Tim U H Baumeister1,2, Nico Ueberschaar3, Wolfgang Schmidt-Heck4, J Frieder Mohr1, Michael Deicke1, Thomas Wichard5, Reinhard Guthke4, Georg Pohnert6,7. 1. Department of Bioorganic Analytics, Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University Jena,, Lessingstr. 8, 07743, Jena, Germany. 2. Max Planck Institute for Chemical Ecology, Max Planck Fellow Group on Plankton Community Interaction, Hans-Knöll-Str. 8, 07745, Jena, Germany. 3. Mass Spectrometric Platform, Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University Jena, Humboldtstr. 8, 07743, Jena, Germany. 4. Department of Systems Biology and Bioinformatics, Hans Knöll Institute (HKI), Leibniz Institute for Natural Product Research and Infection Biology, Beutenbergstr. 11a, 07745, Jena, Germany. 5. Department of Bioorganic Analytics, Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University Jena,, Lessingstr. 8, 07743, Jena, Germany. thomas.wichard@uni-jena.de. 6. Department of Bioorganic Analytics, Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University Jena,, Lessingstr. 8, 07743, Jena, Germany. georg.pohnert@uni-jena.de. 7. Max Planck Institute for Chemical Ecology, Max Planck Fellow Group on Plankton Community Interaction, Hans-Knöll-Str. 8, 07745, Jena, Germany. georg.pohnert@uni-jena.de.
Abstract
INTRODUCTION: Stable isotopic labeling experiments are powerful tools to study metabolic pathways, to follow tracers and fluxes in biotic and abiotic transformations and to elucidate molecules involved in metal complexing. OBJECTIVE: To introduce a software tool for the identification of isotopologues from mass spectrometry data. METHODS: DeltaMS relies on XCMS peak detection and X13CMS isotopologue grouping and then analyses data for specific isotope ratios and the relative error of these ratios. It provides pipelines for recognition of isotope patterns in three experiment types commonly used in isotopic labeling studies: (1) search for isotope signatures with a specific mass shift and intensity ratio in one sample set, (2) analyze two sample sets for a specific mass shift and, optionally, the isotope ratio, whereby one sample set is isotope-labeled, and one is not, (3) analyze isotope-guided perturbation experiments with a setup described in X13CMS. RESULTS: To illustrate the versatility of DeltaMS, we analyze data sets from case-studies that commonly pose challenges in evaluation of natural isotopes or isotopic signatures in labeling experiment. In these examples, the untargeted detection of sulfur, bromine and artificial metal isotopic patterns is enabled by the automated search for specific isotopes or isotope signatures. CONCLUSION: DeltaMS provides a platform for the identification of (pre-defined) isotopologues in MS data from single samples or comparative metabolomics data sets.
INTRODUCTION: Stable isotopic labeling experiments are powerful tools to study metabolic pathways, to follow tracers and fluxes in biotic and abiotic transformations and to elucidate molecules involved in metal complexing. OBJECTIVE: To introduce a software tool for the identification of isotopologues from mass spectrometry data. METHODS: DeltaMS relies on XCMS peak detection and X13CMS isotopologue grouping and then analyses data for specific isotope ratios and the relative error of these ratios. It provides pipelines for recognition of isotope patterns in three experiment types commonly used in isotopic labeling studies: (1) search for isotope signatures with a specific mass shift and intensity ratio in one sample set, (2) analyze two sample sets for a specific mass shift and, optionally, the isotope ratio, whereby one sample set is isotope-labeled, and one is not, (3) analyze isotope-guided perturbation experiments with a setup described in X13CMS. RESULTS: To illustrate the versatility of DeltaMS, we analyze data sets from case-studies that commonly pose challenges in evaluation of natural isotopes or isotopic signatures in labeling experiment. In these examples, the untargeted detection of sulfur, bromine and artificial metal isotopic patterns is enabled by the automated search for specific isotopes or isotope signatures. CONCLUSION: DeltaMS provides a platform for the identification of (pre-defined) isotopologues in MS data from single samples or comparative metabolomics data sets.
Authors: Achuthanunni Chokkathukalam; Dong-Hyun Kim; Michael P Barrett; Rainer Breitling; Darren J Creek Journal: Bioanalysis Date: 2014-02 Impact factor: 2.681
Authors: Matthew C Chambers; Brendan Maclean; Robert Burke; Dario Amodei; Daniel L Ruderman; Steffen Neumann; Laurent Gatto; Bernd Fischer; Brian Pratt; Jarrett Egertson; Katherine Hoff; Darren Kessner; Natalie Tasman; Nicholas Shulman; Barbara Frewen; Tahmina A Baker; Mi-Youn Brusniak; Christopher Paulse; David Creasy; Lisa Flashner; Kian Kani; Chris Moulding; Sean L Seymour; Lydia M Nuwaysir; Brent Lefebvre; Frank Kuhlmann; Joe Roark; Paape Rainer; Suckau Detlev; Tina Hemenway; Andreas Huhmer; James Langridge; Brian Connolly; Trey Chadick; Krisztina Holly; Josh Eckels; Eric W Deutsch; Robert L Moritz; Jonathan E Katz; David B Agus; Michael MacCoss; David L Tabb; Parag Mallick Journal: Nat Biotechnol Date: 2012-10 Impact factor: 54.908