Ultrahigh-throughput metabolomics for large-scale studies using matrix-assisted laser desorption/ionization-ion mobility-mass spectrometry

Abstract

Background: Metabolomics, the comprehensive analysis of low-molecular-weight metabolites, is central to biomarker discovery, drug development, and precision medicine. While large-scale metabolomics from biobanks could greatly advance individualized healthcare, conventional GC–MS and LC-MS approaches remain limited by time-consuming preparation and separation, restricting sample throughput. Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) offers a high-throughput alternative, being robust to contaminants and compatible with automated formats. Moreover, coupling MALDI with ion mobility (IM) expands molecular coverage by combining m/z and collision cross section measurements, positioning MALDI-IM-MS as a scalable platform for next-generation metabolomics and clinical big-data applications. Results: The MALDI-IM-MS workflow, combined with acetonitrile extraction, enabled reproducible detection of >2000 features spanning hydrophilic and lipid metabolites across a broad m/z range. Ion mobility separation enhanced coverage beyond 700 features and resolved isomeric species by collision cross section (CCS) values. Averaging signals from multiple raster positions kept coefficients of variation below 20%, while adduct correlations (R2 > 0.90) indicated strong quantitative potential. UMAP analysis of 34 specimens reliably clustered replicates and separated healthy volunteers from cancer patients, revealing both shared and disease-specific metabolic alterations. Notably, plasma LPC levels, known to decrease in malignancies, were consistently reduced across patient groups, confirming clinical observations. Despite the relatively small cohort, these results demonstrate that MALDI-IM-MS can sensitively capture disease-related metabolic alterations and provides a scalable platform for large-scale cancer metabolomics and precision medicine applications. Significance: The approach presented in this study offers a promising solution for conducting large-scale clinical trials with minimal effort, and is expected to serve as a big data generation platform that contributing to “precision medicine” by utilizing existing biobanks. Furthermore, we believe that this technology can be used not only in clinical research, but also to large-scale chemical and biochemical screening in a wide range of research fields, including pharmaceuticals, food, fisheries, livestock, and agriculture.