Metabolomics is the comprehensive analysis of the metabolome, the complete small-molecule complement of a cell, tissue, organ, organ system, or organism. The metabolome is comprised of all substrates, intermediates, and products of all chemical reactions and metabolic processes. The metabolome is an informative matrix in which chemical “fingerprints” of various processes or perturbations emerge, and metabolomics has advanced biomarker discovery increasingly informs the shift toward precision medicine.

Extraction and Separation: The first decision in sample preparation is usually whether to focus on the entire small-molecule complement, the hydrophobic complement, or the hydrophilic complement. For liquid biological matrices such as urine or blood plasma, sample preparation is relatively straightforward. For solid tissues or where the lipidome (see Lipidomics) is targeted, sample preparation will involve extraction and separation of polar and non-polar complements before final chromatographic separation. Final separation strategies vary depending on the goals of the study and include gas chromatography as well as normal- and reverse-phase high and ultra-performance liquid chromatography (HPLC and UPLC, respectively).

Detection: Metabolomics relies mainly on mass spectrometry (MS) and nuclear magnetic resonance (NMR) technologies. Each has its advantages and disadvantages as well as its respective options and augmentations. Analytic strategies involving MS generally involve a separation technology (e.g., HPLC or UPLC) coupled directly to an ion source fitted to a time-of-flight (TOF) or triple quadrupole mass spectrometer capable of performing tandem mass spectrometry (MS/MS). Molecules of interest may be identified by comparing LC-MS/MS spectra with the standard spectra held in the METLIN Metabolite and Chemical Entity Database, hosted by Scripps Research.

Untargeted versus Targeted: Untargeted metabolomics has been generally thought of as a hypothesis-generating, global “look-and-see” analysis. Untargeted metabolomics is a powerful discovery platform. Targeted metabolomics, an hypothesis-driven approach, becomes the method of choice for validation and quantification of specific features or molecule classes.

Informatics: Metabolomics datasets are large, and feature-finding can be difficult with only classical statistics. Machine learning approaches increase speed and ease of feature-finding. Unsupervised techniques such as principal components analysis and cluster analysis reduce dimensionality/complexity and point the researcher toward those molecules contributing most to class differences. Models based in machine learning algorithms may then be built on training datasets and validated with test datasets.

Nelson Scientific Labs provides comprehensive metabolomic services, from study design all the way to interpreting and reporting your findings.

Contact us today to find out how we may help you with your metabolomics project!