, 14:41 | Cite as

DeltaMS: a tool to track isotopologues in GC- and LC-MS data

  • Tim U. H. Baumeister
  • Nico Ueberschaar
  • Wolfgang Schmidt-Heck
  • J. Frieder Mohr
  • Michael Deicke
  • Thomas WichardEmail author
  • Reinhard Guthke
  • Georg PohnertEmail author
Original Article



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.


To introduce a software tool for the identification of isotopologues from mass spectrometry data.


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.


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.


DeltaMS provides a platform for the identification of (pre-defined) isotopologues in MS data from single samples or comparative metabolomics data sets.

Graphical Abstract


DeltaMS Computer-aided tool Shiny Stable isotope labeling Isotope signature Metallomics 



We gratefully acknowledge the Deutsche Forschungsgemeinschaft (CRC 1067 “AquaDiva” NU, GP and the CRC 1127 “ChemBioSys”; NU WS-H, JFM, TW, RG, GP), the Hans-Böckler-Stiftung (MD) and the Fonds der Chemischen Industrie (TW). The study was co-financed by the state of Thuringia (2015 FGI 0021) with means of the EU in the framework of the EFRE program. Kathleen Thume for the preparation of DMS for the SPME experiments with the GC-Orbitrap Felix Trottmann and Philipp Traber for initial considerations regarding the data analysis and the graphical user interface. Prof. Christian Hertweck for providing the dataset for case study 2 and Prof. Dr. Christoph Steinbeck for the helpful discussions.

Compliance with ethical standards

Conflict of interest

The authors of this manuscript have no competing interests as defined by Springer; they do not have any other interests that influence the results and discussion of this paper.

Research involving with human and animal participants

This article does not contain any studies with human or animal subjects.

Supplementary material

11306_2018_1336_MOESM1_ESM.docx (4.7 mb)
Supplementary material 1 (DOCX 4797 KB)

Supplementary material 2 (MP4 418,929 KB)


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Tim U. H. Baumeister
    • 1
    • 2
  • Nico Ueberschaar
    • 3
  • Wolfgang Schmidt-Heck
    • 4
  • J. Frieder Mohr
    • 1
  • Michael Deicke
    • 1
  • Thomas Wichard
    • 1
    Email author
  • Reinhard Guthke
    • 4
  • Georg Pohnert
    • 1
    • 2
    Email author
  1. 1.Department of Bioorganic Analytics, Institute for Inorganic and Analytical ChemistryFriedrich Schiller University Jena,JenaGermany
  2. 2.Max Planck Institute for Chemical EcologyMax Planck Fellow Group on Plankton Community InteractionJenaGermany
  3. 3.Mass Spectrometric Platform, Institute for Inorganic and Analytical ChemistryFriedrich Schiller University JenaJenaGermany
  4. 4.Department of Systems Biology and Bioinformatics, Hans Knöll Institute (HKI)Leibniz Institute for Natural Product Research and Infection BiologyJenaGermany

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