Dataset Information

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. geoRge is available as an R script at https://github.com/jcapelladesto/geoRge.

INSTRUMENT(S): 6540 Q-TOF LC/MS (Agilent)

SUBMITTER: Sara Samino

PROVIDER: MTBLS213 | MetaboLights | 2015-09-15

REPOSITORIES: MetaboLights

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Dataset's files

Source:

			Action	DRS
	MTBLS213	Other
	FILES	Other
	a_MTBLS213_arpe_george_neg_metabolite_profiling_mass_spectrometry.txt	Txt
	i_Investigation.txt	Txt
	m_MTBLS213_arpe_george_neg_metabolite_profiling_mass_spectrometry_v2_maf.tsv	Tabular

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Publications

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

Capellades Jordi J Navarro Miriam M Samino Sara S Garcia-Ramirez Marta M Hernandez Cristina C Simo Rafael R Vinaixa Maria M Yanes Oscar O

Analytical chemistry 20151218 1

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 analysi ...[more]

PMID: 26639619

Similar Datasets

Project description:This data set is downloaded from MetaboLights (http://www.ebi.ac.uk/metabolights/) accession number MTBLS213 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. geoRge is available as an R script at https://github.com/jcapelladesto/geoRge.

Project description:Studies of isotopically labeled compounds have been fundamental to understanding metabolic pathways and fluxes. They have traditionally, however, been used in conjunction with targeted analyses that identify and quantify a limited number of labeled downstream metabolites. Here we describe an alternative workflow that leverages recent advances in untargeted metabolomic technologies to track the fates of isotopically labeled metabolites in a global, unbiased manner. This untargeted approach can be applied to discover novel biochemical pathways and characterize changes in the fates of labeled metabolites as a function of altered biological conditions such as disease. To facilitate the data analysis, we introduce X13CMS, an extension of the widely used mass spectrometry-based metabolomic software package XCMS. X13CMS uses the XCMS platform to detect metabolite peaks and perform retention-time alignment in liquid chromatography/mass spectrometry (LC/MS) data. With the use of the XCMS output, the program then identifies isotopologue groups that correspond to isotopically labeled compounds. The retrieval of these groups is done without any a priori knowledge besides the following input parameters: (i) the mass difference between the unlabeled and labeled isotopes, (ii) the mass accuracy of the instrument used in the analysis, and (iii) the estimated retention-time reproducibility of the chromatographic method. Despite its name, X13CMS can be used to track any isotopic label. Additionally, it detects differential labeling patterns in biological samples collected from parallel control and experimental conditions. We validated the ability of X13CMS to accurately retrieve labeled metabolites from complex biological matrices both with targeted LC/MS/MS analysis of a subset of the hits identified by the program and with labeled standards spiked into cell extracts. We demonstrate the full functionality of X13CMS with an analysis of cultured rat astrocytes treated with uniformly labeled (U-)13C-glucose during lipopolysaccharide (LPS) challenge. Our results show that out of 223 isotopologue groups enriched from U-13C-glucose, 95 have statistically significant differential labeling patterns in astrocytes challenged with LPS compared to unchallenged control cells. Only two of these groups overlap with the 32 differentially regulated peaks identified by XCMS, indicating that X13CMS uncovers different and complementary information from untargeted metabolomic studies. Like XCMS, X13CMS is implemented in R. It is available from our laboratory website at http://pattilab.wustl.edu/x13cms.php.