Integrating high resolution mass spectrometry with cheminformatics for standardized, routine non-targeted metabolomics
Recorded On: 02/05/2018
Over the past 20 years, metabolomics has evolved into using either multi-targeted assays, usually with nominal mass resolution spectrometers, or non-targeted approaches with high resolution mass spectrometry. We will here show that how to merge targeted approaches with high quality non-targeted discovery metabolomics. We will highlight the importance of advanced, open access data processing, the proper use of quality controls and internal standards, and full reporting of raw data as well as result data. At the NIH West Coast Metabolomics Center, we use 17 mass spectrometers in the central facility for providing data, informatics services and collaborative research for over 400 projects and more than 25,000 samples per year. These services include commercial assays for plasma analytics, the p180 kit, in addition to steroid, bile acid and oxylipin assays for more than 100 target compounds. Most projects, however, use our three integrated non-targeted metabolomics assays: primary metabolism for up to 200 identified compounds per study using GC-TOF MS, complex lipids for more than 600 identified lipids per study using high resolution liquid chromatography / tandem mass spectrometry and more than 150 identified compounds per study for biogenic amines using hydrophilic interaction chromatography/ high resolution mass spectrometry.
We use standardized data processing in free-access MS-DIAL 2.0 software that is far superior standard solutions with respect to data deconvolution, compound identification and false positive/false negative peak detection. This software is now integrated with MS-FINDER 2.0 software for predicting and annotating spectra of biomarkers with unknown chemical structures. Both programs work excellently for high resolution GC-MS and LC-MS data. In addition, we harness the power of legacy data from more than 2,000 projects we have acquired since 2004 that is available to the biomedical and biological research community at large, the BinVestigate interface to our BinBase metabolome database. We showcase how the integrated use of these resources identified novel epimetabolites in cancer metabolism, both on a prospective cohort scale (in lung cancer) and as new epitranscriptome metabolites from modified RNA molecules (in a range of cancers except for liver cancer).
UC Davis, NIH West Coast Metabolomics Center
Prof. Oliver Fiehn has pioneered developments and applications in metabolomics with over 220 publications to date. He aims at understanding metabolism on a comprehensive level. In order to leverage data from these diverse sets of biological systems, his research laboratory focuses on standardizing metabolomic reports and establishing metabolomic databases, for example the MassBank of North America that hosts over 200,000 public metabolite mass spectra and BinBase, a resource of over 100,000 samples covering more than 2,000 studies. He develops and implements new approaches and technologies in analytical chemistry for covering the metabolome, from increasing peak capacity by ion mobility to compound identifications through cheminformatics workflows and software. He collaborates with a range of investigators in human diseases through statistics, text mining and pathway-based mapping. He studies fundamental biochemical questions from metabolite damage repair to the new concept of epimetabolites.