The Healthspan Machine: an automated method to screen for interventions that slow ageing

People are living longer but many suffer several years of ill health leading to an increasing burden to the NHS, families and society in general. A greater understanding of the biology of ageing would allow researchers to design interventions that would keep the elderly mobile and generally healthy for longer. The time that someone stays healthy is termed their "healthspan", and we aim to find interventions that extend this time of health. Some of the most productive research in the field of ageing has used small lab animals because they age quickly and we can work out how their genes and environmental conditions influence ageing. The nematode worm Caenorhabditis elegans has a lifespan of only a few weeks, and studies with this animal have produced several insights in the biology of ageing, as well as in many other areas of biology. One of the strengths of using this worm for biological research is that it can be used for genetic screens. A genetic screen involves searching through a large number of mutant worms to find those that are different. Studying these particular mutants reveals the function of genes that make them different. However, finding a long-lived mutant is very difficult because even between genetically identical animals, ageing is very variable and the worms need to be observed over several weeks. This proposal addresses these problems.
Firstly, rather than looking for worms that live longer, we will find those that stay moving for longer, i.e. those with a longer "healthspan". Secondly, we will use automated techniques to measure worm movement. Our proposal is inspired by the "Lifespan Machine", which was recently developed at Harvard Medical School. Their method uses specially adapted high-specification flatbed scanners to measure the lifespan of large numbers of worms. While this technology works and has been taken up by a number of research groups, it can be challenging and expensive for users to implement and it consumes considerable space and energy. Our proposal overcomes these issues by using several small retail cameras to track worm movement. The "Lifespan Machine" detects the death of each worm. Our approach is to monitor how a group of worms slow down as they age. We have modified the software that runs the cameras so that hundreds of images of a group of worms are taken in a short space of time. Using techniques originally developed to monitor astronomical images, we process the worm images and use the processed images to measure the movement of the worms. Monitoring the groups of worms at regular time intervals will allow us to measure the decline in movement with age. By scaling up this technology we can test 1000s of different genes and conditions that might delay this decay of movement. We have generated a prototype of this "Healthspan Machine" with two cameras trained on two petri dishes containing worms. There are many obstacles to scaling up to a functioning machine using 100+ cameras but we have devised a set of solutions to overcome these challenges and a logical order of developing the machine. For example, we need to be able to store and process a large amount of data very quickly and we plan to do this by using a network of computers that communicate with the cameras. We will start with linking up 6 to 12 cameras to computers, and then link multiple computers together. Each computer will process the raw images from the attached cameras, producing smaller files so that further data processing and storage is easier. By the end of the project, we aim to have a functioning machine that can be used for many different screening experiments and used by other researchers across the world. These experiments will help us understand the genes and environmental conditions that lead to a long healthspan. We can then also use the "Healthspan Machine" to screen compounds for new nutritional and pharmaceutical interventions that keep us healthier for longer.

With extended longevity, ill health in old age is an increasing social and economic problem. Model organisms that age quickly enable genetic research into the biology of ageing. The nematode worm Caenorhabditis elegans has a lifespan of only a few weeks, and studies with this organism have produced several insights into the molecular nature of ageing. A strength of the C. elegans model for biological research is the ability to perform large screens for novel mutants. Screens for long-lived worms are more difficult than for other phenotypes because large cohort sizes are needed and multiple observations are required over several weeks. This proposal addresses these problems. Firstly, we will focus on healthspan, the time that worms stay healthy as measured by the time they move freely. Healthspan is shorter than lifespan, and arguably more relevant to the burden of reduced mobility in the elderly. Secondly, we will use automated imaging techniques to measure movement. Our proposal is inspired by the recently developed "Lifespan Machine", which uses specially adapted high-spec flatbed scanners to measure lifespan using large numbers of worms. This technology has advanced the field, but it can be challenging and expensive for the user to implement and uses considerable space and energy. Our proposal overcomes these issues by using several small retail cameras to track worm movement. The "Lifespan Machine" detects the time of death of each worm. Our approach is to monitor the movement profile of a defined cohort at regular time intervals to measure the decay of health with age, allowing screens for interventions that delay this decay. We have generated a prototype of what we term the "Healthspan Machine" consisting of two cameras trained on two plates of worms. Here we present viable strategies to overcome issues such as data management and analysis that are involved in scaling up to a machine of 100+ cameras. We will share this technology with the C. elegans community.

Academic Impact

The successful delivery of this project will transform the fields of ageing research, because it will enable screens for interventions that slow declines in health with age. It will form part of a general shift towards understanding what limits healthspan rather than what limits lifespan.

The tool will also be of great use for those using C. elegans as models for other diseases, such as infection with pathogenic microbes, neurodegeneration and sarcopenia, and will lead to impacts in those fields

Our group intends to use the "Healthspan Machine" to understand bacteria:animal interactions by screening libraries of bacterial mutants for those that prolong healthspan. Findings from this work will impact research on the microbiome and health.

The technology developed will form part of a community of researchers looking at automated solutions to studying ageing in other model organisms, such as Drosophila melanogaster.

Economic Impact

Pharmaceutical and Nutritional Industries

Drug development requires screens at multiple levels. The "Healthspan Machine" can provide a screening platform to find interventions that prolong healthspan. While it is difficult to get approval for a drug to slow ageing, the machine will be useful to screen for pharmaceuticals that slow the onset of age-related disease such as neurodegeneration and sarcopenia.

The "Healthspan Machine" can be used to screen for compounds and dietary interventions that influence the host:microbe interactions, leading to new products for the pharmaceutical and nutrition industries.

Societal Impact

Ill health in old age is a massive burden for the health service as well as families and carers of the elderly. Any interventions that extend healthspan will have enormous impact on society, especially given the ageing demographic in the UK and other countries.

The research could lead to a better understanding of what limits healthspan, leading to better advice on what to eat to prolong health.