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

Healthspan Machine：一种筛选延缓衰老干预措施的自动化方法

• 批准号: BB/N021649/1
• 负责人: David Weinkove
• 金额: $ 19.29万
• 依托单位: Durham University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FN021649%2F1
• 关键词: Healthspan; Machine; automated; method; screen

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.

人们的寿命越来越长，但许多人的健康状况不佳，导致NHS，家庭和社会的负担越来越重。对衰老生物学的更深入了解将使研究人员能够设计干预措施，使老年人能够更长时间地保持移动的和总体健康。一个人保持健康的时间被称为他们的“健康寿命”，我们的目标是找到延长这一健康时间的干预措施。在衰老领域，一些最有成效的研究使用了小型实验室动物，因为它们衰老得很快，我们可以研究出它们的基因和环境条件如何影响衰老。线虫秀丽隐杆线虫的寿命只有几周，对这种动物的研究在衰老生物学以及许多其他生物学领域产生了一些见解。使用这种蠕虫进行生物研究的优势之一是它可以用于基因筛选。基因筛选包括在大量的突变蠕虫中寻找不同的蠕虫。研究这些特殊的突变体揭示了使它们不同的基因的功能。然而，找到一个长寿的突变体是非常困难的，因为即使在基因相同的动物之间，衰老也是非常可变的，而且需要观察几个星期。这个建议解决了这些问题。首先，我们将找到那些保持运动时间更长的蠕虫，而不是寻找寿命更长的蠕虫，即那些具有更长“健康寿命”的蠕虫。其次，我们将使用自动化技术来测量蠕虫运动。我们的建议是受到“寿命机器”的启发，这是最近在哈佛医学院开发的。他们的方法使用专门改装的高规格平板扫描仪来测量大量蠕虫的寿命。虽然这项技术有效，并且已经被许多研究小组采用，但对于用户来说，实施它可能具有挑战性和昂贵，并且消耗大量的空间和能源。我们的建议通过使用几个小型零售摄像头来跟踪蠕虫运动来克服这些问题。“寿命机器”检测每个蠕虫的死亡。我们的方法是监测一组蠕虫如何随着年龄的增长而变慢。我们已经修改了运行摄像机的软件，以便在短时间内拍摄一组蠕虫的数百张图像。使用最初开发用于监测天文图像的技术，我们处理蠕虫图像，并使用处理后的图像来测量蠕虫的运动。定期监测蠕虫群将使我们能够测量随着年龄的增长而下降的运动。通过扩大这项技术的规模，我们可以测试1000种不同的基因和条件，这些基因和条件可能会延缓这种运动的衰退。我们已经生成了一个原型的这个“健康跨度机”与两个摄像机训练两个培养皿含有蠕虫。使用100多个摄像头来扩展到一台功能正常的机器存在许多障碍，但我们已经设计了一套解决方案来克服这些挑战，并制定了开发机器的逻辑顺序。例如，我们需要能够非常快速地存储和处理大量数据，我们计划通过使用与摄像机通信的计算机网络来实现这一目标。我们将首先将6到12台摄像机连接到计算机，然后将多台计算机连接在一起。每台计算机将处理来自附加相机的原始图像，生成更小的文件，以便进一步的数据处理和存储更容易。到项目结束时，我们的目标是拥有一台功能齐全的机器，可用于许多不同的筛选实验，并供世界各地的其他研究人员使用。这些实验将帮助我们了解导致长寿的基因和环境条件。然后，我们还可以使用“健康跨度机器”来筛选新的营养和药物干预化合物，使我们更健康。

项目成果

Neuronal SKN-1B modulates nutritional signalling pathways and mitochondrial networks to control satiety.

• DOI: 10.1371/journal.pgen.1009358
• 发表时间: 2021-03
• 期刊: PLoS genetics
• 影响因子: 4.5
• 作者: Tataridas-Pallas N;Thompson MA;Howard A;Brown I;Ezcurra M;Wu Z;Silva IG;Saunter CD;Kuerten T;Weinkove D;Blackwell TK;Tullet JMA
• 通讯作者: Tullet JMA

Rapid measurement of ageing by automated monitoring of movement of C. elegans populations.

• DOI: 10.1007/s11357-023-00998-w
• 发表时间: 2024-04
• 期刊: GEROSCIENCE
• 影响因子: 5.6
• 作者: Zavagno, Giulia;Raimundo, Adelaide;Kirby, Andy;Saunter, Christopher;Weinkove, David
• 通讯作者: Weinkove, David