TY - JOUR A1 - Kaltdorf, Kristin Verena A1 - Schulze, Katja A1 - Helmprobst, Frederik A1 - Kollmannsberger, Philip A1 - Dandekar, Thomas A1 - Stigloher, Christian T1 - Fiji macro 3D ART VeSElecT: 3D automated reconstruction tool for vesicle structures of electron tomograms JF - PLoS Computational Biology N2 - Automatic image reconstruction is critical to cope with steadily increasing data from advanced microscopy. We describe here the Fiji macro 3D ART VeSElecT which we developed to study synaptic vesicles in electron tomograms. We apply this tool to quantify vesicle properties (i) in embryonic Danio rerio 4 and 8 days past fertilization (dpf) and (ii) to compare Caenorhabditis elegans N2 neuromuscular junctions (NMJ) wild-type and its septin mutant (unc-59(e261)). We demonstrate development-specific and mutant-specific changes in synaptic vesicle pools in both models. We confirm the functionality of our macro by applying our 3D ART VeSElecT on zebrafish NMJ showing smaller vesicles in 8 dpf embryos then 4 dpf, which was validated by manual reconstruction of the vesicle pool. Furthermore, we analyze the impact of C. elegans septin mutant unc-59(e261) on vesicle pool formation and vesicle size. Automated vesicle registration and characterization was implemented in Fiji as two macros (registration and measurement). This flexible arrangement allows in particular reducing false positives by an optional manual revision step. Preprocessing and contrast enhancement work on image-stacks of 1nm/pixel in x and y direction. Semi-automated cell selection was integrated. 3D ART VeSElecT removes interfering components, detects vesicles by 3D segmentation and calculates vesicle volume and diameter (spherical approximation, inner/outer diameter). Results are collected in color using the RoiManager plugin including the possibility of manual removal of non-matching confounder vesicles. Detailed evaluation considered performance (detected vesicles) and specificity (true vesicles) as well as precision and recall. We furthermore show gain in segmentation and morphological filtering compared to learning based methods and a large time gain compared to manual segmentation. 3D ART VeSElecT shows small error rates and its speed gain can be up to 68 times faster in comparison to manual annotation. Both automatic and semi-automatic modes are explained including a tutorial. KW - Biology KW - Vesicles KW - Caenorhabditis elegans KW - Zebrafish KW - Septins KW - Synaptic vesicles KW - Neuromuscular junctions KW - Computer software KW - Synapses Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-172112 VL - 13 IS - 1 ER - TY - THES A1 - Kaltdorf [geb. Schuch], Kristin Verena T1 - Mikroskopie, Bildverarbeitung und Automatisierung der Analyse von Vesikeln in \(C.\) \(elegans\) und anderen biologischen Strukturen T1 - Microscopy, Image Processing and Automization of Analysis of Vesicles in \(C.\) \(elegans\) and other biological Structures N2 - Thema dieser Thesis ist die Analyse sekretorischer Vesikelpools auf Ultrastrukturebene in unterschiedlichen biologischen Systemen. Der erste und zweite Teil dieser Arbeit fokussiert sich auf die Analyse synaptischer Vesikelpools in neuromuskulären Endplatten (NME) im Modellorganismus Caenorhabditis elegans. Dazu wurde Hochdruckgefrierung und Gefriersubstitution angewandt, um eine unverzügliche Immobilisation der Nematoden und somit eine Fixierung im nahezu nativen Zustand zu gewährleisten. Anschließend wurden dreidimensionale Aufnahmen der NME mittels Elektronentomographie erstellt. Im ersten Teil dieser Arbeit wurden junge adulte, wildtypische C. elegans Hermaphroditen mit Septin-Mutanten verglichen. Um eine umfassende Analyse mit hoher Stichprobenzahl zu ermöglichen und eine automatisierte Lösung für ähnliche Untersuchungen von Vesikelpools bereit zu stellen wurde eine Software namens 3D ART VeSElecT zur automatisierten Vesikelpoolanalyse entwickelt. Die Software besteht aus zwei Makros für ImageJ, eines für die Registrierung der Vesikel und eines zur Charakterisierung. Diese Trennung in zwei separate Schritte ermöglicht einen manuellen Verbesserungsschritt zum Entfernen falsch positiver Vesikel. Durch einen Vergleich mit manuell ausgewerteten Daten neuromuskulärer Endplatten von larvalen Stadien des Modellorganismus Zebrafisch (Danio rerio) konnte erfolgreich die Funktionalität der Software bewiesen werden. Die Analyse der neuromuskulären Endplatten in C. elegans ergab kleinere synaptische Vesikel und dichtere Vesikelpools in den Septin-Mutanten verglichen mit Wildtypen. Im zweiten Teil der Arbeit wurden neuromuskulärer Endplatten junger adulter C. elegans Hermaphroditen mit Dauerlarven verglichen. Das Dauerlarvenstadium ist ein spezielles Stadium, welches durch widrige Umweltbedingungen induziert wird und in dem C. elegans über mehrere Monate ohne Nahrungsaufnahme überleben kann. Da hier der Vergleich der Abundanz zweier Vesikelarten, der „clear-core“-Vesikel (CCV) und der „dense-core“-Vesikel (DCV), im Fokus stand wurde eine Erweiterung von 3D ART VeSElecT entwickelt, die einen „Machine-Learning“-Algorithmus zur automatisierten Klassifikation der Vesikel integriert. Durch die Analyse konnten kleinere Vesikel, eine erhöhte Anzahl von „dense-core“-Vesikeln, sowie eine veränderte Lokalisation der DCV in Dauerlarven festgestellt werden. Im dritten Teil dieser Arbeit wurde untersucht ob die für synaptische Vesikelpools konzipierte Software auch zur Analyse sekretorischer Vesikel in Thrombozyten geeignet ist. Dazu wurden zweidimensionale und dreidimensionale Aufnahmen am Transmissionselektronenmikroskop erstellt und verglichen. Die Untersuchung ergab, dass hierfür eine neue Methodik entwickelt werden muss, die zwar auf den vorherigen Arbeiten prinzipiell aufbauen kann, aber den besonderen Herausforderungen der Bilderkennung sekretorischer Vesikel aus Thrombozyten gerecht werden muss. N2 - Subject of this thesis was the analysis of the ultrastructure of vesicle pools in various biological systems. The first and second part of this thesis is focused on the analysis of synaptic vesicle pools in neuromuscular junctions in the model organism Caenorhabditis elegans. In order to get access of synaptic vesicle pools in their near-to native state high-pressure freezing and freeze substitution was performed. Subsequently three-dimensional imaging of neuromuscular junctions using electron tomography was performed. In the first part young adult wild-type C. elegans hermaphrodites and septin mutants were compared. To enable extensive analysis and to provide an automated solution for comparable studies, a software called 3D ART VeSElecT for automated vesicle pool analysis, was developed. The software is designed as two macros for ImageJ, one for registration of vesicles and one for characterization. This separation allows for a manual revision step in between to erase false positive particles. Through comparison with manually evaluated data of neuromuscular junctions of larval stages of the model organism zebrafish (Danio rerio), functionality of the software was successfully proved. As a result, analysis of C. elegans neuromuscular junctions revealed smaller synaptic vesicles and more densely packed vesicle pools in septin mutants compared to wild-types. In the second part of this thesis NMJs of young adult C. elegans hermaphrodites were compared with dauer larvae. The dauer larva is a special state that is induced by adverse environmental conditions and enables C. elegans to survive several months without any foot uptake. Aiming for an automated analysis of the ratio of two vesicle types, clear core vesicles (CCVs) and dense core vesicles (DCVs), an extension for 3D ART VeSElecT was developed, integrating a machine-learning classifier. As a result, smaller vesicles and an increased amount of dense core vesicles in dauer larvae were found. In the third part of this thesis the developed software, designed for the analysis of synaptic vesicle pools, was checked for its suitability to recognize secretory vesicles in thrombocytes. Therefore, two-dimensional and three-dimensional transmission electron microscopic images were prepared and compared. The investigation has shown that a new methodology has to be developed which, although able to build on the previous work in principle, must meet the special challenges of image recognition of secretory vesicles from platelets. KW - Mikroskopie KW - Bildverarbeitung KW - Registrierung KW - Synaptische Vesikel KW - Bildanalyse KW - Automatisierung der Analyse KW - Automated Image Analysis KW - Caenorhabditis elegans KW - Electron Microscopy KW - Elektronenmikroskopie KW - Caenorhabditis elegans KW - automatisierte Bildanalyse Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-160621 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 -