An approach towards method development for untargeted urinary metabolite profiling in metabonomic research using UPLC/QToF MS.

The application of LC-MS for untargeted urinary metabolite profiling in metabonomic research has gained much interest in recent years. However, the effects of varying sample pre-treatments and LC conditions on generic metabolite profiling have not been studied. We aimed to evaluate the effects of varying experimental conditions on data acquisition in untargeted urinary metabolite profiling using UPLC/QToF MS. In-house QC sample clustering was used to monitor the performance of the analytical platform. In terms of sample pre-treatment, results showed that untreated filtered urine yielded the highest number of features but dilution with methanol provided a more homogenous urinary metabolic profile with less variation in number of features and feature intensities. An increased cycle time with a lower flow rate (400 microl/min vs 600 microl/min) also resulted in a higher number of features with less variability. The step elution gradient yielded the highest number of features and the best chromatographic resolution among three different elution gradients tested. The maximum retention time and mass shift were only 0.03 min and 0.0015 Da respectively over 600 injections. The analytical platform also showed excellent robustness as evident by tight QC sample clustering. To conclude, we have investigated LC conditions by studying variability and repeatability of LC-MS data for untargeted urinary metabolite profiling.