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When placing this tag, consider with a. (August 2010) () Serial block-face scanning electron microscopy ( SBEM, SBSEM or SBFSEM) is a method to generate high resolution three-dimensional images from small samples.

The tehcnique was developed for brain tissue, but it is widely applicable for any biological samples. A serial block-face scanning electron microscope consists of an mounted inside the vacuum chamber of a. Samples are prepared by methods similar to that in (), typically by fixing the sample with aldehyde, staining with heavy metals such as and then embedding in an epoxy resin. The surface of the block of resin-embedded sample is imaged by detection of back-scattered electrons. Following imaging the ultramicrotome is used to cut a thin section (typically around 30 nm) from the face of the block. After the section is cut, the sample block is raised back to the focal plane and imaged again.

This sequence of sample imaging, section cutting and block raising can acquire many thousands of images in perfect alignment in an automated fashion. Practical SBFSEM was invented in 2004 by at the Max-Planck-Institute in Heidelberg and is commercially available from Gatan Inc. And Thermo Fisher Scientific (VolumeScope). Applications [ ] One of the first applications of SBFSEM was to analyze the connectivity of in the brain.

The resolution is sufficient to trace even the thinnest axons and to identify synapses.By now, serial block face imaging contributed to many fields, like developmental biology, plant biology, cancer research, studying neuro-degenerative diseases etc. SBFSEM can generate extremely large data sets, and development of algorithms for automatic of the very large data sets generated is still a challenge. However much work is being done on this area currently. The project harnesses to trace neurons through images of a volume of retina obtained using SBEM. Fanuc Roboguide V7 Crack. Many different samples can be prepared for SBFSEM and the ultramicrotome is able to cut many materials, therefore this technique has wider applicability.

It is starting to find applications in many other areas ranging from cell and developmental biology to materials science. References [ ]. • Denk W, Horstmann H (2004) Serial Block-Face Scanning Electron Microscopy to Reconstruct Three-Dimensional Tissue Nanostructure. PLoS Biol 2(11): e329.: • Mukherjee, Konark; Clark, Helen R.; Chavan, Vrushali; Benson, Emily K.; Kidd, Grahame J.; Srivastava, Sarika (2016-07-09).. Journal of Visualized Experiments (113)...

• Hua, Yunfeng; Laserstein, Philip; Helmstaedter, Moritz (2015-08-03).. Nature Communications. Mark Anderson. Retrieved 2017-10-09.

Retrieved March 27, 2012. External links [ ] • Original Publication in PloS Biology • Gatan's 3View • Cell Centered Data Base, SBEM datasets.

Summary Serial block-face scanning electron microscopy (SBEM) is becoming increasingly popular for a wide range of applications in many disciplines from biology to material sciences. This review focuses on applications for circuit reconstruction in neuroscience, which is one of the major driving forces advancing SBEM. Neuronal circuit reconstruction poses exceptional challenges to volume EM in terms of resolution, field of view, acquisition time and sample preparation.

Mapping the connections between neurons in the brain is crucial for understanding information flow and information processing in the brain. However, information on the connectivity between hundreds or even thousands of neurons densely packed in neuronal microcircuits is still largely missing. Volume EM techniques such as serial section TEM, automated tape-collecting ultramicrotome, focused ion-beam scanning electron microscopy and SBEM (microtome serial block-face scanning electron microscopy) are the techniques that provide sufficient resolution to resolve ultrastructural details such as synapses and provides sufficient field of view for dense reconstruction of neuronal circuits. While volume EM techniques are advancing, they are generating large data sets on the terabyte scale that require new image processing workflows and analysis tools.

In this review, we present the recent advances in SBEM for circuit reconstruction in neuroscience and an overview of existing image processing and analysis pipelines. Lay Description During the last decades, numerous technologies have been developed in order to visualize the brain at high resolution with Volume Electron Microscopy. The visualization in three dimensions and the analysis of an entire brain from an animal model where all cells are visible and can be followed and where all synapses can be examined and counted is considering as a milestone by numerous neuroscientists. One of the technique that could be used to acquire images of an entire brain at nanometre scale is the microtome-based block-face scanning electron microscopy developed by W. Denk and his collaborators.

In this approach, a microtome integrated within the specimen chamber of a scanning electron microscope cuts away nanometres thin slice of the specimen, thus allowing the imaging of the next ‘layer’. These individual 2D images are then assembled to a 3D image of all neurons and their projections. The recent improvements of the technique due to better microscopes and better sample preparation are pushing the limits of data quality and acquisition speed. It is creating data sets requiring important storage space. At the level of analysis, new algorithms are developed to reconstruct manually or with semiautomated interfaces all the neurons present in a volume. • • Volume EM techniques Understanding the information flow in neuronal circuits of the brain requires a detailed map on the connectivity between neurons. In order to map all the connections of even a small circuit, detailed recognition of anatomical structures such as spine necks and synapses is necessary.

It requires 3D ultrastructural resolution on the nanometre scale (0.5 × 0.5 mm 2 at a lateral resolution on the order of 6–10 nm and reliable cuts thousands of sections at section thickness 20–30 nm for neural tissue at an acquisition rate of 0.5 to 2 megapixel s −1 (Briggman et al.,; Helmstaedter, ). In FIBSEM, slices are cut using a gallium-ion beam (Knott et al., ).

This allows to cut sections as thin as 5 nm with a lateral resolution 300 TB storage space. At a typical acquisition speed of 1–4 microseconds pixel −1, the cubic millimetre requires more than 18 years of acquisition time making this kind of project impossible. Thereby, the stage movements for the tiling alone causes an overhead of 4 years. In order to move this kind of projects within the realm of the feasible, both, the instrumentation and the sample preparation, have been improved and optimized in the last couple of years. At the level of instrumentation, progress has been made that now allows now to scan larger field of views with higher scan speed and lower beam voltage. The neuronal tissue used for volume EM is usually embedded in nonconductive resin. If nonconductive regions are scanned in an SBEM, electrons accumulate on the block surface and cause charging artefacts.

Therefore, environmental SEMs have been used for SBEM, where a gaseous agent such as water or nitrogen (Danilatos,; Danilatos,; Danilatos, ) is introduced into the recording chamber and takes up exceeding electrons from the block-face. This reduces any charging artefacts significantly (Denk & Horstmann, ), but requires a lower scanning speed and/or a higher beam voltage in order to compensate for the loss in signal due to electron scattering at the gaseous agent. Because the beam current (number of electrons) of these environmental SEMs is limited to approximately 100 pA, the scan speed/dwell time typically had to be at least around 1–5 microseconds pixel −1 in order to collect enough electrons for a sufficient signal-to-noise ratio (Briggman et al., ).

Similarly, for a decent image quality, the beam voltage (speed of the beam electrons) on these instruments had to be larger than 2 kV. However, the higher the beam voltage, the deeper the electrons penetrate into the block-face, which decreases the Z resolution and increases the minimal section thickness that can be cut (Denk & Horstmann, ). In order to reliably cut 25–30-nm thin sections, a beam voltage 0.5 nA) that allow faster scanning and shorter dwell times (1 nA) scan speed can reach 10 MHz (100 nanoseconds pixel −1) without charging artefact, while keeping the signal-to-noise ratio good enough for image segmentation and neurite reconstruction (Titze & Denk, ).

At a speed of 10 MHz (0.1 microsecond pixel −1) and with continuous scanning along one axis, the millimetre cube could be acquired in about 1.5 year. This is still a significant challenge as these instruments currently cannot be run for more than 1–2 months (Briggman et al.,; Lichtman & Denk,; Helmstaedter et al., ) without failure. However, this calculation is based on a single-beam SEM. With the launch of the multi-SEM 505, the acquisition can be parallelized on 61 beams, leading to a speed of 1220 megapixels s −1 (vs. 1 megapixel s −1 for a single-beam SEM at 1 microsecond pixel −1 equivalent to 1 MHz).

With this, the cubic millimetre could be acquired in about 2 months. Another imaging parameter that influences the effective acquisition time significantly is the size of the acquired field of view. It is therefore advisable to restrict the acquisition to the desired region of interest. However, finding a particular region of interest in an en bloc stained sample is challenging. Possible solutions are targeted near-infrared branding of a region of interest (Bishop et al.,; Maco et al.,; Maco et al., ) and staining specific structures, cells or molecules using genetic labels that accumulate electrodense molecules. (Martell et al., ).

Data preprocessing: alignment in 3D To annotate/reconstruct the imaged sample in silico, image preprocessing is necessary. In particular, images need to be placed correctly in three dimensions to combine the slice data into a volume representing the specimen. Image tiles acquired from the same block-face need to be stitched together in x and y. The overlapping region between neighbouring tiles is scanned multiple times, which increases acquisition time and in addition can compromise the cutting quality and cutting thickness due to multiple beam exposure. It is therefore advisable to minimize both, the number of image tiles as well as the amount of overlap between neighbouring image tiles. A compromise in the amount of overlap has to be found because the overlaps need to be large enough to allow for correct image registration and stitching. Because temperature changes during acquisition can cause drifts of the stage relative to the beam source on the order of several nanometres (Boergens & Denk, ), one cannot rely on stage coordinates alone.

In addition, the actual image positions can also drift due to local changes in the electric field due to charging artefacts. In the case of SBEM, these drifts are mostly limited to translations. The necessary corrections do not need to take into account rotations or nonlinear transforms.

To stitch the overlapping images pixel-perfect, the amount of overlap between adjacent images can be calculated via cross-correlations or by detecting and comparing matching configurations of image features, as with the scale-invariant feature transform algorithm (Lowe, ). Then, the images are stitched to the resulting mosaic by overlapping the tiles and minimizing the global error at the overlaps (Fig. ). Subsequently, the stitched mosaics need to be aligned in the z-direction. Here, image feature-based algorithms suppress overfitting of larger objects which are oriented nonperpendicular to the cutting plane, an artefact typical for cross-correlation-based alignments.

Since EM images of most biological samples contain many objects at smaller scales than the diagonally cut object, their resulting image features do not have a preferential direction and outnumber the features stemming from the larger object. • • (A) Resin-embedded specimen (pyramid) is sectioned from top to bottom, while each block-face is imaged as a mosaic of overlapping tiles (B) For each imaged block-face, the overlapping tiles taken from the same area are compared in order to determine the correct overlap (C) The mosaics of adjacent planes are compared based on image features (as in B) and shifted to form a continuous representation of the specimen (D) Tiles are stitched into a mosaic optimizing the global overlap for each section. The workflow of stitching and aligning needs to be automated to cope with many thousands of images per data set to reduce manual labour. TrakEM2, a free open-source software specifically designed for reconstruction of neural circuits from terabyte EM data sets is a handy tool for this work flow (Cardona et al., ). It uses pyramidal data organization to minimize the RAM consumption and necessary data throughput rates of mass storage.

Image analysis, annotation and segmentation Browsing, analyzing and annotating the massive image data sets generated by SBEM is challenging. The retina stack of Briggman et al. () and Helmstaedter et al. () already required several hundreds of GB storage space, but as the acquisition technology of SBEM progresses, the data set sizes also increase.

The acquisition of a cubic millimetre cortex at a voxel size of 10 × 10 × 30 nm would require more than 300 TB storage space after image preprocessing. Therefore, the vast size of the image data makes it impossible to load the full data set for analysis into the RAM of a normal lab computer.

Therefore, several labs have developed open-source software solutions dedicated for large-scale 3D image data such as KNOSSOS (Helmstaedter et al., ), TrakEM2 (Cardona et al., ) and CATMAID (Saalfeld et al., ). These programs use demand-driven dynamic data loading procedures, in which only the currently viewed subvolume is loaded into memory. As the user browses through the data, the corresponding subvolumes are continuously loaded in the background and therefore allow seamless navigation with minimal memory requirements. As the data sets are getting too big for local storage, both, CATMAID and KNOSSOS, feature online streaming of the data from external servers.

In addition, these programs feature a wide range of manual annotation tools, including feature labelling (e.g. Mitochondria, synapses, etc.), skeleton tracing of neurites and volume segmentation (Figure ). • • Consolidated skeletons of six mitral cells (red) and eight interneurons (blue) projecting into the same protoglomerulus of the olfactory bulb of a larval zebra fish. The consolidated skeletons were calculated from redundant manual reconstructions of three different tracers. The SBEM data set was acquired in a period of 7 weeks and consists of more than 5000 sections of 25 nm thickness with a lateral pixel size of 10 nm. Scale bar: 10 μm.

However, manual annotation and segmentation of large image data sets is tedious, error-prone and can be very time-consuming. For example, the dense skeleton reconstruction and analysis of 950 neurons in the inner plexiform layer of a mouse retina required almost 30,000 human working hours (Perkel, ), despite 50-fold speed-up for manual skeletonization versus manual volume annotation (Helmstaedter et al., 2013). Therefore, new software is under development for computer-assisted, semiautomated large-scale annotation and segmentation (Lowe,; Jain et al.,; Chklovskii et al.,; Kaynig, Fuchs, et al.,; Kim et al.,; Maco et al., ). Although the currently existing automated segmentation algorithms are still far from perfect, they have been successfully combined with manual annotation or proof-reading by humans. CATMAID is used for collaborative annotation efforts distributed over collaborating research groups (Saalfeld et al., ). The SBEM pioneers in the Denk lab recruited hundreds of undergraduates for skeleton tracing of neurons using KNOSSOS (Helmstaedter et al., ).

Thereby, most neurons have been redundantly traced by multiple students in order to form a consensus and in turn reduce reconstruction error rates. Subsequently, these consensus-skeletons have been used for automated volume segmentation of the corresponding neurons.

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