scholarly journals PKA Activation Bypasses the Requirement for UNC-31 in the Docking of Dense Core Vesicles from C. elegans Neurons

Neuron ◽  
2007 ◽  
Vol 56 (4) ◽  
pp. 657-669 ◽  
Author(s):  
Ke-Ming Zhou ◽  
Yong-Ming Dong ◽  
Qian Ge ◽  
Dan Zhu ◽  
Wei Zhou ◽  
...  
Keyword(s):  
Dense Core ◽  
Dense Core Vesicles ◽  
C Elegans

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

2018 ◽  
Author(s):  
Kristin Verena Kaltdorf ◽  
Maria Theiss ◽  
Sebastian Matthias Markert ◽  
Mei Zhen ◽  
Thomas Dandekar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Synaptic Vesicles ◽  
Egg Laying ◽  
Dense Core ◽  
Dense Core Vesicles ◽  
Ground Truth Data ◽  
Neuronal Signaling ◽  
C Elegans ◽  
Morphological Criteria ◽  
Combine Machine

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.

UNC-31/CAPS docks and primes dense core vesicles in C. elegans neurons

2010 ◽  
Vol 397 (3) ◽  
pp. 526-531 ◽  
Author(s):  
Xian-Guang Lin ◽  
Min Ming ◽  
Mao-Rong Chen ◽  
Wei-Pin Niu ◽  
Yong-Deng Zhang ◽  
...  
Keyword(s):  
Dense Core ◽  
Dense Core Vesicles ◽  
C Elegans

Keeping Neuronal Cargoes on the Right Track: New Insights into Regulators of Axonal Transport

2016 ◽  
Vol 23 (3) ◽  
pp. 232-250 ◽  
Author(s):  
Kenneth G. Miller
Keyword(s):  
Retrograde Transport ◽  
Dense Core ◽  
System Model ◽  
Dense Core Vesicles ◽  
C Elegans ◽  
Early Endosomes ◽  
New Findings ◽  
Active Zones ◽  
The Right ◽  
Suppressor Screen

In neurons, a single motor (dynein) transports large organelles as well as synaptic and dense core vesicles toward microtubule minus ends; however, it is unclear why dynein appears more active on organelles, which are generally excluded from mature axons, than on synaptic and dense core vesicles, which are maintained at high levels. Recent studies in Zebrafish and Caenorhabditis elegans have shown that JIP3 promotes dynein-mediated retrograde transport to clear some organelles (lysosomes, early endosomes, and Golgi) from axons and prevent their potentially harmful accumulation in presynaptic regions. A JIP3 mutant suppressor screen in C. elegans revealed that JIP3 promotes the clearance of organelles from axons by blocking the action of the CSS system (Cdk5, SAD Kinase, SYD-2/Liprin). A synthesis of results in vertebrates with the new findings suggests that JIP3 blocks the CSS system from disrupting the connection between dynein and organelles. Most components of the CSS system are enriched at presynaptic active zones where they normally contribute to maintaining optimal levels of captured synaptic and dense core vesicles, in part by inhibiting dynein transport. The JIP3-CSS system model explains how neurons selectively regulate a single minus-end motor to exclude specific classes of organelles from axons, while at the same time ensuring optimal levels of synaptic and dense core vesicles.

Relationship between golgi apparatus and axonal reticulum in sympathetic ganglion cells

1978 ◽  
Vol 36 (2) ◽  
pp. 598-599
Author(s):  
J. Quatacker ◽  
W. De Potter
Keyword(s):  
Golgi Apparatus ◽  
Sympathetic Ganglion ◽  
Phosphotungstic Acid ◽  
Ganglion Cells ◽  
Low Ph ◽  
Dense Core ◽  
Dense Core Vesicles ◽  
Large Dense Core Vesicles ◽  
Cervical Ganglia ◽  
Axoplasmic Reticulum

Mucopolysaccharides have been demonstrated biochemically in catecholamine-containing subcellular particles in different rat, cat and ox tissues. As catecholamine-containing granules seem to arise from the Golgi apparatus and some also from the axoplasmic reticulum we examined wether carbohydrate macromolecules could be detected in the small and large dense core vesicles and in structures related to them. To this purpose superior cervical ganglia and irises from rabbit and cat and coeliac ganglia and their axons from dog were subjected to the chromaffin reaction to show the distribution of catecholamine-containing granules. Some material was also embedded in glycolmethacrylate (GMA) and stained with phosphotungstic acid (PTA) at low pH for the detection of carbohydrate macromolecules.The chromaffin reaction in the perikarya reveals mainly large dense core vesicles, but in the axon hillock, the axons and the terminals, the small dense core vesicles are more prominent. In the axons the small granules are sometimes seen inside a reticular network (fig. 1).

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