Correcting for the effects of natural abundance in stable isotope resolved metabolomics experiments involving ultra-high resolution mass spectrometry

Stable isotope tracing with ultra-high resolution Fourier transform-ion cyclotron resonance-mass spectrometry (FT-ICR-MS) can provide simultaneous determination of hundreds to thousands of metabolite isotopologue species without the need for chromatographic separation. Therefore, this experimental metabolomics methodology may allow the tracing of metabolic pathways starting from stable-isotope-enriched precursors, which can improve our mechanistic understanding of cellular metabolism.

However, contributions to the observed intensities arising from the stable isotopes natural abundance must be subtracted (deisotoped) from the raw isotopologue peaks before interpretation. Previously posed deisotoping problems are sidestepped due to the isotopic resolution and identification of individual isotopologue peaks.

This peak resolution and identification come from the very high mass resolution and accuracy of FT-ICR-MS and present an analytically solvable deisotoping problem, even in the context of stable-isotope enrichment.

Results: We present both a computationally feasible analytical solution and an algorithm to this newly posed deisotoping problem, which both work with any amount of 13C or 15N stable-isotope enrichment. We demonstrate this algorithm and correct for the effects of 13C natural abundance on a set of raw isotopologue intensities for a specific phosphatidylcholine lipid metabolite derived from a 13C-tracing experiment.

Conclusions: Correction for the effects of 13C natural abundance on a set of raw isotopologue intensities is computationally feasible when the raw isotopologues are isotopically resolved and identified.

Such correction makes qualitative interpretation of stable isotope tracing easier and is required before attempting a more rigorous quantitative interpretation of the isotopologue data. The presented implementation is very robust with increasing metabolite size.

Error analysis of the algorithm will be straightforward due to low relative error from the implementation itself. Furthermore, the algorithm may serve as an independent quality control measure for a set of observed isotopologue intensities.

Author: Hunter Moseley
Credits/Source: BMC Bioinformatics 2010, 11:139

Published on: 2010-03-17

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