TY - JOUR A1 - Markert, Sebastian Matthias A1 - Britz, Sebastian A1 - Proppert, Sven A1 - Lang, Marietta A1 - Witvliet, Daniel A1 - Mulcahy, Ben A1 - Sauer, Markus A1 - Zhen, Mei A1 - Bessereau, Jean-Louis A1 - Stigloher, Christian T1 - Filling the gap: adding super-resolution to array tomography for correlated ultrastructural and molecular identification of electrical synapses at the C. elegans connectome JF - Neurophotonics N2 - Correlating molecular labeling at the ultrastructural level with high confidence remains challenging. Array tomography (AT) allows for a combination of fluorescence and electron microscopy (EM) to visualize subcellular protein localization on serial EM sections. Here, we describe an application for AT that combines near-native tissue preservation via high-pressure freezing and freeze substitution with super-resolution light microscopy and high-resolution scanning electron microscopy (SEM) analysis on the same section. We established protocols that combine SEM with structured illumination microscopy (SIM) and direct stochastic optical reconstruction microscopy (dSTORM). We devised a method for easy, precise, and unbiased correlation of EM images and super-resolution imaging data using endogenous cellular landmarks and freely available image processing software. We demonstrate that these methods allow us to identify and label gap junctions in Caenorhabditis elegans with precision and confidence, and imaging of even smaller structures is feasible. With the emergence of connectomics, these methods will allow us to fill in the gap-acquiring the correlated ultrastructural and molecular identity of electrical synapses. KW - caenorhabditis elegans KW - localization micoscopy KW - fluorescent-probes KW - junction proteins KW - resolution limit KW - direct stochasticoptical reconstruction microscopy KW - structured illumination microscopy KW - correlative light and electron microscopy KW - gap junction KW - neural circuits KW - nervous-system KW - image data KW - reconstruction KW - innexins KW - super-resolution microscopy Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-187292 VL - 3 IS - 4 ER - TY - JOUR A1 - Britz, Sebastian A1 - Markert, Sebastian Matthias A1 - Witvliet, Daniel A1 - Steyer, Anna Maria A1 - Tröger, Sarah A1 - Mulcahy, Ben A1 - Kollmannsberger, Philip A1 - Schwab, Yannick A1 - Zhen, Mei A1 - Stigloher, Christian T1 - Structural Analysis of the Caenorhabditis elegans Dauer Larval Anterior Sensilla by Focused Ion Beam-Scanning Electron Microscopy JF - Frontiers in Neuroanatomy N2 - At the end of the first larval stage, the nematode Caenorhabditis elegans developing in harsh environmental conditions is able to choose an alternative developmental path called the dauer diapause. Dauer larvae exhibit different physiology and behaviors from non-dauer larvae. Using focused ion beam-scanning electron microscopy (FIB-SEM), we volumetrically reconstructed the anterior sensory apparatus of C. elegans dauer larvae with unprecedented precision. We provide a detailed description of some neurons, focusing on structural details that were unknown or unresolved by previously published studies. They include the following: (1) dauer-specific branches of the IL2 sensory neurons project into the periphery of anterior sensilla and motor or putative sensory neurons at the sub-lateral cords; (2) ciliated endings of URX sensory neurons are supported by both ILso and AMso socket cells near the amphid openings; (3) variability in amphid sensory dendrites among dauers; and (4) somatic RIP interneurons maintain their projection into the pharyngeal nervous system. Our results support the notion that dauer larvae structurally expand their sensory system to facilitate searching for more favorable environments. KW - FIB-SEM KW - 3D reconstruction KW - neuroanatomy KW - IL2 branching KW - amphids KW - Caenorhabditis elegans (C. elegans) KW - dauer Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-249622 SN - 1662-5129 VL - 15 ER - TY - JOUR A1 - Kaltdorf, Kristin Verena A1 - Theiss, Maria A1 - Markert, Sebastian Matthias A1 - Zhen, Mei A1 - Dandekar, Thomas A1 - Stigloher, Christian A1 - Kollmannsberger, Philipp T1 - Automated classification of synaptic vesicles in electron tomograms of C. elegans using machine learning JF - PLoS ONE N2 - Synaptic vesicles (SVs) are a key component of neuronal signaling and fulfil different roles depending on their composition. In electron micrograms of neurites, two types of vesicles can be distinguished by morphological criteria, the classical “clear core” vesicles (CCV) and the typically larger “dense core” vesicles (DCV), with differences in electron density due to their diverse cargos. Compared to CCVs, the precise function of DCVs is less defined. DCVs are known to store neuropeptides, which function as neuronal messengers and modulators [1]. In C. elegans, they play a role in locomotion, dauer formation, egg-laying, and mechano- and chemosensation [2]. Another type of DCVs, also referred to as granulated vesicles, are known to transport Bassoon, Piccolo and further constituents of the presynaptic density in the center of the active zone (AZ), and therefore are important for synaptogenesis [3]. To better understand the role of different types of SVs, we present here a new automated approach to classify vesicles. We combine machine learning with an extension of our previously developed vesicle segmentation workflow, the ImageJ macro 3D ART VeSElecT. With that we reliably distinguish CCVs and DCVs in electron tomograms of C. elegans NMJs using image-based features. Analysis of the underlying ground truth data shows an increased fraction of DCVs as well as a higher mean distance between DCVs and AZs in dauer larvae compared to young adult hermaphrodites. Our machine learning based tools are adaptable and can be applied to study properties of different synaptic vesicle pools in electron tomograms of diverse model organisms. KW - synaptic vesicles KW - Caenorhabditis elegans KW - machine learning Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-176831 VL - 13 IS - 10 ER -