A Fast Method for the Segmentation of Synaptic Junctions and Mitochondria in Serial Electron Microscopic Images of the Brain

Recent electron microscopy (EM) imaging techniques permit the automatic acquisition of a large number of serial sections from brain samples. Manual segmentation of these images is tedious, time-consuming and requires a high degree of user expertise. Therefore, there is considerable interest in developing automatic segmentation methods. However, currently available methods are computationally demanding in terms of computer time and memory usage, and to work properly many of them require image stacks to be isotropic, that is, voxels must have the same size in the X, Y and Z axes. We present a method that works with anisotropic voxels and that is computationally efficient allowing the segmentation of large image stacks. Our approach involves anisotropy-aware regularization via conditional random field inference and surface smoothing techniques to improve the segmentation and visualization. We have focused on the segmentation of mitochondria and synaptic junctions in EM stacks from the cerebral cortex, and have compared the results to those obtained by other methods. Our method is faster than other methods with similar segmentation results. Our image regularization procedure introduces high-level knowledge about the structure of labels. We have also reduced memory requirements with the introduction of energy optimization in overlapping partitions, which permits the regularization of very large image stacks. Finally, the surface smoothing step improves the appearance of three-dimensional renderings of the segmented volumes.Peer Reviewe

Correlated confocal and super-resolution imaging by VividSTORM

Single-molecule localization microscopy (SMLM) is rapidly gaining popularity in the life sciences as an efficient approach to visualize molecular distribution with nanoscale precision. However, it has been challenging to obtain and analyze such data within a cellular context in tissue preparations. Here we describe a 5-d tissue processing and immunostaining procedure that is optimized for SMLM, and we provide example applications to fixed mouse brain, heart and kidney tissues. We then describe how to perform correlated confocal and 3D-superresolution imaging on these sections, which allows the visualization of nanoscale protein localization within labeled subcellular compartments of identified target cells in a few minutes. Finally, we describe the use of VividSTORM (http://katonalab.hu/index.php/vividstorm), an open-source software for correlated confocal and SMLM image analysis, which facilitates the measurement of molecular abundance, clustering, internalization, surface density and intermolecular distances in a cell-specific and subcellular compartment–restricted manner. The protocol requires only basic skills in tissue staining and microscopy