Meet the Expert: Dr Claudia Manzoni

16 August 2022

Dr Claudia Manzoni is a Lecturer in Translational Neuroscience at the UCL School of Pharmacy and studies the complex nature of neurodegenerations.

Tell us more about your background – it would be great to find out more about your education and career so far.

I have a BSc/MSc in Pharmaceutical Biotechnology from the University of Milan, in Italy. After my degree, I enrolled in a PhD program as “student abroad”, meaning I was an Open University (UK) student while working in Milan at the partner Institution (Mario Negri Institute for Pharmacological Research). My PhD revolved around the toxicity of the amyloid material deposited in the brain during Alzheimer’s disease.

At the end of my PhD, in 2010, I moved to UCL (Institute of Neurology) to study the protein LRRK2 in the context of Parkinson’s disease. I was involved in the first studies that linked LRRK2 to the autophagy process, meaning LRRK2, one of the most frequently mutated genes in Parkinson’s disease, may have a role in controlling how the cells deal with the waste they produce. I was working in a genetic department, therefore, for the first time, I was exposed to population genetics and bioinformatics, and these novel topics really intrigued me.

In 2014 I moved to the University of Reading, still working on LRRK2. However, I kept myself involved in collaborations with geneticists and busy with the study of bioinformatics. Slowly but surely, I transitioned from a fully trained wet-lab researcher to a bioinformatician with a track-record in genomics of complex neurodegenerations.

I returned to UCL in 2020 at the UCL School of Pharmacy as Lecturer in Translational Neuroscience.

What area of your work most excites you and why?

During the past decade, the biomedical field has generated an incredible amount of data and this trend is not going to change anytime soon. On the contrary, due to constant technical improvements and increased availability of informatic platforms we will probably keep on producing even larger amounts of data. Examples of these data are: the sequence of our genome, information related to the expression of different genes in different tissues, levels of proteins and metabolites in biofluids. These data are not used to their full potential yet, in fact, as our knowledge progresses and the amount of accessible data increases, we realize there are novel pipelines we can implement to discover more about disease and potential treatments.

It is very exiting to know that I am part of a very large team of researchers dedicated to the study of large datasets with the goal of translating them into information and applications that will benefit patients.

For example, one of the projects in the lab is focused on teaching a computer how “to read” different types of patients’ data to find patterns that are invisible to the human eye but can be spotted by an algorithm and use them as aid in Parkinson’s diagnostics.

What do you like about working at the UCL School of Pharmacy?

I started at the School of Pharmacy at the beginning of March 2020. I went to the office for the first week, the second week we were suggested to work from home if possible, the third week we were in full lock-down. Despite such a challenging start, I felt I was at home. And this feeling hasn’t changed. My experience at the School of Pharmacy is that of a “family environment”, where people are easy to approach and they are really helping each other, with support and care.

What do you enjoy most about teaching and lecturing your students?

In my opinion, the best moment in teaching is when you realize your student has perfectly understood a critical piece of information or elaborated their way through a complex idea as you have guided them in becoming independent, critical thinkers.

This happens for example when you spend hours thinking about how to explain a complicated concept in the lecture and then you correct the exam papers and students have done a wonderful job in elaborating on it.

It also happens when you put a lot of effort in tutoring your student during their first steps of coding and then, all of the sudden, they reach independence, do not need further help or guidance and rather, become even better than you at programming.

What’s your next big challenge in terms of your research?

Let me start by saying that I believe challenge is the driving force of research.

I mentioned before the latest project for which I have obtained funding is focused on teaching a computer how “to read” different types of patients’ data to find patterns that are invisible to the human eye but can be spotted by an algorithm for Parkinson’s diagnostics. This project sits at the cutting edge of research, it is exciting and at the same time it is a big challenge. This type of approach can be classified with the term “machine learning”. I am extremely excited about the use of machine learning to evaluate large biomedical datasets, as these types of approaches will pave the way for artificial intelligence to become the future of medicine.

What motivates you at the start of each day?

Many years ago, I was watching a movie and a man on a starship suddenly got ill. The doctor scanned the man with a small device that fitted the palm of his hand, to gather all information needed, from the genome of the patient, to the levels of metabolites in the blood, even the presence of toxins in the body. At the end of the analysis, it suggested the best therapy taking all into account. This is my vision for the future of medicine, a day when we will easily collect all the patient’s data and a computer will help us in processing them to find the best therapy tailored for the specific patient in the specific moment of their life (we know this as “precision medicine”). There are hundreds of scientists working for this science-fiction dream to become a reality. Knowing I am one of them is very rewarding and gives me the motivation to continue with my research.

Another motivation to start the day are my students. I am rewarded by the feeling of being helpful while supporting them in setting the roots of their careers.

What advice would you give your younger self?

I would suggest my younger self to look for a wise and caring tutor, someone outside of my research project willing and able not only to advise on a career path but also to support me as an individual (rather than a professional). There have been few occasions through the years in which the presence of such an overarching support would have made things much more simple and less stressful.