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Bladder cancer is the 10th most common cancer and occurs when abnormal tissue growth develops within the bladder lining. Recent bladder cancer studies suggest that a strict distribution pattern of lipid species and enzymes determine cell fate through regulatory mechanisms. Targeted lipid analysis of plasma samples from bladder cancer patients and healthy subjects was conducted using the LipidQuan platform. LipidQuan package was downloaded from the Waters Targeted Omics Method Library (TOML), which provided all the chromatographic settings and MRM transitions required for lipid analysis. Targeted LC-MS data were acquired in positive and negative ion ESI modes. Over 400 lipids were quantified. Quantification was achieved using calibration curves of plasma spiked with known concentrations of SIL standards prior to extraction. By using surrogate standards prepared and analyzed under identical conditions to those of endogenous lipids, the quantification of endogenous lipids within the same class was achieved. The use of a commercially available, premixed SIL solution per lipid class, rather than a SIL standard for each measured lipid, significantly reduces the overall cost of the study. Deuterated standards were used to assess linear response, with typical R2 values ranging from 0.97 - 0.99 for the various lipid classes in both modes of ionization. Ceramides, LPCs, PCs, and SM are differentially expressed in bladder cancer samples when compared to samples from healthy controls. Pathway analysis revealed a number of components related to inflammation, oxidative stress, and immunity as being significant. A rapid, quantitative lipidomics method (LipidQuan) was deployed for the analysis of plasma in a bladder cancer comparative study. This eliminated method development time and allowed implementation of the methods within minutes. This is of great benefit to a core laboratory. LPCs, Cer, and SM were found to be important markers of bladder cancer.

Lipid metabolism is complex and involves a large number of metabolic reactions resulting in an enormous number and variety of actual lipids within living cells. LIPIDMAPS currently stores more than 40000 lipid structures. The dynamic range of lipid concentrations in biological systems can vary by 106 or more (from nanomolar fatty acids to attomolar eicosanoid lipid mediators). The level of precision of most systems-wide measurements is not yet sufficient to detail specific levels or concentrations of cellular components. Comparing lipidomic data across laboratories requires absolute quantification since relative values can vary widely not only between laboratories and between instruments due to various factors including: analyst errors, sample preparation differences (e.g. extraction methods) and ion suppression when using ESI MS. The lack of accurate characterisation of the lipid species also severely hinders interpretation of lipid metabolism associated with disease and physiological states. The proposed platform involves integrated high throughput analytical tool for accurate and robust measurement (>1500 injections) of lipid from sample preparation through to data handling and pathway elucidation. The platform can also be used for more in depth targeting of specific class of lipids of interest. Validation of the chromatographic method is performed at multiple sites by different analysts to show robustness and ease of method transfer. A rapid total lipid extraction method involving IPA and MTBE for plasma shows promising results. This will grant researchers more flexibility depending on their specific needs and requirements. The calibration, system suitability and QC standards used in this platform is sourced pre-mixed from commercial vendors (Avanti Lipids). SymphonyTM Software is used to automate the entire workflow and integrated with Skyline for data processing to enhance efficiency and flexibility. Once quantitative data has been generated and processed, pathway mapping tools can be used to determine the biological relevance of changes in concentration, and make data comparisons between laboratories.

MS interfaced with LC and ion mobility (IM) is routinely used to measure the level and variation of metabolites within biofluids as data generated through metabolomics studies may yield insight into disease onset and progression. LC-IM-MS based metabolomics generates large and complex data sets with analysis and interpretation of the results being the rate determining steps. This has led to a demand for improved data analysis, including processing and advanced multivariate approaches, which are described for the large scale analysis of metabolomics datasets. Urine from a healthy individual was centrifuged and the supernatant diluted. The urine was divided into control, low dosed (LD) and high dosed (HD) groups. To create a sample set, 11 different drugs were differentially spiked into LD and HD urine, contrasted with blank urine. A reversed phase gradient was applied and MS data acquired in positive ion data independent (LC-DIA-MS) and mobility assisted data independent (LC-IM-DIA-MS) modes. Distinguishing biological variation and metabolic change from analytical interference is key to data processing and analysis. Samples were randomized and measured six times, including QC runs, to ensure statistically valid analysis. LC-MS data were retention time aligned and deconvoluted to produce a feature list. Identified features were compound searched and interrogated with multivariate statistics to provide marker ions of interest. Relative high abundance levels of the standards were reported for LD and HD compared to controls, confirmed by trend plots analysis showing an increase in LD and HD groups compared to control. The standards were identified with an average score of 91 and mass error of 1.2 ppm. Three sample clusters were produced with the standards being the most differentiating features (top 20 based on q value) between groups. Functionality of the software will be demonstrated using biological samples.