Since biology is by and large a 3-dimensional phenomenon, 3D imaging has a significant impact on many challenges in life sciences. 3D matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) is an emerging label-free 3D imaging technology with high potential in proteomics and metabolomics. 3D MALDI-IMS is based on 2D MALDI-IMS, which in the last years has proven its value in metabolomics, glycomics, lipidomics, peptidomics, and proteomics. However, 3D MALDI-IMS cannot tap its full potential due to the lack of computational methods for processing large and complex 3D IMS data. Our main goal is to realise 3D label-free proteomics and metabolomics by providing new statistical methods for the reproducible collection of 3D MALDI-IMS data, and for un- and supervised statistical analysis and interpretation of this data. We will validate our methods in diabetes research, surgical metabolomics, and natural products research. To reach our global goal, we will develop methodologies for reproducible collection of 3D MALDI-IMS data, develop a statistical simulator of MALDI-IMS data and evaluation strategies for statistical methods, develop preprocessing, unsupervised, and supervised methods of statistical analysis of 3D MALDI-IMS data, evaluate the developed methods on simulated and real-life proteomics and metabolomics data, and implement them using efficient graphic processing unit (GPU) architecture. This project is of high importance for research-intensive SME partners, Denator and SagivTech, since it would create a new market segment for them. Expertise of partners in biomedicine and their strong connections to clinical partners ensure adoption of the new methods into clinical practice. The developed methods and protocols can be potentially used in any biomedical application where 3D MALDI-IMS is advantageous such as cancer and diabetes diagnostics, disease pathway elucidation, histopathology, and discovery of biomarkers or antibiotics.