Biological ageing can be studied using C. elegans worms which share similarity to mammalian systems. However, manual experiments used in these studies are incredibly time-consuming and must be repeated many times for reproducibility. Scientists in the Kaeberlein Lab, at the University of Washington, have created a new system called the WormBot which allows high-throughput automated experiments with precise results.
Caenorhabditis elegans (C. elegans) is a species of roundworm that is used in many biological experiments as a simple animal model. It is particularly useful for studying ageing, with a view to developing therapies that prolong life while maintaining health. Many of the C. elegans genes are shared with humans, and studies over the past three decades confirm that the biological mechanisms of ageing are highly conserved between worms and people (indicating that they have been maintained by natural selection). The hallmarks of ageing have been investigated in these worms leading to many new targets being discovered.
C. elegans have short lifespans of only a few weeks, which means that we can study ageing much more quickly in worms compared to humans, or even laboratory mice which live a few years. Worms are also very small and have a highly defined cell lineage. All these factors make them the perfect candidates for experiments to determine genetic factors that modulate the ageing process, and to develop drugs that might slow or even reverse it.
Previous experiments on C. elegans ageing have mostly been done manually. This involves looking at each worm individually under a microscope every day to determine how the experiment is progressing. While this is informative, it takes a long time to set up, and there is much room for human error. As with much scientific research, there also a problem with reproducibility because experiments are often underpowered or not sufficiently replicated prior to publication. Automating these methods is key. To overcome some of these shortcomings, the Kaeberlein Lab and investigators at the University of Washington Nathan Shock Center developed the WormBot.
“The WormBot platform consists of an unbiased, high-throughput, automated robotic system and corresponding AI-guided software.”
How the WormBot works
The WormBot is a robotics system which uses an XY plotter robot to move a USB microscope camera over transparent dishes containing multiple small populations of C. elegans. The microscope captures repeated images of each container, and these images are used to measure health and survival throughout life. The system is set up so that hundreds of parallel experiments can run simultaneously. The web-based interface is also designed so that everyone in a lab can analyse and share data with each other. It is easy to use and experiments can be scheduled for maximum efficiency.
Data can be collected from the WormBot in two different ways. The first is the time-lapse feature which takes an image of each population at roughly ten-minute intervals. Once these images are put together it’s possible to get an overview of each animal’s lifespan and determine the time of death. The second type of data collection is the daily-monitor feature, which takes a video lasting up to five minutes, of each population once a day. This can be used to assess the health of worms in each population based on how they are moving, feeding, and reproducing. Most previous experiments focus on the lifespan of a worm, so incorporating these ‘healthspan’ measures is an exciting new feature of the WormBot. The significance of the healthspan cannot be overlooked. If a discovery is made that extends a lifespan, it is only going to be relevant for human use if health is also maintained for quality of life. Therefore, being able to study healthspan using this technology is very valuable.
There are also two different approaches to analyse data produced by the WormBot. First, images can be manually annotated by the user through a graphical interface. This can be helpful for noting novel phenotypes, or if a point of reference is desired – either for rigour and reproducibility, or at the request of a reviewer. The second is a novel machine-learning package that uses state-of-the-art techniques to automatically identify and track individual C. elegans as they age and senesce. This package has been extensively validated against manual experimenter annotation, and accurately detects treatment-induced shifts in lifespan relative to controls. Furthermore, this fully automated analysis also captures pertinent physiological metrics such as rate of growth, behaviour, and body morphology as a function of age. This allows each potential intervention to be simultaneously screened for both longevity and healthspan in a high-throughput, automated manner.
In addition to longer-term experiments, such as lifespans, the WormBot is also useful for shorter assays that sometimes take place over a matter of hours or a few days. Because the robot can image each experiment at ten-minute intervals or even capture real-time movies, it is possible to obtain multiple, high-resolution time-points for even very short-term experiments such as toxicity assays, pathogen sensitivity experiments, and stress resistance. For example, the WormBot has proven useful for detecting the effects of lethal toxins such as cyanide, resistance to infection, and tolerance to heat shock. With the WormBot it is now possible to carry out hundreds of such experiments simultaneously.
The importance of reproducibility
Lack of reproducibility has become recognised as a large problem in scientific research. The WormBot greatly enhances rigour and reproducibility. The high-throughput nature and automation of the WormBot system allows researchers to perform every experiment multiple times with large numbers of animals, providing a high degree of statistical confidence. Because the system is fully automated once the experiments are set up, there is no risk of experimenter bias influencing the collected data. Importantly, every image taken by the WormBot includes an embedded timestamp, and the raw data (images) from every experiment is preserved as a unique video file. Investigators are then able to go back and examine each video after-the-fact and even include videos as supplemental materials in their publications.
“The WormBot’s high-throughput system, where many experiments are carried out at once, ensures data integrity.”
To summarise, Elena Vayndorf explains, ‘The WormBot platform consists of an unbiased, high-throughput, automated robotic system and corresponding AI-guided software, to perform genetic and pharmacological quantification of lifespan and health measures in C. elegans and related nematode species’. The WormBot experiments demonstrate that time of death for these worms can be correctly determined. It can therefore be used in ageing experiments with confidence of accurate results. The WormBot has also been shown to overcome some of the drawbacks associated with other automated systems. Time and money can be saved in setting up and analysing these experiments. The healthspan of the worms can be assessed, which is a fundamental addition to longevity studies. When comparing results to those gained from manual experiments, a similar level of precision is observed at a fraction of the cost in human time and effort. Reproducibility is incredibly important: the WormBot’s high-throughput system, where many experiments are carried out at once, can be used to ensure this data integrity.
Our goal for the future is to increase experimental output by many-fold. We are working on screening several drug and natural products libraries as well as optimising our neural network analysis software to extract additional features associated with health span. We also hope to develop ‘ageing clocks’ that can be used to predict mortality at the individual level based on image features in young animals. Finally, we are extending the power of the WormBot to worm models of human diseases, in order to carry out screens for small molecules that may be novel therapies for a variety of disorders.
References
- Pitt, JN, Strait, NL, Vayndorf, EM, Blue, BW, Tran, CH, et al, (2019) WormBot, an open-source robotics platform for survival and behavior analysis in C. elegans. GeroScience, 41(6), 961–973. doi.org/10.1007/s11357-019-00124-9
10.26904/RF-139-2205900194
Research Objectives
WormBot is an artificial intelligence-coupled robotics system for high-throughput lifespan and behavioural phenotyping in C. elegans.
Funding
University of Washington Nathan Shock Center of Excellence in the Basic Biology of Aging, NIH grant P30AG013280 to MK.
Bio
Dr Elena M Vayndorf is a research scientist at the Department of Laboratory Medicine and Pathology at the University of Washington in Seattle. Her research involves preclinical screening of natural products and FDA-approved drugs to identify candidates that promote healthy ageing and delay the onset of neurodegenerative disease.
Dr Jason N Pitt is a research scientist and engineer in the Department of Laboratory Medicine and Pathology at the University of Washington in Seattle. His research focuses on novel interventions that slow ageing and neurodegenerative disease. He has authored numerous scientific publications and is the creator of the WormBot system (wormbot.org).
Ben Blue (PhD) is a PhD student in the Molecular Medicine and Mechanisms of Disease training programme and is a member of the Kaeberlein lab. His background is in C. elegans ageing and laboratory automation with a current focus on machine learning and drug discovery.
Dr Matt Kaeberlein is a Professor of Laboratory Medicine and Pathology at the University of Washington. Dr Kaeberlein directs the Healthy Aging and Longevity Research Institute, the Nathan Shock Center of Excellence in the Basic Biology of Aging, and the Biological Mechanisms of Healthy Aging Training Program at UW.
Contact
E: kaeber@uw.edu
T: +1 206 221 4849
W: www.wormbot.org
W: halo.dlmp.uw.edu/wormbot
