Matches in SemOpenAlex for { <https://semopenalex.org/work/W2795304244> ?p ?o ?g. }
- W2795304244 abstract "1. 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. 2. Author summary Vesicles 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." @default.
- W2795304244 created "2018-04-06" @default.
- W2795304244 creator A5000765079 @default.
- W2795304244 creator A5005495276 @default.
- W2795304244 creator A5013707111 @default.
- W2795304244 creator A5032541817 @default.
- W2795304244 creator A5047679022 @default.
- W2795304244 creator A5063768132 @default.
- W2795304244 creator A5086202312 @default.
- W2795304244 date "2018-03-29" @default.
- W2795304244 modified "2023-09-27" @default.
- W2795304244 title "Automated classification of synaptic vesicles in electron tomograms of C. elegans using machine learning" @default.
- W2795304244 cites W1510073064 @default.
- W2795304244 cites W1556862815 @default.
- W2795304244 cites W1963760943 @default.
- W2795304244 cites W1966145280 @default.
- W2795304244 cites W1969600766 @default.
- W2795304244 cites W1975227291 @default.
- W2795304244 cites W2038878355 @default.
- W2795304244 cites W2040023425 @default.
- W2795304244 cites W2040910366 @default.
- W2795304244 cites W2046620350 @default.
- W2795304244 cites W2088019468 @default.
- W2795304244 cites W2089039527 @default.
- W2795304244 cites W2140050887 @default.
- W2795304244 cites W2160938187 @default.
- W2795304244 cites W2166987640 @default.
- W2795304244 cites W2518307773 @default.
- W2795304244 cites W2570322818 @default.
- W2795304244 cites W2729786921 @default.
- W2795304244 cites W4245333275 @default.
- W2795304244 cites W973549354 @default.
- W2795304244 doi "https://doi.org/10.1101/291310" @default.
- W2795304244 hasPublicationYear "2018" @default.
- W2795304244 type Work @default.
- W2795304244 sameAs 2795304244 @default.
- W2795304244 citedByCount "0" @default.
- W2795304244 crossrefType "posted-content" @default.
- W2795304244 hasAuthorship W2795304244A5000765079 @default.
- W2795304244 hasAuthorship W2795304244A5005495276 @default.
- W2795304244 hasAuthorship W2795304244A5013707111 @default.
- W2795304244 hasAuthorship W2795304244A5032541817 @default.
- W2795304244 hasAuthorship W2795304244A5047679022 @default.
- W2795304244 hasAuthorship W2795304244A5063768132 @default.
- W2795304244 hasAuthorship W2795304244A5086202312 @default.
- W2795304244 hasBestOaLocation W27953042441 @default.
- W2795304244 hasConcept C113246987 @default.
- W2795304244 hasConcept C120665830 @default.
- W2795304244 hasConcept C121332964 @default.
- W2795304244 hasConcept C12554922 @default.
- W2795304244 hasConcept C130316041 @default.
- W2795304244 hasConcept C148785051 @default.
- W2795304244 hasConcept C154945302 @default.
- W2795304244 hasConcept C169760540 @default.
- W2795304244 hasConcept C186060115 @default.
- W2795304244 hasConcept C187102610 @default.
- W2795304244 hasConcept C193016168 @default.
- W2795304244 hasConcept C202751555 @default.
- W2795304244 hasConcept C41008148 @default.
- W2795304244 hasConcept C41625074 @default.
- W2795304244 hasConcept C55493867 @default.
- W2795304244 hasConcept C65232495 @default.
- W2795304244 hasConcept C75806775 @default.
- W2795304244 hasConcept C86803240 @default.
- W2795304244 hasConcept C93877712 @default.
- W2795304244 hasConcept C95444343 @default.
- W2795304244 hasConceptScore W2795304244C113246987 @default.
- W2795304244 hasConceptScore W2795304244C120665830 @default.
- W2795304244 hasConceptScore W2795304244C121332964 @default.
- W2795304244 hasConceptScore W2795304244C12554922 @default.
- W2795304244 hasConceptScore W2795304244C130316041 @default.
- W2795304244 hasConceptScore W2795304244C148785051 @default.
- W2795304244 hasConceptScore W2795304244C154945302 @default.
- W2795304244 hasConceptScore W2795304244C169760540 @default.
- W2795304244 hasConceptScore W2795304244C186060115 @default.
- W2795304244 hasConceptScore W2795304244C187102610 @default.
- W2795304244 hasConceptScore W2795304244C193016168 @default.
- W2795304244 hasConceptScore W2795304244C202751555 @default.
- W2795304244 hasConceptScore W2795304244C41008148 @default.
- W2795304244 hasConceptScore W2795304244C41625074 @default.
- W2795304244 hasConceptScore W2795304244C55493867 @default.
- W2795304244 hasConceptScore W2795304244C65232495 @default.
- W2795304244 hasConceptScore W2795304244C75806775 @default.
- W2795304244 hasConceptScore W2795304244C86803240 @default.
- W2795304244 hasConceptScore W2795304244C93877712 @default.
- W2795304244 hasConceptScore W2795304244C95444343 @default.
- W2795304244 hasLocation W27953042441 @default.
- W2795304244 hasLocation W27953042442 @default.
- W2795304244 hasLocation W27953042443 @default.
- W2795304244 hasLocation W27953042444 @default.
- W2795304244 hasOpenAccess W2795304244 @default.
- W2795304244 hasPrimaryLocation W27953042441 @default.
- W2795304244 hasRelatedWork W128636301 @default.
- W2795304244 hasRelatedWork W1499946971 @default.
- W2795304244 hasRelatedWork W1970200112 @default.
- W2795304244 hasRelatedWork W1979849724 @default.
- W2795304244 hasRelatedWork W2035068294 @default.
- W2795304244 hasRelatedWork W2052742495 @default.
- W2795304244 hasRelatedWork W2413433610 @default.
- W2795304244 hasRelatedWork W2755798931 @default.