC10344951/ /pubmed/37443183 http://dx.doi.org/10.1038/s41597-023-02359-w 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 Lu, Shaohua Lu, Hong Zheng, Tingkai Yuan, Huiming Du, Hongli Gao, Youhe Liu, Yongtao Pan, Xuanzhen Zhang, Wenlu Fu, Shuying Sun, Zhenghua Jin, Jingjie He, Qing-Yu Chen, Yang Zhang, Gong A multi-omics dataset of human transcriptome and proteome stable reference |
title | A multi-omics dataset of human transcriptome and proteome stable reference |
title_full | A multi-omics dataset of human transcriptome and proteome stable reference |
title_fullStr | A multi-omics dataset of human transcriptome and proteome stable reference |
title_full_unstemmed | A multi-omics dataset of human transcriptome and proteome stable reference |
title_short | A multi-omics dataset of human transcriptome and proteome stable reference |
title_sort | multi-omics dataset of human transcriptome and proteome stable reference |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10344951/ https://www.ncbi.nlm.nih.gov/pubmed/37443183 http://dx.doi.org/10.1038/s41597-023-02359-w |
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