Single cell metabolomics is an emerging and rapidly developing field that complements developments in single cell analysis by genomics and proteomics. Major goals include mapping and quantifying the metabolome in sufficient detail to provide useful information about cellular function in highly heterogeneous systems such as tissue, ultimately with spatial resolution at the individual cell level. The chemical diversity and dynamic range of metabolites poses particular challenges for detection, identification and quantification. In this review we discuss both significant technical issues of measurement and interpretation, and progress toward addressing them, with recent examples from diverse biological systems. We provide a framework for further directions aimed at improving workflow and robustness so that such analyses may become commonly applied, especially in combination with metabolic imaging and single cell transcriptomics and proteomics.

IntroductionFlow infusion electrospray high resolution mass spectrometry (FIE-HRMS) fingerprinting produces complex, high dimensional data sets which require specialist in-silico software tools to process the data prior to analysis.ObjectivesPresent spectral binning as a pragmatic approach to post-acquisition procession of FIE-HRMS metabolome fingerprinting data.MethodsA spectral binning approach was developed that included the elimination of single scan m/z events, the binning of spectra and the averaging of spectra across the infusion profile. The modal accurate m/z was then extracted for each bin. This approach was assessed using four different biological matrices and a mix of 31 known chemical standards analysed by FIE-HRMS using an Exactive Orbitrap. Bin purity and centrality metrics were developed to objectively assess the distribution and position of accurate m/z within an individual bin respectively.ResultsThe optimal spectral binning width was found to be 0.01 amu. 80.8% of the extracted accurate m/z matched to predicted ionisation products of the chemical standards mix were found to have an error of below 3 ppm. The open-source R package binneR was developed as a user friendly implementation of the approach. This was able to process 100 data files using 4 Central Processing Units (CPU) workers in only 55 seconds with a maximum memory usage of 1.36 GB.ConclusionSpectral binning is a fast and robust method for the post-acquisition processing of FIE-HRMS data. The open-source R package binneR allows users to efficiently process data from FIE-HRMS experiments with the resources available on a standard desktop computer.

Acylcarnitines (ACs) are metabolites involved in fatty acid β-oxidation and organic acid metabolism. Metabolic disorders associated to these two processes can be evaluated by determining the complete profile of ACs. In this research, we present an overall strategy for identification, confirmation, and quantitative determination of acylcarnitines in human serum. By this strategy we identified the presence of 47 ACs from C2 to C24 with detection of the unsaturation degree by application of a data-independent acquisition (DIA) liquid chromatography–tandem mass spectrometry (LC–MS/MS) method. Complementary, quantitative determination of ACs is based on a high-throughput and fully automated method consisting of solid-phase extraction on-line coupled to LC–MS/MS in data-dependent acquisition (DDA) to improve analytical features avoiding the errors associated to sample processing. Quantitation limits were at pg mL–1 level, the intra-day and between-day variability were below 15–20%, respectively; and the accuracy, expressed as bias, was always within ± 25%. The proposed method was tested with 40 human volunteers to determine the relative concentration of ACs in serum and identify predominant forms. Significant differences were detected by comparing the ACs profile of obese versus non-obese individuals.

As the most common chronic complication of diabetes mellitus (DM), diabetic peripheral neuropathy (DPN) seriously affects the quality of life of DM patients. So, it is of great significance for the diagnosis and treatment of DPN. In recent years, there have been numerous studies on pathogenesis and biomarkers of DM, but there are few studies on the biomarkers of DPN. This research is intended to identify abnormal metabolic pathways, search for potential biomarkers of DPN, and provide a metabolic basis for the diagnosis and mechanism of DPN. Serum samples from 23 healthy controls (HC), 42 DM patients and 30 DPN patients and urine samples from 42 HC, 40 DM patients, and 30 DPN patients were collected. UPLC-Q-TOF/MS was used to analyze the samples. Potential biomarkers were screened from principal component analysis (PCA) to orthogonal partial least squares discriminant analysis (OPLS-DA) and further evaluated by receiver operating characteristic analysis (ROC). The biomarkers were then enriched and pathway analyzed. 12 potential DPN biomarkers were identified from patient's serum. 11 potential DPN biomarkers were identified from the patient's urine. Among them, the diagnostic ability of gluconic acid, lipoic acid, sphinganine, bilirubin, sphingosine and 4-hydroxybenzoic acid was increased by ROC analysis. Potential biomarkers suggest that the disorder of DPN metabolism may be linked to sphingolipid metabolism. This research laid a theoretical foundation for the diagnosis and pathogenesis of DPN.

IntroductionRecent advances in high-throughput methodologies in the ‘omics’ and synthetic biology fields call for rapid and sensitive workflows in the metabolic phenotyping of complex biological samples.ObjectiveThe objective of this research was to evaluate a straightforward to implement LC–MS metabolomics method using a commercially available chromatography column that provides increased throughput. Reducing run time can potentially impact chromatography and therefore the effects of ion mobility spectrometry to expand peak capacity were also evaluated. Additional confidence provided via collision cross section measurements for detected features was also explored.MethodsA rapid untargeted metabolomics workflow was developed with broad metabolome coverage, combining zwitterionic-phase hydrophilic interaction chromatography (HILIC-Z) with drift tube ion mobility-quadrupole time-of-flight (DTIM-qTOF) mass spectrometry. The analytical performance of our method was explored using extracts from complex biological samples, including a reproducibility study on chicken serum and a simple comparative study on a bacterial metabolome.ResultsThe method is acronymised RHIMMS for rapid HILIC-Z ion mobility mass spectrometry. We present the RHIMMS workflow starting with data acquisition, followed by data processing and analysis. RHIMMS demonstrates improved chromatographic separation for a selection of metabolites with wide physicochemical properties while maintaining reproducibility at better than 20% over 200 injections at 3.5 min per sample for the selected metabolites, and a mean of 13.9% for the top 50 metabolites by intensity. Additionally, the combination of rapid chromatographic separation with ion mobility allows improved annotation and the ability to distinguish isobaric compounds.ConclusionOur results demonstrate RHIMMS to be a rapid, reproducible, sensitive and high-resolution analytical platform that is highly applicable to the untargeted metabolomics analysis of complex samples.

IntroductionTestosterone administration attenuates reductions in total body mass and lean mass during severe energy deficit (SED).ObjectivesThis study examined the effects of testosterone administration on the serum metabolome during SED.MethodsIn a double-blind, placebo-controlled clinical trial, non-obese men were randomized to receive 200-mg testosterone enanthate/wk (TEST) (n = 24) or placebo (PLA) (n = 26) during a 28-d inpatient, severe exercise- and diet-induced energy deficit. This study consisted of three consecutive phases. Participants were free-living and provided a eucaloric diet for 14-d during Phase 1. During Phase 2, participants were admitted to an inpatient unit, randomized to receive testosterone or placebo, and underwent SED for 28-d. During Phase 3, participants returned to their pre-study diet and physical activity habits. Untargeted metabolite profiling was conducted on serum samples collected during each phase. Body composition was measured using dual-energy X-ray absorptiometry after 11-d of Phase 1 and after 25-d of Phase 2 to determine changes in fat and lean mass.ResultsTEST had higher (Benjamini–Hochberg adjusted, q < 0.05) androgenic steroid and acylcarnitine, and lower (q < 0.05) amino acid metabolites after SED compared to PLA. Metabolomic differences were reversed by Phase 3. Changes in lean mass were associated (Bonferroni-adjusted, p < 0.05) with changes in androgenic steroid metabolites (r = 0.42–0.70), acylcarnitines (r = 0.37–0.44), and amino acid metabolites (r = − 0.36–− 0.37). Changes in fat mass were associated (p < 0.05) with changes in acylcarnitines (r = − 0.46–− 0.49) and changes in urea cycle metabolites (r = 0.60–0.62).ConclusionTestosterone administration altered androgenic steroid, acylcarnitine, and amino acid metabolites, which were associated with changes in body composition during SED.

IntroductionAccuracy of feature annotation and metabolite identification in biological samples is a key element in metabolomics research. However, the annotation process is often hampered by the lack of spectral reference data in experimental conditions, as well as logistical difficulties in the spectral data management and exchange of annotations between laboratories.ObjectivesTo design an open-source infrastructure allowing hosting both nuclear magnetic resonance (NMR) and mass spectra (MS), with an ergonomic Web interface and Web services to support metabolite annotation and laboratory data management.MethodsWe developed the PeakForest infrastructure, an open-source Java tool with automatic programming interfaces that can be deployed locally to organize spectral data for metabolome annotation in laboratories. Standardized operating procedures and formats were included to ensure data quality and interoperability, in line with international recommendations and FAIR principles.ResultsPeakForest is able to capture and store experimental spectral MS and NMR metadata as well as collect and display signal annotations. This modular system provides a structured database with inbuilt tools to curate information, browse and reuse spectral information in data treatment. PeakForest offers data formalization and centralization at the laboratory level, facilitating shared spectral data across laboratories and integration into public databases.ConclusionPeakForest is a comprehensive resource which addresses a technical bottleneck, namely large-scale spectral data annotation and metabolite identification for metabolomics laboratories with multiple instruments. PeakForest databases can be used in conjunction with bespoke data analysis pipelines in the Galaxy environment, offering the opportunity to meet the evolving needs of metabolomics research. Developed and tested by the French metabolomics community, PeakForest is freely-available at https://github.com/peakforest.

BackgroundThrough the systematic large-scale profiling of metabolites, metabolomics provides a tool for biomarker discovery and improving disease monitoring, diagnosis, prognosis, and treatment response, as well as for delineating disease mechanisms and etiology. As a downstream product of the genome and epigenome, transcriptome, and proteome activity, the metabolome can be considered as being the most proximal correlate to the phenotype. Integration of metabolomics data with other -omics data in multi-omics analyses has the potential to advance understanding of human disease development and treatment.Aim of reviewTo understand the current funding and potential research opportunities for when metabolomics is used in human multi-omics studies, we cross-sectionally evaluated National Institutes of Health (NIH)-funded grants to examine the use of metabolomics data when collected with at least one other -omics data type. First, we aimed to determine what types of multi-omics studies included metabolomics data collection. Then, we looked at those multi-omics studies to examine how often grants employed an integrative analysis approach using metabolomics data.Key scientific concepts of reviewWe observed that the majority of NIH-funded multi-omics studies that include metabolomics data performed integration, but to a limited extent, with integration primarily incorporating only one other -omics data type. Some opportunities to improve data integration may include increasing confidence in metabolite identification, as well as addressing variability between -omics approach requirements and -omics data incompatibility.