Step-by-step tutorial for analysis of Multiple Reaction Monitoring Profiling of lipidome data for biological interpretation

Multiple Reaction Monitoring (MRM) profiling analysis of lipids provides a sensitive semi-targeted approach to analyze the lipidome of biological samples. The aim of this manuscript is to describe the step-by-step analysis of MRM profiling data following acquisition on a triple quadrupole, with validation of the method demonstrated through the analysis of temporal changes in neonatal piglet liver lipidome between birth and 24 h postnatal. Following extraction of samples using the Bligh and Dyer method, lipids are profiled for a list of MRM scans on a triple quadrupole mass spectrometer. Data standardization is required to reduce technical variability, with the selection of the approach being guided by the biological question, sample type, or system being studied. Four approaches of data standardization are described requiring either pre-acquisition standardization or post-acquisition standardization: 1) raw intensity (pre-acquisition standardization only), 2) application of internal standard (requires both pre-acquisition and post-acquisition standardization), 3) relative abundance (percent) of a lipid relative to others within a class (post-acquisition standardization), or 4) sample injection per MRM list (post-acquisition standardization). After standardization, data are normalized for parametric statistical tests to explore hypothesis, with the use of Metaboanalyst 6.0 tools demonstrated as an option for user-friendly interface in the application of multivariate and univariate analyses. Biological interpretation of findings then proceeds through the use of pathway analysis and lipid ontology tools, such as Lipid Ontology (LION). Additionally, the analysis of differential lipids’ characteristics by carbon length and number of unsaturated bonds within lipid class is demonstrated. The workflow presented provides a practical framework for transforming raw MRM profiling data into biological insights. Standardization method shapes both the statistical outcomes and biological interpretation, emphasizing the importance of selecting an approach that is aligned with the biological question being asked.