Stigloher, C. | bioRxiv Science

bioRxiv2018

Complexin cooperates with Bruchpilot to tether synaptic vesicles to the active zone cytomatrix

Information processing by the nervous system depends on the release of neurotransmitter from synaptic vesicles (SVs) at the presynaptic active zone. Molecular components of the cytomatrix at the active zone (CAZ) regulate the final stages of the SV cycle preceding exocytosis and thereby shape the efficacy and plasticity of synaptic transmission. Part of this regulation is reflected by a physical association of SVs with filamentous CAZ structures. However, our understanding of the protein interactions underlying SV tethering by the CAZ is far from complete. The very C-terminal region of Bruchpilot (Brp), a key component of the Drosophila CAZ, participates in SV tethering. Yet so far, no vesicular or cytoplasmic molecules have been reported to engage in an interaction with Brps C-terminus. Here, we carried out an in vivo screen for molecules that link the Brp C-terminus to SVs. This strategy identified the conserved SNARE (soluble N-ethylmaleimide-sensitive factor (NSF) attachment protein receptor) regulator Complexin (Cpx) as a vesicular interaction partner of Brp. We show that Brp and Cpx interact genetically and functionally. Interfering with Cpx targeting to SVs mirrored distinctive features of a C-terminal Brp truncation: impaired SV recruitment to the CAZ and enhanced short-term synaptic depression. Extending the study beyond Drosophila synapses, we interrogated active zones of mouse rod bipolar cells. Here, too, we collected evidence for an evolutionarily conserved role of Cpx upstream of SNARE complex assembly where it participates in SV tethering to the CAZ.

neuroscience↗

bioRxiv2018

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

1. AbstractSynaptic 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].\n\nTo 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.\n\n2. Author summaryVesicles are important components of the cell, and synaptic vesicles are central for neuronal signaling. Two types of synaptic vesicles can be distinguished by electron microscopy: the classical \"clear core\" vesicles (CCVs) and the typically larger \"dense core\" vesicles (DCVs). The distinct appearance of vesicles is caused by their different cargos. To rapidly distinguish between both vesicle types, we present here a new automated approach to classify vesicles in electron tomograms. We combine machine learning with an extension of our previously developed vesicle segmentation workflow, an ImageJ macro, to reliably distinguish CCVs and DCVs using specific image-based features. The approach was trained and validated using data-sets that were hand curated by microscopy experts. Our technique can be transferred to more extensive comparisons in both stages as well as to other neurobiology questions regarding synaptic vesicles.

neuroscience↗