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Garcia Carlos J., Yang Xiao, Huang Danfeng, Tomas-Barberan Francisco A. Can we trust biomarkers identified using different non-targeted metabolomics platforms? Multi-platform, inter-laboratory comparative metabolomics profiling of lettuce cultivars via UPLC-QTOF-MS.

来源：METABOLOMICS 发布时间：2020-09-17 作者：杨晓 点击数：

标题: Can we trust biomarkers identified using different non-targeted metabolomics platforms? Multi-platform, inter-laboratory comparative metabolomics profiling of lettuce cultivars via UPLC-QTOF-MS
作者: Garcia, CJ (Garcia, Carlos J.); Yang, X (Yang, Xiao); Huang, DF (Huang, Danfeng); Tomas-Barberan, FA (Tomas-Barberan, Francisco A.)
来源出版物: METABOLOMICS  卷: 16  期: 8  文献号: 85  DOI: 10.1007/s11306-020-01705-y  出版年: JUL 31 2020  
影响因子 (2019 年): 2.881
摘要: Introduction Data analysis during UPLC-MS non-targeted metabolomics introduces variation as different manufacturers use specific algorithms for data treatment and this makes untargeted metabolomics an application for the discovery of new biomarkers with low confidence in the reproducibility of the results under the use of different metabolomics platforms. Objectives This study compared the ability of two platforms (Agilent UPLC-ESI-QTOF-MS and Waters UPLC-IMS-QTOF-MS) to identify biomarkers in butterhead and romaine lettuce cultivars. Methods Two case studies by different metabolomics platforms: (1) Waters and Agilent datasets processed by the same data pre-processing software (Progenesis QI), and (2) Datasets processed by different data pre-processing software. Results A higher number of candidate biomarkers shared between sample groups in case 2 (101) than in case 1 (26) was found. Thirteen metabolites were common to both cases. Romaine lettuce was characterised by phenolic compounds including flavonoids, hydroxycinnamate derivatives, and 9-undecenal, while Butterhead showed sesquiterpene lactones and xanthosine. This study demonstrates that high percentages of the most discriminatory entities can be obtained by using the manufacturers' embedded pre-processing software and following the recommended processing data guidelines using commercial software to normalise the data matrix.
入藏号: WOS:000555455400001
PubMed ID: 32737683
语言: English
文献类型: Article
作者关键词: Plant metabolomics; Lettuce biomarkers; Metabolic profiling; Multivariate analysis; Multi-platform analysis
KeyWords Plus: MOBILITY-MASS SPECTROMETRY; UNTARGETED METABOLOMICS; QUANTIFICATION; LIPIDOMICS; SOFTWARE; NITROGEN; MARKER; PLANTS; XCMS; TIME
地址: [Garcia, Carlos J.; Tomas-Barberan, Francisco A.] Spanish Natl Res Council CEBAS CSIC, Res Grp Qual Safety & Bioact Plant Foods, Ctr Appl Soil Sci & Biol Segura, Murcia 30100, Spain.
[Yang, Xiao] Chinese Acad Agr Sci, Inst Urban Agr, Chengdu Natl Agr Sci & Technol Ctr, Chengdu 610213, Peoples R China.
[Yang, Xiao; Huang, Danfeng] Shanghai Jiao Tong Univ, Sch Agr & Biol, Shanghai 200240, Peoples R China.
通讯作者地址: Tomas-Barberan, FA (corresponding author)，Spanish Natl Res Council CEBAS CSIC, Res Grp Qual Safety & Bioact Plant Foods, Ctr Appl Soil Sci & Biol Segura, Murcia 30100, Spain.
Huang, DF (corresponding author)，Shanghai Jiao Tong Univ, Sch Agr & Biol, Shanghai 200240, Peoples R China.
电子邮件地址: hdf@sjtu.edu.cn; fatomas@cebas.csic.es
作者识别号:
作者 Web of Science ResearcherID ORCID 号
Yang, Xiao  P-1700-2016  0000-0002-2511-4640 
Garcia Hernandez-Gil, Carlos    0000-0002-5813-092X 
出版商: SPRINGER
出版商地址: ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
Web of Science 类别: Endocrinology & Metabolism
研究方向: Endocrinology & Metabolism
IDS 号: MU1TJ
ISSN: 1573-3882
eISSN: 1573-3890
29 字符的来源出版物名称缩写: METABOLOMICS
ISO 来源出版物缩写: Metabolomics
来源出版物页码计数: 15
基金资助致谢:
基金资助机构 授权号
CSIC 
201870E014 
Fundacion Seneca de la Region de Murcia, Ayudas a Grupos de Excelencia 
19900/GERM/15 
Central Public-interest Scientific Institution Basal Research Fund 
Y2020XK01 
Sichuan Science and Technology Program 
2020JDRC0043 
Shanghai Municipal Agricultural Commission 
T2017-3-4 

This research was supported by CSIC 201870E014, Fundacion Seneca de la Region de Murcia, Ayudas a Grupos de Excelencia 19900/GERM/15, Central Public-interest Scientific Institution Basal Research Fund (Y2020XK01), Sichuan Science and Technology Program (2020JDRC0043), and Shanghai Municipal Agricultural Commission (T2017-3-4). We acknowledge our colleagues Miss Lina Zhao and Mr Kai Dou, and Dr Shiwei Wei (Shanghai Agrobiological Gene Center) for help with sample preparation, Dr Hongyan Liu (Chinese Academy of Agricultural Sciences) kindly provides some useful suggestions for the manuscript, and Dr Lei Feng (Instrumental Analysis Center, Shanghai Jiao Tong University) helped with the UPLC-QTOF-MS analysis.

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