The open microscopy environment: open image informatics for the life and biomedical sciences
Professor Jason Swedlow, University of Dundee, UK
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.
Does LC/MS metabolomics metabolite annotation make sense for imaging MS?
Dr Steffen Neumann, Leibniz Institute of Plant Biochemistry, Germany
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.
Statistical methods for mass spectrometry-based imaging
Dr Olga Vitek, Northeastern University, USA
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.
Imaging mass spectrometry: unique approaches for the structural identification of biomolecules
Dr Jeffrey Spraggins, Vanderbilt University, USA
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.