Evaluating the performance of sample preparation methods for ultra‐performance liquid chromatography/mass spectrometry based serum metabonomics. (27th February 2019)

• Record Type: Journal Article
• Authors: Chan, Wan; Zhao, Yao; Zhang, Jiayin
• Is Part Of: Rapid communications in mass spectrometry. Volume 33:Number 6(2019)
• Page Start: 561
• Page End: 568
• Publication Date: 2019-02-27
• Subjects: Mass spectrometry -- Periodicals; 543.65
• DOI: 10.1002/rcm.8381
• Languages: English
• ISSNs: 0951-4198

Abstract

Abstract : Rationale: Metabonomics investigating perturbation to endogenous metabolism in response to external stimuli is emerging as a powerful tool for clinical diagnosis as well as in many other areas. The ability to retrieve reliable and reproducible information from complex biological fluids such as serum is crucial for its further applications. Methods: In this study, the performance of the commonly used sample preparation methods for ultra‐performance liquid chromatography/mass spectrometry (UPLC/MS)‐based metabonomics was investigated. Specifically, we compared the extraction efficiencies, the method reproducibility, and the ability to identify potential biomarkers using solvent‐based protein precipitation and solid‐phase extraction (SPE) for serum metabonomic studies. Differences between extraction methods were explored using principal component analysis (PCA) and orthogonal partial least squares‐discrimination analysis (OPLS‐DA). Results: Among the sample preparation methods tested, solvent‐based protein precipitation using methanol has demonstrated the best analytical precision and extraction efficiency. Furthermore, this study revealed, for the first time, gender‐specific differences in levels of two lysophosphatidylcholines (lysoPC 18:0 and lysoPC 18:1) in rat serum samples. Conclusions: The performance of sample preparation methods for UPLC/MS‐based serum metabonomics was evaluated systematically. Results showed sample preparation by solvent precipitation using methanol provided the best analytical precision and extraction efficiency for UPLC/MS‐based serum metabonomics.