Spatial and Spectral Correlations in MALDI Mass Spectrometry Images by Clustering and Multivariate Analysis

Abstract

Matrix-assisted laser desorption/ionization mass spectrometry imaging is a technique for direct analysis of tissue sections without the use of molecular tags or contrast agents. The combination of spatial and mass resolution results in large and complex data sets that require powerful and efficient analysis and interpretation tools. Conventional images, derived from a specific analyte mass, do not identify the spatially localized correlations between analytes that are latent in the data. A new approach to find and visualize these correlations is presented. Clustering methods are used to classify pixels by spectral similarity, facilitating definition of distinct spatial regions. Principal component and discriminant analyses are combined to comprehensively identify changes in the mass spectra between regions. Images are generated by projecting the spectra of each pixel on the discriminant spectra; contrast is then a function of multiple correlated peaks.