JOURNAL ARTICLE

Validation of a Sensitive, Simple and High-Throughput UPLC-MS/MS Method for Quantification of Catecholamines and Their Metabolites in Serum and Urine: Application in Clinical Analysis.

  • Published In: Journal of Chromatographic Science, 2025, v. 63, n. 4. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Wang, Wei; Liu, Tiebing; Li, Qingyan; Ji, Enhui; Xu, Weizhe; Qiao, Shi; Cui, Yujing; Li, Boye; Xu, Haishan 3 of 3

Abstract

This article focuses on the development and validation of a sensitive, simple, and high-throughput ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method for simultaneous quantification of catecholamines (CAs)—dopamine (DA), epinephrine (E), norepinephrine (NE)—and their metabolites metanephrine (MN), normetanephrine (NMN), and 3-methoxytyramine (3-MT) in human serum and urine. The method employs a 96-well solid-phase extraction (SPE) cartridge and a pentafluorophenyl (PFP) column, achieving a short run time of 4 minutes without requiring derivatization, and demonstrates excellent linearity, accuracy, precision, recovery, and stability. For the first time, the study compares analyte levels between plasma and serum, finding significant correlations but also significant differences, underscoring the need for serum-specific reference intervals in clinical diagnostics. The validated method offers a practical tool for clinical and research applications involving CA measurement in multiple biological matrices.

Additional Information

  • Source:Journal of Chromatographic Science. 2025/04, Vol. 63, Issue 4, p1
  • Document Type:Article
  • Subject Area:Health and Medicine
  • Publication Date:2025
  • ISSN:0021-9665
  • DOI:10.1093/chromsci/bmaf021
  • Accession Number:185428179
  • Copyright Statement:Copyright of Journal of Chromatographic Science is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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