Develop a retention time model to identify metabolites by liquid chromatography-mass spectrometry

Join the Monash Proteomics and Metabolomics Platform (MPMP) metabolomics team for an exciting summer project to improve retention time (RT) prediction in hydrophilic interaction liquid chromatography (HILIC) workflows. Accurate RT prediction is crucial for untargeted metabolomics and compound identification to allow biomarker discovery and understand metabolism. In this project, you will help develop and validate an indexed Retention Time (iRT) model specifically for HILIC LC-MS analysis. You will prepare and analyse a panel of chemical standards covering a range of polarities and chromatography behaviour and collect high-quality retention time and fragmentation data across several HILIC gradients and instrument settings. Using these data, you will develop and calibrate an iRT model using linear regression or machine learning approaches. You will evaluate the model's performance across different samples and instrument conditions. During this project you will gain hands-on experience with HILIC LC-MS system operation and metabolomics workflows. Get familiar with chromatographic theory and retention time modelling. Gain exposure to data processing in R and metabolomics software such as MZmine and Skyline. You will also develop practical lab and data skills applicable to systems biology, biomarker discovery, analytical chemistry, and bioinformatics. This project is ideal for summer vacation students in biochemistry, biomedical, analytical sciences, with an interest in metabolomics and LC-MS analysis of biological samples. Previous lab experience is helpful but not required and basic knowledge of chromatography is advantageous.