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      Cryo-soft X-ray tomography: using soft X-rays to explore the ultrastructure of whole cells

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          Abstract

          Cryo-soft X-ray tomography is an imaging technique that addresses the need for mesoscale imaging of cellular ultrastructure of relatively thick samples without the need for staining or chemical modification. It allows the imaging of cellular ultrastructure to a resolution of 25–40 nm and can be used in correlation with other imaging modalities, such as electron tomography and fluorescence microscopy, to further enhance the information content derived from biological samples. An overview of the technique, discussion of sample suitability and information about sample preparation, data collection and data analysis is presented here. Recent developments and future outlook are also discussed.

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          Most cited references80

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          Automated tilt series alignment and tomographic reconstruction in IMOD.

          Automated tomographic reconstruction is now possible in the IMOD software package, including the merging of tomograms taken around two orthogonal axes. Several developments enable the production of high-quality tomograms. When using fiducial markers for alignment, the markers to be tracked through the series are chosen automatically; if there is an excess of markers available, a well-distributed subset is selected that is most likely to track well. Marker positions are refined by applying an edge-enhancing Sobel filter, which results in a 20% improvement in alignment error for plastic-embedded samples and 10% for frozen-hydrated samples. Robust fitting, in which outlying points are given less or no weight in computing the fitting error, is used to obtain an alignment solution, so that aberrant points from the automated tracking can have little effect on the alignment. When merging two dual-axis tomograms, the alignment between them is refined from correlations between local patches; a measure of structure was developed so that patches with insufficient structure to give accurate correlations can now be excluded automatically. We have also developed a script for running all steps in the reconstruction process with a flexible mechanism for setting parameters, and we have added a user interface for batch processing of tilt series to the Etomo program in IMOD. Batch processing is fully compatible with interactive processing and can increase efficiency even when the automation is not fully successful, because users can focus their effort on the steps that require manual intervention.
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            Handbook Of Biological Confocal Microscopy

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              Convolutional Neural Networks for Automated Annotation of Cellular Cryo-Electron Tomograms

              Cellular Electron Cryotomography (CryoET) offers the ability to look inside cells and observe macromolecules frozen in action. A primary challenge for this technique is identifying and extracting the molecular components within the crowded cellular environment. We introduce a method using neural networks to dramatically reduce the time and human effort required for subcellular annotation and feature extraction. Subsequent subtomogram classification and averaging yields in-situ structures of molecular components of interest.
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                Author and article information

                Journal
                Emerg Top Life Sci
                Emerg Top Life Sci
                ppetls
                ETLS
                Emerging Topics in Life Sciences
                Portland Press Ltd.
                2397-8554
                2397-8562
                20 April 2018
                29 March 2018
                : 2
                : 1 , Integrated Structural Biology
                : 81-92
                Affiliations
                Diamond Light Source, Harwell Science and Innovation Campus, Didcot, Oxfordshire, U.K.
                Author notes
                Correspondence: Maria Harkiolaki ( maria.harkiolaki@ 123456diamond.ac.uk )
                Article
                ETLS-2-81
                10.1042/ETLS20170086
                7289011
                33525785
                cf7613d6-7811-4cee-8dfd-bd7b4bf28d50
                © 2018 The Author(s)

                This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and the Royal Society of Biology and distributed under the Creative Commons Attribution License 4.0 (CC BY).

                History
                : 26 November 2017
                : 31 January 2018
                : 2 February 2018
                Categories
                Biotechnology
                Structural Biology
                Biophysics
                Biochemical Techniques & Resources
                Review Articles

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