Funded TREE projects - Spring 2018

Motor Neurone Disease (MND) is a brain disease with no cure that causes loss of motor nerve cells and leads to paralysis and eventually death. A specific class of molecules, called microRNAs, are known to be involved in the disease, but the exact mechanisms are still unknown. Measuring how many microRNA molecules are present as well as how these molecules are controlled in disease tissues is therefore a crucial step in understanding MND. Current techniques require the use of 3 separate methods which become very expensive and cannot be performed in cases where sample availability is low (e.g. clinical samples, post-mortem tissue). We propose to develop software that will use information derived only from a single inexpensive experiment (H3K27Ac ChIP-Seq) to reconstruct all the data necessary for the identification of microRNAs that cause MND.

Human tissue is made up of thousands of different cells and disease is usually the result of a cell malfunction. That is why we need to study individual cells. However, current approaches to isolate and study individual cells are laborious, expensive and require large amount of tissues. Our proposed method will allow us to conduct powerful experiments into how diseased cells function at the single cell level by analysis of thousands of single cells from low amounts of starting material. With this project, we hope to be able to identify the way nerves evolve from normal to being diseased. We will develop a new method in which thousands of neurons can be screened at a time, enabling us to get an overview of the disease progression. This in turn will inform us on the best way to tackle disease and find novel therapeutic approaches. Once developed, our method can be easily applied to the study of other diseases such as cancer. Ultimately, this research will make inroads into the field of personalised medicine: finding treatment targeted to specific individuals.

The development of new drugs to treat human disease is currently a time-consuming and expensive process. This results in delays in the delivery of treatments to patients and high costs of the drugs that are developed.

The aim of this project is to develop new methods that will greatly speed up and reduce the cost of finding new drugs, which is required in the early stages of drug discovery. We have developed a new screening method, called mVDA, which allows more than one possible drug to be tested at the same time, rather than one at a time, as in previous methods. We have already used this method to double the screening efficiency but the method has the potential to improve screening efficiency by at least 20 times.

This seed corn award will allow us to increase the number of genes that can be tested at the same time. We will do this by applying mathematical approaches to distinguish effects caused by each individual gene tested.

After completing this project, we will apply for additional funding to scale-up the method for use in drug-target screens. This will allow us to apply it to identify candidate targets for the development of new cancer treatments.

Senescence is the process of ageing. Senescent cells are old cells and are a good model to study how we age. This project aims to create a mathematical model to help us to understand how cells age. Cells have markers that tell us how old they are. However, none of those markers are unique and specific to senescence. Our plan is to work with different aged cells and measure to common senescence markers. With this data, we will build a mathematical model that will tell us how these markers are related and should be combined to create an index. This index could predict how old a cell is. The project would contribute hugely to ageing research.  Giving an easy new way to quantify the level of senescence, we could compare the effectiveness of anti-ageing therapies.

Cancer is a major health issue in the UK and around the world and is one of the leading causes of human mortality. Cancer is diagnosed by examining a small piece of tissue removed from a patient. The examination is carried out by a pathologist to look out for molecular changes (such as in DNA, protein) or cell shape, size, etc under a microscope. This invasive procedure is often stressful to the patients.  

Mucus, a gel-like substance present in the body also undergoes molecular changes during the process of cancer development. Through this project, we aim to identify such changes in mucus by using infrared light. This approach can reveal information on the type of molecular changes occurring in the samples. Importantly, we will use computer modelling to identify these changes and link them to the presence of cancer.

This approach could be developed into a new way of cancer screening/diagnosis by using mucus that can easily be collected from patients without any surgery. The infrared light based techniques used here can be automated so the routine can be performed rapidly which can reduce the patient waiting times in hospitals.