Innovation: Metabolomics: the apogee of the omics trilogy

Abstract

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.

Figures

Figure 1. The central dogma of biology and the ‘omic’ cascade

While genes and proteins are subject to regulatory epigenetic processes and post-translational modifications respectively, metabolites represent downstream biochemical end products that are closer to the phenotype. Alterations in a single gene (illustrated by blue dots) or a single protein can lead to a cascade of metabolite alterations. In the theoretical schematic shown, up- and down-regulated metabolites are shown in red and unaltered metabolites are shown in grey. Untargeted metabolomics aims at comprehensively profiling metabolites without bias to identify changes that correlate with cellular function or phenotype. By performing meta-analysis, metabolic alterations shared between multiple animal models or multiple genetic modifications may be identified as shown by the superimposed Venn diagram.

Figure 2. The untargeted and targeted workflow for LC/MS-based metabolomics

The untargeted metabolomic workflow (top). Metabolites are first isolated from tissues, biofluids, or cell cultures and subsequently analyzed by LC/MS. After data acquisition, the results are processed by using bioinformatic software such as XCMS to perform nonlinear retention time alignment and identify metabolite features that are changing between the groups of samples measured. Metabolite features of interest are searched in metabolite databases on the basis of accurate mass to obtain putative identifications. Putative identifications are then confirmed by comparison of MS/MS and retention time data to that of standards. The untargeted workflow is global in scope and outputs data related to comprehensive cellular metabolism. The targeted metabolomic workflow (bottom). First, standard compounds for the metabolites of interest are obtained and used to setup selected reaction monitoring methods. Here instrument voltages are established and concentration curves are generated for absolute quantitation. After the targeted methods have been established on the basis of standards, the metabolic extract is analyzed from the research samples. The data output provides quantitation only of those metabolites for which standard methods have been built.

Figure 3. Metabolite characterization in the untargeted metabolomic workflow

In LC/MS-based untargeted metabolomics, metabolites are identified on the basis of accurate mass, retention time, and MS/MS data. Experimental and standard data are shown here for the metabolite A2E (A2-ethanolamine) as an example of the identification process. The accurate mass as measured from the mass spectrometer (obs.) is less than 3 ppm different than that theoretically expected (theo.) on the basis of the compound’s molecular formula. This mass error is within the range expected from most modern mass spectrometers. The retention time of the research sample (38.9 min, black) is then compared to that of a standard (39.0, red). Finally, to confirm an assignment, a follow-up targeted MS/MS analysis is performed. The MS/MS data from the research sample are shown in black and the MS/MS data from the standard are shown in red. As illustrated, all three experimental data parameters are consistent with those obtained from the standard, thereby supporting the identification of A2E in the research sample.

Figure 4. Spatial localization of metabolites in tissue by mass spectrometry-based imaging

An example of a surface-based image of cholesterol from mouse brain by using nanostructure-initiator mass spectrometry (NIMS, reference 2). NIMS is well suited for metabolite imaging because it is highly sensitive and does not suffer from matrix interference in the low-mass range. Sections of frozen tissue are first transferred to a NIMS chip that is subsequently analyzed by using a laser-induced desorption/ionization approach (bottom). By systematically rastering the laser across the tissue, a mass spectrum is generated from each point. The mass spectral intensity of the metabolite of interest is plotted spatially to generate images as shown for cholesterol here (top, m/z 493.26).