Introduction 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. As a result of these pressures rapid methods of untargeted analysis may have value where large numbers of samples require screening. Objectives To 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. Methods A 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. Results The 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. Conclusion A 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.

There has been increasing interest in bacterial lipids in recent years due, in part, to their emerging role as molecular signalling molecules. Bacteroides thetaiotaomicron is an important member of the mammalian gut microbiota that has been shown to produce sphingolipids (SP) that pass through the gut epithelial barrier to impact host SP metabolism and signal into host inflammation pathways. B. thetaiotaomicron also produces a novel family of N-acyl amines (called glycine lipids) that are potent ligands of host Toll-like receptor 2 (TLR2). Here, we specifically examine the lipid signatures of four species of gut-associated Bacteroides. In total we identify 170 different lipids, and we report that the range and diversity of Bacteroides lipids is species specific. Multivariate analysis reveals that the differences in the lipid signatures are largely driven by the presence and absence of plasmalogens, glycerophosphoinositols and certain SP. Moreover, we show that, in B. thetaiotaomicron, mutations altering either SP or glycine lipid biosynthesis result in significant changes in the levels of other lipids, suggesting the existence of a compensatory mechanisms required to maintain the functionality of the bacterial membrane.

Identification of features with high levels of confidence in liquid chromatography–mass spectrometry (LC–MS) lipidomics research is an essential part of biomarker discovery, but existing software platforms can give inconsistent results, even from identical spectral data. This poses a clear challenge for reproducibility in biomarker identification. In this work, we illustrate the reproducibility gap for two open-access lipidomics platforms, MS DIAL and Lipostar, finding just 14.0% identification agreement when analyzing identical LC–MS spectra using default settings. Whilst the software platforms performed more consistently using fragmentation data, agreement was still only 36.1% for MS2 spectra. This highlights the critical importance of validation across positive and negative LC–MS modes, as well as the manual curation of spectra and lipidomics software outputs, in order to reduce identification errors caused by closely related lipids and co-elution issues. This curation process can be supplemented by data-driven outlier detection in assessing spectral outputs, which is demonstrated here using a novel machine learning approach based on support vector machine regression combined with leave-one-out cross-validation. These steps are essential to reduce the frequency of false positive identifications and close the reproducibility gap, including between software platforms, which, for downstream users such as bioinformaticians and clinicians, can be an underappreciated source of biomarker identification errors.

Rationale: A novel data‐independent acquisition method is detailed that incorporates a scanning quadrupole in front of an orthogonal acceleration time‐of‐flight (TOF) mass analyser. This approach is described and the attributes are compared and contrasted to other DIA approaches. Methods: Specific application of the method to both targeted and untargeted lipidomic identification strategies is discussed, with data from both shotgun and LC separated lipidomics experiments presented. Results: The benefits of the fast quadrupole scanning technique are highlighted, and include improvements in speed and specificity for complex mixtures providing high quality qualitative and quantitative data. Conclusions: In particular the high specificity afforded by the scanning quadrupole improves qualitative information for lipid identification.

Prostate cancer accounts for around 15% of male deaths in Western Europe and is the second leading cause of cancer death in men after lung cancer. Mounting evidence suggests that prostate cancer deposits exist within a hypoxic environment and this contributes to radio-resistance thus hampering one of the major therapies for this cancer. Recent reports have shown that nitric oxide (NO) donating non-steroidal anti-inflammatory drugs (NSAIDs) reduced tumour hypoxia as well as maintaining a radio-sensitising/therapeutic effect on prostate cancer cells. The aim of this study was to evaluate the impact of hypoxia on the proteome of the prostate and to establish whether NO-NSAID treatment reverted the protein profiles back to their normoxic status. To this end an established hormone insensitive prostate cancer cell line, PC-3, was cultured under hypoxic and normoxic conditions before and following exposure to NO-NSAID in combination with selected other common prostate cancer treatment types. The extracted proteins were analysed by ion mobility-assisted data independent acquisition mass spectrometry (MS), combined with multivariate statistical analyses, to measure hypoxia-induced alterations in the proteome of these cells. The analyses demonstrated that under hypoxic conditions there were well-defined, significantly regulated/differentially expressed proteins primarily involved with structural and binding processes including, for example, TUBB4A, CIRP and PLOD1. Additionally, the exposure of hypoxic cells to NSAID and NO-NSAID agents, resulted in some of these proteins being differentially expressed; for example, both PCNA and HNRNPA1L were down-regulated, corresponding with disruption in the nucleocytoplasmic shuttling process.