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Of Mice And Water Fleas: A New Way To Study Interventions In Ageing

Posted on 10 March 2022

The majority of evidence for interventions that can extend lifespan comes from just three types of organism: worms, flies, and rodents. The reasons for this are quite simple. These animals are inexpensive to look after, quick to breed, and don’t live very long lives, which means that the effects of interventions targeting ageing can be seen in within a reasonably short timescale. Mice in particular have become the go-to organism for testing therapies targeting ageing prior to human trials. Like humans, mice are mammals and share many similarities with us when it comes to their organs, tissues and genetic makeup.

These models aren’t perfect, of course – no animal model for human ageing ever will be. One major problem, especially for mouse models, is the lack of standardisation across different laboratories. Different studies use different breeds of mouse, some of which are genetically engineered to live shortened lifespans, making it difficult to compare results. Mouse studies also aren’t particularly scalable – running a study with 30 mice is a lot easier than running a study with 3000 mice.

Are there other animal models that can add something to the field of ageing research? Leonid Peshkin, a lecturer in systems biology in the Blavatnik Institute at Harvard Medical School, thinks so. He and his colleagues are working to develop a standardised, scalable system for studying ageing in Daphnia magna, a species of water flea. Daphnia is no stranger to scientific research – being transparent, its organs are clearly visible under a microscope, and it’s also very sensitive to small concentrations of drugs, which need only be introduced into the water that the Daphnia inhabits. These qualities make it well suited for certain research applications such as in toxicology, but what makes scientists think they might be useful for studying ageing?

Despite being invertebrates, Daphnia are surprisingly complex. They have two-chambered hearts and basic immune systems, alongside brains and muscles containing cells that look similar to the human equivalents under the microscope. Daphia live for about a month, and are clonal organisms, meaning that all offspring are genetically identical.

Daphnia undergo easily observable behavioural changes as they age, such as responding less well to light and being unable to jump long distances. Much like humans, the age of Daphnia can be predicted by observing how they move. In the system developed by Leonid Peshkin and colleagues, Daphnia are maintained in a fridge-sized incubator full of flat, one-litre tanks, and cameras record their movements in a one-minute video taken once a day. The movements of the Daphnia are then analysed using machine learning tools. These movements represent what scientists would call a functional biomarker – rather than looking for correlates of ageing at the cellular or molecular level, this system measures how ageing affects the day-to-day function of the animals. This is ultimately what we care about the most when it comes to treating ageing.

In a paper published in Ageing Cell, the researchers describe the system in detail, including exactly how they set up the tank, fed the animals, set the light cycles and so on. The point of this was to develop a set of routines that are needed to raise Daphnia in a standardized way that can be closely replicated and that can also be scaled up as needed.

Daphnia aren’t about to replace mice, which are still biologically closer to humans, when it comes to studying interventions targeting the ageing process. The advantages of this new system are its accessibility (Peshkin says that you could do these experiments in your basement), its scalability and the reproducibility of experiments. Detecting subtle effects of an intervention requires large sample sizes, preferably replicated across multiple laboratories, which this system makes easier. There is further potential for others to improve the system, for example by developing better machine learning algorithms to analyse the video recordings.

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