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Updated: Jan 6, 2026

Metabolic Profile Analysis of Zebrafish Embryos
Published on: January 14, 2013
Evaluation and optimization of sample pretreatment for GC/MS-based metabolomics in embryonic zebrafish
Shi-Chao Yan1, Zhi-Feng Chen1, Hui Zhang1
1Guangzhou Key Laboratory of Environmental Catalysis and Pollution Control, Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control, School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou, 510006, China.
Abstract:
Metabolomics tactics have been applied in the research associated with embryonic zebrafish. However, the report regarding the evaluation of impacts of sample pretreatment on metabolomics results from zebrafish embryos is limited. In the present study, different data normalization approaches, extraction solvents, and extraction strategies for off-line derivatization gas chromatography coupled with mass spectrometry-based metabolomics analysis of zebrafish eleutheroembryos were evaluated and optimized. The results showed that, when 4-chlorophenylalanine normalization, sample homogenization and pure methanol combined with ultrasonic extraction were conducted, better repeatabilities, higher signals and broader coverages of detected metabolites can be achieved. The recovery and standard deviation of most standards were in the range of 82%-121% and 6.6%-12%, respectively, while the relative standard deviation of major detected metabolites ranged from 5.4% to 19%, indicating good extraction efficiencies and method precision. Under the developed method, 87 important endogenous metabolites such as citric acid and hypoxanthine were identified by universal databases or standards among 270 extracted metabolites, which consisted of sugars, amines, amino acids, nucleotides, fatty acids, and sterols. Therefore, the results could provide a proper pretreatment protocol for the analysis of wide-coverage metabolome in embryonic zebrafish. In addition, this study highlights the impact of normalization and extraction methods on the data quality of metabolomics analysis.
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