Metabolic fingerprinting, the main tool in metabolomics, is a non-targeted methodology where all detectable peaks (or signals), including those from unknown analytes, are considered to establish sample classification. After pattern comparison, those signals changing in response to a specific situation under investigation are identified to gain biological insight. For this purpose, gas chromatographymass spectrometry (GC-MS) has a drawback in that only volatile compounds or compounds that can be made volatile after derivatization can be analysed, and derivatization often requires extensive sample treatment. However, once the analysis is focused on low molecular weight metabolites, GC-MS is highly efficient, sensitive, and reproducible. Moreover, it is quantitative, and its compound identification capabilities are superior to other separation techniques because GC-MS instruments obtain mass spectra with reproducible fragmentation patterns, which allow for the creation of public databases. This chapter describes well-established protocols for metabolic fingerprinting (i.e. the comprehensive analysis of small molecules) in plasma and urine using GC-MS. Guidelines will also be provided regarding subsequent data pre-treatment, pattern recognition, and marker identification.
In the search for a noninvasive and reliable rapid screening method to detect biomarkers, a metabolomics fingerprinting approach was developed and applied to rat serum samples using capillary electrophoresis coupled to an electrospray ionization-time of flight-mass spectrometer (CE-TOF-MS). An ultrafiltration method was used for sample pretreatment. To evaluate performance the method was validated with carnitine, choline, ornithine, alanine, acetylcarnitine, betaine, and citrulline, covering the entire electropherogram of pool of rat serum. The linearity for all metabolites was >0.99, with good recovery and precision. Approximately 34 compounds were also confirmed in the pool of rat serum. The method was successfully applied to real serum samples from rats with ventilator-induced lung injury, an experimental rat model for acute lung injury (ALI), giving a total of 1163 molecular features. By use of univariate and multivariate statistics 18 significant compounds were found, of which five were confirmed. The involvement of arginase and nitric oxide synthase has been proved for other lung diseases, meaning the increase of asymmetric dimethyl arginine (ADMA) and ornithine and the decrease of arginine found were in accordance with published literature. Ultimately this fingerprinting approach offers the possibility of identifying biomarkers that could be regularly screened for as part of routine disease control. In this way it might be possible to prevent the development of ALI in patients in critical care units.
The plasma of patients with stable carotid atherosclerosis (n = 9), and healthy subjects (n = 10) have been fingerprinted with both GC-MS and (1)H NMR. Principal component analysis (PCA), partial least-squares-discriminant analysis (PLS-DA) and orthogonal partial least-squares-discriminant analysis (OPLS-DA) have been applied to the profiles from each technique both separately and in combination. These techniques complement each other and enable a clearer picture of the biological samples to be interpreted not only for classification purposes, but also more importantly to define the metabolic state of patients with carotid atherosclerosis. The results showed at least 24 metabolites that were significantly modified in the group of atherosclerotic patients by this nontargeted procedure. Most of the changes can be associated to alterations of the metabolism characteristics of insulin resistance that can be strongly related to the metabolic syndrome. In addition, correlations among variables accounting for the classification show amino acids as variables whose changes showed a high degree of correlation. GC-MS and (1)H NMR fingerprints can provide complementary information in the identification of altered metabolic pathways in patients with carotid atherosclerosis. Moreover, correlations among the results with both techniques, instead of a single study, can provide a deeper insight into the patient state.
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