Nico Verbeeck (Presenter)
Delft University of Technology
Bio: Nico Verbeeck recently joined the Delft University of Technology in the Netherlands as a post-doctoral researcher within the Delft Center for Systems and Control. He obtained his PhD in 2014 at the University of Leuven (KU Leuven) in Belgium in the STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics. He holds a Master in Engineering in Nanotechnology (KU Leuven, 2008) and a Master in Artificial Intelligence (KU Leuven, 2009). Dr. Verbeeck works on new methods for automated interpretation and exploration of imaging mass spectrometry (IMS) data, combining IMS data with external information sources such as anatomical atlases to leverage prior information and enable guided exploration of large datasets. Further research topics include dimensionality reduction in IMS data and multivariate decomposition techniques for comparison of multiple IMS datasets.
Authorship: Nico Verbeeck (1), Jeffrey Spraggins (2), Yousef El Aalamat (3), Junhai Yang (2), Richard M. Caprioli (2), Bart De Moor (3), Etienne Waelkens (3), Raf Van de Plas (1,2).
(1) Delft University of Technology, Delft, The Netherlands (2) Vanderbilt University, Nashville, TN (3) KU Leuven, Leuven, Belgium
| Short Abstract In recent years, imaging mass spectrometry (IMS) has gained increasing interest as a biomolecular screening tool, capable of detecting deviations in protein content between multiple tissue sections. In this work we aid the differential analysis of IMS data collected from healthy and diseased mouse brain tissue, by linking these data to an anatomical atlas. Differential protein signatures between the tissues are extracted using multivariate analysis techniques, and are then automatically interpreted in terms of anatomical structures using the Allen Mouse Brain Atlas. The automated interpretation of these inter-experiment differences can greatly accelerate differential exploration of IMS data sets by avoiding the time- and resource-intensive step of manually interpreting differential patterns using anatomical terms. |
Long Abstract
In recent years, imaging mass spectrometry (IMS) has gained increasing interest as a biomolecular screening tool, capable of detecting deviations in protein content between multiple tissue sections. In this work we aid the differential analysis of IMS data collected from healthy and diseased mouse brain tissue, by linking these data to an anatomical atlas. Differential protein signatures between the tissues are extracted using multivariate analysis techniques, and are then automatically interpreted in terms of anatomical structures using the Allen Mouse Brain Atlas. The automated interpretation of these inter-experiment differences can greatly accelerate differential exploration of IMS data sets by avoiding the time- and resource-intensive step of manually interpreting differential patterns using anatomical terms.
References & Acknowledgements:
Reference:
N. Verbeeck, J. Yang, B. De Moor, R. M. Caprioli, E. Waelkens, and R. Van de Plas, “Automated anatomical interpretation of ion distributions in tissue: linking imaging mass spectrometry to curated atlases.,” Anal. Chem., vol. 86, no. 18, pp. 8974–8982, Sep. 2014.
Acknowledgments:
This work was supported by the U.S. National Institutes of Health Grants NIH/NIGMS R01 GM058008-14 and NIH/NIGMS P41 GM103391-03. This research was funded by a PhD grant of the Agency for Innovation by Science and Technology (IWT), the Research Council KU Leuven: GOA/10/09 MaNet, GOA/12/24, Industrial Research fund (IOF): IOF/HB/13/027 Logic Insulin, the Flemish Government: FWO: projects: G.0871.12N (Neural circuits), IWT: TBM-Logic Insulin (100793), TBM Rectal Cancer (100783), TBM IETA (130256), PhD grants, iMinds Medical Information Technologies SBO 2014, VLK Stichting E. van der Schueren: rectal cancer, Federal Government: FOD: Cancer Plan 2012- 2015 KPC-29-023 (prostate), IAP P7/13, COST: Action BM1104: Mass Spectrometry Imaging.
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