Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non‐destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

The clinical utility of metabolomics hinges on reproducibly detecting differences between groups across analytical platforms. A first step toward assuring clinical reliability is establishing the ability to correctly classify heterogeneous mixtures across multiple platforms. No multi-lab studies to date have attempted to use metabolomic profiles to reproducibly cluster heterogeneous mixtures. We evaluated the reproducibility of the TruQuant metabolomics approach in eight labs in the US and UK. Each lab received identical aliquots of 32 samples, containing known mixtures of beef (A), chicken (B), and/or pork (C) extracts of varying homogeneity levels. Metabolomic profiles were generated for each mixture in duplicate using each lab’s LC−MS methods. Anonymized, processed data were analyzed centrally. Despite differences in the number of features detected (560−1727 features), all labs correctly differentiated sample mixture types via unsupervised approaches, with generally strong clustering across labs (NMF-adjusted rand index (ARI) = 1 for all labs except one (ARI = 0.84), unsupervised random forest silhouette score mean = 0.58 ± 0.14). Pearson correlations of suppression-corrected metabolite abundances, compared between lab pairs, were also strong for metabolites that were matched using authentic standards (mean = 0.87 ± 0.12 across all labs). Notably, we evaluated the suitability and impact of different data transformation approaches to make lab-to-lab comparisons. Overall, this “round-robin” study design and data show that metabolomic analyses, with appropriate study designs and data handling, enable reproducible conclusions across labs and platforms.

Large-scale radiation catastrophes and localized exposure incidents remain major threats to public health. Effective treatment following radiation exposure is challenging because radiation dose, timing, and affected tissues can vary widely between individuals. To better understand the biological response to radiation injury, we employed a multi-omics approach integrating transcriptomics, proteomics, and metabolomics. In this study, mouse bone marrow was analyzed at 1, 6, and 24 h after whole-body irradiation and compared with bone marrow from non-irradiated mice. Multivariate and univariate analyses revealed time-dependent changes across transcriptomic, proteomic, and metabolomic profiles. We further applied machine learning-based integrative analysis and biomarker discovery approaches to identify key molecular responses associated with radiation exposure. Integrated analysis showed that the acute radiation response in bone marrow included immune-related processes, followed by intermediate metabolic reprogramming, and later disruptions in cell cycle regulation. Biomarker analysis identified several promising candidates, including the transcripts Sapcd2 and Cd28, proteins FRIL1 and CDK1, and the metabolites 3-hydroxyisobutyrate and taurine. Overall, our findings demonstrate that bone marrow from irradiated mice undergoes dynamic and coordinated molecular changes over time. These responses include activation of ferroptosis defense mechanisms, mitotic dysfunction, and pathways associated with programmed cell death.