Metabolomics by Gas Chromatography–Mass Spectrometry: Combined Targeted and Untargeted Profiling

Abstract

Gas chromatography-mass spectrometry (GC-MS)-based metabolomics is ideal for identifying and quantitating small molecular metabolites (<650 daltons), including small acids, alcohols, hydroxyl acids, amino acids, sugars, fatty acids, sterols, catecholamines, drugs, and toxins, often using chemical derivatization to make these compounds volatile enough for gas chromatography. This unit shows that on GC-MS- based metabolomics easily allows integrating targeted assays for absolute quantification of specific metabolites with untargeted metabolomics to discover novel compounds. Complemented by database annotations using large spectral libraries and validated, standardized standard operating procedures, GC-MS can identify and semi-quantify over 200 compounds per study in human body fluids (e.g., plasma, urine or stool) samples. Deconvolution software enables detection of more than 300 additional unidentified signals that can be annotated through accurate mass instruments with appropriate data processing workflows, similar to liquid chromatography-MS untargeted profiling (LC-MS). Hence, GC-MS is a mature technology that not only uses classic detectors (‘quadrupole’) but also target mass spectrometers (‘triple quadrupole’) and accurate mass instruments (‘quadrupole-time of flight’). This unit covers the following aspects of GC-MS-based metabolomics: (i) sample preparation from mammalian samples, (ii) acquisition of data, (iii) quality control, and (iv) data processing.

Metabolic Reprogramming in Astrocytes Distinguishes Region-Specific Neuronal Susceptibility in Huntington Mice

“…Multivariate statistics confirmed brain region is a dominant factor for the metabolic discrimination (Figure 3A) and that the metabolic profiles of each region differed with respect to genotype and age (Figure 3B). Indeed, the array of metabolites in each region was identified and visually displayed using MetaMapp (Figure 4A) (Fiehn, 2016; Lee et al, 2013), a published graphing approach that groups metabolites based on chemical similarity and enzymatic transformation (Figure 4) (Barupal et al, 2012; Fiehn et al, 2011; Lee et al, 2013). The resulting metabolic network consisted of six distinctive clusters (FAs, steroids, carbohydrates, organic acids, amino acids, and nucleic acids) (Figure 4A).…”

Metabolite Measurement: Pitfalls to Avoid and Practices to Follow

“…Detailed protocols have been published to maintain analysis quality (85), but staff needs to be trained in detail. First, liners (used to hold the injection gas cloud) need to be kept meticulously clean and changed regularly.…”

“…Involatile matrix components deposit on the guard column’s start site. The guard column may then be cut in quality maintenance procedures without compromising GC separations (85). Alternatively, users can resort to using more than one derivatization reaction (and more than one analytical run) to enhance precision and accuracy, e.g., trimethylsilylation for general metabolome profiling (for sugars, hydroxy acids, and similar compounds) and tertiary-butyldimethylsilylation for amines and amino acids (86, 87).…”

Technical Challenges in Mass Spectrometry-Based Metabolomics

“…ere are many comprehensive review articles on the basic concepts, methodology, and examples of the application of metabolomics, which are cited in the subsequent sections of this article. 11,[14][15][16][17][18][19] …”

“…103) One of the problems intrinsic to metabolome analysis using LC/MS is ion suppression by ESI. 104) In addition to improving the ionization e ciency of ESI, developing new complete ionization methods for bioanalysis such as the reconsideration of the historic interfaces such as the frit-FAB (fast atom bombardment) for LC/MS 105) and the chemical ionization and derivatization techniques of GC-MS, 19) remains to be investigated. As is the case with gene expression analysis, the nal goal of the quantitative analysis is molecular counting in samples without using a calibration curve.…”