Advertisement

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

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.

Graphical Abstract

This is a preview of subscription content, log in to check access.

Access options

Buy single article

Instant unlimited access to the full article PDF.

US$ 39.95

Price includes VAT for USA

Subscribe to journal

Immediate online access to all issues from 2019. Subscription will auto renew annually.

US$ 99

This is the net price. Taxes to be calculated in checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

References

  1. Allaire, J., Cheng, J., Xie, Y., McPherson, J., Chang, W., Allen, J., et al. (2016). rmarkdown: Dynamic documents for R. https://CRAN.R-project.org/package=rmarkdown. Accessed 2 Feb 2018.

  2. Antoniewicz, M. R. (2013). 13C metabolic flux analysis: Optimal design of isotopic labeling experiments. Current Opinion in Biotechnology, 24(6), 1116–1121. https://doi.org/10.1016/j.copbio.2013.02.003.

  3. Attali, D. (2016). Easily improve the user experience of your shiny apps in seconds. https://CRAN.R-project.org/package=shinyjs. Accessed 2 Feb 2018.

  4. Audi, G., & Wapstra, A. H. (1993). The 1993 atomic mass evaluation. Nuclear Physics A, 565(1), 1–65. https://doi.org/10.1016/0375-9474(93)90024-R.

  5. Audi, G., & Wapstra, A. H. (1995). The 1995 update to the atomic mass evaluation. Nuclear Physics A, 595(4), 409–480. https://doi.org/10.1016/0375-9474(95)00445-9.

  6. Baars, O., Morel, F. M., & Perlman, D. H. (2014). ChelomEx: Isotope-assisted discovery of metal chelates in complex media using high-resolution LC-MS. Analytical Chemistry, 86(22), 11298–11305. https://doi.org/10.1021/ac503000e.

  7. Bailey, E. (2015). shinyBS: Twitter bootstrap components for shiny. https://CRAN.R-project.org/package=shinyBS. Accessed 2 Feb 2018.

  8. Banci, L., & Bertini, I. (2013). Metallomics and the cell: Some definitions and general comments. In L. Banci (Ed.), Metallomics and the Cell (pp. 1–13). Dordrecht: Springer.

  9. Böcker, S., Letzel, M. C., Lipták, Z., & Pervukhin, A. (2009). SIRIUS: Decomposing isotope patterns for metabolite identification. Bioinformatics, 25(2), 218–224. https://doi.org/10.1093/bioinformatics/btn603.

  10. Boiteau, R. M., & Repeta, D. J. (2015). An extended siderophore suite from Synechococcus sp. PCC 7002 revealed by LC-ICPMS-ESIMS. Metallomics, 7(5), 877–884. https://doi.org/10.1039/c5mt00005j.

  11. Bueschl, C., Kluger, B., Neumann, N. K. N., Doppler, M., Maschietto, V., Thallinger, G. G., et al. (2017). MetExtract II: A software suite for stable isotope-assisted untargeted metabolomics. Analytical Chemistry, 89(17), 9518–9526. https://doi.org/10.1021/acs.analchem.7b02518.

  12. Bueschl, C., Krska, R., Kluger, B., & Schuhmacher, R. (2013). Isotopic labeling-assisted metabolomics using LC–MS. Analytical and Bioanalytical Chemistry, 405(1), 27–33. https://doi.org/10.1007/s00216-012-6375-y.

  13. Capellades, J., Navarro, M., Samino, S., Garcia-Ramirez, M., Hernandez, C., Simo, R., et al. (2016). geoRge: A computational tool to detect the presence of stable isotope labeling in LC/MS-based untargeted metabolomics. Analytical Chemistry, 88(1), 621–628. https://doi.org/10.1021/acs.analchem.5b03628.

  14. Castro-Falcón, G., Hahn, D., Reimer, D., & Hughes, C. C. (2016). Thiol probes to detect electrophilic natural products based on their mechanism of action. Chemistry & Biology, 11(8), 2328–2336. https://doi.org/10.1021/acschembio.5b00924.

  15. Chambers, M. C., Maclean, B., Burke, R., Amodei, D., Ruderman, D. L., Neumann, S., et al. (2012). A cross-platform toolkit for mass spectrometry and proteomics. Nature Biotechnology, 30(10), 918–920. https://doi.org/10.1038/nbt.2377.

  16. Chang, W. (2016). shinythemes: Themes for Shiny. https://CRAN.R-project.org/package=shinythemes. Accessed 2 Feb 2018.

  17. Chang, W., Cheng, J., Allaire, J., Xie, Y., & McPherson, J. (2017). shiny: Web application framework for R. https://CRAN.R-project.org/package=shiny. Accessed 2 Feb 2018.

  18. Chokkathukalam, A., Jankevics, A., Creek, D. J., Achcar, F., Barrett, M. P., & Breitling, R. (2013). mzMatch–ISO: An R tool for the annotation and relative quantification of isotope-labelled mass spectrometry data. Bioinformatics, 29(2), 281–283. https://doi.org/10.1093/bioinformatics/bts674.

  19. Chokkathukalam, A., Kim, D.-H., Barrett, M. P., Breitling, R., & Creek, D. J. (2014). Stable isotope-labeling studies in metabolomics: New insights into structure and dynamics of metabolic networks. Bioanalysis, 6(4), 511–524. https://doi.org/10.4155/bio.13.348.

  20. Conley, C. J., Smith, R., Torgrip, R. J., Taylor, R. M., Tautenhahn, R., & Prince, J. T. (2014). Massifquant: Open-source Kalman filter-based XC-MS isotope trace feature detection. Bioinformatics, 30(18), 2636–2643. https://doi.org/10.1093/bioinformatics/btu359.

  21. Dai, Z., & Locasale, J. W. (2017). Understanding metabolism with flux analysis: From theory to application. Metabolomic Engineering, 43, 94–102. https://doi.org/10.1016/j.ymben.2016.09.005.

  22. Deicke, M., Mohr, J. F., Bellenger, J. P., & Wichard, T. (2014). Metallophore mapping in complex matrices by metal isotope coded profiling of organic ligands. Analyst, 139(23), 6096–6099. https://doi.org/10.1039/c4an01461h.

  23. Drexler, H. G. (1994). Leukemia cell lines: In vitro models for the study of chronic myeloid leukemia. Leukemia Research, 18(12), 919–927.

  24. Dunn, W. B., Bailey, N. J., & Johnson, H. E. (2005). Measuring the metabolome: Current analytical technologies. Analyst, 130(5), 606–625. https://doi.org/10.1039/b418288j.

  25. Filer, C. N. (1999). Isotopic fractionation of organic compounds in chromatography. Journal of Labelled Compounds and Radiopharmaceuticals, 42(2), 169–197.

  26. Gribble, G. W. (2015). Biological activity of recently discovered halogenated marine natural products. Marine Drugs, 13(7), 4044–4136. https://doi.org/10.3390/md13074044.

  27. Grossmann, K., Niggeweg, R., Christiansen, N., Looser, R., & Ehrhardt, T. (2010). The Herbicide saflufenacil (KixorTM) is a new inhibitor of protoporphyrinogen IX oxidase activity. Weed Science, 58, 1–9. https://doi.org/10.1614/WS-D-09-00004.1.

  28. Hegeman, A. D., Schulte, C. F., Cui, Q., Lewis, I. A., Huttlin, E. L., Eghbalnia, H., et al. (2007). Stable isotope assisted assignment of elemental compositions for metabolomics. Analytical Chemistry, 79(18), 6912–6921. https://doi.org/10.1021/ac070346t.

  29. Hiller, K., Wegner, A., Weindl, D., Cordes, T., Metallo, C. M., Kelleher, J. K., et al. (2013). NTFD-a stand-alone application for the non-targeted detection of stable isotope-labeled compounds in GC/MS data. Bioinformatics, 29(9), 1226–1228. https://doi.org/10.1093/bioinformatics/btt119.

  30. Huang, X., Chen, Y. J., Cho, K., Nikolskiy, I., Crawford, P. A., & Patti, G. J. (2014a). X13CMS. Accessed January 30, 2018 from http://pattilab.wustl.edu/software/x13cms/x13cms.php.

  31. Huang, X., Chen, Y. J., Cho, K., Nikolskiy, I., Crawford, P. A., & Patti, G. J. (2014b). X13CMS: Global tracking of isotopic labels in untargeted metabolomics. Analytical Chemistry, 86(3), 1632–1639. https://doi.org/10.1021/ac403384n.

  32. Kuhl, C., Tautenhahn, R., Bottcher, C., Larson, T. R., & Neumann, S. (2012). CAMERA: An integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets. Analytical Chemistry, 84(1), 283–289. https://doi.org/10.1021/ac202450g.

  33. Kumar, B. (2011). Isotopic signatures. In V. P. Singh, P. Singh & U. K. Haritashya (Eds.), Encyclopedia of snow, ice and glaciers (pp. 669–669). Dordrecht: Springer.

  34. Levin, Y. (2011). The role of statistical power analysis in quantitative proteomics. Proteomics, 11(12), 2565–2567. https://doi.org/10.1002/pmic.201100033.

  35. Lisovich, A., & Day, R. (2014). rChoiceDialogs: rChoiceDialogs collection. https://CRAN.R-project.org/package=rChoiceDialogs.

  36. Loos, M. (2016) EnviPick: Peak picking for high resolution mass spectrometry data, R package. Accessed January 30, 2018 from https://CRAN.R-project.org/package=enviPick.

  37. Millard, P., Portais, J. C., & Mendes, P. (2015). Impact of kinetic isotope effects in isotopic studies of metabolic systems. BMC Systems Biology, 9, 64. https://doi.org/10.1186/s12918-015-0213-8.

  38. Murray, K. K., Boyd, R. K., Eberlin, M. N., Langley, G. J., Li, L., & Naito, Y. (2013). Definitions of terms relating to mass spectrometry (IUPAC Recommendations 2013). Pure and Applied Chemistry, 85(7), 1515–1609.

  39. Pluskal, T., Castillo, S., Villar-Briones, A., & Oresic, M. (2010). MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinformatics, 11, 395. https://doi.org/10.1186/1471-2105-11-395.

  40. R-Core-Team (R Foundation for Statistical Computing). (2017). Accessed January 30, 2018 from https://www.R-project.org/.

  41. Rosman, K. J. R. (1999). Atomic weights of the elements 1997. Pure and Applied Chemistry, 71(8), 1593–1607.

  42. Scheltema, R. A., Jankevics, A., Jansen, R. C., Swertz, M. A., & Breitling, R. (2011). PeakML/mzMatch: A file format, Java library, R library, and tool-chain for mass spectrometry data analysis. Analytical Chemistry, 83(7), 2786–2793. https://doi.org/10.1021/ac2000994.

  43. Schoenheimer, R., & Rittenberg, D. (1938). The application of isotopes to the study of intermediary metabolism. Science, 87(2254), 221. https://doi.org/10.1126/science.87.2254.221.

  44. Signorell, A., Aho, K., Alfons, A., Anderegg, N., Aragon, T., Arppe, A., et al. (2017). DescTools: Tools for descriptive statistics. Accessed January 30, 2018 from https://cran.r-project.org/web/packages/DescTools/index.html.

  45. Smith, C. A., Want, E. J., O’Maille, G., Abagyan, R., & Siuzdak, G. (2006a). Accessed January 30, 2018 from https://bioconductor.org/packages/release/bioc/html/xcms.html.

  46. Smith, C. A., Want, E. J., O’Maille, G., Abagyan, R., & Siuzdak, G. (2006b). XCMS: Processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Analytical Chemistry, 78(3), 779–787. https://doi.org/10.1021/ac051437y.

  47. Tautenhahn, R., Patti, G. J., Rinehart, D., & Siuzdak, G. (2012). XCMS Online: A web-based platform to process untargeted metabolomic data. Analytical Chemistry, 84(11), 5035–5039. https://doi.org/10.1021/ac300698c.

  48. Tsednee, M., Huang, Y. C., Chen, Y. R., & Yeh, K. C. (2016). Identification of metal species by ESI-MS/MS through release of free metals from the corresponding metal-ligand complexes. Scientific Reports, 6, 26785. https://doi.org/10.1038/srep26785.

  49. Ueberschaar, N., Dahse, H.-M., Bretschneider, T., & Hertweck, C. (2013a). Rational design of an apoptosis-inducing photoreactive DNA intercalator. 52(24), 6185–6189. https://doi.org/10.1002/ange.201302439.

  50. Ueberschaar, N., Meyer, F., Dahse, H. M., & Hertweck, C. (2016). Bipiperidine conjugates as soluble sugar surrogates in DNA-intercalating antiproliferative polyketides. Chemical Communications, 52(27), 4894–4897. https://doi.org/10.1039/c6cc00890a.

  51. Ueberschaar, N., Xu, Z., Scherlach, K., Metsä-Ketelä, M., Bretschneider, T., Dahse, H.-M., et al. (2013b). Synthetic remodeling of the chartreusin pathway to tune antiproliferative and antibacterial activities. Journal of the American Chemical Society, 135(46), 17408–17416. https://doi.org/10.1021/ja4080024.

  52. Weindl, D., Wegner, A., & Hiller, K. (2016). MIA: Non-targeted mass isotopolome analysis. Bioinformatics, 32(18), 2875–2876. https://doi.org/10.1093/bioinformatics/btw317.

  53. Wichard, T. (2016). Identification of metallophores and organic ligands in the chemosphere of the marine macroalga Ulva (Chlorophyta) and at Land-Sea Interfaces. Frontiers in Marine Science, 3, 131. https://doi.org/10.3389/fmars.2016.00131.

  54. Zhang, R., Sioma, C. S., Wang, S., & Regnier, F. E. (2001). Fractionation of mics. Analytical Chemistry, 73(21), 5142–5149. https://doi.org/10.1021/ac010583a.

Download references

Acknowledgements

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.

Author information

Correspondence to Thomas Wichard or Georg Pohnert.

Ethics declarations

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.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 2 (MP4 418,929 KB)

Supplementary material 1 (DOCX 4797 KB)

Supplementary material 2 (MP4 418,929 KB)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Baumeister, T.U.H., Ueberschaar, N., Schmidt-Heck, W. et al. DeltaMS: a tool to track isotopologues in GC- and LC-MS data. Metabolomics 14, 41 (2018). https://doi.org/10.1007/s11306-018-1336-x

Download citation

Keywords

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