Articles by Elizabeth M.H. Duke in JoVE
Volume Segmentation and Analysis of Biological Materials Using SuRVoS (Super-region Volume Segmentation) Workbench Michele C. Darrow1, Imanol Luengo1,2, Mark Basham1, Matthew C. Spink1, Sarah Irvine1, Andrew P. French2, Alun W. Ashton1, Elizabeth M.H. Duke1 1Science Division, Harwell Science and Innovation Campus, Diamond Light Source, 2School of Computer Science, University of Nottingham Segmentation of three-dimensional data from many imaging techniques is a major bottleneck in analysis of complex biological systems. Here, we describe the use of SuRVoS Workbench to semi-automatically segment volumetric data at various length-scales using example datasets from cryo-electron tomography, cryo soft X-ray tomography, and phase contrast X-ray tomography techniques.
Other articles by Elizabeth M.H. Duke on PubMed
New Light for Science: Synchrotron Radiation in Structural Medicine Trends in Biotechnology. Nov, 2006 | Pubmed ID: 17005277 Macromolecular crystallography (MX) is a powerful method for obtaining detailed three-dimensional structural information about macromolecules. MX using synchrotron X-rays has contributed, significantly, to both fundamental and applied research, including the structure-based design of drugs to combat important diseases. New third-generation synchrotrons offer substantial improvements in terms of quality and brightness of the X-ray beams they produce. Important classes of macromolecules, such as membrane proteins (including many receptors) and macromolecular complexes, are difficult to obtain in quantity and to crystallise, which has hampered analysis by MX. Intensely bright X-rays from the latest synchrotrons will enable the use of extremely small crystals, and should usher in a period of rapid progress in resolving these previously refractory structures.
Imaging Endosomes and Autophagosomes in Whole Mammalian Cells Using Correlative Cryo-fluorescence and Cryo-soft X-ray Microscopy (cryo-CLXM) Ultramicroscopy. Aug, 2014 | Pubmed ID: 24238600 Cryo-soft X-ray tomography (cryo-SXT) is a powerful imaging technique that can extract ultrastructural information from whole, unstained mammalian cells as close to the living state as possible. Subcellular organelles including the nucleus, the Golgi apparatus and mitochondria have been identified by morphology alone, due to the similarity in contrast to transmission electron micrographs. In this study, we used cryo-SXT to image endosomes and autophagosomes, organelles that are particularly susceptible to chemical fixation artefacts during sample preparation for electron microscopy. We used two approaches to identify these compartments. For early and recycling endosomes, which are accessible to externally-loaded markers, we used an anti-transferrin receptor antibody conjugated to 10nm gold particles. For autophagosomes, which are not accessible to externally-applied markers, we developed a correlative cryo-fluorescence and cryo-SXT workflow (cryo-CLXM) to localise GFP-LC3 and RFP-Atg9. We used a stand-alone cryo-fluorescence stage in the home laboratory to localise the cloned fluorophores, followed by cryo-soft X-ray tomography at the synchrotron to analyse cellular ultrastructure. We mapped the 3D ultrastructure of the endocytic and autophagic structures, and discovered clusters of omegasomes arising from 'hotspots' on the ER. Thus, immunogold markers and cryo-CLXM can be used to analyse cellular processes that are inaccessible using other imaging modalities.
SuRVoS: Super-Region Volume Segmentation Workbench Journal of Structural Biology. Apr, 2017 | Pubmed ID: 28246039 Segmentation of biological volumes is a crucial step needed to fully analyse their scientific content. Not having access to convenient tools with which to segment or annotate the data means many biological volumes remain under-utilised. Automatic segmentation of biological volumes is still a very challenging research field, and current methods usually require a large amount of manually-produced training data to deliver a high-quality segmentation. However, the complex appearance of cellular features and the high variance from one sample to another, along with the time-consuming work of manually labelling complete volumes, makes the required training data very scarce or non-existent. Thus, fully automatic approaches are often infeasible for many practical applications. With the aim of unifying the segmentation power of automatic approaches with the user expertise and ability to manually annotate biological samples, we present a new workbench named SuRVoS (Super-Region Volume Segmentation). Within this software, a volume to be segmented is first partitioned into hierarchical segmentation layers (named Super-Regions) and is then interactively segmented with the user's knowledge input in the form of training annotations. SuRVoS first learns from and then extends user inputs to the rest of the volume, while using Super-Regions for quicker and easier segmentation than when using a voxel grid. These benefits are especially noticeable on noisy, low-dose, biological datasets.