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

As imaging mass spectrometry (IMS) has grown in popularity in recent years, the applications of this technique have become increasingly diverse. Currently there is a need for sophisticated data processing strategies that maximize the information gained from large IMS data sets. Traditional two-dimensional heat maps of single ions generated in IMS experiments lack analytical detail, yet manual analysis of multiple peaks across hundreds of pixels within an entire image is time-consuming, tedious and subjective. Here, various chemometric methods were used to analyze data sets obtained by matrixa-ssisted laser desorption/ ionization (MALDI) IMS of multicellular spheroids. HT-29 colon carcinoma multicellular spheroids are an excellent in vitro model system that mimic the three dimensional morphology of tumors in vivo. 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.