Abstract
IntroductionAs large scale metabolic phenotyping is increasingly employed in preclinical studies and in the investigation of human health and disease the current LC–MS/MS profiling methodologies adopted for large sample sets can result in lengthy analysis times, putting strain on available resources. As a result of these pressures rapid methods of untargeted analysis may have value where large numbers of samples require screening.ObjectivesTo develop, characterise and evaluate a rapid UHP-HILIC-MS-based method for the analysis of polar metabolites in rat urine and then extend the capabilities of this approach by the addition of IMS to the system.MethodsA rapid untargeted HILIC LC–MS/MS profiling method for the analysis of small polar molecules has been developed. The 3.3 min separation used a Waters BEH amide (1 mm ID) analytical column on a Waters Synapt G2-Si Q-Tof enabled with ion mobility spectrometry (IMS). The methodology, was applied to the metabolic profiling of a series of rodent urine samples from vehicle-treated control rats and animals administered tienilic acid. The same separation was subsequently linked to IMS and MS to evaluate the benefits that IMS might provide for metabolome characterisation.ResultsThe rapid HILIC–MS method was successfully applied to rapid analysis of rat urine and found, based on the data generated from the data acquired for the pooled quality control samples analysed at regular intervals throughout the analysis, to be robust. Peak area and retention times for the compounds detected in these samples showed good reproducibility across the batch. When used to profile the urine samples obtained from vehicle-dosed control and those administered tienilic acid the HILIC-MS method detected 3007 mass/retention time features. Analysis of the same samples using HILIC–IMS–MS enabled the detection of 6711 features. Provisional metabolite identification for a number of compounds was performed using the high collision energy MS/MS information compared against the Metlin MS/MS database and, in addition, both calculated and measured CCS values from an experimentally derived CCS database.ConclusionA rapid metabolic profiling method for the analysis of polar metabolites has been developed. The method has the advantages of speed and both reducing sample and solvent consumption compared to conventional profiling methods. The addition of IMS added an additional dimension for feature detection and the identification of metabolites.
Highlights
As large scale metabolic phenotyping is increasingly employed in preclinical studies and in the investigation of human health and disease the current LC–mass spectrometry (MS)/MS profiling methodologies adopted for large sample sets can result in lengthy analysis times, putting strain on available resources
The key analytical tools employed in metabolic profiling are currently nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), with the latter either via direct infusion (DIMS) or coupled to a chromatographic separation technique such as gas (GC–MS) or high performance liquid/ supercritical fluid chromatography (LC–MS/SFC–MS) or alternatively a separation based on capillary electrophoresis (CE–MS) (Begou et al 2017; Putri et al 2013)
The numerical challenge provided by the need for higher throughput in drug discovery programs and larger studies (> 500) such as those associated biobanking/epidemiology etc., would clearly benefit from a more efficient solution with the ability to perform rapid and reproducible acquisitions to help address the analysis time burden associated with the metabolic profiling of large cohorts
Summary
As large scale metabolic phenotyping is increasingly employed in preclinical studies and in the investigation of human health and disease the current LC–MS/MS profiling methodologies adopted for large sample sets can result in lengthy analysis times, putting strain on available resources. In LC, the performance improvements in metabolic profiling delivered by ultra (high) performance liquid chromatography (U(H)PLC) (Wilson et al 2005), in terms of resolution, speed and sensitivity (a three- to fivefold increase), have resulted in analysis times in the range of 10–20 min per sample (Dunn et al 2011; Lewis et al 2016; Paglia et al 2012) compared to 40–60 min for conventional HPLC (Clayton et al 1998) This increase in throughput is generally acceptable for small-scale animal studies, where sample numbers are typically in the 100–500 range. Lengthy analytical runs, or running large studies in multiple batches, can lead to a variation in ion response when looking at data across different days and increase the possibility of errors in data processing
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