A Software Tool for Rapid and Automated Preprocessing of Large-Scale Serum Metabolomic Data by Multisegment...

Area of Science:

• Analytical Chemistry
• Metabolomics
• Bioinformatics

Background:

• Mass spectrometry (MS)-based metabolomics requires efficient separation techniques for complex biological samples.
• Low sample throughput and complex data preprocessing hinder large-scale, affordable metabolomic studies.
• High-throughput separation platforms are needed to overcome current limitations in metabolomic data acquisition.

Purpose of the Study:

• To introduce PeakMeister, a novel software tool for standardized processing of serum metabolomic data.
• To enable high-throughput analysis of serum samples using multisegment injection-capillary electrophoresis-mass spectrometry (MSI-CE-MS).
• To validate PeakMeister's performance in large-scale metabolomic studies.

Main Methods:

• Development of PeakMeister software within the R statistical environment.
• Utilized multisegment injection-capillary electrophoresis-mass spectrometry (MSI-CE-MS) for high-throughput sample analysis (<4 min/sample).
• Introduced a migration time index using internal standards for accurate peak annotation and integration.
• Validated PeakMeister using 5,000 serum samples and 420 quality control samples from the ENANI-2019 study.

Main Results:

• PeakMeister accelerated data preprocessing 30-fold compared to manual methods.
• Achieved excellent peak annotation fidelity (median accuracy >99.9%) and acceptable intermediate precision (median CV = 16.0%).
• Demonstrated good agreement with NIST SRM-1950 plasma metabolite quantification (mean bias = -1.3%).
• Reported reference ranges for 40 serum metabolites in Brazilian children.

Conclusions:

• PeakMeister enables rapid and automated processing of large-scale metabolomic studies.
• MSI-CE-MS combined with PeakMeister overcomes limitations of nonlinear migration time shifts.
• The software facilitates affordable and scalable metabolomic research for population studies.