Chemometric analysis of MALDI mass spectrometric images of three-dimensional cell culture systems

. These data are especially challenging to process because, while different microenvironments exist, the cells are clonal which can result in strong similarities in the mass spectral profiles within the image. In this proof-of-concept study, a combination of principal component analysis (PCA), clustering methods, and linear discriminant analysis was used to identify unique spectral features present in spatially heterogeneous locations within the image. Overall, the application of these exploratory data analysis tools allowed for the isolation and detection of proteomic changes within IMS data sets in an easy, rapid, and unsupervised manner. Furthermore, a simplified, non-mathematical theoretical introduction to the techniques is provided in addition to full command routines within the MATLAB programming environment, allowing others to easily utilize and adapt this approach.