Ralf J. M. Weber

Characterization of Isotopic Abundance Measurements in High Resolution FT-ICR and Orbitrap Mass Spectra for Improved Confidence of Metabolite Identification

Currently there is limited information available on the accuracy and precision of relative isotopic abundance (RIA) measurements using high-resolution direct-infusion mass spectrometry (HR DIMS), and it is unclear if this information can benefit automated peak annotation in metabolomics. Here we characterize the accuracy of RIA measurements on the Thermo LTQ FT Ultra (resolution of 100,000-750,000) and LTQ Orbitrap (R = 100,000) mass spectrometers. This first involved reoptimizing the SIM-stitching method (Southam, A. D. Anal. Chem. 2007, 79, 4595-4602) for the LTQ FT Ultra, which achieved a ca. 3-fold sensitivity increase compared to the original method while maintaining a root-mean-squared mass error of 0.16 ppm. Using this method, we show the quality of RIA measurements is highly dependent on signal-to-noise ratio (SNR), with RIA accuracy increasing with higher SNR. Furthermore, a negative offset between the theoretical and empirically calculated numbers of carbon atoms was observed for both mass spectrometers. Increasing the resolution of the LTQ FT Ultra lowered both the sensitivity and the quality of RIA measurements. Overall, although the errors in the empirically calculated number of carbons can be large (e.g., 10 carbons), we demonstrate that RIA measurements do improve automated peak annotation, increasing the number of single empirical formula assignments by >3-fold compared to using accurate mass alone.

Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control

Direct-infusion mass spectrometry (DIMS) metabolomics is an important approach for characterising molecular responses of organisms to disease, drugs and the environment. Increasingly large-scale metabolomics studies are being conducted, necessitating improvements in both bioanalytical and computational workflows to maintain data quality. This dataset represents a systematic evaluation of the reproducibility of a multi-batch DIMS metabolomics study of cardiac tissue extracts. It comprises of twenty biological samples (cow vs. sheep) that were analysed repeatedly, in 8 batches across 7 days, together with a concurrent set of quality control (QC) samples. Data are presented from each step of the workflow and are available in MetaboLights. The strength of the dataset is that intra- and inter-batch variation can be corrected using QC spectra and the quality of this correction assessed independently using the repeatedly-measured biological samples. Originally designed to test the efficacy of a batch-correction algorithm, it will enable others to evaluate novel data processing algorithms. Furthermore, this dataset serves as a benchmark for DIMS metabolomics, derived using best-practice workflows and rigorous quality assessment.

Non-targeted UHPLC-MS metabolomic data processing methods: a comparative investigation of normalisation, missing value imputation, transformation and scaling

IntroductionThe generic metabolomics data processing workflow is constructed with a serial set of processes including peak picking, quality assurance, normalisation, missing value imputation, transformation and scaling. The combination of these processes should present the experimental data in an appropriate structure so to identify the biological changes in a valid and robust manner.ObjectivesCurrently, different researchers apply different data processing methods and no assessment of the permutations applied to UHPLC-MS datasets has been published. Here we wish to define the most appropriate data processing workflow.MethodsWe assess the influence of normalisation, missing value imputation, transformation and scaling methods on univariate and multivariate analysis of UHPLC-MS datasets acquired for different mammalian samples.ResultsOur studies have shown that once data are filtered, missing values are not correlated with m/z, retention time or response. Following an exhaustive evaluation, we recommend PQN normalisation with no missing value imputation and no transformation or scaling for univariate analysis. For PCA we recommend applying PQN normalisation with Random Forest missing value imputation, glog transformation and no scaling method. For PLS-DA we recommend PQN normalisation, KNN as the missing value imputation method, generalised logarithm transformation and no scaling. These recommendations are based on searching for the biologically important metabolite features independent of their measured abundance.ConclusionThe appropriate choice of normalisation, missing value imputation, transformation and scaling methods differs depending on the data analysis method and the choice of method is essential to maximise the biological derivations from UHPLC-MS datasets.

Exo-metabolome of Pseudovibrio sp. FO-BEG1 analyzed by ultra-high resolution mass spectrometry and the effect of phosphate limitation.

Oceanic dissolved organic matter (DOM) is an assemblage of reduced carbon compounds, which results from biotic and abiotic processes. The biotic processes consist in either release or uptake of specific molecules by marine organisms. Heterotrophic bacteria have been mostly considered to influence the DOM composition by preferential uptake of certain compounds. However, they also secrete a variety of molecules depending on physiological state, environmental and growth conditions, but so far the full set of compounds secreted by these bacteria has never been investigated. In this study, we analyzed the exo-metabolome, metabolites secreted into the environment, of the heterotrophic marine bacterium Pseudovibrio sp. FO-BEG1 via ultra-high resolution mass spectrometry, comparing phosphate limited with phosphate surplus growth conditions. Bacteria belonging to the Pseudovibrio genus have been isolated worldwide, mainly from marine invertebrates and were described as metabolically versatile Alphaproteobacteria. We show that the exo-metabolome is unexpectedly large and diverse, consisting of hundreds of compounds that differ by their molecular formulae. It is characterized by a dynamic recycling of molecules, and it is drastically affected by the physiological state of the strain. Moreover, we show that phosphate limitation greatly influences both the amount and the composition of the secreted molecules. By assigning the detected masses to general chemical categories, we observed that under phosphate surplus conditions the secreted molecules were mainly peptides and highly unsaturated compounds. In contrast, under phosphate limitation the composition of the exo-metabolome changed during bacterial growth, showing an increase in highly unsaturated, phenolic, and polyphenolic compounds. Finally, we annotated the detected masses using multiple metabolite databases. These analyses suggested the presence of several masses analogue to masses of known bioactive compounds. However, the annotation was successful only for a minor part of the detected molecules, underlining the current gap in knowledge concerning the biosynthetic ability of marine heterotrophic bacteria.

Community detection in social and biological networks using differential evolution

The community detection in complex networks is an important problem in many scientific fields, from biology to sociology. This paper proposes a new algorithm, Differential Evolution based Community Detection (DECD), which employs a novel optimization algorithm, differential evolution (DE) for detecting communities in complex networks. DE uses network modularity as the fitness function to search for an optimal partition of a network. Based on the standard DE crossover operator, we design a modified binomial crossover to effectively transmit some important information about the community structure in evolution. Moreover, a biased initialization process and a clean-up operation are employed in DECD to improve the quality of individuals in the population. One of the distinct merits of DECD is that, unlike many other community detection algorithms, DECD does not require any prior knowledge about the community structure, which is particularly useful for its application to real-world complex networks where prior knowledge is usually not available. We evaluate DECD on several artificial and real-world social and biological networks. Experimental results show that DECD has very competitive performance compared with other state-of-the-art community detection algorithms.

Galaxy-M: a Galaxy workflow for processing and analyzing direct infusion and liquid chromatography mass spectrometry-based metabolomics data

BackgroundMetabolomics is increasingly recognized as an invaluable tool in the biological, medical and environmental sciences yet lags behind the methodological maturity of other omics fields. To achieve its full potential, including the integration of multiple omics modalities, the accessibility, standardization and reproducibility of computational metabolomics tools must be improved significantly.ResultsHere we present our end-to-end mass spectrometry metabolomics workflow in the widely used platform, Galaxy. Named Galaxy-M, our workflow has been developed for both direct infusion mass spectrometry (DIMS) and liquid chromatography mass spectrometry (LC-MS) metabolomics. The range of tools presented spans from processing of raw data, e.g. peak picking and alignment, through data cleansing, e.g. missing value imputation, to preparation for statistical analysis, e.g. normalization and scaling, and principal components analysis (PCA) with associated statistical evaluation. We demonstrate the ease of using these Galaxy workflows via the analysis of DIMS and LC-MS datasets, and provide PCA scores and associated statistics to help other users to ensure that they can accurately repeat the processing and analysis of these two datasets. Galaxy and data are all provided pre-installed in a virtual machine (VM) that can be downloaded from the GigaDB repository. Additionally, source code, executables and installation instructions are available from GitHub.ConclusionsThe Galaxy platform has enabled us to produce an easily accessible and reproducible computational metabolomics workflow. More tools could be added by the community to expand its functionality. We recommend that Galaxy-M workflow files are included within the supplementary information of publications, enabling metabolomics studies to achieve greater reproducibility.

HAMMER: automated operation of mass frontier to construct in silico mass spectral fragmentation libraries

Summary: Experimental MSn mass spectral libraries currently do not adequately cover chemical space. This limits the robust annotation of metabolites in metabolomics studies of complex biological samples. In silico fragmentation libraries would improve the identification of compounds from experimental multistage fragmentation data when experimental reference data are unavailable. Here, we present a freely available software package to automatically control Mass Frontier software to construct in silico mass spectral libraries and to perform spectral matching. Based on two case studies, we have demonstrated that high-throughput automation of Mass Frontier allows researchers to generate in silico mass spectral libraries in an automated and high-throughput fashion with little or no human intervention required. Availability and implementation: Documentation, examples, results and source code are available at http://www.biosciences-labs.bham.ac.uk/viant/hammer/. Contact: m.viant@bham.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Computational tools and workflows in metabolomics: An international survey highlights the opportunity for harmonisation through Galaxy

This work was completed through funding provided by the BBSRC [BB/L005077/1 and BB/M019985/1] and Wellcome Trust [202952/Z/16/Z].

MaConDa: a publicly accessible mass spectrometry contaminants database

UNLABELLED Mass spectrometry is widely used in bioanalysis, including the fields of metabolomics and proteomics, to simultaneously measure large numbers of molecules in complex biological samples. Contaminants routinely occur within these samples, for example, originating from the solvents or plasticware. Identification of these contaminants is crucial to enable their removal before data analysis, in particular to maintain the validity of conclusions drawn from uni- and multivariate statistical analyses. Although efforts have been made to report contaminants within mass spectra, this information is fragmented and its accessibility is relatively limited. In response to the needs of the bioanalytical community, here we report the creation of an extensive manually well-annotated database of currently known small molecule contaminants. AVAILABILITY The Mass spectrometry Contaminants Database (MaConDa) is freely available and accessible through all major browsers or by using the MaConDa web service http://www.maconda.bham.ac.uk.

Microbial Community Structure Affects Marine Dissolved Organic Matter Composition

Marine microbes are critical players in the global carbon cycle, affecting both the reduction of inorganic carbon and the remineralization of reduced organic compounds back to carbon dioxide. Members of microbial consortia all depend on marine dissolved organic matter (DOM) and in turn, affect the molecules present in this heterogeneous pool. Our understanding of DOM produced by marine microbes is biased towards single species laboratory cultures or simplified field incubations, which exclude large phototrophs and protozoan grazers. Here we explore the interdependence of DOM composition and bacterial diversity in two mixed microbial consortia from coastal seawater: a whole water community and a <1.0-μm community dominated by heterotrophic bacteria. Each consortium was incubated with isotopically-labeled glucose for 9 days. Using stable-isotope probing techniques and electrospray ionization Fourier-transform ion cyclotron resonance mass spectrometry, we show that the presence of organisms larger than 1.0-μm is the dominant factor affecting bacterial diversity and low-molecular-weight (<1000 Da) DOM composition over this experiment. In the <1.0-μm community, DOM composition was dominated by compounds with lipid and peptide character at all time points, confirmed by fragmentation spectra with peptide-containing neutral losses. In contrast, DOM composition in the whole water community was nearly identical to that in the initial coastal seawater. These differences in DOM composition persisted throughout the experiment despite shifts in bacterial diversity, underscoring an unappreciated role for larger microorganisms in constraining DOM composition in the marine environment.