Jaber, Malak
(2019)
Development and application of an enhanced lipidomic profiling methods using stable isotope-assisted LC-MS.
PhD thesis, University of Nottingham.
Abstract
Quantification is a critical step in comprehensive lipidomics studies. Although LC-MS is considered as an available tool for simultaneous detection and quantification of hundreds or thousands of lipid species, the analysed samples are subjected to unwanted variations at multiple stages, including study design, sampling and storage procedures, analytical and data acquisition procedures, that could affect the quality of the results. For targeted lipidomics analysis, these errors can be corrected by the appropriate use of standards. However, for untargeted analysis, it is still problematic to correct the data especially across multiple batches where usually many thousands of closely related lipid species need to be measured in a complex biological matrix.
In this thesis, an approach has been investigated for the first time to correct these variations by adapting an existing normalisation strategy routinely applied in targeted analysis based on standards to untargeted lipidomics studies. Although the cost and availability of authentic synthesised standards limits the practical usefulness of this approach, an alternative in vivo isotopic labelling strategy was evaluated. The aim was to present an approach that allows correcting variations introduced during the study and eventually provide a more reliable estimate of lipids in the studied samples.
To find out the source for the generation of labelled standards in complex samples, five different microorganism species were investigated and compared including E. coli MG1655, spirulina, S. cerevisiae CEN.PK 1137D, S. cerevisiae BY4741, and P. pastoris NCYC 175. Comparison of the lipid profiles and the efficacy of in vivo labelling strategy in these species according to the results revealed that the yeast P. pastoris NCYC 175 proved to be the optimum source of isotopically labelled standards leading the way to comprehensive direct and indirect normalisation in quantitative mass spectrometry assays in complex biological samples. After that, the optimum 13C-IS mixture was utilised to develop and validate a novel normalisation approach for untargeted lipidomics studies on plasma samples. An extraction protocol was optimised to ensure maximum efficacy and sensitivity that enabled detection of a higher number of 13C-IS in a reproducible manner. Then, the 13C-IS mixture was used as an internal standard introduced at the initial stages of samples preparation. From the data presented, the labelled internal standard mixture has shown to be effective in reducing technical and analytical variations introduced during samples preparation, analysis and special situations where the mass spectrometry response starts to fluctuate such as in long analysis time or from day to day that ultimately provides a reliable estimate of those ions.
Subsequently, the developed method was successfully applied to two clinical lipidomic studies. Where for the first one, the method was used to explore the level of intra-tumoral heterogeneity in patients diagnosed with low grade glioma and the results showed a clear distinct lipidomic profile in spatially resolved regions of the tumour and between patients indicating inter- and intra-tumoral heterogeneity that could affect treatment output, survival and quality of life before and after treatment. This highlight the importance of personalised tumour-specific strategies to accommodate these variations. In the second application, the method was applied to study the lipidomic signatures associated in patients diagnosed with diabetes mellitus and to study the effect of an oral supplement of L-carnitine on plasma lipidomic profile on T2DM patients. In conclusion, the developed method highlights the benefit of in vivo labelling strategy in the generation of a versatile number of labelled standards that can be used to correct data in untargeted lipidomics efficiently.
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