Quantitative Measurement of 2000+ Metabolites in Biofluids Using the Agilent 6495D LC/TQ System
Metabolomics is reaching an inflection point. Either it can continue with traditional, manually- intensive, nontargeted approaches and remain a niche field of scientific research -- or it can change to become a truly quantitative, fully automated endeavor, serving as the main vehicle to advance the next generation of clinical, pharmaceutical, environmental, and forensic applications.
In this presentation I will describe how it is possible to develop and implement a truly quantitative, fully-automated metabolomic assay that can routinely measure 2000+ metabolites (including metabolite sums and ratios) in less than 20 minutes on the Agilent 6495D triple quadrupole LC/MS system.
In implementing this assay (called the GIGA assay) we have taken advantage of the enhanced sensitivity of the Agilent iFunnel technology, the AI-based autotuning, and the submillisecond dwell times that allowed us to rapidly collect high-quality MS/MS data on hundreds of metabolites. The improved collision cell design of the 6495D LC/TQ also allowed us to perform much more accurate quantification, even for low S/N situations.
These technology advances allowed us to rapidly evolve an assay that could measure just 650 metabolites to one that could measure 2000 metabolites with just a few weeks of effort. I will describe the assay design, the assay specifications and performance assessment, and examples of its application to real metabolomics studies.
I will also describe the software we have developed (called LC-AutoFit) to automatically process the MS/MS spectra collected on the 6495D LC/TQ and the low-cost robotic systems we have created to both prepare the assay kits (in a 96-well format) and to run the GIGA assay.
We believe this new, fast, low-cost, and fully-automated approach to doing comprehensive, quantitative metabolite measurement will enable metabolomics to be an integral part of next-generation applications in clinical, pharmaceutical, environmental, and forensic chemistry.
Presenter: David Wishart (Distinguished University Professor, Depts. of Computing Science and Biological Sciences, University of Alberta)
Dr. David Wishart (PhD Yale, 1991) is a Distinguished University Professor in the Departments of Biological Sciences and Computing Science at the University of Alberta. He also holds adjunct appointments with the Faculty of Pharmaceutical Sciences and with the Department of Pathology and Laboratory Medicine. Dr. Wishart's research interests are very wide ranging, covering metabolomics, analytical chemistry, drug chemistry, natural product chemistry and molecular biology. For many years Dr. Wishart been using machine learning and artificial intelligence to help create a variety of popular chemistry databases, such as HMDB, DrugBank, FooDB, T3DB, NP-MRD and the Norman Suspect List Exchange as well as software tools (such as MetaboAnalyst, CFM-ID and BioTransformer) to help with the characterization and identification of metabolites, natural products, drugs, pesticides, pollutants and harmful chemicals. Over the course of his career Dr. Wishart has published more than 500 research papers in high profile journals on a wide variety of subject areas.
