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Metabolomics
(2024)
Raw data from metabolomics experiments are initially subjected to peak identification and signal deconvolution to generate raw data matrices m × n, where m are samples and n are metabolites. We describe here simple statistical procedures on such multivariate data matrices, all provided as functions in the programming environment R, useful to normalize data, detect biomarkers, and perform sample classification.
Climate change and related energy policies, exacerbated by unforeseen geopolitical developments, pose new challenges for gas analytics, such as the use of hydrogen, hydrogen-containing alternative gaseous fuels (NH3, etc.), the use of alternative methane-based energy gases (LNG, LPG, etc.) or decarbonisation via CCSU. In all topics, the quality, i.e. the actual chemical composition of the gases, naturally plays a decisive role. BAM is meeting this strategic importance with the further development of hydrogen analytics and is continuing to develop the methods used in order to support the German economy and research landscape with traceability, reference materials and analytical procedures as quickly as possible.
Mass spectrometry plays an important role for trace analysis in hydrogen matrix. The presentation shows first experimental results from the application of PTR-TOF-MS (Proton Transfer Reaction Time-of-Flight Mass Spectrometry).
The investigation of metabolic fluxes and metabolite distributions within cells by means of tracer molecules is a valuable tool to unravel the complexity of biological systems. Technological advances in mass spectrometry (MS) technology such as atmospheric pressure chemical ionization (APCI) coupled with high resolution (HR), not only allows for highly sensitive analyses but also broadens the usefulness of tracer-based experiments, as interesting signals can be annotated de novo when not yet present in a compound library. However, several effects in the APCI ion source, i.e., fragmentation and rearrangement, lead to superimposed mass isotopologue distributions (MID) within the mass spectra, which need to be corrected during data evaluation as they will impair enrichment calculation otherwise. Here, we present and evaluate a novel software tool to automatically perform such corrections. We discuss the different effects, explain the implemented algorithm, and show its application on several experimental datasets. This adjustable tool is available as an R package from CRAN.
Analysis of electrochemical and liver microsomal transformation products of lasalocid by LC/HRMS
(2022)
Rationale: Lasalocid (LAS), an ionophore, is used in cattle and poultry farming as feed additive for its antibiotic and growth-promoting properties. Literature on transformation products (TP) resulting from LAS degradation is limited. So far, only hydroxylation is found to occur as the metabolic reaction during the LAS degradation. To investigate potential TPs of LAS, we used electrochemistry (EC) and liver microsome (LM) assays to synthesize TPs, which were identified using liquid chromatography high-resolution mass spectrometry (LC/HRMS).
Methods: Electrochemically produced TPs were analyzed online by direct coupling of the electrochemical cell to the electrospray ionization (ESI) source of a Sciex Triple-TOF high resolution mass spectrometer. Then, EC-treated LAS solution was collected and analyzed offline using LC/HRMS to confirm stable TPs and improve their annotation with a chemical structure due to informative MS/MS spectra. In a complementary approach, TPs formed by rat and human microsomal incubation were investigated using LC/HRMS. The resulting data were used to investigate LAS modification reactions and elucidate the chemical structure of obtained TPs.
Results: The online measurements identified a broad variety of TPs, resulting from modification reactions like (de-)hydrogenation, hydration, methylation, oxidation as well as adduct formation with methanol. We consistently observed different ion complexations of LAS and LAS-TPs (Na+; 2Na+ K+; NaNH4 +; KNH4 +). Two stable methylated EC-TPs were found, structurally annotated, and assigned to a likely modification reaction. Using LM incubation, seven TPs were formed, mostly by oxidation/hydroxylation. After the identification of LM-TPs as Na+-complexes, we identified LM-TPs as K+-complexes.
Conclusion: We identified and characterized TPs of LAS using EC- and LM-based methods. Moreover, we found different ion complexes of LAS-based TPs. This knowledge, especially the different ion complexes, may help elucidate the metabolic and environmental degradation pathways of LAS.
Towards Unbiased Evaluation of Ionization Performance in LC-HRMS Metabolomics Method Development
(2022)
As metabolomics increasingly finds its way from basic science into applied and regulatory environments, analytical demands on nontargeted mass spectrometric detection methods continue to rise. In addition to improved chemical comprehensiveness, current developments aim at enhanced robustness and repeatability to allow long-term, inter-study, and meta-analyses. Comprehensive metabolomics relies on electrospray ionization (ESI) as the most versatile ionization technique, and recent liquid chromatography-high resolution mass spectrometry (LC-HRMS) instrumentation continues to overcome technical limitations that have hindered the adoption of ESI for applications in the past. Still, developing and standardizing nontargeted ESI methods and instrumental setups remains costly in terms of time and required chemicals, as large panels of metabolite standards are needed to reflect biochemical diversity. In this paper, we investigated in how far a nontargeted pilot experiment, consisting only of a few measurements of a test sample dilution series and comprehensive statistical analysis, can replace conventional targeted evaluation procedures. To examine this potential, two instrumental ESI ion source setups were compared, reflecting a common scenario in practical method development. Two types of feature evaluations were performed, (a) summary statistics solely involving feature intensity values, and (b) analyses additionally including chemical interpretation.
Results were compared in detail to a targeted evaluation of a large metabolite standard panel. We reflect on the advantages and shortcomings of both strategies in the context of current harmonization initiatives in the metabolomics field.
The investigation of metabolic fluxes and metabolite distributions within cells by means of tracer molecules is a valuable tool to unravel the complexity of biological systems. Technological advances in mass spectrometry (MS) technology such as atmospheric pressure chemical ionization (APCI) coupled with high resolution (HR), not only allows for highly sensitive analyses but also broadens the usefulness of tracer‐based experiments, as interesting signals can be annotated de novo when not yet present in a compound library. However, several effects in the APCI ion source, i.e., fragmentation and rearrangement, lead to superimposed mass isotopologue distributions (MID) within the mass spectra, which need to be corrected during data evaluation as they will impair enrichment calculation otherwise. Here, we present and evaluate a novel software tool to automatically perform such corrections. We discuss the different effects, explain the implemented
algorithm, and show its application on several experimental datasets. This adjustable tool is available as an R package from CRAN.
Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are valuable analytical and quality control methods for most industrial chemical processes as they provide information on the concentrations of individual compounds and by-products. These processes are traditionally carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been realized to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra, to train an ANN with better prediction performance and speed than state-of-the-art analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a strongly simplified use of MS and MRS and the accompanying data evaluation by machine-supported procedures, and can utilize both methods much wider for reaction and process monitoring or quality control.
A combination of acoustic levitation, laser vaporization, and atmospheric pressure chemical ionization mass spectrometry (APCI-MS) is presented in this study that enabled sensitive analysis of pharmaceutical drugs from an aqueous sample matrix. An unfocused pulsed infrared laser provided contactless sample desorption from the droplets trapped inside an acoustic levitator by activation of the OH stretching band of aqueous and alcoholic solvents. Subsequent atmospheric pressure chemical ionization was used between the levitated droplet and the mass spectrometer for postionization. In this setup, the unfocused laser gently desorbed the analytes by applying very mild repulsive forces. Detailed plume formation studies by temporally resolved schlieren experiments were used to characterize the liquid gas transition in this process. In addition, the role of different additives and solvent composition was examined during the ionization process. The analytical application of the technique and the proof-of-concept for quantitative analysis were demonstrated by the determination of selected pharmaceutical drugs in aqueous matrix with limits of quantification at the lower nanomolar level and a linear dynamic range of 3–4 orders of magnitude.
Concomitant species that appear at the same or very similar times in a mass-spectral analysis can clutter a spectrum because of the coexistence of many analyte-related ions (e.g., molecular ions, adducts, fragments). One method to extract ions stemming from the same origin is to exploit the chemical information encoded in the time domain, where the individual temporal appearances inside the complex structures of chronograms or chromatograms differ with respect to analytes. By grouping ions with very similar or identical time-domain structures, single-component mass spectra can be reconstructed, which are much easier to interpret and are library-searchable. While many other approaches address similar objectives through the Pearson’s correlation coefficient, we explore an alternative method based on a modified cross-correlation algorithm to compute a metric that describes the degree of similarity between features inside any two ion chronograms. Furthermore, an automatic workflow was devised to be capable of categorizing thousands of mass-spectral peaks into different groups within a few seconds. This approach was tested with direct mass-spectrometric analyses as well as with a simple, fast, and poorly resolved LC–MS analysis. Single-component mass spectra were extracted in both cases and were identified based on accurate mass and a mass-spectral library search.