Abstract New methods are required to image the metabolic hallmarks of cancerous tissues and correlate this information with patient tumour pathology, cancer phenotype and treatment outcome. Mass spectrometry imaging (MSI) offers a powerful suite of techniques for imaging metabolites, drugs and proteins in cells and tissues. Here we present a multi-modal MSI strategy and use it to profile PDX, GEMM and biopsy samples. Human and murine colon and small intestine samples were sectioned onto glass slides and stored at -80°C until use. MALDI-MSI (Waters and Bruker) was carried out with pixel sizes between 10 and 50 microns, DESI (Waters) at 20-50 microns and SIMS (IONTOF) at approximately 200 nm per pixel. Data were converted from proprietary format to imzML and analysed in MatLab (2015b, MathWorks) via the SpectralAnalysis software package using non-negative matrix factorisation (NMF) and principal component analysis (PCA). Further reduction and segmentation was achieved using stacked autoencoders and t-distributed stochastic neighbour embedding (t-SNE). To assign the peaks detected to molecular identities, peaks picked from mean spectra were matched to the human metabolome database (HMDB) using custom Matlab scripts. Possible adducts were queried against the database masses, and filtered based on comparison of expected and observed mass, and correlation between the images of the monoisotopic mass and 13C isotope image. Preliminary data show the successful preparation and analysis of multiple GEMM and human tissue samples across several sites by MALDI-MSI, DESI-MSI and SIMS imaging. Converting data to imzML and combining into large, single multi-file images enabled multivariate analysis (MVA) to be performed simultaneously within the same comparison. Of these MVA methods, mass spectrometry imaging (MSI) in combination with t-SNE has been shown to differentiate tumour subpopulations linked with patient outcomes. Current t-SNE methods suffer from three main limitations as compared to other MVA methods; poor computational scaling to large datasets, inability to add new data into the embedding, and inability to return to spectral contributions. By combining t-SNE performed on a subset of data and learning the embedding via deep learning, almost limitless size datasets can be analysed by t-SNE, new data can be incorporated into the embedding, and the spectral contributions towards the three dimensional space can be determined. Database matching on the data from these GEMM samples to the HMDB reveals over 1000 possible molecular assignments, primarily structural lipids, within 10 ppm mass accuracy and with isotope image correlations above 0.6. From these methods, comparisons can be made between the unique molecules detected in different tissues, as well as the relative changes in intensity of those detected between different tissues. Citation Format: Josephine Bunch, Rory T. Steven, Adam J. Taylor, Spencer A. Thomas, Alan M. Race, Alex Dexter, Gregory Hamm, Nicole Strittmatter, Rasmus Havelund, Renata F. Soares, Andrew D. Campbell, Owen J. Sansom, Richard J. Goodwin, Zoltan Takats. A multi modal mass spectrometry imaging strategy to profile the metabolic hallmarks of colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5661.