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].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.2.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.
Neuronal Signaling by Thy-1 in Nanodomains With Specific Ganglioside Composition: Shall We Open the Door to a New Complexity?
