Massively parallel C. elegans tracking provides multi-dimensional fingerprints for phenotypic discovery.
Hardenberg, Maarten C
Roode, Lianne WY
Nollen, Ellen AA
Journal of neuroscience methods
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Perni, M., Challa, P., Kirkegaard, J. B., Limbocker, R., Koopman, M., Hardenberg, M. C., Sormanni, P., et al. (2018). Massively parallel C. elegans tracking provides multi-dimensional fingerprints for phenotypic discovery.. Journal of neuroscience methods, 306 57-67. https://doi.org/10.1016/j.jneumeth.2018.02.005
BACKGROUND: The nematode worm C. elegans is a model organism widely used for studies of genetics and of human disease. The health and fitness of the worms can be quantified in different ways, such as by measuring their bending frequency, speed or lifespan. Manual assays, however, are time consuming and limited in their scope providing a strong motivation for automation. NEW METHOD: We describe the development and application of an advanced machine vision system for characterising the behaviour of C. elegans, the Wide Field-of-View Nematode Tracking Platform (WF-NTP), which enables massively parallel data acquisition and automated multi-parameter behavioural profiling of thousands of worms simultaneously. RESULTS: We screened more than a million worms from several established models of neurodegenerative disorders and characterised the effects of potential therapeutic molecules for Alzheimer's and Parkinson's diseases. By using very large numbers of animals we show that the sensitivity and reproducibility of behavioural assays is very greatly increased. The results reveal the ability of this platform to detect even subtle phenotypes. COMPARISON WITH EXISTING METHODS: The WF-NTP method has substantially greater capacity compared to current automated platforms that typically either focus on characterising single worms at high resolution or tracking the properties of populations of less than 50 animals. CONCLUSIONS: The WF-NTP extends significantly the power of existing automated platforms by combining enhanced optical imaging techniques with an advanced software platform. We anticipate that this approach will further extend the scope and utility of C. elegans as a model organism.
Animals, Caenorhabditis elegans, Neurodegenerative Diseases, Disease Models, Animal, Data Interpretation, Statistical, Reproducibility of Results, Drug Evaluation, Preclinical, Behavior, Animal, Phenotype, Software, Pattern Recognition, Automated, Optical Imaging, Machine Learning
External DOI: https://doi.org/10.1016/j.jneumeth.2018.02.005
This record's URL: https://www.repository.cam.ac.uk/handle/1810/278494
Attribution-NonCommercial-NoDerivatives 4.0 International
Licence URL: http://creativecommons.org/licenses/by-nc-nd/4.0/
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