Exploring Mass Spectrometry Suitability to Examine Human Liver Graft Metabonomic Profiles

Background. Transplant surgeons rely on morphologic aspects of the organ as well as clinical and histologic data to decide whether to use a graft. Metabonomics measures the "downstream" products of proteins and genes; these metabolic profiles are particularly good reporters of tissue physiologic features. Sample preparation and data acquisition are generally considered limiting steps in metabonome analysis because they are important sources of variability. State-of-the-art mass spectrometry and multivariate statistical analysis have been used to explore the suitability of a metabonomic platform as a liver tissue metabonomic profiling method. Objective. To develop robust and reliable sample processing and mass spectrometry protocols for studying human liver metabonomic profiles. Materials and Methods. Liquid chromatography coupled with mass spectrometry was used to analyze 20 liver tissue samples from 10 discarded and 10 transplanted grafts. Principal component analysis (PCA) and projection to latent structures discriminant analysis (PLS-DA) were used for data interpretation. Results. Standard operating protocols for sample processing (tissue homogenization) and data acquisition were developed. The quantification of the quality controls present in the test mix demonstrated coefficients of variation less than 15%. The PCA score plot revealed that the sample triplicate cluster was quite close. Furthermore, PLS-DA analysis demonstrated a clear separation (transplanted vs discarded) along the first component. Discussion. Multivariate data analysis (PCA and PLS-DA) indicated that protocols developed in-house for sample processing and mass spectrometry data acquisition were sufficiently sensitive (approximately 1245 features) and reproducible (sample triplicate clusters and test mix quantification) to perform liver tissue metabonomic profiling. In addition, a reduced set of metabolites was selected as potential biomarkers responsible for sample discrimination. These findings encourage ongoing research into the development of a metabonomic model to assess liver graft quality and function before transplantation.