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

点击翻译按钮获取中文摘要

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.