Multiplatform analytical methodology for metabolic fingerprinting of lung tissue

Using multiplatform approaches providing wider information about the metabolome, is currently the main topic in the area of metabolomics, choosing from liquid chromatography-mass spectrometry (LC-MS), gas chromatography/mass spectrometry (GC/MS), capillary electrophoresis-mass spectrometry (CE-MS), and nuclear magnetic resonace (NMR). However, the reliability and suitability of sample treatment, data acquisition, data preprocessing, and data analysis are prerequisites for correct biological interpretation in metabolomics studies. The significance of differences between samples can only be determined when the performance characteristics of the entire method are known. This leads to performing method validation in order to assess the performance and the fitness-for-purpose of a method or analytical system for metabolomics research. The present study was designed for developing a nontargeted global fingerprinting approach of lung tissue, for the first time, applying multiple complementary analytical techniques (LC-MS, GC/MS, and CE-MS) with regards to analytical method optimization (sample treatment + analytical method), characterization, and validation as well as application to real samples. An initial solvent for homogenization has been optimized, which is usually overseen in the tissue homogenization protocol. A nontargeted fingerprinting approach was applied to a pooled sample of lung tissue using these three instruments to cover a wider range of metabolites. The linearity of the validated method for all metabolites was >0.99, with good recovery and precision in all techniques. The method has been successfully applied to lung samples from rats with sepsis compared to the control samples. Only 20 mg of tissue is required for the three analytical techniques, where only one metabolite was found in common between LC-MS and CE-MS analysis as statistically significant. This proves the importance of applying a multiplatform approach in a metabolomics study as well as for biomarker discovery.