Spheroids—three-dimensional aggregates of cells grown from a cancer cell line—represent a model of living tissue for chemotherapy investigation. Distribution of chemotherapeutics in spheroid sections was determined using the matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI). Proliferating or apoptotic cells were immunohistochemically labeled and visualized by laser scanning confocal fluorescence microscopy (LSCM). Drug efficacy was evaluated by comparing coregistered MALDI MSI and LSCM data of drug-treated spheroids with LSCM only data of untreated control spheroids. We developed a fiducial-based workflow for coregistration of low-resolution MALDI MS with high-resolution LSCM images. To allow comparison of drug and cell distribution between the drug-treated and untreated spheroids of different shapes or diameters, we introduced a common diffusion-related coordinate, the distance from the spheroid boundary. In a procedure referred to as “peeling”, we correlated average drug distribution at a certain distance with the average reduction in the affected cells between the untreated and the treated spheroids. This novel approach makes it possible to differentiate between peripheral cells that died due to therapy and the innermost cells which died naturally. Two novel algorithms—for MALDI MS image denoising and for weighting of MALDI MSI and LSCM data by the presence of cell nuclei—are also presented.

We present the first demonstration of a general method for the chemical characterization of small surface features at high magnification via simultaneous collection of mass spectrometry (MS) imaging and tandem MS imaging data. High lateral resolution tandem secondary ion MS imaging is employed to determine the composition of surface features on poly(ethylene terephthalate) (PET) that precipitate during heat treatment. The surface features, probed at a lateral resolving power of<200 nm using a surface-sensitive ion beam, are found to be comprised of ethylene terephthalate trimer at a greater abundance than is observed in the surrounding polymer matrix. This is the first chemical identification of PET surface precipitates made without either an extraction step or the use of a reference material. The new capability employed for this study achieves the highest practical lateral resolution ever reported for tandem MS imaging.

In proteomics study, Imaging Mass Spectrometry (IMS) is an emerging and very promisingnew technique for protein analysis from intact biological tissues. Though it has showngreat potential and is very promising for rapid mapping of protein localization and thedetection of sizeable differences in protein expression, challenges remain in dataprocessing due to the difficulty of high dimensionality and the fact that the number ofinput variables in prediction model is significantly larger than the number ofobservations. To obtain a complete overview of IMS data and find trace features based onboth spectral and spatial patterns, one faces a global optimization problem. In thispaper, we propose a weighted elastic net (WEN) model based on IMS data processing needs ofusing both the spectral and spatial information for biomarker selection andclassification. Properties including variable selection accuracy of the WEN model arediscussed. Experimental IMS data analysis results show that such a model not only reducesthe number of side features but also helps new biomarkers discovery.