Project Summary This project aims to develop a powerful new approach to improve the spatially resolved extraction and identification of proteins from tissue. Detection of proteins for imaging mass spectrometry analysis is challenging because of limited ionization. To address this issue, electrophoresis will be employed to extract proteins from tissues and into adjacent enzymatic membranes for proteolytic digestion. The resulting peptides will be collected on a capture membrane for Matrix Assisted Laser/Desorption Ionization Imaging Mass Spectrometry (MALDI-IMS) analysis. This approach both increases the number of proteins that can be identified from tissues and preserves their spatial distribution in the original tissue. The project is constructed around three sets of activities. First, the spatial diffusion of the resulting peptides will be measured after they are digested in the enzymatic membrane under a range of experimental conditions. A standard mixture of proteins will be separated by gel electrophoresis and then the proteins will be electrophoresed in an orthogonal direction, through an enzymatic membrane. The digested peptides will be collected on a capture membrane for mass spectrometry analysis and compared against optical images of the protein gels prior to electrodigestion to determine the lateral diffusion. A range of experimental conditions will be evaluated, including voltages, membrane pore sizes and denaturants, to determine the optimal conditions. These optimized conditions will be applied to the study of proteins in mouse brains in the second set of experiments. In the third and final set of experiments, pepsin membranes will be added to the workflow to increase proteomic coverage. The public health benefits of the project lie in the promise of a powerful new tool to analyze protein expression in tissues for both diagnostic purposes and mechanistic studies. This approach will make it possible for researchers to build a coherent picture of the protein expression levels that underlie the development of diseases, thus improving patient outcomes.