Serial block face scanning electron microscopy – Big data results for life sciences and materials science An issue for measurement and analysis 1,2
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A. Zankel , M. Nachtnebel , C. Mayrhofer , H. Schroettner , S. Wernitznig , G. Leitinger 1
Institute of Electron Microscopy and Nanoanalysis, NAWI Graz, Graz University of Technology, Steyrergasse 17, Austria 2 Graz Centre for Electron Microscopy, Steyrergasse 17, Austria 3 Research Unit Electron Microscopic Techniques, Division of Cell Biology, Histology and Embryology, Gottfried Schatz Research Center, Medical University of Graz, Neue Stiftingtalstraße 6, 8010 Graz
Introduction The trend in recent years shows increased amounts of data that need to be collected for the investigation in almost all areas of science. This is also true for investigations in 3D and the reconstruction of bigger volumes. On the one hand, this gives a deeper insight into the sample under investigation, but on the other hand requires also a time consuming handling and processing of the big datasets. In this poster two different approaches to handle with such datasets are presented.
Method Serial blockface scanning electron microscopy (SBFSEM, SBEM) was developed in the context of neuroscience [1], where it is nowadays well-established. Since then it was applied in different fields of research even in materials science [2]. Here one example from materials science and one of life sciences are presented explaining how these different fields require data analysis in different ways. Results were gained using the scanning electron microscope ESEM Quanta 600 FEG (FEI, Eindhoven, the Netherlands) and the serial block face sectioning and TM TM imaging tool, 3View from Gatan, Inc. (Pleasanton, CA, USA), which is controlled by the software Digital Micrograph . For materials science investigations commonly quite large volumes have to be recorded, to obtain a statistically evaluable dataset. The example presented deals with a tensile failure test of polymers. Here informations are obtained about the spatial distribution of fine cracks or crazes in polymeric blends after tensile tests were stopped at low forces. Therefore, after the image acquisition the images are segmented into the matrix (in this case polypropylene (PP)), the filler particles (ethylene proplyene rubber (EPR)) and the developed cracks. This gives information, among others, if such filler particles are acting as initiators or rather as stoppers of cracks. Because of the substantial size of the dataset of several hundreds of slices a manual segmentation would be too time consuming. Therefore, a sophisticated automated segmentation algorithm was developed, to segmentate all images into the three stated phases (see the left image in figure 1). With this strategy it was possible to obtain the needed binary images, which were subsequently 3D-reconstructed and analyzed (see the right image in figure 1) [3].
Figure 1. Left: Automated image segmentation strategy for segmentation into three phases (matrix, particles, cracks) at given big dataset. Right: Section of the 3D-reconstruction of polymeric blend after tensile testing: blue-cracks, greenERP particles (unit: microns).
As an example from biology the investigation of a neuronal circuit in the visual system of locusts is presented. The computation of visual stimuli which are important for predator detection within the neuronal circuit can only be realiably described with the help of ultra-structural data. Using SBEM a specific neuron, a trans-medullary Figure 2. A: Reconstruction of a traced neuron (TmA neuron, yellow) and parts of the lobula giant movement detector afferent (TmA) neuron, was traced from its (LGMD) neuron (blue). Colour code of background indicates three different neuronal regions in the optic lobe. Beside: designated output area (lobula complex) to the Detail of TmA neuron in the medulla (black rectangle) with several neurons (in different colours) which show synaptic assumed input area (medulla) in the locust's optic connections with the TmA neuron. B Reconstruction process of the LGMD. i original electron micrograph, black rectangle lobe. The total covered distance for the tracing of indicates a large profile of a neuron (L). ii snapshot of semi-automatic segmentation of LGMD in progress with manually the neuron was about 375 µm or 7500 sections set seed (yellow) and automatically segmented area (red). iii part of reconstruction of semi-automatically segmented (Figure 1). The segmentation of such a huge data set is challenging, even for just a few neurons. The segmentation can be done manually, semi-automatically or automatically. In our case we used, beside the conventional manual approach, a special algorithm for the segmentation process was used [4]. This semiautomatic approach allowed us to segment neurons in 3D saving up to 50% of the time needed manually. Example of a neuron segmented with the semiautomated application is shown in the right part of figure 2.
Acknowledgements and references The authors thanks the Austrian Research Promotion Agency FFG (project 856321) for funding and the „Land Steiermark“, departmant air quality monitoring for providing materials. [1] W. Denk , H. Horstmann, PLoS Biol 2(2004) e329. [2] A. Zankel et al., Micron 62 (2014) p. 66 [3] M. Nachtnebel, PhD Dissertation TU Graz (2017) [4] St. Wernitznig et al., Journal of Neuroscience Methods 264 (2016), p. 16
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