4.7 Article

Large-scale targeted metabolomics method for metabolite profiling of human samples

期刊

ANALYTICA CHIMICA ACTA
卷 1125, 期 -, 页码 144-151

出版社

ELSEVIER
DOI: 10.1016/j.aca.2020.05.053

关键词

Mass spectrometry; Targeted metabolomics; Human samples; Biomarker discovery

资金

  1. National Key Research and Development Program of China [2017YFC1600500]
  2. National Natural Science Foundation of China [21575120, 21707112, 81802276]
  3. Hong Kong General Research Fund [12302317, 12303919]
  4. Technology and Innovation Commission of Shenzhen [JCYJ20160531193901593]

向作者/读者索取更多资源

Targeted metabolomics has significant advantages for quantification but suffers from reduced metabolite coverage. In this study, we developed a large-scale targeted metabolomics method and expanded its applicability to various human samples. This approach initially involved unbiased identification of metabolites in human cells, tissues and body fluids using ultra high-performance liquid chromatography (UHPLC) coupled to high-resolution Orbitrap mass spectrometry (MS). Targeted metabolomics method was established with utility of UHPLC-triple quadrupole MS, which enables targeted profiling of over 400 biologically important metabolites (e.g., amino acids, sugars, nucleotides, dipeptides, coenzymes, and fatty acids), covering 92 metabolic pathways (e.g., Krebs cycle, glycolysis, amino acids metabolism, ammonia recycling, and one-carbon metabolism). The present method displayed better sensitivity, repeatability and linearity than the Orbitrap MS-based untargeted metabolomics approach and demonstrated excellent performance in lung cancer biomarker discovery, in which 107 differential metabolites were able to discriminate between carcinoma and adjacent normal tissues, implicating the Warburg effect, alteration of redox state, and nucleotide metabolism of lung cancer. This new method is flexible and expandable and offers many advantages for metabolomics analysis, such as wide metabolite coverage, good repeatability and linearity and excellent capability in biomarker discovery, making it useful for both basic and clinical metabolic research. (C) 2020 Elsevier B.V. All rights reserved.

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