Towards a more reliable identification of isomeric metabolites using pattern guided retention validation

Reliable analyte identification is critical in metabolomics experiments to ensure proper interpretation of data. Due to chemical similarity of metabolites (as isobars and isomers) identification by mass spectrometry or chromatography alone can be difficult. Here we show that isomeric compounds are quite common in the metabolic space as given in common metabolite databases. Further, we show that retention information can shift dramatically between different experiments decreasing the value of external or even in-house compound databases. As a consequence the retention information in compound databases should be updated regularly, to allow a reliable identification. To do so we present a feasible and budget conscious method to guarantee updates of retention information on a regular basis using well designed compound mixtures. For this we combine compounds in “Ident-Mixes”, showing a way to distinctly identify chemically similar compounds through combinatorics and principle of exclusion. We illustrate the feasibility of this approach by comparing Gas chromatography (GC)–columns with identical properties from three different vendors and by creating a compound database from measuring these mixtures by Liquid chromatography–mass spectrometry (LC–MS). The results show the high influence of used materials on retention behavior and the ability of our approach to generate high quality identifications in a short time

Optimized workflow for on-line derivatization for targeted metabolomics approach by gas chromatography-mass spectrometry

Using manual derivatization in gas chromatography-mass spectrometry samples have varying equilibration times before analysis which increases technical variability and limits the number of potential samples analyzed. By contrast, automated derivatization methods can derivatize and inject each sample in an identical manner. We present a fully automated (on-line) derivatization method used for targeted analysis of different matrices. We describe method optimization and compare results from using off-line and on-line derivatization protocols, including the robustness and reproducibility of the methods. Our final parameters for the derivatization process were 20 µL of methoxyamine (MeOx) in pyridine for 60 min at 30 °C followed by 80 µL N-Methyl-N-trimethylsilyltrifluoracetamide (MSTFA) for 30 min at 30 °C combined with 4 h of equilibration time. The repeatability test in plasma and liver revealed a median relative standard deviation (RSD) of 16% and 10%, respectively. Serum samples showed a consistent intra-batch median RSD of 20% with an inter-batch variability of 27% across three batches. The direct comparison of on-line versus off-line demonstrated that on-line was fit for purpose and improves repeatability with a measured median RSD of 11% compared to 17% using the same method off-line. In summary, we recommend that optimized on-line methods may improve results for metabolomics and should be used where available

Exercise blood-drop metabolic profiling links metabolism with perceived exertion

BACKGROUND: Assessing detailed metabolism in exercising persons minute-to-minute has not been possible. We developed a “drop-of-blood” platform to fulfill that need. Our study aimed not only to demonstrate the utility of our methodology, but also to give insights into unknown mechanisms and new directions. METHODS: We developed a platform, based on gas chromatography and mass spectrometry, to assess metabolism from a blood-drop. We first observed a single volunteer who ran 13 km in 60 min. We particularly monitored relative perceived exertion (RPE). We observed that 2,3-bisphosphoglycerate peaked at RPE in this subject. We next expanded these findings to women and men volunteers who performed an RPE-based exercise protocol to RPE at Fi O 2 20.9% or Fi O 2 14.5% in random order. RESULTS: At 6 km, our subject reached his maximum relative perceived exertion (RPE); however, he continued running, felt better, and finished his run. Lactate levels had stably increased by 2 km, ketoacids increased gradually until the run’s end, while the hypoxia marker, 2,3 bisphosphoglycerate, peaked at maximum relative perceived exertion. In our normal volunteers, the changes in lactate, pyruvate, ß hydroxybutyrate and a hydroxybutyrate were not identical, but similar to our model proband runner. CONCLUSION: Glucose availability was not the limiting factor, as glucose availability increased towards exercise end in highly exerted subjects. Instead, the tricarboxylic acid?oxphos pathway, lactate clearance, and thus and the oxidative capacity appeared to be the defining elements in confronting maximal exertion. These ideas must be tested further in more definitive studies. Our preliminary work suggests that our single-drop methodology could be of great utility in studying exercise physiology

A mass spectrometry approach for identification and quantification of both polar and apolar metabolites from a single drop of human capillary blood

In the global effort of translating systems biology research into clinical applicability, metabolomics and lipidomics harbor great potential for blood-based medical diagnostics. Our lab has already established a robust analytical pipeline for the identification and quantification of polar metabolites by GC-MS. However, apolar metabolites, and lipids in particular, cover an important part of cell metabolism. Here we present a combined approach in order to detect (and quantify) both polar metabolites and lipid species from human blood using a two-step extraction procedure. Moreover, we developed a data analysis pipeline, which allows for the fast and robust quantification and identification of lipid species from high-resolution MS data. Isotope-corrected and calibrated mass traces are matched to an in-house database combining data from LipidMaps and HMDB. Internal standards serve as control for mass calibration as well as for abundance normalization for their respective lipid class. As proof of principle, we combined our approach to a simple, fast, and minimally invasive blood sampling method. Starting from 20 \ub5L human capillary blood of 15 volunteers, about 100 known (+ 200 unidentified) polar metabolites and on average around 300 lipid species covering all main lipid classes were identified and quantified by GC-MS and direct infusion-MS, respectively

Towards diagnostic medicine : metabolomic and lipidomic analysis of human capillary blood

In the global effort of translating systems biology research into clinical applicability, metabolomics and lipidomics harbor great potential for blood-based medical diagnostics. Gas chromatography mass spectrometry (MS) and direct-infusion electrospray-mass spectrometry are popular platforms for the identification and quantification of metabolites and lipid species in biological samples. By combining a simple, fast, and minimally invasive blood sampling method with these technologies, we are developing an analysis pipeline that allows for both the detailed determination of the current metabolic state as well as metabolic responses to experimental stimuli in vivo in humans. A proof-of-principle study monitored metabolomic changes in the capillary blood of a human volunteer undergoing an exercise regime. In addition to reproducing previous experimental findings, the analysis provided a metabolic mechanism for \u201chitting the wall\u201d, a phenomenon experienced during strenuous exercise. This insight was possible only in light of (1) the frequent sampling facilitated by our sampling strategy, and (2) the identification and quantification of both metabolites that are commonly not analyzed due to technical limitations as well as those that originate in erythrocytes, and can therefore only be systemically interpreted in full blood samples. Furthermore, the lipidomic analysis, which allows for the measurement of free fatty acids, triacylglycerols, and diacylglycerols among others, has indicated that human lipidomic profiles are highly individual and might, aside from representing underlying genetic variation, be strongly influenced by lifestyle and pathological alterations, making the technology ideal for use in preventative and diagnostic medicine. In summary, we have developed a minimally invasive sampling method coupled to a MS-based analysis pipeline that allows for the decoding of human metabolic physiology in health and disease

Analyzing and targeting cancer metabolism in vivo

Metabolic reprogramming is a key step in oncogenic transformation including the activation of energy- and anabolic metabolism. Thus, differentiated cells reenter the cell cycle and proliferate. The central metabolism is the ultimate source of energy and building blocks enabling growth and proliferation. Specifically the time resolved analysis of central metabolic pathways allows an insight in metabolic dynamics and allows the comparison and even quantification of pathway usage (Liu et al. 2012). Glucose and glutamine were identified as the major fuel sources for cancer cells. Thus, glycolysis and glutaminolysis are central metabolic pathways that may possess molecular targets for an effective metabolic treatment of cancer cells not only in vitro but also in vivo. However, until now the activity of glutaminolysis in vivo is still under debate. Although a high glutaminolytic activity can be observed in cell cultures the in vivo-activity of this pathway has rarely been shown. To better characterize the metabolic activity of hepatocellular carcinoma (HCC) we studied a mouse model for HCC by analyzing the metabolome, proteome and usage of glucose and glutamine by 13C stable isotope labeling in vivo. The data show that in vivo-metabolism is strongly transformed (metabolic reprogramming) and characterized by altered usage of glucose and glutamine. Further analyses and integration of the multilevel data may allow identifying molecular targets within central metabolism. Liu L. et al. (2012) Deregulated MYC expression induces dependence upon AMPK-related kinase 5. Nature 483(7391):608-12

WiPP: Workflow for Improved Peak Picking for Gas Chromatography-Mass Spectrometry (GC-MS) Data

The authors would like to thank Mathias Kuhring for his feedback on the usage of the pipelineLack of reliable peak detection impedes automated analysis of large-scale gas chromatography-mass spectrometry (GC-MS) metabolomics datasets. Performance and outcome of individual peak-picking algorithms can differ widely depending on both algorithmic approach and parameters, as well as data acquisition method. Therefore, comparing and contrasting between algorithms is difficult. Here we present a workflow for improved peak picking (WiPP), a parameter optimising, multi-algorithm peak detection for GC-MS metabolomics. WiPP evaluates the quality of detected peaks using a machine learning-based classification scheme based on seven peak classes. The quality information returned by the classifier for each individual peak is merged with results from different peak detection algorithms to create one final high-quality peak set for immediate down-stream analysis. Medium- and low-quality peaks are kept for further inspection. By applying WiPP to standard compound mixes and a complex biological dataset, we demonstrate that peak detection is improved through the novel way to assign peak quality, an automated parameter optimisation, and results in integration across different embedded peak picking algorithms. Furthermore, our approach can provide an impartial performance comparison of different peak picking algorithms. WiPP is freely available on GitHub (https://github.com/bihealth/WiPP) under MIT licence