Background: The investigation of intracellular metabolism is the mainstay in the biotechnology and physiology\nsettings. Intracellular metabolic rates are commonly evaluated using labeling pattern of the identified metabolites\nobtained from stable isotope labeling experiments. The labeling pattern or mass distribution vector describes the\nfractional abundances of all isotopologs with different masses as a result of isotopic labeling, which are typically\nresolved using mass spectrometry. Because naturally occurring isotopes and isotopic impurity also contribute to\nmeasured signals, the measured patterns must be corrected to obtain the labeling patterns. Since contaminant\nisotopologs with the same nominal mass can be resolved using modern mass spectrometers with high mass\nresolution, the correction process should be resolution dependent.\nResults: Here we present a software tool, ElemCor, to perform correction of such data in a resolution-dependent\nmanner. The tool is based on mass difference theory (MDT) and information from unlabeled samples (ULS) to\naccount for resolution effects. MDT is a mathematical theory and only requires chemical formulae to perform\ncorrection. ULS is semi-empirical and requires additional measurement of isotopologs from unlabeled samples. We\nvalidate both methods and show their improvement in accuracy and comprehensiveness over existing methods\nusing simulated data and experimental data from Saccharomyces cerevisiae. The tool is available at https://github.\ncom/4dsoftware/elemcor.\nConclusions: We present a software tool based on two methods, MDT and ULS, to correct LC-MS data from\nisotopic labeling experiments for natural abundance and isotopic impurity. We recommend MDT for low-mass\ncompounds for cost efficiency in experiments, and ULS for high-mass compounds with relatively large spectral\ninaccuracy that can be tracked by unlabeled standards.
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