geoRge: A Computational Tool To Detect the Presence of Stable Isotope Labeling in LC/MS-Based Untargeted Metabolomics

Abstract

Studying the flow of chemical moieties through the complex set of metabolic reactions that happen in the cell is essential to understanding the alterations in homeostasis that occur in disease. Recently, LC/MS-based untargeted metabolomics and isotopically labeled metabolites have been used to facilitate the unbiased mapping of labeled moieties through metabolic pathways. However, due to the complexity of the resulting experimental data sets few computational tools are available for data analysis. Here we introduce geoRge, a novel computational approach capable of analyzing untargeted LC/MS data from stable isotope-labeling experiments. geoRge is written in the open language R and runs on the output structure of the XCMS package, which is in widespread use. As opposed to the few existing tools, which use labeled samples to track stable isotopes by iterating over all MS signals using the theoretical mass difference between the light and heavy isotopes, geoRge uses unlabeled and labeled biologically equivalent samples to compare isotopic distributions in the mass spectra. Isotopically enriched compounds change their isotopic distribution as compared to unlabeled compounds. This is directly reflected in a number of new m/z peaks and higher intensity peaks in the mass spectra of labeled samples relative to the unlabeled equivalents. The automated untargeted isotope annotation and relative quantification capabilities of geoRge are demonstrated by the analysis of LC/MS data from a human retinal pigment epithelium cell line (ARPE-19) grown on normal and high glucose concentrations mimicking diabetic retinopathy conditions in vitro. In addition, we compared the results of geoRge with the outcome of X(13)CMS, since both approaches rely entirely on XCMS parameters for feature selection, namely m/z and retention time values. To ensure data traceability and reproducibility, and enabling for comparison with other existing and future approaches, raw LC/MS files have been deposited in MetaboLights (MTBLS213) and geoRge is available as an R script at https://github.com/jcapelladesto/geoRge.

Metabolomics: beyond biomarkers and towards mechanisms

“…However, such analyses are computationally highly complex for dynamic experiments, leading to a decrease in accuracy 35 . In order to overcome this, algorithms have recently been developed that combine both stable isotope analysis and untargeted metabolomics 52–55 . This technology, called global isotope metabolomics, provides comprehensive differential labelling between two biological conditions, offering further understanding of metabolism at a systems level.…”

From chromatogram to analyte to metabolite. How to pick horses for courses from the massive web resources for mass spectral plant metabolomics

“…However, the approach requires the labeled fraction to be fully labeled and the tracer to be highly pure to get the proper isotopic distributions. X13CMS [83] and geoRge [84], both run on the R platform using GC-MS output. The former algorithm iterates over MS signals in each mass spectra using the mass difference due to the label, while the latter uses statistical testing to distinguish spectral peaks originating from labeled metabolites, resulting in significantly less false positives.…”

Analysis of stable isotope assisted metabolomics data acquired by high resolution mass spectrometry

“…Creek et al combined multiple software packages to analyze the SIAM data 1 , and Huang and colleagues developed the software package X 13 CMS to track isotopic labels 4 . Other developments include geoRge 5 , mzMatch-ISO 6 , and MIRACLE 7 . However, technical challenges persist, especially with respect to automatic and accurate assignment and quantification of isotopologue peaks.…”