U.S. Department of Energy

Pacific Northwest National Laboratory

Metabolomics and Lipidomics

Overview and History

Metabonomics, or metabolomics, is the least mature of the systems biology triad, which also includes genomics and proteomics. The distinction between metabonomics and metabolomics has oftentimes been confusing and inconsistent in the literature. Nicholson et al. initially defined ‘metabonomics’ as the quantitative measurement of perturbations in the metabolite complement of an integrated biological system in response to some stimuli, whereas ‘metabolomics’ was considered to be these measurements in individual cells or cell types [1-4]. For the most part, these terms have been used interchangeably by individuals reporting deviations in metabolite concentrations both system-wide and on cellular levels due to disease, drug administration, or different growth conditions. In the Omics Separations & Mass Spectrometry group, we consider 'metabolomics' as the quantitative determination of time-related or stimuli-dependent changes in the small-molecular weight complement of either an integrated biological system, cell, or cell types. Similarly, 'lipidomics' is a subset of metabolomics devoted to the quantitative measurement of lipids.

Metabolomics has its origins in the early orthomolecular medicine work pioneered by Robinson and Pauling [5-10], as well as in the metabolic flux and metabolic control analysis work of Kacser [11-16] (additionally, the influence of metabolic profiling in the diagnosis and screening for inborn errors of metabolism [17-19] cannot be ignored, but will not be discussed here). Pauling defined orthomolecular medicine as both the preservation of optimum health and the treatment of disease through variation of the concentrations of endogenous substances required for health [5]. An essential component of orthomolecular medicine that directly relates to metabolomics is orthomolecular diagnosis, or the process of determining the concentrations of various substances in the human body and how they may relate to a given disease state [5]. The initial efforts of Robinson and Pauling focused on the design and implementation of instrumentation for reliable quantitative measurements of volatiles in human urine and breath [20-22], which were soon followed by generally untargeted and quantitative measurements of as many substances as possible in a given analysis coupled with pattern recognition calculations in order to assign individuals to various disease states [5-7] or age groups [9,10,23]. Their untargeted analyses of low-molecular weight substances combined with pattern recognition techniques to distinguish healthy from disease (or otherwise normal from perturbed states) amounts to the current concept of metabolomics, as generally accepted today. Similarly, Kacser has contributed to the overall understanding of the parameters and variables that should be considered in properly designed metabolic control analyses [11]. Parameters, such as enzyme Michaelis constants (Km), turnover numbers (kcat), and inhibition constants (Ki), represent the constant constraints of a given system; others such as enzyme quantity and quality are considered to be under the control (within limits) of the researcher.  Alternatively, the variables represent the levels of metabolites themselves, which are directly determined by a system’s parameters [11]. Of particular importance was the insight that metabolite pools and their fluxes were not only dependent upon those components of the pathway to which they were traditionally thought to belong (eg. fumarate in the citric acid cycle), but also to any pathway to which or from which they may contribute or be derived (eg. fumarate in tyrosine metabolism). This thinking may have inspired Nicholson’s concept of the ‘superorganism’, which describes the interactions between the metabolome of a complex animal with those of the various microorganisms living symbiotically within that animal [3,24,25].

Despite the essential conceptualization of metabolomics by Robinson and Pauling, a number of researchers have contributed in parallel to the refinement of that concept into a format that is consistent with other major omics approaches. The laboratories of Laseter [26-28], Novotny [29,30], and Sweeley [31] had also developed and applied gas chromatography-based methods in comparative metabolic profiling studies of various biological samples during the same timeframe. Similarly, the work of van der Graaf [32-34], among others [30,35], has furthered the use of pattern recognition approaches (also known as ‘chemometrics’) to process data from metabolic profiling experiments in order to differentiate among comparative samples. These myriad efforts culminated in the first printed reference to the ‘metabolome’ [36] and the first occurrence of the word in a title [37] in 1998. Fiehn has further clarified this field by defining four basic types of metabolite analyses: 1) targeted metabolite analysis, 2) metabolic profiling, 3) metabolomics, and 4) metabolic fingerprinting [38,39]. Again, the terms describing these types of metabolite analyses tend to be used interchangeably and often incorrectly (as defined by Fiehn) in the literature. However, standardization of nomenclature is only one of the many goals established by both the Metabolomics Society [40] and the U.S. National Institutes of Health (at the Metabolomics Standards Workshop) [41].

Metabolomics Technologies

The majority of pre-metabolomics and early metabolomics studies have utilized nuclear magnetic resonance (NMR) spectroscopy- [1,2,42-48] and gas chromatography (GC)-based approaches [6-10,20-23,26,29,31]. However, investigators have also applied high performance liquid chromatography (HPLC) coupled with UV detection [49], pyrolysis-mass spectrometry (MS) [50,51], and inductively-coupled plasma (ICP) atomic emission and ICP-MS [52] among other techniques. Current metabolomics and metabolic profiling studies rely almost exclusively on 1H NMR, GC-MS and LC-MS due to the technological maturity of the corresponding instrumentation, the recent independent advancements made in all three fields, and the realization by the instrumentation industry that these three approaches offer the most potential for successful results.

Despite this solid foundation and the great strides made in technology and knowledge over the past decade, present capabilities still fall below desired levels in terms of analytical throughput, sensitivity, comprehensiveness, and data quality required to support many important applications. The complexity and heterogeneity of metabolites are considerably greater than those seen with genes or proteins, despite the fact that the average elemental composition (in terms of C, H, N, O, S, and P) of a metabolite is C4.938H6.793N0.4279O1.774S0.04590P0.06987, which is not terribly different from that of a peptide, i.e., C4.938H7.758N1.358O1.477S0.0417 [53].  Yet it is the manner in which these elements are arranged in a metabolite that governs its chemical properties and leads to the technical challenges in providing high throughput, sensitive, and comprehensive analyses of the metabolome.  As a result, comprehensive, high quality metabolomics and lipidomics investigations require diverse, specialized equipment and highly trained personnel with interdisciplinary expertise in bioinformatics, biochemistry, physiology, and spectrometry.

Omics Separations & Mass Spectrometry Group Metabolomics and Lipidomics Capabilities

Sample preparation and instrumental methods

Based on previous studies (e.g. Figure 1), we find that the Folch method is amenable for the extraction of the metabolome and lipidome in untargeted studies. Briefly, samples are treated with chloroform/methanol (2:1, v/v) in a 5-fold excess to the sample volume, followed by agitation to thoroughly mix the solution. Samples are then centrifuged to separate precipitated protein from water- and lipid-soluble metabolites. The aqueous and lipid fractions are removed and dried in vacuo.

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For polar metabolite analysis, residues from aqueous fractions are reconstituted in pyridine containing methoxyamine, prior to methoxyamination of reactive carbonyl groups. Hydroxyl and amine groups are subsequently derivatized using N-methyl-N-(trimethylsilyl)trifluoroacetamide with 1% trimethylchlorosilane. Derivatized samples are then analyzed using GC-MS according to a method developed by the Oliver Fiehn laboratory. GC is unsurpassed in terms of separation peak capacity, which is a measure of the number of chromatographic peaks that can be baseline-resolved within the analysis time. Because the stationary phase is coated on the inside of the unpacked GC column, system back pressure is negligible, and separations are typically performed on 30 m capillary columns at carrier gas flow rates of ~1 L/min, leading to highly efficient separations with chromatographic peak widths of a few seconds.  In addition, standardization of the electron ionization source to 70 eV by the GC-MS industry, coupled with its overall high reproducibility has enabled development and utilization of commercial and custom mass spectral databases for high-throughput and automated metabolite identifications. Because GC-MS analyses are performed in the gas phase and chemical derivatization is required to increase the volatility of sample constituents, it is not amenable to the analysis of relatively large biomolecules (e.g., >500 Da), as they either exceed the upper mass limit of the mass spectrometer (~1000 m/z for some instruments) or cannot be made volatile within the temperature limits of the instrument.  However, GC-MS has proven to be an exceptional tool for the untargeted, quantitative analysis of small, polar metabolites (e.g., amino and small organic acids, sugars, etc.) and fatty acids.

For lipid analysis, dried lipid extracts are reconstituted in isopropanol and analyzed directly using capillary LC coupled with high resolution MS and employing a combined top down/bottom up lipidomics approach. With this approach, complex lipid molecular species are separated using reversed-phase chromatography and identified based on accurate mass and characteristic fragment ions in sequential full scan and tandem (MS/MS) mass spectra. In addition to adding confidence to the identification of detected lipids, the high mass resolution provided by TOF and Orbitrap MS instruments increases the coverage of the lipidome. Figure 2 illustrates this point, where both a major (in terms of abundance) and minor lipid species differing by 46 ppm are detected in the same spectra.  Despite the high quality LC separations in routine operation at PNNL, the complex nature of biological samples prevents complete chromatographic separation of all sample constituents and only mass spectrometers with sufficient high resolution are capable of resolving molecules with such similar m/z. We have also recently developed a novel software, LIQUID (Lipid Informed Quantitation and Identification) for identification of lipids detected in LC-MS/MS-based lipidomics analyses. For more details on LIQUID, see the Algorithm Development page.

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Instrumentation

Robust and comprehensive capabilities for both untargeted metabolite and lipid measurements are based on established state-of-the-art platforms. The measurement platforms consist of GC and high resolution capillary LC coupled to mass spectrometers or ion mobility spectrometry MS systems, the majority of which provide high mass accuracy measurements (e.g. Orbitrap, TOF). When possible, we construct or purchase metabolite and lipid databases that consist of molecular species annotated by measured retention times and masses, which allows us to exploit the PNNL-developed accurate mass and time tag approach for identifying biomolecules detected using MS and also leverage existing advanced MS data processing tools. Our chromatography-MS metabolite libraries include the Agilent Fiehn Metabolomics Retention Time Locked Library, the Wiley FAMEs Fatty Acid Methyl Esters Database, and an in-house developed library of lipid molecular species – collectively, these libraries contain validated retention times and masses for >1300 metabolites. We continually augment these libraries with new entries as we obtain chemical standards or confidently identify new molecules.

 

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