Optimization of large-scale pseudotargeted metabolomics method based on liquid chromatography-mass spectrometry

Area of Science:

• Metabolomics
• Analytical Chemistry
• Genomics

Background:

• Liquid chromatography-mass spectrometry (LC-MS) is crucial for large-scale metabolic phenotyping and understanding genomic functions.
• Repeatability remains a significant challenge in LC-MS-based methods, especially for long-term analyses.
• Previous pseudotargeted methods offer a practical approach for high-quality, information-rich data.

Purpose of the Study:

• To develop and optimize a comprehensive strategy for large-scale metabolomics using pseudotargeted LC-MS.
• To enhance the repeatability and reliability of metabolic profiling over extended analytical runs.
• To validate the strategy's performance across multiple batches and time spans.

Main Methods:

• Integration of blank-wash, pooled quality control (QC) samples, and post-calibration into a pseudotargeted LC-MS workflow.
• Optimization of pre- and post-acquisition parameters, including QC sample selection and insertion frequency.
• Application of the strategy to combine 3 independent batches over 5 weeks for proof of concept.

Main Results:

• The developed strategy demonstrated improved stability and suitability for large-scale metabolic profiling.
• Approximately 54% of features exhibited coefficients of variation (CV) below 15% when combining 3 batches over 5 weeks.
• Analytical batch stability was extended to at least 282 injections (110 hours), with 63% of metabolic features showing CV < 15%.

Conclusions:

• The enhanced pseudotargeted LC-MS strategy significantly improves repeatability for large-scale metabolomics.
• This reliable protocol supports long-term metabolic profiling with high data quality.
• The findings provide a robust method for advancing metabolic phenotyping in biological research.