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EM allows analysis of cellular context with high resolution imaging of macromolecules in biological context. However, this typically limits the field of view. Larger scale 3D EM is particularly suitable for mapping neuronal connectivity by creating nanoscale resolution 3 dimensional reconstructions, requiring complex data processing10. In contrast, 2D large scale EM requires only a single section and stitching of the imaging data, and assessment of the data is possible by anyone with access to an Internet browser. We and others previously used large scale EM to analyze tissues and whole animals. As for most approaches, TEM and SEM-based stitching do have their own advantages. Here, scanning transmission EM (STEM) was used that allows generation of a large field of view at high resolution. Typically, one STEM image is equivalent to the fields of view of approximately 100 TEM images, significantly reducing the amount of stitching when imaging large fields of view at high resolution. If higher resolution is needed, TEM may be advantageous. STEM has the advantage over TEM that non-contrasted samples can be used with good ultrastructural contrast6 .
Additionally, the method described here can be simply adjusted for use with back scattered electron detection (BSD) on sections mounted on silica wafers, broadening the use to multiple microscope systems. HTML-zoomable tissue EM files are very useful for quantification, sharing data that may not have been analyzed to its full content, combining LM and EM data (CLEM)8, presentation purposes in scientific research, to analyze patient data, and for education. Alternatively, in large-scale EM in a SEM, but not in a TEM, silica wafers can be used, which has two main advantages: BSD can be used, which is more generally available on scanning electron microscopes than STEM detection. Second, mounting of large sections (> 1 mm2 areas of interest) is straightforward. Mounting on single slotted grids is labor intensive and technically challenging. Detailed comparison between TEM, SEM and STEM is detailed elsewhere6. A disadvantage of BSD imaging is that, compared to STEM, images have increased noise. This can partly be compensated by increasing the pixel dwell time, resulting in much longer acquisition times.
Although for sample preparation relatively standard EM processing (fixation, embedding and sectioning) is needed5-7 , it is technically challenging to cut large ultrathin sections completely devoid of artifacts. Sections are very fragile, easily break, fold or are destroyed during imaging, which typically takes multiple hours per dataset. However, because of the online sharing of the raw unbiased data, it should become possible to compare more easily to published data, and use published dataset in the open domain as controls. Currently, few EM devices capable of large-scale analysis are in use, and therefore accessibility to this technique is somewhat limited, though most imaging centers welcome collaborative efforts.
For large-scale sections the tissue has to be properly fixed throughout the sample. That is why a mixture of the fast but moderate fixative PFA is used in combination with the slow but strong fixative GA. Cutting and picking up large sections without artifacts is difficult. Working with wafers in combination with BSD is easier compared to collecting sections on single hole grids. Heavy metal post staining is more critical compared to classical EM. Since the whole section is imaged every artifact will be visible. TEM users typically find it difficult to switch to a SEM, because of the difference in microscope operation.
Quantification and data sharing - Quantifying subcellular features is difficult in single EM images. The possibility to zoom in and out of large-scale images readily allows identification of cells of interest, which can be followed by nanoscale measurements inside the cells. These datasets indicate that specific cell types can be rapidly identified and quantified in an unbiased manner in these large tissue sections, based on features detectable at different scales. For example microglia can be identified based on their morphology and dense cytoplasm. Subsequently, upon zooming in on the individual cells, nanoscale subcellular and molecular features of these cells can be measured within the same dataset, as we previously showed for ER width within a large scale EM tissue culture dataset2. An additional advantage of nanotomy is that hosting of large scale datasets online will allow others to inspect the data, maybe for other features, and draw their conclusions on new hypothesis.
CLEM - In addition to facilitating quantitative EM, large scale EM makes it easier to correlate light microscopic labeling to EM level8. In the present example the presence of phagocytic microglia in a zebrafish ablation model is shown. A major question in neuroscience is what the individual functions and contributions are of microglia and potential other sources of phagocytic and immune cells. Early EM studies have shown distinctive subcellular features of microglia in disease18. Unfortunately, it is difficult to selectively label microglia in particular in a pathological setting, as they exhibit large overlap with other immune cells in gene expression, morphology and function. Therefore, it is unclear whether and when microglia at the ultrastructural level differ from immune cells from other sources including monocyte derived infiltrating macrophages. Understanding whether there are ultrastructural differences between these cells will provide a starting point for analysis of functional differences. Combining selective transgenic or expression markers and CLEM allows detection of ultrastructural features selective to specific populations.
Diagnosis & presentation and education - By visualizing nanoscale to microscale within a single dataset EM data is greatly facilitated to a broad audience. With the increased possibilities and tools for correlative and large-scale EM we anticipate a revival of EM in basic and medical research. Our method represented here is applied to a zebrafish brain injury model5, but has been used on human tissue7, rat brain3, in a rat model for diabetes4 and in cell culture2, and can also be used in combination with a TEM-based approach1 showing the versatility of this method. The microscope operator is no longer recording highly selected, and hence biased images, but all numerous ultrastructural features are recorded and open for worldwide analysis.