Discovery of novel biomarkers
Our research develops diagnostic and predictive biomarkers for cancer, cardiovascular disease, neurodegeneration, and infection. We generate data from patient samples including urine, blood, and tissue, to understand who will respond to specific treatment, or who may be more at risk of developing a life-threatening disease. We use a combination of quantitative analysis of proteins, advanced microscopy, nanovesicle-based protein detection, and high resolution and high throughput quantitative profiling of disease relevant nanoparticles using AI-based recognition. Using patient samples our research helps to understand biological changes that underpin disease and to enable rapid and early detection of changes in health. We develop new nanotechnologies to monitor changes at the cellular and subcellular level that can help inform early diagnosis of disease.
Our work is funded by the BBSRC, EPSRC, ERC Fellowship, British Heart Foundation, Tenovus Scotland, PanACEA & TB Alliance.
Biomarkers in Prostate Cancer Diagnosis, Digital pathology and AI research in digital diagnostics
The Powis laboratory is working on using extracellular vesicles present in the bloodstream as potential biomarkers for the early diagnosis and monitoring of a range of conditions including cancer, with current funded projects on breast cancer, lung cancer, melanoma and Covid-19. In particular, the laboratory is studying the peptide antigens presented on extracellular vesicles by human leucocyte antigen (HLA) proteins, that determine T cell immune responses in cancer and viral infections.
We are using quantitative mass spectrometry (SWATH-MS) to measure the levels of thousands of different proteins in patient plasma and will evaluate whether the levels of some of these molecules can predict a patient’s response to therapy. This will also give important information about how the body fights cancer in response to treatment and provide information regarding the tumour, such as how likely the tumour is to move to other parts of the body.
We use high resolution mass spectrometry to identify and quantify metabolites and changes in nucleotide pools that occur during cancer and in the presence of different treatments. This work helps us identify mechanisms of action and resistance to treatments, but also to confirm whether pathways for prodrug activation/degradation are taking place. This information advises on treatment success and options for combination therapy.
Diagnostic and predictive biomarkers for cardiovascular disease
Led by Dr Samantha Pitt
The Pitt lab is interested in molecular mechanisms of heart failure. A current focus of the group is identification of biomarkers that predispose individuals to develop anti-cancer drug induced cardiac toxicity to enable a more personalised approach to cancer therapy.
Diagnostic and predictive biomarkers for neurodegenerative disease
We are interested in the detection and characterisation of the “main suspects” of toxicity in neurodegenerative diseases such as Alzheimer’s or Parkinson’s. These species are ultra-small aggregates of proteins which contain amyloid structure and are highly heterogeneous in morphology and size. We develop high-resolution fluorescence techniques capable of characterising these amyloidogenic aggregates in human bio-fluids at a single-molecule level.
The Neurogenetics group led by Silvia Paracchini is studying the genetics of neurodevelopmental disorders to understand the nature of this condition and improve earlier and more accurate diagnosis. In addition to studies focusing on specific molecule we are active members of the GenLang Consortium (http://genlang.org/) aimed at dissecting the genetics of language disorders.
We are interested in why risk factors for disease translate into neurodegeneration. By studying the blood-based markers that change before patients get symptoms, we aim to prevent or halt the progress of neurodegeneration by designing targeted strategies to remove the risk. We use cell-based models of neurodegeneration to identify how neurodegeneration arises and what we can do to prevent damage translating to disease.
Diagnostic and predictive biomarkers for infection
Led by Dr Robert Hammond
Lipid inclusion vesicles (lipid bodies) have been shown to be associated with mycobacterial drug resistance and tolerance. Our work focusses on discovering the link between lipid body expression and treatment failure and relapse in patients. Having a toolkit to diagnose these patients earlier will mean that they will have more favourable outcomes but will also reduce the chance of antimicrobial resistance evolving in the population, further driving the global burden of TB.
Emerging nanotechnologies to detect biological changes for early diagnosis of disease
The Lucocq lab has developed high resolution/high throughput quantitative profiling of disease relevant nanoparticles using AI-based recognition/measurement from picolitre samples of human body fluids. Current projects include (1) plasma lipoprotein profiling for identification of CVD risk and genetic disease associations, (2) CSF/tear/plasma-based markers of neurodegenerative disease (dementia and Parkinson’s disease), (3) rapid virus diagnosis and (4) nanovesicle-based cancer detection.
The Penedo lab is interested in the development and application of single-molecule fluorescence methods to sense biomolecular interactions and to investigate the recognition of small organic molecules by protein complexes and nucleic acid sequences. Currently, we are focus on two main research areas: i) understanding the mechanisms of DNA repair and ii) visualizing gene regulation by non-coding RNAs in bacterial organisms as a potential tool to develop novel antibiotics. Because single-molecule detection provides the highest sensitivity level, the fundamental knowledge extracted from these studies will help us to develop fluorescence methods to report the presence of specific proteins and nucleic acid sequences in vitro and in vivo, from single cells to biological fluids.
The Di Falco lab is interested in the development of photonic membranes and nanosensors to measure early changes in cellular mechanics in disease.