Untargeted Metabolomics Workflow Research Articles

Nephron Toxicity Profiling via Untargeted Metabolome Analysis Employing a High Performance Liquid Chromatography-Mass Spectrometry-based Experimental and Computational Pipeline

Untargeted metabolomics has the potential to improve the predictivity of in vitro toxicity models and therefore may aid the replacement of expensive and laborious animal models. Here we describe a long term repeat dose nephrotoxicity study conducted on the human renal proximal tubular epithelial cell line, RPTEC/TERT1, treated with 10 and 35 μmol·liter(-1) of chloroacetaldehyde, a metabolite of the anti-cancer drug ifosfamide. Our study outlines the establishment of an automated and easy to use untargeted metabolomics workflow for HPLC-high resolution mass spectrometry data. Automated data analysis workflows based on open source software (OpenMS, KNIME) enabled a comprehensive and reproducible analysis of the complex and voluminous metabolomics data produced by the profiling approach. Time- and concentration-dependent responses were clearly evident in the metabolomic profiles. To obtain a more comprehensive picture of the mode of action, transcriptomics and proteomics data were also integrated. For toxicity profiling of chloroacetaldehyde, 428 and 317 metabolite features were detectable in positive and negative modes, respectively, after stringent removal of chemical noise and unstable signals. Changes upon treatment were explored using principal component analysis, and statistically significant differences were identified using linear models for microarray assays. The analysis revealed toxic effects only for the treatment with 35 μmol·liter(-1) for 3 and 14 days. The most regulated metabolites were glutathione and metabolites related to the oxidative stress response of the cells. These findings are corroborated by proteomics and transcriptomics data, which show, among other things, an activation of the Nrf2 and ATF4 pathways.

Untargeted profiling of tracer-derived metabolites using stable isotopic labeling and fast polarity-switching LC-ESI-HRMS.

An untargeted metabolomics workflow for the detection of metabolites derived from endogenous or exogenous tracer substances is presented. To this end, a recently developed stable isotope-assisted LC–HRMS-based metabolomics workflow for the global annotation of biological samples has been further developed and extended. For untargeted detection of metabolites arising from labeled tracer substances, isotope pattern recognition has been adjusted to account for nonlabeled moieties conjugated to the native and labeled tracer molecules. Furthermore, the workflow has been extended by (i) an optional ion intensity ratio check, (ii) the automated combination of positive and negative ionization mode mass spectra derived from fast polarity switching, and (iii) metabolic feature annotation. These extensions enable the automated, unbiased, and global detection of tracer-derived metabolites in complex biological samples. The workflow is demonstrated with the metabolism of 13C9-phenylalanine in wheat cell suspension cultures in the presence of the mycotoxin deoxynivalenol (DON). In total, 341 metabolic features (150 in positive and 191 in negative ionization mode) corresponding to 139 metabolites were detected. The benefit of fast polarity switching was evident, with 32 and 58 of these metabolites having exclusively been detected in the positive and negative modes, respectively. Moreover, for 19 of the remaining 49 phenylalanine-derived metabolites, the assignment of ion species and, thus, molecular weight was possible only by the use of complementary features of the two ion polarity modes. Statistical evaluation showed that treatment with DON increased or decreased the abundances of many detected metabolites.

Untargeted soil metabolomics methods for analysis of extractable organic matter

The cycling of soil organic matter (SOM) by microorganisms is a critical component of the global carbon cycle but remains poorly understood. There is an emerging view that much of SOM, and especially the dissolved fraction (DOM), is composed of small molecules of plant and microbial origin resulting from lysed cells and released metabolites. Unfortunately, little is known about the small molecule composition of soils and how these molecules are cycled (by microbes or plants or by adsorption to mineral surfaces). The water-extractable organic matter (WEOM) fraction is of particular interest given that this is presumably the most biologically-accessible component of SOM. Here we describe the development of a simple soil metabolomics workflow and a novel spike recovery approach using 13C bacterial lysates to assess the types of metabolites remaining in the WEOM fraction. Soil samples were extracted with multiple mass spectrometry-compatible extraction buffers (water, 10 mM K2SO4 or NH4HCO3, 10–100% methanol or isopropanol/methanol/water [3:3:2 v/v/v]) with and without prior chloroform vapor fumigation. Profiling of derivatized extracts was performed using gas chromatography/mass spectrometry (GC/MS) with 55 metabolites identified by comparing fragmentation patterns and retention times with authentic standards. As expected, fumigation, which is thought to lyse microbial cells, significantly increased the range and abundance of metabolites relative to unfumigated samples. To assess the types of microbial metabolites from lysed bacterial cells that remain in the WEOM fraction, an extract was prepared from the soil bacterium Pseudomonas stutzerii RCH2 grown on 13C acetate. This approach produced highly labeled metabolites that were easily discriminated from the endogenous soil metabolites. Comparing the composition of the fresh bacterial extract with what was recovered following a 15 min incubation with soil revealed that only 27% of the metabolites showed >50% recovery in the WEOM. Many, especially cations (polyamines) and anions, showed <10% recovery. These represent metabolites that may be inaccessible to microbes in this environment and would be most likely to accumulate as SOM presumably due to binding with minerals and negatively-charged clay particles. This study presents a simple untargeted metabolomics workflow for extractable organic matter and an approach to estimate microbial metabolite availability in soils. These methods can be used to further our understanding of SOM and DOM composition and examine the link between metabolic pathways and microbial communities to terrestrial carbon cycling.

Improving natural products identification through targeted LC-MS/MS in an untargeted secondary metabolomics workflow.

Tandem mass spectrometry is a widely applied and highly sensitive technique for the discovery and characterization of microbial natural products such as secondary metabolites from myxobacteria. Here, a data mining workflow based on MS/MS precursor lists targeting only signals related to bacterial metabolism is established using LC-MS data of crude extracts from shaking flask fermentations. The devised method is not biased toward specific compound classes or structural features and is capable of increasing the information content of LC-MS/MS analyses by directing fragmentation events to signals of interest. The approach is thus contrary to typical auto-MS(2) setups where precursor ions are usually selected according to signal intensity, which is regarded as a drawback for metabolite discovery applications when samples contain many overlapping signals and the most intense signals do not necessarily represent compounds of interest. In line with this, the method described here achieves improved MS/MS scan coverage for low-abundance precursor ions not captured by auto-MS(2) experiments and thereby facilitates the search for new secondary metabolites in complex biological samples. To underpin the effectiveness of the approach, the identification and structure elucidation of two new myxobacterial secondary metabolite classes is reported.

Robust Automated Mass Spectra Interpretation and Chemical Formula Calculation Using Mixed Integer Linear Programming

Untargeted metabolite profiling using liquid chromatography and mass spectrometry coupled via electrospray ionization is a powerful tool for the discovery of novel natural products, metabolic capabilities, and biomarkers. However, the elucidation of the identities of uncharacterized metabolites from spectral features remains challenging. A critical step in the metabolite identification workflow is the assignment of redundant spectral features (adducts, fragments, multimers) and calculation of the underlying chemical formula. Inspection of the data by experts using computational tools solving partial problems (e.g., chemical formula calculation for individual ions) can be performed to disambiguate alternative solutions and provide reliable results. However, manual curation is tedious and not readily scalable or standardized. Here we describe an automated procedure for the robust automated mass spectra interpretation and chemical formula calculation using mixed integer linear programming optimization (RAMSI). Chemical rules among related ions are expressed as linear constraints and both the spectra interpretation and chemical formula calculation are performed in a single optimization step. This approach is unbiased in that it does not require predefined sets of neutral losses and positive and negative polarity spectra can be combined in a single optimization. The procedure was evaluated with 30 experimental mass spectra and was found to effectively identify the protonated or deprotonated molecule ([M + H](+) or [M - H](-)) while being robust to the presence of background ions. RAMSI provides a much-needed standardized tool for interpreting ions for subsequent identification in untargeted metabolomics workflows.

An Untargeted Metabolomic Workflow to Improve Structural Characterization of Metabolites

Mass spectrometry-based metabolomics relies on MS(2) data for structural characterization of metabolites. To obtain the high-quality MS(2) data necessary to support metabolite identifications, ions of interest must be purely isolated for fragmentation. Here, we show that metabolomic MS(2) data are frequently characterized by contaminating ions that prevent structural identification. Although using narrow-isolation windows can minimize contaminating MS(2) fragments, even narrow windows are not always selective enough, and they can complicate data analysis by removing isotopic patterns from MS(2) spectra. Moreover, narrow windows can significantly reduce sensitivity. In this work, we introduce a novel, two-part approach for performing metabolomic identifications that addresses these issues. First, we collect MS(2) scans with less stringent isolation settings to obtain improved sensitivity at the expense of specificity. Then, by evaluating MS(2) fragment intensities as a function of retention time and precursor mass targeted for MS(2) analysis, we obtain deconvolved MS(2) spectra that are consistent with pure standards and can therefore be used for metabolite identification. The value of our approach is highlighted with metabolic extracts from brain, liver, astrocytes, as well as nerve tissue, and performance is evaluated by using pure metabolite standards in combination with simulations based on raw MS(2) data from the METLIN metabolite database. A R package implementing the algorithms used in our workflow is available on our laboratory website ( http://pattilab.wustl.edu/decoms2.php ).

Comprehensive Analysis of LC/MS Data Using Pseudocolor Plots

We have developed new applications of the pseudocolor plot for the analysis of LC/MS data. These applications include spectral averaging, analysis of variance, differential comparison of spectra, and qualitative filtering by compound class. These applications have been motivated by the need to better understand LC/MS data generated from analysis of human biofluids. The examples presented use data generated to profile steroid hormones in urine extracts from a Cushing's disease patient relative to a healthy control, but are general to any discovery-based scanning mass spectrometry technique. In addition to new visualization techniques, we introduce a new metric of variance: the relative maximum difference from the mean. We also introduce the concept of substructure-dependent analysis of steroid hormones using precursor ion scans. These new analytical techniques provide an alternative approach to traditional untargeted metabolomics workflow. We present an approach to discovery using MS that essentially eliminates alignment or preprocessing of spectra. Moreover, we demonstrate the concept that untargeted metabolomics can be achieved using low mass resolution instrumentation.

A view from above: cloud plots to visualize global metabolomic data.

Global metabolomics describes the comprehensive analysis of small molecules in a biological system without bias. With mass spectrometry-based methods, global metabolomic data sets typically comprise thousands of peaks, each of which is associated with a mass-to-charge ratio, retention time, fold change, p-value, and relative intensity. Although several visualization schemes have been used for metabolomic data, most commonly used representations exclude important data dimensions and therefore limit interpretation of global data sets. Given that metabolite identification through tandem mass spectrometry data acquisition is a time-limiting step of the untargeted metabolomic workflow, simultaneous visualization of these parameters from large sets of data could facilitate compound identification and data interpretation. Here, we present such a visualization scheme of global metabolomic data using a so-called "cloud plot" to represent multidimensional data from septic mice. While much attention has been dedicated to lipid compounds as potential biomarkers for sepsis, the cloud plot shows that alterations in hydrophilic metabolites may provide an early signature of the disease prior to the onset of clinical symptoms. The cloud plot is an effective representation of global mass spectrometry-based metabolomic data, and we describe how to extract it as standard output from our XCMS metabolomic software.

XCMS Online: a web-based platform to process untargeted metabolomic data.

Recently, interest in untargeted metabolomics has become prevalent in the general scientific community among an increasing number of investigators. The majority of these investigators, however, do not have the bioinformatic expertise that has been required to process metabolomic data by using command-line driven software programs. Here we introduce a novel platform to process untargeted metabolomic data that uses an intuitive graphical interface and does not require installation or technical expertise. This platform, called XCMS Online, is a web-based version of the widely used XCMS software that allows users to easily upload and process liquid chromatography/mass spectrometry data with only a few mouse clicks. XCMS Online provides a solution for the complete untargeted metabolomic workflow including feature detection, retention time correction, alignment, annotation, statistical analysis, and data visualization. Results can be browsed online in an interactive, customizable table showing statistics, chromatograms, and putative METLIN identities for each metabolite. Additionally, all results and images can be downloaded as zip files for offline analysis and publication. XCMS Online is available at https://xcmsonline.scripps.edu.

Separation strategies for untargeted metabolomics

Metabolomics has rapidly become a profiling technique of choice for biomarker elucidation and molecular diagnostics in addition to studies focused on understanding disease pathogenesis. Key to the success of metabolomics in these areas has been the techniques to separate and analyze the chemically diverse group of compounds comprising the metabolome by using global and untargeted approaches. Untargeted metabolomic efforts have the goal of examining as many metabolites as possible simultaneously and most frequently use an LC/MS-based approach. Here, the importance of LC in an untargeted metabolomic workflow is outlined and separation strategies for optimization are reviewed within the context of these criteria.