geoRge

geoRge analyzes untargeted liquid chromatography–mass spectrometry (LC/MS) data from stable isotope-labeling experiments to detect and quantify isotopic enrichment and map labeled moieties in metabolic studies.

Key Features:

• Implementation: Implemented in R for computational analysis of LC/MS feature tables.
• XCMS integration: Operates on the output structure of the XCMS package for processed LC/MS features.
• Isotopic distribution comparison: Compares isotopic distributions between unlabeled and labeled biologically equivalent samples to detect enrichment manifested as new m/z peaks and increased peak intensities.
• Automated annotation and quantification: Performs untargeted isotope annotation and relative quantification of enriched features.
• Demonstrated dataset: Applied to LC/MS data from ARPE-19 human retinal pigment epithelium cells cultured under normal and high glucose conditions (diabetic retinopathy in vitro model).
• Comparative benchmarking: Compared against X(13)CMS using XCMS parameters for feature selection (m/z and retention time).
• Data deposition: Raw LC/MS files used in analyses deposited in MetaboLights (MTBLS213).

Scientific Applications:

• Untargeted metabolomics: Mapping labeled moieties through metabolic pathways without prior compound targeting.
• Metabolic flux analysis: Detecting changes in isotopic distribution to infer enrichment and label propagation in metabolic networks.
• Disease metabolic profiling: Investigating metabolic alterations associated with disease states such as diabetes, exemplified by ARPE-19 high versus normal glucose comparisons.

Methodology:

Implemented in R; accepts XCMS-processed LC/MS feature output; compares isotopic distributions between unlabeled and labeled biologically equivalent samples, detects enrichment as new m/z peaks and intensity shifts, performs untargeted isotope annotation and relative quantification, and was benchmarked against X(13)CMS using m/z and retention time feature selection.