Advances in Computational Methods for Tissue Imaging by Mass Spectrometry Raf Van de Plas (Presenter) Delft University of Technology Presenter Bio: Raf Van de Plas is assistant professor at the Delft University of Technology (TU Delft) in the Netherlands. At the Delft Center for Systems and Control, he works on computational analysis of molecular imaging modalities such as imaging mass spectrometry and microscopy. He holds an adjunct assistant professor position in biochemistry at Vanderbilt University School of Medicine in Nashville, TN. He holds a PhD in Engineering (KU Leuven, 2010), a degree in Industrial Engineering (Group T, 2002), and an MSc in Artificial Intelligence (KU Leuven, 2003). Before his appointment at TU Delft, he held a research faculty position at the Mass Spectrometry Research Center at Vanderbilt University with Dr. Caprioli. His research focuses on the interface between (i) mathematical engineering and machine learning; (ii) analytical chemistry and instrumentation; and (iii) life sciences and medicine.

Authors: Raf Van de Plas (1,2,3), Jeffrey Spraggins (2,3,4), Boone M. Prentice (2,3), Junhai Yang (2,3), Richard M. Caprioli (2,3,4,5,6)
(1) Delft Center for Systems and Control, Delft University of Technology, Delft, Netherlands; (2) Mass Spectrometry Research Center, Vanderbilt University, Nashville, TN, USA; (3) Department of Biochemistry, Vanderbilt University, Nashville, TN, USA; (4) Department of Chemistry, Vanderbilt University, Nashville, TN, USA; (5) Department of Pharmacology, Vanderbilt University, Nashville, TN, USA; (6) Department of Medicine, Vanderbilt University, Nashville, TN, USA;

Imaging Mass Spectrometry (IMS) has made rapid progress as an imaging modality that can map the spatial distribution of molecules in tissue. In recent years, novel computational developments have become an increasingly important part of major advancements in this field. This talk presents several computational techniques developed in our group, specifically relevant to molecular imaging in medicine and the clinical practice. We show recent work in low-level signal processing, where in silico integration of isolation windows enables High-Dynamic-Range mass spectrometry, substantially increasing MS sensitivity. We also address advancements in data-driven image fusion, a multi-modal data mining methodology that drives the automated discovery of biomolecular relationships between stained microscopy and IMS, thus directly linking exploratory tissue analysis to established clinical targets.

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Van de Plas R., Yang J., Spraggins J., and Caprioli R.M., Image fusion of mass spectrometry and microscopy: a multimodality paradigm for molecular tissue mapping. Nature methods, 2015, 12(4), pp.366-372.