Literature DB >> 28669031

Experimental design and reporting standards for metabolomics studies of mammalian cell lines.

Sarah Hayton1,2, Garth L Maker3,4, Ian Mullaney2, Robert D Trengove1.   

Abstract

Metabolomics is an analytical technique that investigates the small biochemical molecules present within a biological sample isolated from a plant, animal, or cultured cells. It can be an extremely powerful tool in elucidating the specific metabolic changes within a biological system in response to an environmental challenge such as disease, infection, drugs, or toxins. A historically difficult step in the metabolomics pipeline is in data interpretation to a meaningful biological context, for such high-variability biological samples and in untargeted metabolomics studies that are hypothesis-generating by design. One way to achieve stronger biological context of metabolomic data is via the use of cultured cell models, particularly for mammalian biological systems. The benefits of in vitro metabolomics include a much greater control of external variables and no ethical concerns. The current concerns are with inconsistencies in experimental procedures and level of reporting standards between different studies. This review discusses some of these discrepancies between recent studies, such as metabolite extraction and data normalisation. The aim of this review is to highlight the importance of a standardised experimental approach to any cultured cell metabolomics study and suggests an example procedure fully inclusive of information that should be disclosed in regard to the cell type/s used and their culture conditions. Metabolomics of cultured cells has the potential to uncover previously unknown information about cell biology, functions and response mechanisms, and so the accurate biological interpretation of the data produced and its ability to be compared to other studies should be considered vitally important.

Entities:  

Keywords:  Cell culture; Experimental design; In vitro; Metabolomics; Methods; Standardisation

Mesh:

Year:  2017        PMID: 28669031     DOI: 10.1007/s00018-017-2582-1

Source DB:  PubMed          Journal:  Cell Mol Life Sci        ISSN: 1420-682X            Impact factor:   9.261


  75 in total

Review 1.  The current status of alternatives to animal testing and predictive toxicology methods using liver microfluidic biochips.

Authors:  Jean Matthieu Prot; Eric Leclerc
Journal:  Ann Biomed Eng       Date:  2011-12-10       Impact factor: 3.934

2.  Strategy for choosing extraction procedures for NMR-based metabolomic analysis of mammalian cells.

Authors:  Estelle Martineau; Illa Tea; Gregory Loaëc; Patrick Giraudeau; Serge Akoka
Journal:  Anal Bioanal Chem       Date:  2011-08-13       Impact factor: 4.142

Review 3.  Single-cell metabolomics: analytical and biological perspectives.

Authors:  R Zenobi
Journal:  Science       Date:  2013-12-06       Impact factor: 47.728

4.  GC-TOFMS analysis of metabolites in adherent MDCK cells and a novel strategy for identifying intracellular metabolic markers for use as cell amount indicators in data normalization.

Authors:  Bei Cao; Jiye Aa; Guangji Wang; Xiaolan Wu; Linsheng Liu; Mengjie Li; Jian Shi; Xinwen Wang; Chunyan Zhao; Tian Zheng; Sheng Guo; Jinao Duan
Journal:  Anal Bioanal Chem       Date:  2011-05-02       Impact factor: 4.142

5.  NMR-based metabolomics of mammalian cell and tissue cultures.

Authors:  Nelly Aranibar; Michael Borys; Nancy A Mackin; Van Ly; Nicholas Abu-Absi; Susan Abu-Absi; Matthias Niemitz; Bernhard Schilling; Zheng Jian Li; Barry Brock; Reb J Russell; Adrienne Tymiak; Michael D Reily
Journal:  J Biomol NMR       Date:  2011-03-04       Impact factor: 2.835

6.  Statistical analysis and modeling of mass spectrometry-based metabolomics data.

Authors:  Bowei Xi; Haiwei Gu; Hamid Baniasadi; Daniel Raftery
Journal:  Methods Mol Biol       Date:  2014

7.  Metabolic profiling reveals disorder of carbohydrate metabolism in mouse fibroblast cells induced by titanium dioxide nanoparticles.

Authors:  Chengyu Jin; Yumin Liu; Limin Sun; Tianlu Chen; Yinan Zhang; Aihua Zhao; Xiaoyan Wang; Melanie Cristau; Kaisheng Wang; Wei Jia
Journal:  J Appl Toxicol       Date:  2012-09-20       Impact factor: 3.446

Review 8.  Toxicity testing in the 21st century: a vision and a strategy.

Authors:  Steven Gibb
Journal:  Reprod Toxicol       Date:  2007-11-01       Impact factor: 3.143

9.  Standard reporting requirements for biological samples in metabolomics experiments: microbial and in vitro biology experiments.

Authors:  Mariët J van der Werf; Ralf Takors; Jørn Smedsgaard; Jens Nielsen; Tom Ferenci; Jean Charles Portais; Christoph Wittmann; Mark Hooks; Alberta Tomassini; Marco Oldiges; Jennifer Fostel; Uwe Sauer
Journal:  Metabolomics       Date:  2007-08-20       Impact factor: 4.290

10.  Feature Selection Methods for Early Predictive Biomarker Discovery Using Untargeted Metabolomic Data.

Authors:  Dhouha Grissa; Mélanie Pétéra; Marion Brandolini; Amedeo Napoli; Blandine Comte; Estelle Pujos-Guillot
Journal:  Front Mol Biosci       Date:  2016-07-08
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  17 in total

Review 1.  Decoding the Metabolome and Lipidome of Child Malnutrition by Mass Spectrometric Techniques: Present Status and Future Perspectives.

Authors:  Iqbal Mahmud; Mamun Kabir; Rashidul Haque; Timothy J Garrett
Journal:  Anal Chem       Date:  2019-11-14       Impact factor: 6.986

2.  Therapeutic Effects of Salvianolic Acid B on Angiotensin II-Induced Atrial Fibrosis by Regulating Atrium Metabolism via Targeting AMPK/FoxO1/miR-148a-3p Axis.

Authors:  Jie Liu; Qijuan Sun; Xiaotong Sun; Qian Wang; Guangchen Zou; Dewei Wang; Baoxiang Zhuang; Zhaodong Juan; Rui Zhang; Daoliang Zhang
Journal:  J Cardiovasc Transl Res       Date:  2022-08-19       Impact factor: 3.216

Review 3.  Considerations for using isolated cell systems to understand cardiac metabolism and biology.

Authors:  Lindsey A McNally; Tariq R Altamimi; Kyle Fulghum; Bradford G Hill
Journal:  J Mol Cell Cardiol       Date:  2020-12-21       Impact factor: 5.000

Review 4.  Metabolomics in asthma: A platform for discovery.

Authors:  Shengjie Xu; Reynold A Panettieri; Joseph Jude
Journal:  Mol Aspects Med       Date:  2021-07-17

5.  The General Explanation Method with NMR Spectroscopy Enables the Identification of Metabolite Profiles Specific for Normal and Tumor Cell Lines.

Authors:  Klemen Pečnik; Vesna Todorović; Maša Bošnjak; Maja Čemažar; Igor Kononenko; Gregor Serša; Janez Plavec
Journal:  Chembiochem       Date:  2018-09-14       Impact factor: 3.164

Review 6.  Aryl hydrocarbon receptor activation mediates kidney disease and renal cell carcinoma.

Authors:  Hui Zhao; Lin Chen; Tian Yang; Ya-Long Feng; Nosratola D Vaziri; Bao-Li Liu; Qing-Quan Liu; Yan Guo; Ying-Yong Zhao
Journal:  J Transl Med       Date:  2019-09-05       Impact factor: 5.531

7.  Non-Targeted UHPLC-Q-TOF/MS-Based Metabolomics Reveals a Metabolic Shift from Glucose to Glutamine in CPB Cells during ISKNV Infection Cycle.

Authors:  Xiaozhe Fu; Xixi Guo; Shiwei Wu; Qiang Lin; Lihui Liu; Hongru Liang; Yinjie Niu; Ningqiu Li
Journal:  Metabolites       Date:  2019-09-04

8.  Exposure of HepaRG Cells to Sodium Saccharin Underpins the Importance of Including Non-Hepatotoxic Compounds When Investigating Toxicological Modes of Action Using Metabolomics.

Authors:  Matthias Cuykx; Charlie Beirnaert; Robim Marcelino Rodrigues; Kris Laukens; Tamara Vanhaecke; Adrian Covaci
Journal:  Metabolites       Date:  2019-11-04

9.  Influence of Drying Method on NMR-Based Metabolic Profiling of Human Cell Lines.

Authors:  Irina Petrova; Shenyuan Xu; William C Joesten; Shuisong Ni; Michael A Kennedy
Journal:  Metabolites       Date:  2019-10-31

10.  Comprehensive Metabolomic Analysis Reveals Dynamic Metabolic Reprogramming in Hep3B Cells with Aflatoxin B1 Exposure.

Authors:  Shufeng Wang; Xin Yang; Feng Liu; Xinzheng Wang; Xuemin Zhang; Kun He; Hongxia Wang
Journal:  Toxins (Basel)       Date:  2021-05-27       Impact factor: 4.546

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