Widely-targeted metabolomics is essentially a combination of two assays. First, untargeted metabolomics using high-resolution mass spectrometer is performed to collect primary and secondary mass spectrometry data from mixed biological samples. These data are compared against databases for high throughput metabolite identification. Then, targeted metabolomics using low-resolution QQQ mass spectrometer in MRM (multiple reaction monitoring) mode is performed to collect mass spectrometry data and metabolite quantity from each sample based on the metabolites detected from the high-resolution mass spectrometer. As a result, widely-targeted metabolomics provides accurate annotation and accurate relative quantification of metabolites in biological samples.
Another important factor which strengthens the use of this proprietary approach in the biomarker discovery, is its high reproducibility across studies. The CV values of internal standards across different studies showed that Widely-Targeted Metabolomics is a highly stable assay. Our internal tests showed that quantification of 6 internal standards across thousands of samples remain stable: within 15% CV. Figure 1 shows CV distribution of 6 internal standards in 8 separate projects, while Figure 2 highlights the stability of signals measured across 990 samples processed over 25 days.
Figure 1. CV distribution of 6 internal standards (IS01-IS06) in 8 separate projects (A-H). The number for each project indicates the number of samples.
Figure 2. Signal intensity of an internal standard (IS-6) across 990 samples processed over 25 days.
With all those benefits, Widely-Targeted Metabolomics technology is particularly suited for multi-center, multi-stage biomarker discovery studies. Below are some publications highlighting the use of this technology in studying human and animal samples, as well as in research with plant samples.
Biomedical research examples
Paper published in Clinica Chimica Acta “Plasma metabolic signatures for intracranial aneurysm and its rupture identified by pseudotargeted metabolomics” proposed the diagnostics model by analyzing metabolic profiles in human’s plasma samples. Graphical abstract shown below (Figure 3) highlights sample collection and preparation steps, widely-targeted metabolomics steps, and data analysis up to biomarker evaluation stage. The metabolite panels may serve as potential non-invasive diagnostic and risk stratification tool for intracranial aneurysm.
Figure 3. Graphical abstract for the paper “Plasma metabolic signatures for intracranial aneurysm and its rupture identified by pseudotargeted metabolomics”
Follicular development is discussed in an open access paper in Nature’s Communications Biology “Metabolic signatures in human follicular fluid identify lysophosphatidylcholine as a predictor of follicular development”. Authors characterized the follicular fluid metabolic signatures from ovarian follicles of different developmental stages. Lysophosphatidylcholine can be used as a biomarker of follicular development and ovarian sensitivity.
Plant samples study examples
A recent open access publication “QTL analysis of important agronomic traits and metabolites in foxtail millet (Setaria italica) by RIL population and widely targeted metabolome” looked into plant architecture and height in foxtail millet. Using Widely-Targeted Metabolomics for Plants, a total of 3,452 reproducible metabolite signals were detected, of which 381 metabolites were qualitatively analyzed through standard comparison and annotated. These annotated metabolites included Flavonoids, Lipids, Phenolic acids, Amino acids and derivatives, Organic acids, Nucleotide and derivatives, Alkaloids, Anthocyanins, Lignans and Coumarins.
Another open access study highlights integrative analyses of metabolome and transcriptome data combined with a series of physiological and experimental analyses in the ‘Keitt’ mango. Authors characterized changes in accumulation of specific metabolites at different stages during fruit development and ripening. Paper is entitled “Metabolomic and transcriptomic analyses reveal new insights into the role of abscisic acid in modulating mango fruit ripening”, and it was published in Horticulture Research. In total, 617 metabolites were detected, including 126 phenolic acids, 80 lipids, 77 amino acids and derivatives, 72 organic acids, 61 flavonoids, 46 saccharides and alcohols, 36 nucleotides and derivatives, 33 alkaloids, 20 tannins, 15 lignans and coumarins, 14 vitamins, 9 terpenoids, 4 xanthones, and 24 other compounds. Heatmap of metabolites is shown in the Figure 4.
In summary, widely-targeted metabolomics exhibits great analytical characteristics, including accurate annotation, accurate relative quantification and high reproducibility, which supports its application not only in studies with human and animal samples, but also supports plants research. Interested in further details about metabolomics services? Click the button below.