Matrix-assisted laser desorption/ionisation mass spectrometry imaging of lipids in rat brain tissue with integrated unsupervised and supervised multivariant statistical analysis

To date matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI) analysis has been largely concerned with mapping the distribution of known analytes in tissues. An important step in the progression of its applications is the determination of unknown variants for metabolite and protein profiling in both clinical studies and studies of disease. Principal component analysis (PCA) is a statistical approach which can be used as a means of determining latent variables in multivariate data sets. In the work reported here, PCA, in both unsupervised and supervised modes, has been used to differentiate brain regions based on their lipid composition determined by MALDI-MSI. PCA has been shown to be useful in the determination of hidden variables between spectra taken from six regions of brain tissue. It is possible to identify ions of interest from the loadings plot which are likely to be more prominent in the different regions of the brain and thus differentiating between white and grey matter. It is also possible to distinguish between the grey Cerebellar Cortex and the Hippocampal formation, due to the grey Cerebellar Cortex having a positive PC2 and the Hippocampal formation having a negative PC2 score; this is only possible in supervised PCA with this data set because with unsupervised PCA the two regions overlap.