scholarly journals Action potentials contribute to neuronal signaling in C. elegans

2008 ◽  
Vol 11 (8) ◽  
pp. 865-867 ◽  
Author(s):  
Jerry E Mellem ◽  
Penelope J Brockie ◽  
David M Madsen ◽  
Andres V Maricq
Keyword(s):  
Action Potentials ◽  
Neuronal Signaling ◽  
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.

scholarly journals The quest for action potentials in C. elegans neurons hits a plateau

2009 ◽  
Vol 12 (4) ◽  
pp. 377-378 ◽  
Author(s):  
Shawn R Lockery ◽  
Miriam B Goodman
Keyword(s):  
Action Potentials ◽  
C Elegans

scholarly journals Reply to “First report of action potentials in a C. elegans neuron is premature”

2009 ◽  
Vol 12 (4) ◽  
pp. 366-366 ◽  
Author(s):  
Jerry E Mellem ◽  
Penelope J Brockie ◽  
David M Madsen ◽  
Andres V Maricq
Keyword(s):  
Action Potentials ◽  
First Report ◽  
C Elegans

scholarly journals First report of action potentials in a C. elegans neuron is premature

2009 ◽  
Vol 12 (4) ◽  
pp. 365-366 ◽  
Author(s):  
Shawn R Lockery ◽  
Miriam B Goodman ◽  
Serge Faumont
Keyword(s):  
Action Potentials ◽  
First Report ◽  
C Elegans

scholarly journals The C.elegans AWA Olfactory Neuron Fires Calcium-Mediated All-or-None Action Potentials

2018 ◽  
Author(s):  
Qiang Liu ◽  
Philip B. Kidd ◽  
May Dobosiewicz ◽  
Cornelia I. Bargmann
Keyword(s):  
Calcium Imaging ◽  
Action Potentials ◽  
Neural Coding ◽  
Patch Clamp Recording ◽  
Olfactory Neurons ◽  
C Elegans ◽  
Coding Schemes ◽  
Current Voltage ◽  
Ion Substitution ◽  
Specialized Function

SummaryWe find, unexpectedly, that C. elegans neurons can encode information through regenerative all-or-none action potentials. In a survey of current-voltage relationships in C. elegans neurons, we discovered that AWA olfactory neurons generate membrane potential spikes with defining characteristics of action potentials. Ion substitution experiments, pharmacology, and mutant analysis identified a voltage-gated CaV1 calcium channel and a Shaker-type potassium channel that underlie action potential dynamics in AWA. Simultaneous patch-clamp recording and calcium imaging in AWA revealed spike-associated calcium signals that were also observed after odor stimulation of intact animals, suggesting that natural odor stimuli induce AWA action potentials. The stimulus regimes that elicited action potentials match AWA’s proposed specialized function in climbing odor gradients. Our results provide evidence that C. elegans can use digital as well as analog coding schemes, expand the computational repertoire of its nervous system, and inform future modeling of its neural coding and network dynamics.

scholarly journals EGL-19 and EXP-2 Ion Channels Modelling within C. Elegans Pharyngeal Muscle Cell Allows Reproduction of CA2+ Driven Action Potentials, Both Single Events and Trains

2017 ◽  
Author(s):  
Andrey Yu. Palyanov ◽  
Khristina V. Samoilova ◽  
Natalia V. Palyanova
Keyword(s):  
Ion Channels ◽  
Muscle Cell ◽  
Action Potentials ◽  
Computational Models ◽  
Signal Transmission ◽  
Open Science ◽  
Time Profile ◽  
Simulation Environment ◽  
Pharyngeal Muscle ◽  
C Elegans

One of the current problems at the interface between neuroscience, biophysics, and computational modeling is the reverse-engineering and reproduction of Caenorhabditis elegans using computer simulation. The aim of our research was to develop the computational models and techniques for solving this problem while participating in the international open science OpenWorm Project. We have suggested models of a typical C. elegans neuron and a pharyngeal muscle cell, which were constructed and optimized using the NEURON simulation environment. The available experimental data about EGL-19 and EXP-2 ion channels allowed the model of a muscle to reproduce the action potential time profile correctly. Also, the model of a neuron reproduces quite accurately the mechanism of neural signal transmission based on passive propagation. We believe our models to be promising for better representing the specifics of various nervous and muscular cell classes when adding the corresponding ion channel models. Moreover, they can be used to construct the networks of such elements.

scholarly journals Genetic dissection of ion currents underlying all-or-none action potentials in C. elegans body-wall muscle cells

2010 ◽  
Vol 589 (1) ◽  
pp. 101-117 ◽  
Author(s):  
P. Liu ◽  
Q. Ge ◽  
B. Chen ◽  
L. Salkoff ◽  
M. I. Kotlikoff ◽  
...  
Keyword(s):  
Action Potentials ◽  
Body Wall ◽  
Muscle Cells ◽  
Body Wall Muscle ◽  
Genetic Dissection ◽  
Ion Currents ◽  
C Elegans

scholarly journals The Ephrin VAB-2/EFN-1 Functions in Neuronal Signaling to Regulate Epidermal Morphogenesis in C. elegans

Cell ◽  
1999 ◽  
Vol 99 (7) ◽  
pp. 781-790 ◽  
Author(s):  
Ian D Chin-Sang ◽  
Sean E George ◽  
Mei Ding ◽  
Sarah L Moseley ◽  
Andrew S Lynch ◽  
...  
Keyword(s):  
Neuronal Signaling ◽  
C Elegans ◽  
Epidermal Morphogenesis
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