Concept and objectives
Biology is by and large a 3-dimensional phenomenon; it is hardly surprising that 3D imaging has a significant impact on many challenges in life sciences. Current 3D imaging technologies (CT, MRI, PET, SPECT, ultrasound) are either targeted or labeled, i.e they either recover anatomy or trace a specific inserted compound in the body. They are of no use in proteomics or metabolomics discovery studies, neither for discovery of biomarkers and drugs, nor for analysis of disease pathways.
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 decade has proven its value in metabolomics, glycomics, lipidomics, peptidomics, and proteomics. MALDI-IMS allows the spatial localization of precise molecular masses. It serves as a superior discovery tool in addition to e.g. drieddroplets mass spectrometry and 2D gel electrophoresis, or as a method to image the spatial distribution of molecular compounds, thereby complementing immunohistochemistry or methods based on genetics, like in situ hybridization.
3D MALDI-IMS inherits the advantages of 2D MALDI-IMS over other technologies. It is a targetfree, highly sensitive, semi-quantitative detection technology for a wide range of biomolecules, and it can be combined with MS/MS for subsequent identification of biomolecular species. Several proof-ofprinciple experiments demonstrated the feasibility of 3D MALDI-IMS and its advantages over 2D MALDI-IMS. Even manual data mining of 3D MALDI-IMS data already produces results beyond the capabilities of 2D MALDI-IMS data analysis. However, 3D MALDI-IMS cannot tap its full potential due to the lack of computational methods for processing large and complex 3D IMS data.
It is our main goal to realise 3D label-free proteomics and metabolomics by 3D MALDI-IMS. We will develop statistical methods for the reproducible collection of 3D MALDI-IMS data, and for unsupervised and supervised statistical analysis and interpretation of this data. We will validate our methods in three applications, in which 3D imaging is critical: diabetes research, surgical metabolomics, and natural products research.