Natural isotope correction of MS/MS measurements for metabolomics and 13C fluxomics

• Published in: Biotechnology and bioengineering Vol. 113; no. 5; pp. 1137 - 1147
• Main Authors: Niedenführ, Sebastian, ten Pierick, Angela, van Dam, Patricia T.N., Suarez-Mendez, Camilo A., Nöh, Katharina, Wahl, S. Aljoscha
• Format: Journal Article
• Language: English
• Published: New York Blackwell Publishing Ltd 01.05.2016; Wiley Subscription Services, Inc
• Subjects: 13C fluxomics; Bioengineering; carbon labeling experiments; Cellular biology; Isotopes; mass spectrometry; metabolic flux ratio analysis; Metabolism; metabolomics; MS/MS
• ISSN: 0006-3592; 1097-0290
• DOI: 10.1002/bit.25859

ABSTRACT Fluxomics and metabolomics are crucial tools for metabolic engineering and biomedical analysis to determine the in vivo cellular state. Especially, the application of 13C isotopes allows comprehensive insights into the functional operation of cellular metabolism. Compared to single MS, tandem mass spectrometry (MS/MS) provides more detailed and accurate measurements of the metabolite enrichment patterns (tandem mass isotopomers), increasing the accuracy of metabolite concentration measurements and metabolic flux estimation. MS‐type data from isotope labeling experiments is biased by naturally occurring stable isotopes (C, H, N, O, etc.). In particular, GC–MS(/MS) requires derivatization for the usually non‐volatile intracellular metabolites introducing additional natural isotopes leading to measurements that do not directly represent the carbon labeling distribution. To make full useof LC‐ and GC–MS/MS mass isotopomer measurements, the influence of natural isotopes has to be eliminated (corrected). Our correction approach is analyzed for the two most common applications; 13C fluxomics and isotope dilution mass spectrometry (IDMS) based metabolomics. Natural isotopes can have an impact on the calculated flux distribution which strongly depends on the substrate labeling and the actual flux distribution. Second, we show that in IDMS based metabolomics natural isotopes lead to underestimated concentrations that can and should be corrected with a nonlinear calibration. Our simulations indicate that the correction for natural abundance in isotope based fluxomics and quantitative metabolomics is essential for correct data interpretation. Biotechnol. Bioeng. 2016;113: 1137–1147. © 2015 Wiley Periodicals, Inc. Fluxomics and metabolomics have become crucial tools for metabolic engineering and biomedical analysis. The authors focus on the first steps in data processing of MS/MS measurements, namely eliminating the effect of natural isotopes that are inherently measured together with the carbon mass isotopomers. This step should be included for unbiased 13C flux analysis as well as metabolomics studies.