Chairs
Professor Dr Ron Heeren, FOM Institute AMOLF, The Netherlands
Professor Dr Ron Heeren, FOM Institute AMOLF, The Netherlands
Prof. Dr. Ron M.A. Heeren obtained a PhD degree in technical physics in 1992 at the University of Amsterdam on plasma-surface interactions. In the period 1995-2014 he has been developing new approaches towards high spatial resolution and high throughput molecular imaging mass spectrometry at FOM-AMOLF. In 2001 he was appointed professor at the chemistry faculty of Utrecht University lecturing on the physical aspects of biomolecular mass spectrometry. In 2014 he was appointed as distinguished professor and Limburg Chair at the University of Maastricht where he now is the director of M4I, the Maastricht MultiModal Molecular Imaging institute and heads the division of imaging MS. His academic research interests are the fundamental studies of the energetics of macromolecular systems, conformational studies of non-covalently bound protein complexes, high-throughput bioinformatics and the development and validation of new mass spectrometry based proteomic imaging techniques for the life sciences.
13:30-14:00
The open microscopy environment: open image informatics for the life and biomedical sciences
Professor Jason Swedlow, University of Dundee, UK
Abstract
Despite significant advances in biological imaging and analysis, major informatics challenges remain unsolved: file formats are proprietary, storage and analysis facilities are lacking, as are standards for sharing image data and results. The Open Microscopy Environment (OME) is an open-source software framework developed to address these challenges. OME has three components—an open data model for biological imaging: OME data model; standardised file formats (OME-TIFF) and software libraries for file conversion (Bio-Formats); and a software platform for image data management and analysis (OMERO). The Java-based OMERO client-server platform comprises an image metadata store, an image repository, visualisation and analysis by remote access, enabling sharing and publishing of image data. OMERO’s model-based architecture has enabled its extension into a range of imaging domains, including light and electron microscopy, high content screening and recently into applications using non-image data from clinical and genomic studies Our current version, OMERO-5 improves support for large datasets and reads images directly from their original file format, allowing access by third party software. OMERO and Bio-Formats run the JCB DataViewer, the world’s first on-line scientific image publishing system and several other institutional image data repositories.
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Professor Jason Swedlow, University of Dundee, UK
Professor Jason Swedlow, University of Dundee, UK
Dr Swedlow received his Ph.D. in Biophysics from the University of California, San Francisco after completing a B.A. in Chemistry at Brandeis University in Massachusetts. He joined the Wellcome Trust Biocentre at the University of Dundee in the United Kingdom following postdoctoral training at UCSF and Harvard Medical School in the U.S. Dr Swedlow is currently Professor of Quantitative Cell Biology at the University of Dundee. His research interests are the mechanisms and regulation of chromosome segregation during mitotic cell division, and the development of software tools for accessing, processing, sharing and publishing large scientific image datasets. Dr Swedlow is a co-founder of the Open Microscopy Environment (OME), an international consortium that develops and releases open source software for biological imaging. He also cofounded Glencoe Software, Inc., which commercializes and customizes OME technology for use in biopharma and data publishing, and BioImagingUK, a consortium of UK imaging scientsts who develop, use, or administer imaging solutions for life sciences research. In 2011, Dr Swedlow was named Social and Overall Innovator of the Year by the BBSRC. In 2012, Dr Swedlow was named a Fellow of the Royal Society of Edinburgh.
14:00-14:15
Does LC/MS metabolomics metabolite annotation make sense for imaging MS?
Dr Steffen Neumann, Leibniz Institute of Plant Biochemistry, Germany
Abstract
Metabolite profiling via LC/MS can reveal ‘interesting’ features, and subsequent tandem MS experiments provide powerful structural hints for the elucidation of these unknown mass spectral features. Reference libraries like MassBank and in-silico methods such as MetFrag help to identify compounds with tandem MS among candidate structures obtained from general purpose compound libraries. I won't give an answer to the question in the title; that will be part of the discussion.
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Dr Steffen Neumann, Leibniz Institute of Plant Biochemistry, Germany
Dr Steffen Neumann, Leibniz Institute of Plant Biochemistry, Germany
Steffen Neumann studied computer science and bioinformatics in Bielefeld, and now his group focuses on the development of tools and databases for metabolomics and computational mass spectrometry. The group develops algorithms for data processing of metabolite profiling experiments, which are available in several Open Source Bioconductor packages, and addresses the most pressing bottleneck in Metabolomics: the identification of unknowns in mass spectrometry data. The institute is part of the MassBank consortium and operated the first MassBank server in Europe. The MetFrag and MetFusion tools allow the identification of compounds where no reference spectra are available. To compare such identification methods on common challenge data, Steffen Neumann initiated the CASMI contest in 2012, which is now going into its third year. Within the EU project COSMOS, he develops and promotes the use of data standards to enable data sharing and -archiving.
14:15-14:30
Statistical methods for mass spectrometry-based imaging
Dr Olga Vitek, Northeastern University, USA
Abstract
Statistical methods are key for detecting systematic signal (e.g., caused by an intervention or a disease) in presence of variation and uncertainty, and for making objective and reproducible conclusions. This is particularly important for mass spectrometry-based imaging, where signals are obscured by 3 types of variation: the variation between different biological replicates, the spatial variation within images of a same biological replicate, and the technical variation due to sample handling and spectral acquisition. Moreover, the large-scale nature of mass spectrometry imaging experiments presents an additional challenge. As spatial and mass resolution increase, the experiments become more prone to generating spurious associations, and to amplifying bias and confounding. This talk will discuss the importance of statistical inference when designing and analysing mass spectrometry-based imaging experiments, as well as statistical methods and open-source software designed to facilitate the statistical inference tasks.
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Dr Olga Vitek, Northeastern University, USA
Dr Olga Vitek, Northeastern University, USA
Dr. Vitek holds a B.S. degree from University of Geneva, Switzerland, and a M.S., a PhD in Statistics from Purdue University, and a post-doctoral training in the Aebersold lab at the Institute for Systems Biology in Seattle. Between 2006-20014 Dr. Vitek was a faculty in the Departments of Statistics and Computer Science at Purdue University. She is currently a Sy and Laurie Sternberg Interdisciplinary Associate Professor College of Science and College of Computer and Information Science at Northeastern University.
Dr. Vitek‘s group develops statistical and computational methods and software for high-throughput quantitative experiments in molecular biology that rely on mass spectrometric workflows. The contributions from the Vitek lab help increase the accuracy of these experiments and their insight into the biological function. Dr. Vitek serves on the Editorial Boards of Molecular & Cellular Proteomics and of the Journal of Statistical Planning and Inference. She was a recipient of the NSF CAREER award (2011), and was distinguished as Purdue University Faculty Scholar.
14:30-14:45
Imaging mass spectrometry: unique approaches for the structural identification of biomolecules
Dr Jeffrey Spraggins, Vanderbilt University, USA
Abstract
Imaging mass spectrometry (IMS) is a rapidly advancing technology, however the identification of species detected from tissue remains a significant challenge. Biomolecular identification strategies for IMS fall into two general categories: on-tissue fragmentation and indirect identification approaches. Since IMS analysis often ablates all material from the measurement area, on-tissue identification is typically performed using serial tissue sections or unmeasured regions of the sample. This can prove problematic because, for many ions, optical inspection alone is insufficient to determine their location, making manual prediction of where to focus fragmentation experiments impractical. Indirect identification is performed by using secondary information such as mass accuracy to link separate IMS and LC-ESI MS/MS experiments. This approach is often hampered by insufficient mass resolving power and accuracy for the imaging experiment to correlate results with high confidence. Here we describe novel methods for the identification of metabolites, lipids and proteins in molecular imaging experiments using high performance instrumentation and advanced computational approaches.
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Dr Jeffrey Spraggins, Vanderbilt University, USA
Dr Jeffrey Spraggins, Vanderbilt University, USA
Jeffrey M. Spraggins received his B.A. in Chemistry from the College of Wooster and his Ph.D. in Analytical Chemistry from the University of Delaware (2009), where he studied gas-phase fragmentation mechanisms of modified biomolecules and metal-sulfide cluster ion-molecule reactions. Following graduate school, Dr. Spraggins continued his training as a postdoctoral research fellow in Richard Caprioli’s research group and is currently a Research Assistant Professor in the Department of Biochemistry and a member of the Mass Spectrometry Research Center at Vanderbilt University. His research focuses on developing new mass spectrometric technologies to enhance biomolecular imaging experiments. Specifically, he is working on expanding the application of FTICR MS for the spatial analysis and structural identification of metabolites, lipids, peptides and proteins in biological tissues.
16:00-17:00
Further discussion