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Record W2056544905 · doi:10.3791/50226

Microfluidic-based Electrotaxis for On-demand Quantitative Analysis of <em>Caenorhabditis elegans</em>' Locomotion

2013· article· en· W2056544905 on OpenAlexafffund
Justin Tong, Pouya Rezai, Sangeena Salam, P. Ravi Selvaganapathy, Bhagwati P. Gupta

Bibliographic record

VenueJournal of Visualized Experiments · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Aging, and Longevity in Model Organisms
Canadian institutionsMcMaster University
FundersCanadian Institutes of Health ResearchOntario Ministry of Research and InnovationNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsCaenorhabditis elegansNeurodegenerationModel organismBiologyNeuroscienceDopaminergicNeuroprotectionGeneDiseaseGeneticsMedicinePathology

Abstract

fetched live from OpenAlex

The nematode Caenorhabditis elegans is a versatile model organism for biomedical research because of its conservation of disease-related genes and pathways as well as its ease of cultivation. Several C. elegans disease models have been reported, including neurodegenerative disorders such as Parkinson's disease (PD), which involves the degeneration of dopaminergic (DA) neurons (1). Both transgenes and neurotoxic chemicals have been used to induce DA neurodegeneration and consequent movement defects in worms, allowing for investigations into the basis of neurodegeneration and screens for neuroprotective genes and compounds (2,3). Screens in lower eukaryotes like C. elegans provide an efficient and economical means to identify compounds and genes affecting neuronal signaling. Conventional screens are typically performed manually and scored by visual inspection; consequently, they are time-consuming and prone to human errors. Additionally, most focus on cellular level analysis while ignoring locomotion, which is an especially important parameter for movement disorders. We have developed a novel microfluidic screening system (Figure 1) that controls and quantifies C. elegans' locomotion using electric field stimuli inside microchannels. We have shown that a Direct Current (DC) field can robustly induce on-demand locomotion towards the cathode ("electrotaxis") (4). Reversing the field's polarity causes the worm to quickly reverse its direction as well. We have also shown that defects in dopaminergic and other sensory neurons alter the swimming response (5). Therefore, abnormalities in neuronal signaling can be determined using locomotion as a read-out. The movement response can be accurately quantified using a range of parameters such as swimming speed, body bending frequency and reversal time. Our work has revealed that the electrotactic response varies with age. Specifically, young adults respond to a lower range of electric fields and move faster compared to larvae (4). These findings led us to design a new microfluidic device to passively sort worms by age and phenotype (6). We have also tested the response of worms to pulsed DC and Alternating Current (AC) electric fields. Pulsed DC fields of various duty cycles effectively generated electrotaxis in both C. elegans and its cousin C. briggsae (7). In another experiment, symmetrical AC fields with frequencies ranging from 1 Hz to 3 KHz immobilized worms inside the channel (8). Implementation of the electric field in a microfluidic environment enables rapid and automated execution of the electrotaxis assay. This approach promises to facilitate high-throughput genetic and chemical screens for factors affecting neuronal function and viability.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.347
Teacher spread0.329 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations19
Published2013
Admission routes2
Has abstractyes

Explore more

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