Synaptic Specializations of Melanopsin-Retinal Ganglion Cells in Multiple Brain Regions Revealed by Genetic Label for Light and Electron Microscopy.

The form and synaptic fine structure of melanopsin-expressing retinal ganglion cells, also called intrinsically photosensitive retinal ganglion cells (ipRGCs), were determined using a new membrane-targeted version of a genetic probe for correlated light and electron microscopy (CLEM). ipRGCs project to multiple brain regions, and because the method labels the entire neuron, it was possible to analyze nerve terminals in multiple retinorecipient brain regions, including the suprachiasmatic nucleus (SCN), olivary pretectal nucleus (OPN), and subregions of the lateral geniculate. Although ipRGCs provide the only direct retinal input to the OPN and SCN, ipRGC terminal arbors and boutons were found to be remarkably different in each target region. A network of dendro-dendritic chemical synapses (DDCSs) was also revealed in the SCN, with ipRGC axon terminals preferentially synapsing on the DDCS-linked cells. The methods developed to enable this analysis should propel other CLEM studies of long-distance brain circuits at high resolution.

Probability Map Viewer: near real-time probability map generator of serial block electron microscopy collections.

SUMMARY: To expedite the review of semi-automated probability maps of organelles and other features from 3D electron microscopy data we have developed Probability Map Viewer, a Java-based web application that enables the computation and visualization of probability map generation results in near real-time as the data are being collected from the microscope. Probability Map Viewer allows the user to select one or more voxel classifiers, apply them on a sub-region of an active collection, and visualize the results as overlays on the raw data via any web browser using a personal computer or mobile device. Thus, Probability Map Viewer accelerates and informs the image analysis workflow by providing a tool for experimenting with and optimizing dataset-specific segmentation strategies during imaging. AVAILABILITY AND IMPLEMENTATION: https://github.com/crbs/probabilitymapviewer. CONTACT: mellisman@ucsd.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A workflow for the automatic segmentation of organelles in electron microscopy image stacks.

Electron microscopy (EM) facilitates analysis of the form, distribution, and functional status of key organelle systems in various pathological processes, including those associated with neurodegenerative disease. Such EM data often provide important new insights into the underlying disease mechanisms. The development of more accurate and efficient methods to quantify changes in subcellular microanatomy has already proven key to understanding the pathogenesis of Parkinson's and Alzheimer's diseases, as well as glaucoma. While our ability to acquire large volumes of 3D EM data is progressing rapidly, more advanced analysis tools are needed to assist in measuring precise three-dimensional morphologies of organelles within data sets that can include hundreds to thousands of whole cells. Although new imaging instrument throughputs can exceed teravoxels of data per day, image segmentation and analysis remain significant bottlenecks to achieving quantitative descriptions of whole cell structural organellomes. Here, we present a novel method for the automatic segmentation of organelles in 3D EM image stacks. Segmentations are generated using only 2D image information, making the method suitable for anisotropic imaging techniques such as serial block-face scanning electron microscopy (SBEM). Additionally, no assumptions about 3D organelle morphology are made, ensuring the method can be easily expanded to any number of structurally and functionally diverse organelles. Following the presentation of our algorithm, we validate its performance by assessing the segmentation accuracy of different organelle targets in an example SBEM dataset and demonstrate that it can be efficiently parallelized on supercomputing resources, resulting in a dramatic reduction in runtime.