Combining Analytical Chemistry with Multivariate Statistical Methods at the Interface of Medicine and Biology
My research focuses on using a multidisciplinary combination of analytical chemistry, mathematics, and biology techniques to understand the chemistry of small molecule pathways associated with disease. I am particularly interested in better understanding the chemistry of small molecule pathways associated with inflammation in the brain and the effect of therapy on such pathways, with the overall aim of identifying novel drug targets, developing therapeutics, and improving diagnostics for neurodegenerative diseases.
Working with Us
If you are interesting in working with us on multidisciplinary projects at the interface of Chemistry and Medicine as either a Part II, DPhil, or PDR please contact Dr Probert directly.
Further information on applying for DPhil funding in Chemistry as part of the Oxford Centre for Doctoral Training can be found here.
Inflammatory Neurodegenerative Diseases
Neurodegenerative diseases, including Alzheimer’s disease and Multiple Sclerosis (MS), are the leading cause of disability and second leading cause of death world-wide. Although chronic inflammation is a hallmark of such diseases, the mechanisms by which inflammation contributes to cell death are not understood. However, we have previously shown that inflammation in the brain generates profound small molecule changes in blood which are consistent with significantly compromised energy metabolism [PMID: 29208041, 31659123, 31693024, 32709911]. Our current work aims to better understand the relationship between these small molecule patterns (the metabolome) in the blood and the chemical changes associated with different cell types of the brain. We are establishing the viability of using nuclear magnetic resonance spectroscopy (NMR) analysis of blood as a cost effective, non-invasive, and rapid method of diagnosing and monitoring MS progression.
The field of metabolomics uses analytical chemistry methods (predominantly NMR and mass spectrometry) and advanced statistical analysis to characterise small molecules (metabolites) in biological samples (human, rodent, cell culture) with the aim of better understanding the chemistry associated with disease in complex biological systems, identifying novel biomarkers, and discovering novel drug targets. Metabolomics is a rapidly growing field with a wide range of applications in disease diagnostics, personalised medicine, toxicology, food technology, and systems biology. The direct link between the metabolome and the molecular phenotype means that this powerful approach is ideal for the study of diseases where environment, lifestyle, and genetic factors are believed to play a combined role.
Nuclear Magnetic Resonance Analysis of Biofluids
NMR is a highly reproducible technique that requires minimal sample preparation and allows analysis of blood samples in under 10 minutes. The intrinsically quantitative and non-selective nature of NMR allows every molecule in a blood sample (above a sensitivity threshold) to be measured simultaneously. While the basic molecules that make up blood remain the same across individuals, the relative quantities of the metabolites differ and produce a distinctive pattern that is unique. Thus, a single NMR experiment provides a huge amount of information within a molecular ‘fingerprint’, which is representative of the blood sample. As a result, NMR provides significant advantages over other techniques that can only quantify a single molecule at a time and, by analysing the fingerprint of metabolites in combination, can provide a more powerful means of diagnosis. For example, an increase in the amount of molecule A alone may not be sufficient to diagnose or stage a disease while increases in molecules A and B, combined with a decrease in molecule C could enable accurate diagnosis.
Machine Learning & AI
Owing to the information-rich nature of NMR, advanced statistical techniques (multivariate analysis and machine learning) are utilized to extract the molecular patterns that relate disease stage and prognosis. These statistical tools build complex equations able to distinguish NMR spectra of blood samples from different groups of patients. Once these equations have been calculated and independently tested on a large cohort, they can be used to classify new blood samples. This not only provides a means of diagnosing disease but, by interrogating the metabolites responsible for the discrimination, provides clues into the underlying pathology of the disease.
A blood-based metabolomics test to distinguish relapsing-remitting and secondary progressive multiple sclerosis: addressing practical considerations for clinical application.
Yeo T, Sealey M, Zhou Y, Saldana L, Loveless S, Claridge TDW, Robertson N, DeLuca G, Palace J, Anthony DC, Probert F.Sci Rep. 2020 Jul 24;10(1):12381. doi: 10.1038/s41598-020-69119-3.
Reliable, high-quality suppression of NMR signals arising from water and macromolecules: application to bio-fluid analysis.
Aguilar JA , Cassani J , Probert F , Palace J , Claridge TDW , Botana A , Kenwright AM .Analyst. 2019 Dec 2;144(24):7270-7277. doi: 10.1039/c9an01005j.
Classifying the antibody-negative NMO syndromes: Clinical, imaging, and metabolomic modeling.
Yeo T, Probert F, Jurynczyk M, Sealey M, Cavey A, Claridge TDW, Woodhall M, Waters P, Leite MI, Anthony DC, Palace J.Neurol Neuroimmunol Neuroinflamm. 2019 Oct 28;6(6):e626. doi: 10.1212/NXI.0000000000000626. Print 2019 Nov.
Plasma Nuclear Magnetic Resonance Metabolomics Discriminates Between High and Low Endoscopic Activity and Predicts Progression in a Prospective Cohort of Patients With Ulcerative Colitis.
Probert F, Walsh A, Jagielowicz M, Yeo T, Claridge TDW, Simmons A, Travis S, Anthony DC.J Crohns Colitis. 2018 Nov 15;12(11):1326-1337. doi: 10.1093/ecco-jcc/jjy101.
Metabolomics reveals distinct, antibody-independent, molecular signatures of MS, AQP4-antibody and MOG-antibody disease.
Jurynczyk M, Probert F, Yeo T, Tackley G, Claridge TDW, Cavey A, Woodhall MR, Arora S, Winkler T, Schiffer E, Vincent A, DeLuca G, Sibson NR, Isabel Leite M, Waters P, Anthony DC, Palace J.Acta Neuropathol Commun. 2017 Dec 6;5(1):95. doi: 10.1186/s40478-017-0495-8.
NMR analysis reveals significant differences in the plasma metabolic profiles of Niemann Pick C1 patients, heterozygous carriers, and healthy controls.
Probert F, Ruiz-Rodado V, Vruchte DT, Nicoli ER, Claridge TDW, Wassif CA, Farhat N, Porter FD, Platt FM, Grootveld M.Sci Rep. 2017 Jul 24;7(1):6320. doi: 10.1038/s41598-017-06264-2.