Dataset Information

Untargeted extraction of metabolites 13C labeling profiles from time course labeling switch experiment

ABSTRACT: Dynamic isotope labeling data provide crucial information about the operation of metabolic pathways, and are commonly generated via liquid chromatography mass spectrometry (LC-MS). Metabolome-wide analysis is challenging as it requires grouping of metabolite features over different samples. We developed DynaMet for fully automated investigations of isotope labeling experiments from LC-high resolution MS raw data. DynaMet enables untargeted extraction of metabolite labeling profiles and provides integrated tools for expressive data visualization. For this study we generated labeling data of the model strain Bacillus methanolicus from 13C methanol resulting in complex spectra in multi carbon compounds. Analysis of two biological replicates revealed high robustness and reproducibility of the pipeline. In total, DynaMet extracted 386 features showing dynamic labeling within ten minutes. Of these features, 357 could be fitted by implemented kinetic models. Feature identification against KEGG database resulted in 247 matches covering multiple pathways of core metabolism and major biosynthetic routes. The study results reported here are only a summary. To reproduce the complete results including plots, DynaMet software is required.

INSTRUMENT(S): LTQ Orbitrap Classic (Thermo Scientific)

SUBMITTER: Patrick Kiefer

PROVIDER: MTBLS228 | MetaboLights | 2015-10-19

REPOSITORIES: MetaboLights

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Publications

DynaMet: a fully automated pipeline for dynamic LC-MS data.

Kiefer Patrick P Schmitt Uwe U Müller Jonas E N JE Hartl Johannes J Meyer Fabian F Ryffel Florian F Vorholt Julia A JA

Analytical chemistry 20151001 19

Dynamic isotope labeling data provides crucial information about the operation of metabolic pathways and are commonly generated via liquid chromatography-mass spectrometry (LC-MS). Metabolome-wide analysis is challenging as it requires grouping of metabolite features over different samples. We developed DynaMet for fully automated investigations of isotope labeling experiments from LC-high-resolution MS raw data. DynaMet enables untargeted extraction of metabolite labeling profiles and provides ...[more]

PMID: 26366644

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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.

Dataset Information

Untargeted extraction of metabolites 13C labeling profiles from time course labeling switch experiment

Publications

DynaMet: a fully automated pipeline for dynamic LC-MS data.

Similar Datasets

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