Untargeted metabolomics is a systematic identification analysis of the entire living organism metabolome with limited background knowledge to obtain metabolite data from which differential metabolites can be identified.
Figure 1. Untargeted metabolomics workflow (Lee et al., 2010).
Untargeted metabolomics consists of the following processes:
1. Sample collection and processing
Common samples for metabolomics analysis include plasma, urine, tissues, cells, organelles, etc. A sufficiently large sample size can reduce errors caused by individual differences in the samples. These complex samples contain many other components that may interfere with the results and are a key factor in the success of metabolomics studies. Common sample processing methods include protein precipitation, differential centrifugation and extraction (solid phase extraction, liquid-liquid extraction, supercritical fluid extraction, accelerated solvent extraction).
2. Instrumental analysis
Metabolomics often requires the use of multiple analytical techniques to meet different experimental needs. Common analytical techniques for metabolomics include nuclear magnetic resonance (NMR), liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), capillary electrophoresis-mass spectrometry (CD-MS), and HILIC-MS. High-resolution mass spectrometry techniques mainly include TOF-MS, FTICR-MS, Orbitrap-MS, and Sector-MS, etc.
3. Data analysis
1) Data pre-processing: QC samples are tested first to evaluate the stability of the system, and at the same time help researchers to screen data. QC samples are usually prepared by mixing equal amounts of all samples. Missing values are then evaluated. In metabolomics studies, the presence of a large number of missing values and the different methods of missing value filling can have an impact on the subsequent statistical analysis, as the technique and the sample may contain about 20% of missing values. The commonly used missing value filtering method is the "80% rule", but other methods are also available.
Other operations are included to provide a more reliable data set for the next statistical analysis by removing system noise signals, removing disturbing signals caused by system instability, eliminating operational errors, and other steps. These steps mainly include normalization, scaling, centering, etc. Each step of the operation has different methods and also different combinations of sequences. Different methods of data pre-processing have been shown to have a significant impact on the results of statistical analysis.
Raw data can be processed with the help of tools such as XCMS, MZmine and MarkerView.
2) Identification of differential metabolites: Common analysis methods include principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA). The data analysis results are also subjected to t-test and variable importance in projection (VIP) values to screen differential metabolites. It is generally considered that variables that satisfy both P<0.05 and VIP>1.0 are differential metabolites.
3) Metabolic pathway analysis: Common metabolomics pathway databases include HMDB, KEGG, Reactome, BioCyc, MetaCyc and other databases, which can be used for metabolic pathway and interoperability network analysis.
4) Multi-omics analysis: Multi-omics analysis is already a trend in histological discovery. Available databases and tools include the IMPaLA website, iPEAP software, MetaboAnalyst website, SAMNetWeb website, pwOMICS, MetaMapR, MetScape, Grinn, WGCNA, MixOmic, DiffCorr, qpgraph, hug, etc.
- Lee, D. Y., Bowen, B. P., & Northen, T. R. (2010). Mass spectrometry—based metabolomics, analysis of metabolite-protein interactions, and imaging. Biotechniques, 49(2), 557-565.
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