Untargeted metabolomics and lipidomics LC–MS experiments produce complex datasets, usually containing tens of thousands of features from thousands of metabolites whose annotation requires additional MS/MS experiments and expert knowledge. All-ion fragmentation (AIF) LC–MS/MS acquisition provides fragmentation data at no additional experimental time cost. However, analysis of such datasets requires reconstruction of parent–fragment relationships and annotation of the resulting pseudo-MS/MS spectra. Here, we propose a novel approach for automated annotation of isotopologues, adducts, and in-source fragments from AIF LC–MS datasets by combining correlation-based parent–fragment linking with molecular fragment matching. Our workflow focuses on a subset of features rather than trying to annotate the full dataset, saving time and simplifying the process. We demonstrate the workflow in three human serum datasets containing 599 features manually annotated by experts. Precision and recall values of 82–92% and 82–85%, respectively, were obtained for features found in the highest-rank scores (1–5). These results equal or outperform those obtained using MS-DIAL software, the current state of the art for AIF data annotation. Further validation for other biological matrices and different instrument types showed variable precision (60–89%) and recall (10–88%) particularly for datasets dominated by nonlipid metabolites. The workflow is freely available as an open-source R package, MetaboAnnotatoR, together with the fragment libraries from Github (https://github.com/gggraca/MetaboAnnotatoR).

While the combination of liquid chromatography and tandem mass spectrometry (LC-MS/MS) is commonly used for feature annotation in untargeted omics experiments, ensuring these prioritized features originate from endogenous metabolism remains challenging. Isotopologue workflows, such as isotopic ratio outlier analysis (IROA), mass isotopomer ratio analysis of U-13C labeled extracts (MIRACLE), and credentialing incorporate isotopic labels directly into metabolic precursors, guaranteeing that all features of interest are unequivocal byproducts of cellular metabolism. Furthermore, comprehensive separation and annotation of small molecules continue to challenge the metabolomics field, particularly for isomeric systems. In this paper, we evaluate the analytical utility of incorporating ion mobility spectrometry (IMS) as an additional separation mechanism into standard LC-MS/MS isotopologue workflows. Since isotopically labeled molecules codrift in the IMS dimension with their 12C versions, LC-IMS-CID-MS provides four dimensions (LC, IMS, MS, and MS/MS) to directly investigate the metabolic activity of prioritized untargeted features. Here, we demonstrate this additional selectivity by showcasing how a preliminary data set of 30 endogeneous metabolites are putatively annotated from isotopically labeled Escherichia coli cultures when analyzed by LC-IMS-CID-MS. Metabolite annotations were based on several molecular descriptors, including accurate mass measurement, carbon number, annotated fragmentation spectra, and collision cross section (CCS), collectively illustrating the importance of incorporating IMS into isotopologue workflows. Overall, our results highlight the enhanced separation space and increased annotation confidence afforded by IMS for metabolic characterization and provide a unique perspective for future developments in isotopically labeled MS experiments.

High-throughput multiplexed protein quantification using mass spectrometry is steadily increasing in popularity, with the two major techniques being data-dependent acquisition (DDA) and targeted acquisition using selected reaction monitoring (SRM). However, both techniques involve extensive data processing, which can be performed by a multitude of different software solutions. Analysis of quantitative LC-MS/MS data is mainly performed in three major steps: processing of raw data, normalization, and statistical analysis. To evaluate the impact of data processing steps, we developed two new benchmark data sets, one each for DDA and SRM, with samples consisting of a long-range dilution series of synthetic peptides spiked in a total cell protein digest. The generated data were processed by eight different software workflows and three postprocessing steps. The results show that the choice of the raw data processing software and the postprocessing steps play an important role in the final outcome. Also, the linear dynamic range of the DDA data could be extended by an order of magnitude through feature alignment and a charge state merging algorithm proposed here. Furthermore, the benchmark data sets are made publicly available for further benchmarking and software developments.

Dynamic isotope labeling data provide crucial information about the operation of metabolic pathways, and are commonly generated via liquid chromatography mass spectrometry (LC-MS). Metabolome-wide analysis is challenging as it requires grouping of metabolite features over different samples. We developed DynaMet for fully automated investigations of isotope labeling experiments from LC-high resolution MS raw data. DynaMet enables untargeted extraction of metabolite labeling profiles and provides integrated tools for expressive data visualization. For this study we generated labeling data of the model strain Escherichia coli from 13C glucose labeling switch experiments. Analysis of two biological replicates revealed high robustness and reproducibility of the pipeline. DynaMet analysis of two data sets each comprising 19 samples resulted in 286 and 293 features, respectively, with detection in at least 80% of all samples analyzed. Of these, 222 and 230, respectively, could be fitted with the implemented model. After removing false positives and merging both data sets DynaMet revealed a total number of 291 features with labeling profiles whereof 285 could be generated with implemented fitting function. Comparison with KEGG database resulted in 125 matches corresponding to 98 metabolites covering multiple pathways of core metabolism and major biosynthetic routes. The study results reported here are only a summary. To reproduce the complete results i.e. labeling profiles and corresponding kinetic fits including plots, DynaMet software is required.

Metabolic profiling is routinely performed on multiple analytical platforms to increase the coverage of detected metabolites, and it is often necessary to distribute biological and clinical samples from a study between instruments of the same type to share the workload between different laboratories. The ability to combine metabolomics data arising from different sources is therefore of great interest, particularly for large-scale or long-term studies, where samples must be analyzed in separate blocks. This is not a trivial task, however, due to differing data structures, temporal variability, and instrumental drift. In this study, we employed blood serum and plasma samples collected from 29 subjects diagnosed with small cell lung cancer and analyzed each sample on two liquid chromatography–mass spectrometry (LC-MS) platforms. We describe a method for mapping retention times and matching metabolite features between platforms and approaches for fusing data acquired from both instruments. Calibration transfer models were developed and shown to be successful at mapping the response of one LC-MS instrument to another (Procrustes dissimilarity = 0.04; Mantel correlation = 0.95), allowing us to merge the data from different samples analyzed on different instruments. Data fusion was assessed in a clinical context by comparing the correlation of each metabolite with subject survival time in both the original and fused data sets: a simple autoscaling procedure (Pearson’s R = 0.99) was found to improve upon a calibration transfer method based on partial least-squares regression (R = 0.94).

Untargeted liquid chromatography–mass spectrometry metabolomics studies are typically performed under roughly identical experimental settings. Measurements acquired with different LC-MS protocols or following extended time intervals harbor significant variation in retention times and spectral abundances due to altered chromatographic, spectrometric, and other factors, raising many data analysis challenges. We developed a computational workflow for merging and harmonizing metabolomics data acquired under disparate LC-MS conditions. Plasma metabolite profiles were collected from two sets of maternal subjects three years apart using distinct instruments and LC-MS procedures. Metabolomics features were aligned using metabCombiner to generate lists of compounds detected across all experimental batches. We applied data set-specific normalization methods to remove interbatch and interexperimental variation in spectral intensities, enabling statistical analysis on the assembled data matrix. Bioinformatics analyses revealed large-scale metabolic changes in maternal plasma between the first and third trimesters of pregnancy and between maternal plasma and umbilical cord blood. We observed increases in steroid hormones and free fatty acids from the first trimester to term of gestation, along with decreases in amino acids coupled to increased levels in cord blood. This work demonstrates the viability of integrating nonidentically acquired LC-MS metabolomics data and its utility in unconventional metabolomics study designs.

Eucalyptus rust is caused by the biotrophic fungus, Austropuccinia psidii, which affects commercial plantations of Eucalyptus, a major raw material for the pulp and paper industry in Brazil. In this manuscript we aimed to uncover the molecular mechanisms involved in rust resistance and susceptibility in Eucalyptus grandis. Epifluorescence microscopy was used to follow the fungus development inside the leaves of two contrasting half-sibling genotypes (rust-resistance and rust-susceptible), and also determine the comparative time-course of changes in metabolites and proteins in plants inoculated with rust. Within 24 h of complete fungal invasion, the analysis of 709 metabolomic features showed the suppression of many metabolites 6 h after inoculation (hai) in the rust-resistant genotype, with responses being induced after 12 hai. In contrast, the rust-susceptible genotype displayed more induced metabolites from 0 to 18 hai time-points, but a strong suppression occurred at 24 hai. Multivariate analyses of genotypes and time points were used to select 16 differential metabolites mostly classified as phenylpropanoid-related compounds. Applying the Weighted Gene Co-Expression Network Analysis (WGCNA), rust-resistant and rust-susceptible genotypes had, respectively, 871 and 852 proteins grouped into 5 and 6 modules, of which 5 and 4 of them were significantly correlated to the selected metabolites. Functional analyses revealed roles for photosynthesis and oxidative-dependent responses leading to temporal activity of metabolites and related enzymes after 12 hai in rust-resistance; while the initial over-accumulation of those molecules and suppression of supporting mechanisms at 12 hai caused a lack of progressive metabolite-enzyme responses after 12 hai in rust-susceptible genotype. This study provides some insights on how E. grandis plants are functionally modulated to integrate secondary metabolites and related enzymes from phenylpropanoid pathway and lead to temporal divergences of resistance and susceptibility responses to rust.

Eucalyptus rust is caused by the biotrophic fungus, Austropuccinia psidii, which affects commercial plantations of Eucalyptus, a major raw material for the pulp and paper industry in Brazil. In this manuscript we aimed to uncover the molecular mechanisms involved in rust resistance and susceptibility in Eucalyptus grandis. Epifluorescence microscopy was used to follow the fungus development inside the leaves of two contrasting half-sibling genotypes (rust-resistance and rust-susceptible), and also determine the comparative time-course of changes in metabolites and proteins in plants inoculated with rust. Within 24 h of complete fungal invasion, the analysis of 709 metabolomic features showed the suppression of many metabolites 6 h after inoculation (hai) in the rust-resistant genotype, with responses being induced after 12 hai. In contrast, the rust-susceptible genotype displayed more induced metabolites from 0 to 18 hai time-points, but a strong suppression occurred at 24 hai. Multivariate analyses of genotypes and time points were used to select 16 differential metabolites mostly classified as phenylpropanoid-related compounds. Applying the Weighted Gene Co-Expression Network Analysis (WGCNA), rust-resistant and rust-susceptible genotypes had, respectively, 871 and 852 proteins grouped into 5 and 6 modules, of which 5 and 4 of them were significantly correlated to the selected metabolites. Functional analyses revealed roles for photosynthesis and oxidative-dependent responses leading to temporal activity of metabolites and related enzymes after 12 hai in rust-resistance; while the initial over-accumulation of those molecules and suppression of supporting mechanisms at 12 hai caused a lack of progressive metabolite-enzyme responses after 12 hai in rust-susceptible genotype. This study provides some insights on how E. grandis plants are functionally modulated to integrate secondary metabolites and related enzymes from phenylpropanoid pathway and lead to temporal divergences of resistance and susceptibility responses to rust.