Abstract
High-resolution mass spectrometry coupled with pattern recognition techniques is an established tool to perform comprehensive metabolite profiling of biological datasets. Interpreting untargeted metabolomic data requires robust, reproducible and reliable analytical methods to translate results into biologically relevant and actionable knowledge. The analyses of biological samples were developed based on ultra-high performance liquid chromatography (UHPLC) coupled to ion mobility - mass spectrometry (IM-MS). An innovative strategy for optimizing simultaneously the analytical conditions for untargeted UHPLC-IM-MS methods is proposed using an experimental design approach. Optimization experiments were conducted through a screening process designed to identify the influential factors that have significant effects on the selected responses (total number of peaks and number of reliable peaks). For this purpose, full and fractional factorial designs were used while partial least squares regression was used for experimental design modeling and optimization of parameter values. The total number of peaks yielded the best predictive model and is used for the optimization of parameters settings. Fifty-five urine samples have been used to assess the developed method. Twenty-two controles and 33 diseases allocated to five different groups: propionic aciduria, cystinuria, tyrosiniemia, mucopolysaccharidoses (I, IV and VI) and creatine metabolism defect have been metabolically profiled. Multivariate modeling has been performed on the data. This pilot study yielded predictive models that allowed clear classification of the different diseases. These promising results highlight the potential of this new approach in the screening of IEMs and in patient stratification. An assay on a larger cohort is needed to assess the clinical validity of the method. (This work is co-supported by European Union, Region Normandie, Inserm, CNRS, Normandy University and IRIB. Europe gets involved in Normandie with European Regional Development Fund (ERDF).)
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