PR001429 (Project)

Description:Data analysis and mass spectrometry tools have advanced significantly in the last decade. This ongoing revolution has elevated the status of analytical chemistry within the big-data omics era. High resolution mass spectrometers (HRMS) can now distinguish different metabolites with mass to charge ratios (i.e. m/z) that differ by 0.01 Da or less. This unprecedented level of resolution not only enables identification of previously unknown compounds but also presents an opportunity to establish active metabolic pathways through quantification of isotope enrichment. Studies with stable isotope tracers continue to contribute to our knowledge of biological pathways in human, plant and bacterial species, however most current studies have been based on targeted analyses. The capacity of HRMS to resolve near-overlapping isotopologues and identify compounds with high mass precision presents a strategy to assess ‘active’ pathways de novo from data generated in an untargeted way, that is blind to the metabolic network and therefore unbiased. Currently, identifying metabolic features, enriched with stable isotopes, at an ‘omics’ level remains an experimental bottleneck, limiting our capacity to understand biological network operation at the metabolic level. We developed data analysis tools that: i) use labeling information and exact mass to determine the elemental composition of each isotopically enriched ion, ii) apply correlation-based approaches to cluster metabolite peaks with similar patterns of isotopic labels and, iii) leverage this information to build directed metabolic networks de novo. Using Camelina sativa, an emerging oilseed model, we demonstrate the power of stable isotope labeling in combination with imaging and HRMS to reconstruct lipid metabolic networks in developing seeds and are currently addressing questions about lipid and central metabolism. Tools developed in this study will have a broader application to assess context specific operation of metabolic pathways.