Targeted studies using serial block face and focused ion beam scan electron microscopy

This protocol allows for the efficient and effective imaging of cell or tissue samples in three dimensions at the resolution level of electron microscopy. For many years electron microscopy (EM) has remained an inherently two-dimensional technique. With the advent of serial scanning electron microscope imaging techniques (volume EM), using either an integrated microtome or focused ion beam to slice then view embedded tissues, the third dimension becomes easily accessible. Serial block face scanning electron microscopy (SBF-SEM) uses an ultramicrotome enclosed in the SEM chamber. It has the capability to handle large specimens (1,000 mu m x 1,000 mu m) and image large fields of view at small X,Y pixel size, but is limited in the Z dimension by the diamond knife. Focused ion beam SEM (FIB-SEM) is not limited in 3D resolution, (isotropic voxels of <= 5 nm are achievable), but the field of view is much more limited. This protocol demonstrates a workflow for combining the two techniques to allow for finding individual regions of interest (ROls) in a large field and then imaging the subsequent targeted volume at high isotropic voxel resolution. Preparing fixed cells or tissues is more demanding for volume EM techniques due to the extra contrasting needed for efficient signal generation in SEM imaging. Such protocols are time consuming and labor intensive. This protocol also incorporates microwave assisted tissue processing facilitating the penetration of reagents, which reduces the time needed for the processing protocol from days to hours

Collaborating by courier, imaging by mail

Core facilities offer visiting scientists access to equipment and expertise to generate and analyze data. For some projects, it might however be more efficient to collaborate remotely by sending in samples

Three-dimensional visualization of APEX2-tagged Erg11 in Saccharomyces cerevisiae using Focused Ion Beam Scanning Electron Microscopy

The determination of the exact location of a protein in the cell is essential to the understanding of biological processes. Here, we report for the first time the visualization of a protein of interest in Saccharomyces cerevisiae using focused ion beam scanning electron microscopy (FIB-SEM). As a proof of concept, the integral endoplasmic reticulum (ER) membrane protein Erg11 has been C-terminally tagged with APEX2, which is an engineered peroxidase that catalyzes an electron-dense deposition of 3,3'-diaminobenzidine (DAB), as such marking the location of the fused protein of interest in electron microscopic images. As DAB is unable to cross the yeast cell wall to react with APEX2, cell walls have been partly removed by the formation of spheroplasts. This has resulted in a clear electron-dense ER signal for the Erg11 protein using FIB-SEM. With this study, we have validated the use of the APEX2 tag for visualization of yeast proteins in electron microscopy. Furthermore, we have introduced a methodology that enables precise and three-dimensional (3D) localization studies in yeast, with nanometer resolution and without the need for antibody staining. Because of these properties, the described technique can offer valuable information on the molecular functions of studied proteins. IMPORTANCE With this study, we have validated the use of the APEX2 tag to define the localization of proteins in the model yeast S. cerevisiae. As such, FIB-SEM can identify the exact 3D location of a protein of interest in the cell with nanometerscale resolution. Such detailed imaging could provide essential information on the elucidation of various biological processes. APEX2, which adds electron density to a fused protein of interest upon addition of the substrate DAB, originally was used in mammalian studies. With this study, we expand its use to protein localization studies in one of the most important models in molecular biology

Cytosolic delivery of nanolabels prevents their asymmetric inheritance and enables extended quantitative in vivo cell imaging

Long-term in vivo imaging of cells is crucial for the understanding of cellular fate in biological processes in cancer research, immunology, or in cell-based therapies such as beta cell transplantation, in type I diabetes or stem cell therapy. Traditionally, cell labeling with the desired contrast agent occurs ex vivo via spontaneous endocytosis, which is a variable and slow process that requires optimization for each particular label-cell type combination. Following endocytic uptake, the contrast agents mostly remain entrapped in the endolysosomal compartment, which leads to signal instability, cytotoxicity, and asymmetric inheritance of the labels upon cell division. Here, we demonstrate that these disadvantages can be circumvented by delivering contrast agents directly into the cytoplasm via vapor nanobubble photoporation. Compared to classic endocytic uptake, photoporation resulted in :50 and 3 times higher loading of fluorescent dextrans and quantum dots, respectively, with improved signal stability and reduced cytotoxicity: Most: interestingly, cytosolic delivery by iihotoporation prevented asymmetric inheritance of labels by daughter cells over subsequent cell' generations. Instead, unequal inheritance of endocytosed labels resulted in a dramatic increase in polydispersity of the amount of labels per cell with each cell division, hindering accurate quantification of cell numbers in vivo over time. The combined benefits of cell labeling by photoporation resulted in a marked improvement in long-term cell visibility in vivo where an insulin producing cell line (INS-1E cell line) labeled with fluorescent dextrans could be tracked for up to two months in Swiss nude mice compared to 2-Weeks for cells labeled by endocytosis

Beclin 1 A role in membrane dynamics and beyond

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Regulation of the expression and processing of caspase-12

Phylogenetic analysis clusters caspase-12 with the inflammatory caspases 1 and 11. We analyzed the expression of caspase-12 in mouse embryos, adult organs, and different cell types and tested the effect of interferons (IFNs) and other proinflammatory stimuli. Constitutive expression of the caspase-12 protein was restricted to certain cell types, such as epithelial cells, primary fibroblasts, and L929 fibrosarcoma cells. In fibroblasts and B16/B16 melanoma cells, caspase-12 expression is stimulated by IFN-γ but not by IFN-α or -β. The effect is increased further when IFN-γ is combined with TNF, lipopolysaccharide (LPS), or dsRNA. These stimuli also induce caspase-1 and -11 but inhibit the expression of caspase-3 and -9. In contrast to caspase-1 and -11, no caspase-12 protein was detected in macrophages in any of these treatments. Transient overexpression of full-length caspase-12 leads to proteolytic processing of the enzyme and apoptosis. Similar processing occurs in TNF-, LPS-, Fas ligand–, and thapsigargin (Tg)-induced apoptosis. However, B16/B16 melanoma cells die when treated with the ER stress–inducing agent Tg whether they express caspase-12 or not

An interactive ImageJ plugin for semi-automated image denoising in electron microscopy

The recent advent of 3D in electron microscopy (EM) has allowed for detection of nanometer resolution structures. This has caused an explosion in dataset size, necessitating the development of automated workflows. Moreover, large 3D EM datasets typically require hours to days to be acquired and accelerated imaging typically results in noisy data. Advanced denoising techniques can alleviate this, but tend to be less accessible to the community due to low-level programming environments, complex parameter tuning or a computational bottleneck. We present DenoisEM: an interactive and GPU accelerated denoising plugin for ImageJ that ensures fast parameter tuning and processing through parallel computing. Experimental results show that DenoisEM is one order of magnitude faster than related software and can accelerate data acquisition by a factor of 4 without significantly affecting data quality. Lastly, we show that image denoising benefits visualization and (semi-)automated segmentation and analysis of ultrastructure in various volume EM datasets