Journal of Lipid Research (Jan 2021)

Integrated LC-MS metabolomics with dual derivatization for quantification of FFAs in fecal samples of hepatocellular carcinoma patients

  • Jiangang Zhang,
  • Shuai Yang,
  • Jingchun Wang,
  • Yanquan Xu,
  • Huakan Zhao,
  • Juan Lei,
  • Yu Zhou,
  • Yu Chen,
  • Lei Wu,
  • Mingyue Zhou,
  • Yan Li,
  • Yongsheng Li

Journal volume & issue
Vol. 62
p. 100143

Abstract

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FFAs display pleiotropic functions in human diseases. Short-chain FAs (SCFAs), medium-chain FAs, and long-chain FAs are derived from different origins, and precise quantification of these FFAs is critical for revealing their roles in biological processes. However, accessing stable isotope-labeled internal standards is difficult, and different chain lengths of FFAs challenge the chromatographic coverage. Here, we developed a metabolomics strategy to analyze FFAs based on isotope-free LC-MS-multiple reaction monitoring integrated with dual derivatization. Samples and dual derivatization internal standards were synthesized using 2-dimethylaminoethylamine or dansyl hydrazine as a “light” label and N,N-diethyl ethylene diamine or N,N-diethyldansulfonyl hydrazide as a “heavy” label under mild and efficient reaction conditions. General multiple reaction monitoring parameters were designed to analyze these FFAs. The limit of detection of SCFAs varied from 0.5 to 3 nM. Furthermore, we show that this approach exhibits good linearity (R2 = 0.99374–0.99929), there is no serious substrate interference, and no quench steps are required, confirming the feasibility and reliability of the method. Using this method, we successfully quantified 15 types of SCFAs in fecal samples from hepatocellular carcinoma patients and healthy individuals; among these, propionate, butyrate, isobutyrate, and 2-methylbutyrate were significantly decreased in the hepatocellular carcinoma group compared with the healthy control group. These results indicate that the integrated LC-MS metabolomics with isotope-free and dual derivatization is an efficient approach for quantifying FFAs, which may be useful for identifying lipid biomarkers of cancer.

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