scholarly journals Electric egg-laying: a new approach for regulating C. elegans egg-laying behaviour in a microchannel using electric field

Lab on a Chip ◽  
2021 ◽  
Author(s):  
Khaled Youssef ◽  
Daphne Archonta ◽  
Terrance J. Kubiseski ◽  
Anurag Tandon ◽  
Pouya Rezai
Keyword(s):  
Electric Field ◽  
Egg Laying ◽  
Cellular Functions ◽  
New Approach ◽  
C Elegans ◽  
On Demand ◽  
Electrical Pulses ◽  
Egg Laying Behaviour

C. elegans egg laying behavior and the associated cellular functions can be stimulated on-demand using electrical pulses in a microchannel.

scholarly journals Electric Egg-Laying: Effect of Electric Field in a Microchannel on C. elegans Egg-Laying Behavior

2020 ◽  
Author(s):  
Khaled Youssef ◽  
Daphne Archonta ◽  
Terrance J. Kubiseski ◽  
Anurag Tandon ◽  
Pouya Rezai
Keyword(s):  
Electric Field ◽  
Neural Circuit ◽  
Response Duration ◽  
Egg Laying ◽  
Fluorescent Imaging ◽  
The Novel ◽  
Neural Basis ◽  
C Elegans ◽  
Intracellular Calcium Dynamics ◽  
Reduced Rate

ABSTRACTIn this paper, the novel effect of electric field (EF) on adult C. elegans egg-laying in a microchannel is discovered and correlated with neural and muscular activities. The quantitative effects of worm aging and EF strength, direction, and exposure duration on egg-laying is studied phenotypically using egg-count, body length, head movement, and transient neuronal activity readouts. Electric egg-laying rate increases significantly when worms face the anode and the response is EF-dependent, i.e. stronger (6V/cm) and longer EF (40s) exposure result in a shorter egg laying response duration. Worm aging significantly deteriorates the electric egg-laying behaviour with 88% decrease in the egg-count from Day-1 to Day-4 post young-adult stage. Fluorescent imaging of intracellular calcium dynamics in the main parts of the egg-laying neural circuit demonstrate the involvement and sensitivity of the serotonergic hermaphrodite specific neurons (HSNs), vulva muscles, and ventral cord neurons to the EF. HSN mutation also results in a reduced rate of electric egg-laying allowing the use of this technique for cellular screening and mapping of the neural basis of electrosensation in C. elegans. This novel assay can be parallelized and performed in a high-throughput manner for drug and gene screening applications.

On-demand dielectrophoretic immobilization and high-resolution imaging of C. elegans in microfluids

2018 ◽  
Vol 259 ◽  
pp. 703-708 ◽  
Author(s):  
Liang Huang ◽  
Peng Zhao ◽  
Junhan Wu ◽  
Han-Sheng Chuang ◽  
Wenhui Wang
Keyword(s):  
High Resolution ◽  
High Resolution Imaging ◽  
C Elegans ◽  
On Demand ◽  
Resolution Imaging

scholarly journals Luqin-like RYamide peptides regulate food-evoked responses in C. elegans

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Hayao Ohno ◽  
Morikatsu Yoshida ◽  
Takahiro Sato ◽  
Johji Kato ◽  
Mikiya Miyazato ◽  
...  
Keyword(s):  
Critical Role ◽  
Egg Laying ◽  
Serotonergic Neuron ◽  
Pacemaker Neurons ◽  
C Elegans ◽  
Peptide Signaling ◽  
Multiple Organs ◽  
Locomotory Behavior ◽  
Control Food ◽  
And Control

Peptide signaling controls many processes involving coordinated actions of multiple organs, such as hormone-mediated appetite regulation. However, the extent to which the mode of action of peptide signaling is conserved in different animals is largely unknown, because many peptides and receptors remain orphan and many undiscovered peptides still exist. Here, we identify two novel Caenorhabditis elegans neuropeptides, LURY-1-1 and LURY-1-2, as endogenous ligands for the neuropeptide receptor-22 (NPR-22). Both peptides derive from the same precursor that is orthologous to invertebrate luqin/arginine-tyrosine-NH2 (RYamide) proneuropeptides. LURY-1 peptides are secreted from two classes of pharyngeal neurons and control food-related processes: feeding, lifespan, egg-laying, and locomotory behavior. We propose that LURY-1 peptides transmit food signals to NPR-22 expressed in feeding pacemaker neurons and a serotonergic neuron. Our results identified a critical role for luqin-like RYamides in feeding-related processes and suggested that peptide-mediated negative feedback is important for satiety regulation in 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.

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