Volume Segmentation and Analysis of Biological Materials Using SuRVoS (Super-region Volume Segmentation) Workbench.

Segmentation is the process of isolating specific regions or objects within an imaged volume, so that further study can be undertaken on these areas of interest. When considering the analysis of complex biological systems, the segmentation of three-dimensional image data is a time consuming and labor intensive step. With the increased availability of many imaging modalities and with automated data collection schemes, this poses an increased challenge for the modern experimental biologist to move from data to knowledge. This publication describes the use of SuRVoS Workbench, a program designed to address these issues by providing methods to semi-automatically segment complex biological volumetric data. Three datasets of differing magnification and imaging modalities are presented here, each highlighting different strategies of segmenting with SuRVoS. Phase contrast X-ray tomography (microCT) of the fruiting body of a plant is used to demonstrate segmentation using model training, cryo electron tomography (cryoET) of human platelets is used to demonstrate segmentation using super- and megavoxels, and cryo soft X-ray tomography (cryoSXT) of a mammalian cell line is used to demonstrate the label splitting tools. Strategies and parameters for each datatype are also presented. By blending a selection of semi-automatic processes into a single interactive tool, SuRVoS provides several benefits. Overall time to segment volumetric data is reduced by a factor of five when compared to manual segmentation, a mainstay in many image processing fields. This is a significant savings when full manual segmentation can take weeks of effort. Additionally, subjectivity is addressed through the use of computationally identified boundaries, and splitting complex collections of objects by their calculated properties rather than on a case-by-case basis.

SuRVoS: Super-Region Volume Segmentation workbench.

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.

Imaging endosomes and autophagosomes in whole mammalian cells using correlative cryo-fluorescence and cryo-soft X-ray microscopy (cryo-CLXM).

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.

New light for science: synchrotron radiation in structural medicine.

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.