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GC-MS Profiling of Leukemia Cells: An Optimized Extraction Protocol for Intracellular Metabolome


The sample preparation is an important step in untargeted cell metabolomics workflow. In this study, a gas chromatography-mass spectrometry (GC-MS)-based metabolome preparation protocol was optimized aiming at global, reliable and reproducible intracellular metabolite profiling of leukemia cell. We evaluated and compared 8 sample preparation methods: 4 extraction solvents (i.e., pure methanol, aqueous acetonitrile, methanol/methanol/water, and aqueous methanol) coupled with 2 common trimethylsilylation reagents (i.e., N, O-Bis(trimethylsilyl)trifluoroacetamide and N-methyl-N-(trimethylsilyl)trifluoroacetamide) respectively. The extraction solvents and derivatization agents were evaluated from their suitability for leukemia cells by criteria the number of total detected metabolites, extraction efficiency and repeatability. Based on the overall performance, methanol/methanol/water was chosen as the optimal extraction solvent for covering the highest number of intracellular metabolites with the best reproducibility. The derivatization agent N, O-Bis(trimethylsilyl)trifluoroacetamide (BSTFA) was proved to be more suitable for profiling of leukemia cell intracellular metabolome with better repeatability than N-methyl-N-(trimethylsilyl)trifluoroacetamide (MSTFA). This optimized method provided a robust, efficient and reproducible way to gather information on the metabolome of leukemia cell. It also offers a solid basis to further understand the physiology of leukemia cell and unravel the mechanism of leukemia’s multidrug resistance from the perspective of metabolomics.

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Publication details

The article was received on 03 Nov 2017, accepted on 01 Feb 2018 and first published on 01 Feb 2018

Article type: Paper
DOI: 10.1039/C7AY02578E
Citation: Anal. Methods, 2018, Accepted Manuscript
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    GC-MS Profiling of Leukemia Cells: An Optimized Extraction Protocol for Intracellular Metabolome

    Y. He, Z. Zhang, P. Ma, H. Ji and H. Lu, Anal. Methods, 2018, Accepted Manuscript , DOI: 10.1039/C7AY02578E

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