Morphology and location of dense-core vesicles in the stomatogastric ganglion of the lobster, Panulirus interruptus

1976 ◽  
Vol 175 (3) ◽  
Author(s):  
BrendaJ. Friend
Keyword(s):  
Stomatogastric Ganglion ◽  
Dense Core ◽  
Dense Core Vesicles ◽  
Panulirus Interruptus

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).

Synaptic regulation of cellular properties and burst oscillations of neurons in gastric mill system of spiny lobsters, Panulirus interruptus

1984 ◽  
Vol 52 (1) ◽  
pp. 54-73 ◽  
Author(s):  
D. F. Russell ◽  
D. K. Hartline
Keyword(s):  
Motor Neurons ◽  
Pattern Generator ◽  
Stomatogastric Ganglion ◽  
Gastric Mill ◽  
Panulirus Interruptus ◽  
Synaptic Regulation ◽  
Gastric Cells ◽  
Current Pulses ◽  
Regenerative Property ◽  
Stimulation Of

The properties of neurons in the stomatogastric ganglion (STG) participating in the pattern generator for the gastric mill rhythm were studied by intracellular current injection under several conditions: during ongoing gastric rhythms, in the nonrhythmic isolated STG, after stimulation of the nerve carrying central nervous system (CNS) inputs to the STG, or under Ba2+ or Sr2+. Slow regenerative depolarizations during ongoing rhythms were demonstrated in the anterior median, cardiopyloric, lateral cardiac, gastropyloric, and continuous inhibitor (AM, CP, LC, GP, and CI) neurons according to criteria such as voltage dependency, burst triggering, and termination by brief current pulses, etc. Experiments showed that regenerative-like behavior was not due to synaptic network interactions. The slow regenerative responses were abolished by isolating the stomatogastric ganglion but could be reestablished by stimulating the input nerve. This indicates that certain CNS inputs synaptically induce the regenerative property in specific gastric neurons. Slow regenerative depolarizations were not demonstrable in gastric mill (GM) motor neurons. Their burst oscillations and firing rate were instead proportional to injected current. CNS inputs evoked a prolonged depolarization in GM motor neurons, apparently by a nonregenerative mechanism. All the gastric cells showed prolonged regenerative potentials under 0.5-1.5 mM Ba2+. We conclude that the gastric neurons of the STG can be divided into three types according to their properties: those with a regenerative capability, a repetitively firing type, and a nonregenerative "proportional" type. The cells are strongly influenced by several types of CNS inputs, including "gastric command fibers."

scholarly journals HID-1 controls formation of large dense core vesicles by influencing cargo sorting and trans-Golgi network acidification

2017 ◽  
Vol 28 (26) ◽  
pp. 3870-3880 ◽  
Author(s):  
Blake H. Hummer ◽  
Noah F. de Leeuw ◽  
Christian Burns ◽  
Lan Chen ◽  
Matthew S. Joens ◽  
...  
Keyword(s):  
Biochemical Properties ◽  
Neuroendocrine Cells ◽  
Dense Core ◽  
Core Formation ◽  
Dense Core Vesicles ◽  
Atpase Subunit ◽  
Trans Golgi Network ◽  
Large Dense Core Vesicles ◽  
Golgi Network ◽  
Cargo Sorting

Large dense core vesicles (LDCVs) mediate the regulated release of neuropeptides and peptide hormones. They form at the trans-Golgi network (TGN), where their soluble content aggregates to form a dense core, but the mechanisms controlling biogenesis are still not completely understood. Recent studies have implicated the peripheral membrane protein HID-1 in neuropeptide sorting and insulin secretion. Using CRISPR/Cas9, we generated HID-1 KO rat neuroendocrine cells, and we show that the absence of HID-1 results in specific defects in peptide hormone and monoamine storage and regulated secretion. Loss of HID-1 causes a reduction in the number of LDCVs and affects their morphology and biochemical properties, due to impaired cargo sorting and dense core formation. HID-1 KO cells also exhibit defects in TGN acidification together with mislocalization of the Golgi-enriched vacuolar H+-ATPase subunit isoform a2. We propose that HID-1 influences early steps in LDCV formation by controlling dense core formation at the TGN.

scholarly journals Synaptotagmin VII Is Targeted to Dense-core Vesicles and Regulates Their Ca2+-dependent Exocytosis in PC12 Cells

2004 ◽  
Vol 279 (50) ◽  
pp. 52677-52684 ◽  
Author(s):  
Mitsunori Fukuda ◽  
Eiko Kanno ◽  
Megumi Satoh ◽  
Chika Saegusa ◽  
Akitsugu Yamamoto
Keyword(s):  
Plasma Membrane ◽  
Neuropeptide Y ◽  
Cell Line ◽  
Pc12 Cells ◽  
Pc12 Cell ◽  
Small Interfering Rna ◽  
Dense Core ◽  
Dense Core Vesicles ◽  
Pc12 Cell Line ◽  
Interfering Rna

It has recently been proposed that synaptotagmin (Syt) VII functions as a plasma membrane Ca2+sensor for dense-core vesicle exocytosis in PC12 cells based on the results of transient overexpression studies using green fluorescent protein (GFP)-tagged Syt VII; however, the precise subcellular localization of Syt VII is still a matter of controversy (plasma membraneversussecretory granules). In this study we established a PC12 cell line “stably expressing” the Syt VII-GFP molecule and demonstrated by immunocytochemical and immunoelectron microscopic analyses that the Syt VII-GFP protein is localized on dense-core vesicles as well as in other intracellular membranous structures, such as thetrans-Golgi network and lysosomes. Syt VII-GFP forms a complex with endogenous Syts I and IX, but not with Syt IV, and it colocalize well with Syts I and IX in the cellular processes (where dense-core vesicles are accumulated) in the PC12 cell line. We further demonstrated by an N-terminal antibody-uptake experiment that Syt VII-GFP-containing dense-core vesicles undergo Ca2+-dependent exocytosis, the same as endogenous Syt IX-containing vesicles. Moreover, silencing of Syt VII-GFP with specific small interfering RNA dramatically reduced high KCl-dependent neuropeptide Y secretion from the stable PC12 cell line (∼60% of the control cells), whereas the same small interfering RNA had little effect on neuropeptide Y secretion from the wild-type PC12 cells (∼85–90% of the control cells), indicating that the level of endogenous expression of Syt VII molecules must be low. Our results indicate that the targeting of Syt VII-GFP molecules to specific membrane compartment(s) is affected by the transfection method (transient expressionversusstable expression) and suggested that Syt VII molecule on dense-core vesicles functions as a vesicular Ca2+sensor for exocytosis in endocrine cells.

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.

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