Gary J. Patti

Innovation: Metabolomics: the apogee of the omics trilogy

Metabolites, the chemical entities that are transformed during metabolism, provide a functional readout of cellular biochemistry. With emerging technologies in mass spectrometry, thousands of metabolites can now be quantitatively measured from minimal amounts of biological material, which has thereby enabled systems-level analyses. By performing global metabolite profiling, also known as untargeted metabolomics, new discoveries linking cellular pathways to biological mechanism are being revealed and are shaping our understanding of cell biology, physiology and medicine.

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.

An accelerated workflow for untargeted metabolomics using the METLIN database.

Metabolites, typically recognized as small molecules that are involved in cellular reactions, provide a functional signature of phenotype that is complimentary to the upstream biochemical information obtained from genes, transcripts, and proteins. The high-level of correlation between metabolites and phenotype has created a surge of interest in the field that is reflected in the number of metabolomic publications growing from just a few articles in 1999 to over five thousand in 2011. Although relatively new compared to its genomic and proteomic predecessors, already metabolomics has led to the discovery of biomarkers for disease, fundamental insights into cellular biochemistry, and clues related to disease pathogenesis.1,2

Expanding Coverage of the Metabolome for Global Metabolite Profiling

Mass spectrometry-based metabolomics is the comprehensive study of naturally occurring small molecules collectively known as the metabolome. Given the vast structural diversity and chemical properties of endogenous metabolites, biological extraction and chromatography methods bias the number, property, and concentration of metabolites detected by mass spectrometry and creates a challenge for global untargeted studies. In this work, we used Escherichia coli bacterial cells to explore the influence of solvent polarity, temperature, and pH in extracting polar and nonpolar metabolites simultaneously. In addition, we explored chromatographic conditions involving different stationary and mobile phases that optimize the separation and ionization of endogenous metabolite extracts as well as a mixture of synthetic standards. Our results reveal that hot polar solvents are the most efficient in extracting both hydrophilic and hydrophobic metabolites simultaneously. In addition, ammonium fluoride in the mobile phase substantially improved ionization efficiency in negative electrospray ionization mode by an average increase in signal intensity of 5.7 and over a 2-fold increase in the total number of features detected. The improvement in sensitivity with ammonium fluoride resulted in 3.5 times as many metabolite hits in databases compared to ammonium acetate or formic acid enriched mobile phases and allowed for the identification of unique metabolites involved in fundamental cellular pathways.

Liquid chromatography quadrupole time-of-flight mass spectrometry characterization of metabolites guided by the METLIN database

Untargeted metabolomics provides a comprehensive platform for identifying metabolites whose levels are altered between two or more populations. By using liquid chromatography quadrupole time-of-flight mass spectrometry (LC-Q-TOF-MS), hundreds to thousands of peaks with a unique m/z ratio and retention time are routinely detected from most biological samples in an untargeted profiling experiment. Each peak, termed a metabolomic feature, can be characterized on the basis of its accurate mass, retention time and tandem mass spectral fragmentation pattern. Here a seven-step protocol is suggested for such a characterization by using the METLIN metabolite database. The protocol starts from untargeted metabolomic LC-Q-TOF-MS data that have been analyzed with the bioinformatics program XCMS, and it describes a strategy for selecting interesting features as well as performing subsequent targeted tandem MS. The seven steps described will require 2–4 h to complete per feature, depending on the compound.

Interactive XCMS Online: Simplifying Advanced Metabolomic Data Processing and Subsequent Statistical Analyses

XCMS Online (xcmsonline.scripps.edu) is a cloud-based informatic platform designed to process and visualize mass-spectrometry-based, untargeted metabolomic data. Initially, the platform was developed for two-group comparisons to match the independent, “control” versus “disease” experimental design. Here, we introduce an enhanced XCMS Online interface that enables users to perform dependent (paired) two-group comparisons, meta-analysis, and multigroup comparisons, with comprehensive statistical output and interactive visualization tools. Newly incorporated statistical tests cover a wide array of univariate analyses. Multigroup comparison allows for the identification of differentially expressed metabolite features across multiple classes of data while higher order meta-analysis facilitates the identification of shared metabolic patterns across multiple two-group comparisons. Given the complexity of these data sets, we have developed an interactive platform where users can monitor the statistical output of univariate (cloud plots) and multivariate (PCA plots) data analysis in real time by adjusting the threshold and range of various parameters. On the interactive cloud plot, metabolite features can be filtered out by their significance level (p-value), fold change, mass-to-charge ratio, retention time, and intensity. The variation pattern of each feature can be visualized on both extracted-ion chromatograms and box plots. The interactive principal component analysis includes scores, loadings, and scree plots that can be adjusted depending on scaling criteria. The utility of XCMS functionalities is demonstrated through the metabolomic analysis of bacterial stress response and the comparison of lymphoblastic leukemia cell lines.

Metabolomics implicates altered sphingolipids in chronic pain of neuropathic origin

Neuropathic pain is a debilitating condition for which the development of effective treatments has been limited by an incomplete understanding of its chemical basis. We show by using untargeted metabolomics that sphingomyelin-ceramide metabolism is altered in the dorsal horn of rats with neuropathic pain and that the upregulated, endogenous metabolite N,N-dimethylsphingosine induces mechanical hypersensitivity in vivo. These results demonstrate the utility of metabolomics to implicate unexplored biochemical pathways in disease.

Nanostructure-initiator mass spectrometry metabolite analysis and imaging.

Nanostructure-Initiator Mass Spectrometry (NIMS) is a matrix-free desorption/ionization approach that is particularly well-suited for unbiased (untargeted) metabolomics. An overview of the NIMS technology and its application in the detection of biofluid and tissue metabolites are presented. (To listen to a podcast about this feature, please go to the Analytical Chemistry multimedia page at pubs.acs.org/page/ancham/audio/index.html .).

Variability Analysis of Human Plasma and Cerebral Spinal Fluid Reveals Statistical Significance of Changes in Mass Spectrometry-Based Metabolomics Data

Analytical and biological variability are issues of central importance to human metabolomics studies. Here both types of variation are examined in human plasma and cerebrospinal fluid (CSF) using a global liquid chromatography/mass spectrometry (LC/MS) metabolomics strategy. The platform shows small analytical variation with a median coefficient of variation (CV) of 15-16% for both plasma and CSF sample matrixes when the integrated area of each peak in the mass spectra is considered. Analysis of biological variation shows that human CSF has a median CV of 35% and plasma has a median CV of 46%. To understand the difference in CV between the biofluids, we compared plasma and CSF independently obtained from different healthy humans. Additionally, we analyzed another group of patients from whom we compared matched CSF and plasma (plasma and CSF obtained from the same human subject). A similar number of features was observed in both biofluids, although the majority of features appeared with greater intensity in plasma. More than a dozen metabolites shared between the human CSF and plasma metabolomes were identified based on accurate mass measurements, retention times, and MS/MS spectra. The fold change in these metabolites was consistent with the median biological CV determined for all peaks. The measured median biological CV together with analysis of intragroup variation of healthy individuals suggests that fold changes above 2 in metabolomics studies investigating plasma or CSF are statistically relevant with respect to the inherent variability of a healthy control group. These data demonstrate the reproducibility of the global metabolomics platform using LC/MS and reveal the robustness of the approach for biomarker discovery.

Metabolism Links Bacterial Biofilms and Colon Carcinogenesis

Bacterial biofilms in the colon alter the host tissue microenvironment. A role for biofilms in colon cancer metabolism has been suggested but to date has not been evaluated. Using metabolomics, we investigated the metabolic influence that microbial biofilms have on colon tissues and the related occurrence of cancer. Patient-matched colon cancers and histologically normal tissues, with or without biofilms, were examined. We show the upregulation of polyamine metabolites in tissues from cancer hosts with significant enhancement of N(1), N(12)-diacetylspermine in both biofilm-positive cancer and normal tissues. Antibiotic treatment, which cleared biofilms, decreased N(1), N(12)-diacetylspermine levels to those seen in biofilm-negative tissues, indicating that host cancer and bacterial biofilm structures contribute to the polyamine metabolite pool. These results show that colonic mucosal biofilms alter the cancer metabolome to produce a regulator of cellular proliferation and colon cancer growth potentially affecting cancer development and progression.