iMet-Q: A User-Friendly Tool for Label-Free Metabolomics Quantitation Using Dynamic Peak-Width Determination

Area of Science:

• Metabolomics
• Analytical Chemistry
• Bioinformatics

Background:

• Accurate quantitation of metabolites from LC-MS data is crucial for high-throughput metabolomics.
• Existing tools may have limitations in quantitation accuracy and metabolite identification support.

Purpose of the Study:

• To develop and validate an automated tool, iMet-Q, for label-free metabolomics quantitation from MS1 data.
• To enhance metabolite identification through charge state and isotope ratio determination.

Main Methods:

• iMet-Q performs automated peak detection and alignment for quantitation.
• It calculates ion abundance at replicate and sample levels.
• Charge states and isotope ratios of metabolite peaks are determined.

Main Results:

• iMet-Q demonstrated a lower quantitation error (12%) compared to other tools using standard mixture data.
• It accurately determined charge states and facilitated filtering of metabolite candidates using isotope ratios.
• In an Arabidopsis dataset, iMet-Q detected all internal standards and elucidated metabolites with high abundance correlation.

Conclusions:

• iMet-Q provides efficient and accurate label-free metabolomics quantitation.
• The tool aids in metabolite identification by providing charge state and isotope ratio information.
• iMet-Q is a user-friendly, publicly available solution for metabolomics data analysis.