Metware Biotechnology Co., Ltd.

Pioneer of Widely-Targeted Metabolomics

Metware’s proprietary Widely-Targeted Metabolomics is profiled in the May issue of MetaboNews (see page 11). We would like to highlight here some of the basics and major benefits of this technology.


Metabolomics has a long history of development, with commonly seen approaches such as targeted and untargeted metabolomics. Each of them has its strengths and weaknesses in its throughput, qualitative detection, and quantitative analysis.


Untargeted metabolomics, using DDA (data dependent acquisition) mode from high-resolution mass spectrometers offer precise molecular mass measurement and allows detection of thousands of metabolites in a single run. Targeted metabolomics, using MRM (multiple reaction monitoring) mode allows highly sensitive detection and accurate quantification. These methods are not without limitations and challenges. For untargeted metabolomics, it heavily depends on databases, which contain limited endogenous metabolites from plants or animals, resulting in a limited ability to identify metabolites. Secondly, high-resolution mass spectrometry is less sensitive (by 1-2 orders of magnitude) than low-resolution mass spectrometry, making it difficult to detect low-level metabolites. Lastly, many TOF (Time of Flight) mass spectrometers suffer from dead time problems in their operation mode, leading to inaccurate metabolite quantification. For targeted metabolomics, its disadvantage lies in the limitation of its detection mode, allowing the detection of only a few dozen metabolites in a single run (low throughput), and its dependence on chemical standards for the qualitative identification and quantification of metabolites.


These challenges propelled us to develop the Widely-Targeted Metabolomics technology, a process that combines DDA and MRM data acquisition modes based on Q-TOF and QQQ (triple quadrupole) mass spectrometers. This process was also made possible by the construction of a large, curated database for animals, plants, and humans, allowing for accurate and high-throughput detection of metabolites with ultra-sensitivity and wide coverage.


Widely-Targeted Metabolomics was initially developed to create an exclusive database of endogenous metabolites in paddy rice to upgrade the throughput of detected metabolites and assist in studying biological problems1. Since then, it has been refined into a metabolomics assay based on a multi-species in-house database. This technique uses the MIM-EPI (Multiple Ion Monitoring–Enhanced Product Ions) mode of a low-resolution mass spectrometer to obtain secondary mass spectra of metabolites. Meanwhile, it refines the parameters required for metabolite identification by utilizing primary and secondary qualitative results from high-resolution mass spectrometry and external sources, such as public databases, standard sample data, and literature. This process culminates in the construction of an MS2 database. The MRM mode is then utilized to optimize collision energy and de-clustering potential for metabolite detection and to select the appropriate Q3 (fragment ion) as the quantitative ion for analysis. By establishing a species-specific endogenous metabolite database, this technique ensures accurate metabolite identification and quantification while greatly improving metabolite detection efficiency.


Widely-targeted metabolomics is a two-step process. First, untargeted metabolomics using high-resolution mass spectrometers is performed to collect primary and secondary mass spectrometry data from mixed biological samples. These data are compared against databases (public database + in-house library) for high throughput metabolite identification. Then, targeted metabolomics using low-resolution QQQ mass spectrometers in MRM 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, this two-step process achieves the following features:

a)    Accurate annotation

Widely-targeted metabolomics analysis involves collecting secondary spectra of metabolites in a biological sample using high-resolution mass spectrometry and comparing them with standard spectra in the database. The alignment to the metabolite database is performed using MultiQuant (v 3.0.3) and proprietary software that takes into consideration the retention time (RT), MS1, and MS2 information. The confidence of the alignment is scored using the dot product method.

b)    Accurate quantification

The MRM mode of a QQQ mass spectrometer allows highly specific screening of characteristic ion pairs and reduces the interference from other ions. The MRM mode also boasts a wide linear dynamic range spanning 4 to 5 orders of magnitude, which facilitates the detection of metabolites across a wide range of concentrations in complex samples. Data processing from MRM scanning is also simplified due to predetermination of selected metabolites.

c)    Large Curated Database

Plant database:

Plant metabolites are generally under-represented in the public databases. MetwareBio has constructed a plant metabolite specific database and has been expanding over the last 6 years. It houses over 30,000 plant metabolites to-date that includes primary metabolites (sugars, amino acids, lipids, and nucleotides) and secondary metabolites such as flavonoids, phenolic acids, terpenoids, and alkaloids.

Human and Animal database:

Our curated database contains over 280,000 metabolites, which includes an in-house database containing over 3,000 metabolites constructed from standards, a curated public database containing over 150,000 metabolites, and an AI predicted structural database containing over 130,000 metabolites. The in-house standard database contains metabolites across 13 different categories as shown in the Table 1 below:




Representative compounds

Amino acids and their metabolites


Glycine, L-threonine, L-arginine, N-acetyl-L-alanine

Organic acids and their derivatives


3-hydroxybutyric acid, adipic acid, hippuric acid, kynurenine

Nucleotides and their metabolites


Adenine, 5'-Adenine Nucleotide, Guanine, 2'-Deoxycytidine

Carbohydrates and their metabolites


D-glucose, glucosamine, D-fructose 6-phosphate



O-acetylcarnitine, γ-linolenic acid, lysophosphatidylcholine 22:4

Benzene and its derivatives


Benzoic acid, 3,4-dimethoxyphenylacetic acid, 4-hydroxybenzoic acid

Coenzymes and vitamins


Folic acid, pantothenic acid, vitamin D3

Alcohols, amines


Dopamine, histamine, DL-1-amino-2-propanol

Aldehydes, ketones, esters


Furfural, ethyl butyrate, α-pentyl cinnamaldehyde

Heterocyclic compound


Pyridoxal, biopterin, indole-3-acetic acid

bile acid


Glycocholic acid, deoxycholic acid, taurolithocholic acid

Hormones and hormone-related substances


Juvenile hormone 3, epinephrine, 3,3'-diiodo-L-thyroxine

Tryptamine, choline, pigment


Serotonin, bilirubin (E-E), urobilin



Astaxanthin, hydroxyurea




Table 1. MetwareBio’s In-House Human/Animal-focused Database



d)    High reproducibility

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, see one of the previous posts). Widely-Targeted Metabolomics technology is particularly suited for multi-center, multi-stage biomarker discovery studies. This technology has been applied to identify biomarkers for distinguishing intracranial aneurysm2, COVID-19 severity3, and follicular development4.


Owing to the large plant metabolite database, Widely-Targeted Metabolomics has also been applied to numerous studies for identifying metabolite biomarkers associated with important agronomic traits, including fruit mass in tomato5, plant architecture and height in foxtail millet6, and fruit development modulation in mango7.


Stay tuned for our next blog post where we will talk more about Widely-Targeted metabolomics detection results in human and animal samples, as well as in plant samples.


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1. Chen, W. et al.Mol. Plant 6, 1769–1780 (2013).

2. Sun, K. et al. Clin. Chim. Acta 538, 36–45 (2023).

3. Wu, D. et al. Natl Sci Rev 7, 1157–1168 (2020).

4. Yang, J. et al. Commun Biol 5, 763 (2022).

5. Zhu, G. et al. Cell 172, 249–261.e12 (2018).

6. Wei, W. et al. Front. Plant Sci. 13, 1035906 (2022).

7. Wu, S. et al. Hortic Res 9, uhac102 (2022).