Metabolomics

Services

Untargeted Metabolomics

The research object of untargeted metabolomics is the endogenous small molecule substances with molecular weights below 1500 Da in samples. Untargeted metabolomics is used for unbiased detection and analysis of metabolites in a sample by LC-MS (Liquid Chromatography-Mass Spectrometry) and obtain the qualitative and quantitative information. Compared to genomics, transcriptomics, and proteomics, metabolomics can more directly and promptly reflect an individual's disease state, potentially making it the most sensitive approach for identifying pathological variations. This is because even minor changes in protein expression or structure can lead to significant alterations in protein activity and metabolite levels.

Technical Advantages

- High Throughput
Detection and analysis of all metabolites in the sample
- Comprehensive Database
Integrated public databases and in-house standard database
- High Sensitivity
Ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS) platform for better separation and high sensitivity
- Amplification Effect
Changes in metabolites can reflect physiological and pathological changes in the organism more directly and accurately, and even minor changes in genes or proteins can be amplified in metabolites.

Application Areas

Untargeted metabolomics, by comparing differences in the metabolic fingerprint profiles of samples, can provide clues and directions for research into disease biomarkers, disease pathogenesis, and drug treatment. Currently, it has been widely applied in research fields such as cancer, biomedicine, and nutrition.

Research on Disease Biomarkers

Mechanistic Studies of Metabolic Diseases

Disease Classification and Personalized Treatment

Tumor Metabolism Research

Modernization of Traditional Chinese Medicine

Research

Research on Drug Efficacy Evaluation

References

[1]Shao, Y., Li, T., Liu, Z., Wang, X., Xu, X., Li, S., Xu, G., & Le, W. (2021). Comprehensive metabolic profiling of Parkinson's disease by liquid chromatography-mass spectrometry. Molecular neurodegeneration, 16(1), 4. 阅读全文
[2]Zhou, Z., Luo, M., Zhang, H., Yin, Y., Cai, Y., & Zhu, Z. J. (2022). Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer metabolic networking. Nature communications, 13(1), 6656. 阅读全文
[3]Lai, Z., Tsugawa, H., Wohlgemuth, G., Mehta, S., Mueller, M., Zheng, Y., Ogiwara, A., Meissen, J., Showalter, M., Takeuchi, K., Kind, T., Beal, P., Arita, M., & Fiehn, O. (2018). Identifying metabolites by integrating metabolome databases with mass spectrometry cheminformatics. Nature methods, 15(1), 53–56. 阅读全文
[4]Darghouth, D., Koehl, B., Madalinski, G., Heilier, J. F., Bovee, P., Xu, Y., Olivier, M. F., Bartolucci, P., Benkerrou, M., Pissard, S., Colin, Y., Galacteros, F., Bosman, G., Junot, C., & Roméo, P. H. (2011). Pathophysiology of sickle cell disease is mirrored by the red blood cell metabolome. Blood, 117(6), e57–e66. 阅读全文