ElemCor

ElemCor performs resolution-dependent correction of LC-MS stable isotope labeling data to separate true metabolite labeling patterns from contributions of natural isotopes and isotopic impurities, improving interpretation of mass distribution vectors.

Key Features:

• Resolution-dependent correction: Distinguishes true labeling patterns from contaminant signals that share nominal masses, leveraging resolving power of high-resolution mass spectrometry.
• Mass Difference Theory (MDT): Implements a mathematical correction that relies solely on chemical formulae to correct isotopic contributions, recommended for low-mass compounds.
• Unlabeled Sample (ULS) method: Applies a semi-empirical correction using measurements from unlabeled samples to account for resolution effects and correct spectral inaccuracies, suitable for high-mass compounds.
• Labeling pattern analysis: Processes mass distribution vectors of metabolites to support downstream metabolic interpretation.

Scientific Applications:

• Intracellular metabolism analysis: Applied to stable isotope labeling experiments for studying intracellular metabolic activities relevant to biotechnology and physiological research.
• Metabolic flux and pathway inference: Enables more accurate inference of metabolic rates and pathways from corrected LC-MS labeling data.
• Metabolomics data correction: Improves accuracy and comprehensiveness of isotopic labeling data correction in LC-MS datasets.
• Validation: Performance demonstrated on simulated datasets and experimental data derived from Saccharomyces cerevisiae.

Methodology:

Performs resolution-dependent correction using Mass Difference Theory (MDT) for formula-based mathematical correction and Unlabeled Sample (ULS) semi-empirical correction using unlabeled sample measurements to account for resolution effects and spectral inaccuracies while analyzing labeling patterns/mass distribution vectors.