A Software Tool for Rapid and Automated Pre-Processing of Large-Scale Serum Metabolomic Data by Multisegment Injection-Capillary Electrophoresis-Mass Spectrometry

Mass spectrometry (MS)-based metabolomics often rely on separation techniques when analyzing complex biological specimens to improve method resolution, metabolome coverage, quantitative performance, and/or unknown identification. However, low sample throughput and complicated data pre-processing procedures remain major barriers to affordable metabolomic studies that are also scalable to large populations. Herein, we introduce PeakMeister as a new software tool in the R statistical environment to enable the automated processing of serum metabolomic data acquired by multisegment injection-capillary electrophoresis-mass spectrometry (MSI-CE-MS) under positive ion mode. MSI-CE-MS takes advantage of a multiplexed separation format involving serial injection of 13 serum/plasma filtrate samples, quality controls, calibrants and/or blanks introduced within a single analytical run (< 4 min/sample). We performed a rigorous validation of PeakMeister by analyzing 47 cationic metabolites consistently measured in 5,000 serum samples from the Brazilian National Survey on Child Nutrition (ENANI-2019) comprising a total of 224,983 sample peaks analyzed in three batches over an eight-month period. A migration time index using a panel of internal standards was introduced to correct for large variations in apparent migration times, which allowed for reliable peak picking, peak integration and sample position assignment for serum metabolites having a single co-migrating stable-isotope internal standard or two flanking internal standards. PeakMeister accelerated data pre-processing times by 30-fold compared to manual processing of MSI-CE-MS data by an experienced analyst using vendor software, while also achieving excellent peak annotation fidelity (median accuracy > 99.9%), acceptable intermediate precision (median CV = 16.0 %), consistent metabolite peak integration (mean bias = 2.1%), and good mutual agreement when quantifying 16 plasma metabolites from NIST SRM-1950 (mean bias = -1.3%). We also report reference intervals for 40 serum metabolites in a national nutritional survey of children under 5 years of age (ENANI-2019). MSI-CE-MS in conjunction with PeakMeister allows for rapid and automated data pre-processing of large-scale metabolomic studies while tolerating long-term migration time shifts without the need for complicated dynamic time warping or effective mobility scale transformations.