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Correlative microscopy incorporates the specificity of fluorescent protein labeling into high-resolution electron micrographs. Several approaches exist for correlative microscopy, most of which have used the green fluorescent protein (GFP) as the label for light microscopy. Here we use chemical tagging and synthetic fluorophores instead, in order to achieve protein-specific labeling, and to perform multicolor imaging. We show that synthetic fluorophores preserve their post-embedding fluorescence in the presence of uranyl acetate. Post-embedding fluorescence is of such quality that the specimen can be prepared with identical protocols for scanning electron microscopy (SEM) and transmission electron microscopy (TEM); this is particularly valuable when singular or otherwise difficult samples are examined. We show that synthetic fluorophores give bright, well-resolved signals in super-resolution light microscopy, enabling us to superimpose light microscopic images with a precision of up to 25 nm in the x-y plane on electron micrographs. To exemplify the preservation quality of our new method we visualize the molecular arrangement of cadherins in adherens junctions of mouse epithelial cells.
Cryo-electron tomography provides a snapshot of the cellular proteome. With template matching, the spatial positions of various macromolecular complexes within their native cellular context can be detected. However, the growing awareness of the reference bias introduced by the cross-correlation based approaches, and more importantly the lack of a reliable confidence measurement in the selection of these macromolecular complexes, has restricted the use of these applications. Here we propose a heuristic, in which the reference bias is measured in real space in an analogous way to the R-free value in X-ray crystallography. We measure the reference bias within the mask used to outline the area of the template, and do not modify the template itself. The heuristic works by splitting the mask into a working and a testing area in a volume ratio of 9:1. While the working area is used during the calculation of the cross-correlation function, the information from both areas is explored to calculate the M-free score. We show using artificial data, that the M-free score gives a reliable measure for the reference bias. The heuristic can be applied in template matching and in sub-tomogram averaging. We further test the applicability of the heuristic in tomograms of purified macromolecules, and tomograms of whole Mycoplasma cells.