I define myself as an NMR (Nuclear Magnetic Resonance) spectroscopist and throughout my career, I have worked in a variety of fields with NMR being a constant theme. I started my career as inorganic chemist working on the characterisation of the metal centre of metaloproteins and soon after developed a strong interest structural biology and the characterisation of protein function. To solve the solution structure of a protein it is necessary to acquire multidimensional NMR spectra. The experiments can be complicated and require detailed knowledge of different NMR parameters and how they can be manipulated to solve the structure of a protein. This led me to work in NMR pulse sequence development- in order to be able to optimise the experiments myself- and obtain more information about my protein samples.
As I have always been interested in how NMR can be used to answer biologically relevant questions, I soon became interested in Metabonomics and have now been working in the field for about six years. The NMR knowledge required to obtain the necessary data is relatively basic but provides detailed information about the biochemistry of a biological system that can be used for diagnostics, prognostics and for monitoring drug metabolism (amongst many other applications).
I have now been working at Imperial College for two years where I in charge of the NMR section of the Clinical Phenotyping Center. It is a very challenging and exciting role that aims to bring together medicine and research.
Our focus is applying NMR-based metabolomics to the diagnosis and prognosis of disease. To do so we have recently installed three NMR spectrometers in a hospital setting (Saint Mary's) where scientists and clinicians work closely to make this project a reality.
et al., 2021, A computationally lightweight algorithm for deriving reliable metabolite panel measurements from 1D 1H NMR., Analytical Chemistry, Vol:93, ISSN:0003-2700, Pages:4995-5000
et al., 2021, Neuroendocrine neoplasms: identification of novel metabolic circuits of potential diagnostic utility, Cancers, Vol:13, ISSN:2072-6694
et al., 2020, Urinary metabolic phenotyping for Alzheimer's disease, Scientific Reports, Vol:10, ISSN:2045-2322
et al., 2020, SMolESY: An efficient and quantitative alternative to on-instrument macromolecular ¹H-NMR signal suppression, Chemical Science, Vol:11, ISSN:2041-6520, Pages:6000-6011
et al., 2020, Performance of metabonomic serum analysis for diagnostics in paediatric tuberculosis, Scientific Reports, Vol:10, ISSN:2045-2322, Pages:1-11