@article{MarkertBritzProppertetal.2016,
  author    = {Markert, Sebastian Matthias and Britz, Sebastian and Proppert, Sven and Lang, Marietta and Witvliet, Daniel and Mulcahy, Ben and Sauer, Markus and Zhen, Mei and Bessereau, Jean-Louis and Stigloher, Christian},
  title     = {Filling the gap: adding super-resolution to array tomography for correlated ultrastructural and molecular identification of electrical synapses at the C. elegans connectome},
  series = {Neurophotonics},
  volume    = {3},
  journal   = {Neurophotonics},
  number    = {4},
  doi       = {10.1117/1.NPh.3.4.041802},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-187292},
  pages     = {041802},
  year      = {2016},
  abstract  = {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.},
  language  = {en}
}
@phdthesis{Markert2021,
  author    = {Markert, Sebastian Matthias},
  title     = {Enriching the understanding of synaptic architecture from single synapses to networks with advanced imaging techniques},
  doi       = {10.25972/OPUS-18993},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-189935},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2021},
  abstract  = {Because of its complexity and intricacy, studying the nervous system is often challenging. Fortunately, the small nematode roundworm Caenorhabditis elegans is well established as a model system for basic neurobiological research. The C. elegans model is also the only organism with a supposedly complete connectome, an organism-wide map of synaptic connectivity resolved by electron microscopy, which provides some understanding of how the nervous system works as a whole. However, the number of available data-sets is small and the connectome contains errors and gaps. One example of this concerns electrical synapses. Electrical synapses are formed by gap junctions and difficult to map due to their often ambiguous morphology in electron micrographs, leading to misclassification or omission. On the other hand, chemical synapses are more easily mapped, but many aspects of their mode of operation remain elusive and their role in the C. elegans connectome is oversimplified. A comprehensive understanding of signal transduction of neurons between each other and other cells will be indispensable for a comprehensive understanding of the nervous system. In this thesis, I approach these challenges with a combination of advanced light and electron microscopy techniques. First, this thesis describes a strategy to increase synaptic specificity in connectomics. Specifically, I classify gap junctions with a high degree of confidence. To achieve this, I utilized array tomography (AT). In this thesis, AT is adapted for high-pressure freezing to optimize for structure preservation and for super-resolution light microscopy; in this manner, I aim to bridge the gap between light and electron microscopy resolutions. I call this adaptation super-resolution array tomography (srAT). The srAT approach made it possible to clearly identify and map gap junctions with high precision and accuracy. The results from this study showcased the feasibility of incorporating electrical synapses into connectomes in a systematic manner, and subsequent studies have used srAT for other models and questions. As mentioned above, the C. elegans connectomic model suffers from a shortage of datasets. For most larval stages, including the special dauer larval stage, connectome data is completely missing up to now. To obtain the first partial connectome data-set of the C. elegans dauer larva, we used focused ion-beam scanning electron microscopy (FIB-SEM). This technique offers an excellent axial resolution and is useful for acquiring large volumes for connectomics. Together with our collaborators, I acquired several data-sets which enable the analysis of dauer stage-specific "re-wiring" of the nervous system and thus offer valuable insights into connectome plasticity/variability. While chemical synapses are easy to map relative to electrical synapses, signal transduction via chemical transmitters requires a large number of different proteins and molecular processes acting in conjunction in a highly constricted space. Because of the small spatial scale of the synapse, investigating protein function requires very high resolution, which electron tomography provides. I analyzed electron tomograms of a worm-line with a mutant synaptic protein, the serine/threonine kinase SAD-1, and found remarkable alterations in several architectural features. My results confirm and re-contextualize previous findings and provide new insight into the functions of this protein at the chemical synapse. Finally, I investigated the effectiveness of our methods on "malfunctioning," synapses, using an amyotrophic lateral sclerosis (ALS) model. In the putative synaptopathy ALS, the mechanisms of motor neuron death are mostly unknown. However, mutations in the gene FUS (Fused in Sarcoma) are one known cause of the disease. The expression of the mutated human FUS in C. elegans was recently shown to produce an ALS-like phenotype in the worms, rendering C. elegans an attractive disease model for ALS. Together with our collaboration partners, I applied both srAT and electron tomography methods to "ALS worms" and found effects on vesicle docking. These findings help to explain electrophysiological recordings that revealed a decrease in frequency of mini excitatory synaptic currents, but not amplitudes, in ALS worms compared to controls. In addition, synaptic endosomes appeared larger and contained electron-dense filaments in our tomograms. These results substantiate the idea that mutated FUS impairs vesicle docking and also offer new insights into further molecular mechanisms of disease development in FUS-dependent ALS. Furthermore, we demonstrated the broader applicability of our methods by successfully using them on cultured mouse motor neurons. Overall, using the C. elegans model and a combination of light and electron microscopy methods, this thesis helps to elucidate the structure and function of neuronal synapses, towards the aim of obtaining a comprehensive model of the nervous system.},
  subject      = {Caenorhabditis elegans},
  language  = {en}
}
@article{BritzMarkertWitvlietetal.2021,
  author    = {Britz, Sebastian and Markert, Sebastian Matthias and Witvliet, Daniel and Steyer, Anna Maria and Tr{\"o}ger, Sarah and Mulcahy, Ben and Kollmannsberger, Philip and Schwab, Yannick and Zhen, Mei and Stigloher, Christian},
  title     = {Structural Analysis of the Caenorhabditis elegans Dauer Larval Anterior Sensilla by Focused Ion Beam-Scanning Electron Microscopy},
  series = {Frontiers in Neuroanatomy},
  volume    = {15},
  journal   = {Frontiers in Neuroanatomy},
  issn      = {1662-5129},
  doi       = {10.3389/fnana.2021.732520},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-249622},
  year      = {2021},
  abstract  = {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.},
  language  = {en}
}
@article{KaltdorfTheissMarkertetal.2018,
  author    = {Kaltdorf, Kristin Verena and Theiss, Maria and Markert, Sebastian Matthias and Zhen, Mei and Dandekar, Thomas and Stigloher, Christian and Kollmannsberger, Philipp},
  title     = {Automated classification of synaptic vesicles in electron tomograms of C. elegans using machine learning},
  series = {PLoS ONE},
  volume    = {13},
  journal   = {PLoS ONE},
  number    = {10},
  doi       = {10.1371/journal.pone.0205348},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-176831},
  pages     = {e0205348},
  year      = {2018},
  abstract  = {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.},
  language  = {en}
}