Open AccessDEVELOPMENT AND APPLICATION OF A TEST MIXTURE FOR UNTARGETED LIQUID CHROMATOGRAPHY-MASS SPECTROMETRY ANALYSIS OF URINE SAMPLES
Author(s) -
Clarisse L. Torres,
Vinícius Figueiredo Sardela,
Fernanda B. Scalco,
Francisco R. de Aquino,
Rafael Garrett
Publication year - 2021
Publication title -
química nova
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.214
H-Index - 73
eISSN - 1678-7064
pISSN - 0100-4042
DOI - 10.21577/0100-4042.20170796
Subject(s) - chromatography , metabolomics , chemistry , mass spectrometry , analyte , electrospray ionization , ion suppression in liquid chromatography–mass spectrometry , elution , urine , liquid chromatography–mass spectrometry , sample preparation , gradient elution , electrospray , high performance liquid chromatography , biochemistry
Metabolic profiling of complex biological matrices based on liquid chromatography-mass spectrometry (LC-MS) allows detecting a wide range of metabolites with distinct concentrations and physicochemical properties. Given the complexity of samples and the necessity of a comprehensive approach in untargeted metabolomics, quality control strategies are mandatory to obtain high-quality data. The LC-MS performance must be monitored and evaluated to guarantee data reliability. In this study, a test mixture (TM) was developed, systematically evaluated, and applied to untargeted metabolomics of urine samples from individuals suspected of inborn errors of metabolism. The TM was composed of fifteen analytes that eluted across the entire gradient in reversed-phase columns and ionized in positive/negative electrospray modes. It helped set the LC-MS conditions for urine analysis, from sample reconstitution solvent to selecting the MS ion source parameters. The TM quickly indicated column stationary phase degradation during the batch analysis when employed to monitor and evaluate the LC-MS system in an untargeted metabolomic analysis. Thus, in addition to pooled QC samples, a TM can be employed in untargeted metabolomics to rapidly assess the system performance avoiding unnecessary efforts for further data treatment and multivariate analysis of poor-quality data