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Journal of The American Society for Mass Spectrometry

, Volume 30, Issue 9, pp 1733–1741 | Cite as

Mass Accuracy Check Using Common Background Peaks for Improving Metabolome Data Quality in Chemical Isotope Labeling LC-MS

  • Yunong Li
  • Liang LiEmail author
Research Article

Abstract

Chemical isotope labeling (CIL) LC-MS is a highly sensitive and quantitative method for metabolome analysis. Because of a large number of peaks detectable in a sample and the need of running many samples in a metabolomics project, any significant change in mass measurement accuracy during the whole period of running samples can adversely affect the downstream peak alignment and quantitative analysis. Herein, we report a rapid method to check the mass accuracy of individual spectra in each CIL LC-MS run in order to flag up any run containing spectra with accuracy drift that falls outside the expected error. The flagged run may be re-run or discarded before merging with other runs for peak alignment and analysis. This method is based on the observation that some background signals are commonly detected in almost all spectra collected in CIL LC-MS runs. A mass accuracy check (MAC) software program has been developed to first find the common background mass peaks and then use them as mass references to calculate any mass shifts over the course of multiple sample runs. Using a metabolome dataset of 324 human cerebrospinal fluid (CSF) samples and 35 quality control (QC) samples produced by CIL LC-MS, we show that this accuracy check method can streamline the initial raw data processing for downstream analysis in metabolomics.

Keywords

Chemical isotope labeling LC-MS Mass accuracy Peak alignment Metabolome analysis Metabolomics 

Notes

Acknowledgements

This work was supported by the Natural Sciences and Engineering Research Council of Canada, CIHR, Canada Research Chairs, Canada Foundation of Innovations, Genome Canada and Alberta Innovates. We thank Ms. Xinyun Gu for providing the CSF data.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no competing interest.

Supplementary material

13361_2019_2248_MOESM1_ESM.pdf (187 kb)
ESM 1 (PDF 186 kb)
13361_2019_2248_MOESM2_ESM.pdf (31 kb)
ESM 2 (PDF 31 kb)

References

  1. 1.
    Guo, K., Li, L.: Differential 12C-/13C-isotope dansylation labeling and fast liquid chromatography/mass spectrometry for absolute and relative quantification of the metabolome. Anal. Chem. 81, 3919–3932 (2009)CrossRefGoogle Scholar
  2. 2.
    Guo, K., Li, L.: High-performance isotope labeling for profiling carboxylic acid-containing metabolites in biofluids by mass spectrometry. Anal. Chem. 82, 8789–8793 (2010)CrossRefGoogle Scholar
  3. 3.
    Zhao, S., Luo, X., Li, L.: Chemical isotope labeling LC-MS for high coverage and quantitative profiling of the hydroxyl submetabolome in metabolomics. Anal. Chem. 88, 10617–10623 (2016)Google Scholar
  4. 4.
    Zhao, S., Dawe, M., Guo, K., Li, L.: Development of high-performance chemical isotope labeling LC-MS for profiling the carbonyl submetabolome. Anal. Chem. 89, 6758–6765 (2017)Google Scholar
  5. 5.
    Dai, W.D., Huang, Q., Yin, P.Y., Li, J., Zhou, J., Kong, H.W., Zhao, C.X., Lu, X., Xu, G.W.: Comprehensive and highly sensitive urinary steroid hormone profiling method based on stable isotope-labeling liquid chromatography mass spectrometry. Anal. Chem. 84, 10245–10251 (2012)CrossRefGoogle Scholar
  6. 6.
    Hao, L., Johnson, J., Lietz, C.B., Buchberger, A., Frost, D., Kao, W.J., Li, L.J.: Mass defect-based N,N-dimethyl Leucine labels for quantitative proteomics and amine metabolomics of pancreatic cancer cells. Anal. Chem. 89, 1138–1146 (2017)CrossRefGoogle Scholar
  7. 7.
    Leng, J.P., Wang, H.Y., Zhang, L., Zhang, J., Wang, H., Guo, Y.L.: A highly sensitive isotope-coded derivatization method and its application for the mass spectrometric analysis of analytes containing the carboxyl group. Anal. Chim. Acta. 758, 114–121 (2013)CrossRefGoogle Scholar
  8. 8.
    Mochizuki, T., Todoroki, K., Inoue, K., Min, J.Z., Toyo’oka, T.: Isotopic variants of light and heavy L-pyroglutamic acid succinimidyl esters as the derivatization reagents for DL-amino acid chiral metabolomics identification by liquid chromatography and electrospray ionization mass spectrometry. Anal. Chim. Acta. 811, 51–59 (2014)CrossRefGoogle Scholar
  9. 9.
    Tayyari, F., Gowda, G.A.N., Gu, H.W., Raftery, D.: N-15-cholamine-a smart isotope tag for combining NMR- and MS-based metabolite profiling. Anal. Chem. 85, 8715–8721 (2013)CrossRefGoogle Scholar
  10. 10.
    Wong, J.M.T., Malec, P.A., Mabrouk, O.S., Ro, J., Dus, M., Kennedy, R.T.: Benzoyl chloride derivatization with liquid chromatography-mass spectrometry for targeted metabolomics of neurochemicals in biological samples. J. Chromatogr. A. 1446, 78–90 (2016)CrossRefGoogle Scholar
  11. 11.
    Yuan, W., Edwards, J.L., Li, S.W.: Global profiling of carbonyl metabolites with a photo-cleavable isobaric labeling affinity tag. Chem. Commun. 49, 11080–11082 (2013)CrossRefGoogle Scholar
  12. 12.
    Zhou, R., Tseng, C.-L., Huan, T., Li, L.: IsoMS: automated processing of LC-MS data generated by a chemical isotope labeling metabolomics platform. Anal. Chem. 86, 4675–4679 (2014)Google Scholar
  13. 13.
    Huan, T., Li, L.: Quantitative Metabolome analysis based on chromatographic peak reconstruction in chemical isotope labeling liquid chromatography mass spectrometry. Anal. Chem. 87, 7011–7016 (2015)Google Scholar
  14. 14.
    Han, W., Sapkota, S., Camicioli, R., Dixon, R.A., Li, L.: Profiling novel metabolic biomarkers for Parkinson’s disease using in-depth metabolomic analysis. Mov. Disord. 32, 1710–1728 (2017)Google Scholar
  15. 15.
    Huan, T., Li, L.: Counting missing values in a metabolite-intensity data set for measuring the analytical performance of a metabolomics platform. Anal. Chem. 87, 1306–1313 (2015)Google Scholar
  16. 16.
    Zhang, N., Fountain, S.T., Bi, H., Rossi, D.T.: Quantification and rapid metabolite identification in drug discovery using API time-of-flight LC/MS. Anal. Chem. 72, 800–806 (2000)CrossRefGoogle Scholar
  17. 17.
    Eckers, C., Wolff, J.-C., Haskins, N.J., Sage, A.B., Giles, K., Bateman, R.: Accurate mass liquid chromatography/mass spectrometry on orthogonal acceleration time-of-flight mass analyzers using switching between separate sample and reference sprays. 1. Proof of concept. Anal. Chem. 72, 3683–3688 (2000)CrossRefGoogle Scholar
  18. 18.
    Schlosser, A., Volkmer-Engert, R.: Volatile polydimethylcyclosiloxanes in the ambient laboratory air identified as source of extreme background signals in nanoelectrospray mass spectrometry. J. Mass Spectrom. 38, 523–525 (2003)CrossRefGoogle Scholar
  19. 19.
    Barry, J.A., Robichaud, G., Muddiman, D.C.: Mass recalibration of FT-ICR mass spectrometry imaging data using the average frequency shift of ambient ions. J. Am. Soc. Mass Spectrom. 24, 1137–1145 (2013)CrossRefGoogle Scholar
  20. 20.
    Olsen, J.V., de Godoy, L.M.F., Li, G., Macek, B., Mortensen, P., Pesch, R., Makarov, A., Lange, O., Horning, S., Mann, M.: Parts per million mass accuracy on an orbitrap mass spectrometer via lock mass injection into a C-trap. Mol. Cell. Proteomics. 4, 2010–2021 (2005)CrossRefGoogle Scholar

Copyright information

© American Society for Mass Spectrometry 2019

Authors and Affiliations

  1. 1.Department of ChemistryUniversity of AlbertaEdmontonCanada

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