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Identification of xenobiotic metabolites from biological fluids using flow injection analysis high‐resolution mass spectrometry and post‐acquisition data filtering
Author(s) -
RathahaoParis Estelle,
Paris Alain,
Bursztyka Julian,
Jaeg JeanPhilippe,
Cravedi JeanPierre,
Debrauwer Laurent
Publication year - 2014
Publication title -
rapid communications in mass spectrometry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.528
H-Index - 136
eISSN - 1097-0231
pISSN - 0951-4198
DOI - 10.1002/rcm.7066
Subject(s) - chemistry , metabolite , orbitrap , chromatography , mass spectrometry , flow injection analysis , tandem mass spectrometry , electrospray ionization , detection limit , biochemistry
RATIONALE Concern for public health entails the need to evaluate the degree of exposure of population to toxicants. To do this, robust high‐throughput approaches are required to be able to perform a large number of analyses in cohort studies. In this study, a data‐filtering procedure was applied to mass spectral data acquired by direct analysis of biological fluids leading to rapid detection of metabolites in a model xenobiotic system. METHODS Flow injection analysis (FIA) coupled to negative electrospray ionization (ESI)‐LTQ Orbitrap Fourier transform mass spectrometry was used to directly analyze urine of rats treated with vinclozolin. Tandem mass spectrometry (MS/MS) experiments were subsequently performed for confirmation of a new metabolite structure. The isotope filtering based on the difference between accurate masses of 35 Cl and 37 Cl was applied to the raw data for the specific detection of ions containing at least one chlorine atom. RESULTS Seven metabolites of vinclozolin were manually identified thanks to the characteristic isotope pattern of dichlorinated compounds. A new metabolite of vinclozolin was detected for the first time and identified as a sulfate conjugate. The application of an isotope‐filtering procedure allowed the selective extraction of pertinent signals from the data. The processed mass spectrum was greatly simplified, significantly facilitating the detection of the seven metabolites previously identified. CONCLUSIONS The use of FIA‐HRMS in combination with dedicated bio‐informatics data processing is shown to be an efficient approach for the rapid detection of metabolites in biological fluids. This is a very promising high‐throughput approach for rapid characterization of the exposure status to xenobiotics. Copyright © 2014 John Wiley & Sons, Ltd.