Strategy for optimizing LC-MS data processing in metabolomics: a design of experiments approach.

Area of Science:

• Analytical Chemistry
• Biochemistry
• Bioinformatics

Background:

• Liquid chromatography-mass spectrometry (LC-MS) is a powerful technique for metabolomics.
• Optimizing data processing is crucial for accurate metabolite identification and quantification.
• Current default settings in software packages may not yield optimal results.

Purpose of the Study:

• To propose and validate a novel strategy for optimizing LC-MS metabolomics data processing.
• To enhance the reliability and accuracy of metabolite peak detection and quantification.
• To demonstrate the broad applicability of the strategy across different biological samples and software.

Main Methods:

• A sequential design of experiments (DoE) approach was developed.
• The strategy utilizes a dilution series of pooled samples.
• A reliability index metric, based on peak linearity and response, was employed for optimization.
• The strategy was implemented and tested using the XCMS package in R on human and plant metabolomics data.

Main Results:

• The proposed DoE optimization strategy resulted in over 57% improvement in the reliability index compared to default settings.
• The strategy effectively favors reliable peaks while down-weighting unreliable ones.
• Successful application was demonstrated on both human and plant biology datasets.
• The method showed potential for automation and integration into existing metabolomics pipelines.

Conclusions:

• The developed strategy offers a robust method for optimizing LC-MS metabolomics data processing parameters.
• This optimization significantly enhances the quality and reliability of metabolomics data.
• The approach is versatile and can be adapted for various software packages like MZmine 2.
• The strategy facilitates automated and more efficient metabolomics data analysis.