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      A reliable LC-MS/MS method for the quantification of natural amino acids in mouse plasma: Method validation and application to a study on amino acid dynamics during hepatocellular carcinoma progression

      , , , , ,
      Journal of Chromatography B
      Elsevier BV

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          Abstract

          A simple and fast LC-MS/MS method was developed and validated for the quantification of 20 proteinogenic L-amino acids (AAs) in a small volume (5 μL) of mouse plasma. Chromatographic separation was achieved on an Intrada Amino Acid column within 13 min via gradient elution with an aqueous solution containing 100 mM ammonium formate and an organic mobile phase containing acetonitrile, water and formic acid ( v : v : v = 95: 5: 0.3), at the flow rate of 0.6 mL/min. Individual AAs and corresponding stable-isotope-labeled AAs internal standards were analyzed by multiple reaction monitoring (MRM) in positive ion mode under optimized conditions. Method validation consisted of linearity, sensitivity, accuracy and precision, recovery, matrix effect, and stability, and the results demonstrated this LC-MS/MS method as a specific, accurate, and reliable assay. This LC-MS/MS method was thus utilized to compare the dynamics of individual plasma AAs between healthy and orthotopic hepatocellular carcinoma (HCC) xenograft mice housed under identical conditions. Our results revealed that, 5 weeks after HCC tumor progression, plasma L-arginine concentrations were significantly decreased in HCC mice while L-alanine and L-threonine levels were sharply increased. These findings support the utilities of this LC-MS/MS method and the promise of specific AAs as possible biomarkers for HCC.

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          Author and article information

          Journal
          Journal of Chromatography B
          Journal of Chromatography B
          Elsevier BV
          15700232
          August 2019
          August 2019
          : 1124
          : 72-81
          Article
          10.1016/j.jchromb.2019.05.039
          6626562
          31177050
          fb8e64db-14a2-4d21-a986-778d05735da3
          © 2019

          https://www.elsevier.com/tdm/userlicense/1.0/

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