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Automated classification of synaptic vesicles in electron tomograms of C. elegans using machine learning

Please always quote using this URN: urn:nbn:de:bvb:20-opus-176831
  • 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 modulatorsSynaptic 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.show moreshow less

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Metadaten
Author: Kristin Verena Kaltdorf, Maria Theiss, Sebastian Matthias Markert, Mei Zhen, Thomas Dandekar, Christian StigloherORCiD, Philipp KollmannsbergerORCiD
URN:urn:nbn:de:bvb:20-opus-176831
Document Type:Journal article
Faculties:Fakultät für Biologie / Theodor-Boveri-Institut für Biowissenschaften
Fakultät für Biologie / Center for Computational and Theoretical Biology
Language:English
Parent Title (English):PLoS ONE
Year of Completion:2018
Volume:13
Issue:10
Pagenumber:e0205348
Source:PLoS ONE 2018, 13(10):e0205348. DOI: 10.1371/journal.pone.0205348
DOI:https://doi.org/10.1371/journal.pone.0205348
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie
Tag:Caenorhabditis elegans; machine learning; synaptic vesicles
Release Date:2019/02/27
Collections:Open-Access-Publikationsfonds / Förderzeitraum 2018
Licence (German):License LogoCC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International