Systematic Characterization of Sesquiterpenes from Dendrobium nobile through Offline Two-Dimensional Chromatography Tandem Mass Spectrometry and Target Isolation.
Han-Ze WangJia-Yuan LiChang-Liang YaoMeng-Meng LiYan ZhangLin FengDe-An GuoPublished in: Journal of agricultural and food chemistry (2024)
Dendrobium nobile is a species of the genus Dendrobium that can be used as both a medicinal herb and healthy food. The sesquiterpenes in D. nobile have attracted extensive attention in recent years. In this study, Amide × RP offline two-dimensional chromatography separation tandem high-resolution mass spectrometry combined with GNPS (Global Natural Product Social Molecular Networking) was developed for the characterization of sesquiterpenes in D. nobile . After first-dimensional amide separation, the 70% ethanol extract of D. nobile was divided into 40 fractions, which were analyzed by second-dimensional reverse-phase system separation and LTQ-Orbitrap detection. The raw data was imported into the GNPS, resulting in the efficient clustering of similar substances. Finally, 594 sesquiterpene compounds were characterized, and 25 compounds were isolated based on molecular network analysis, including six new compounds. In vitro bioassays, the isolated compounds decreased NO production in the LPS-induced microglial BV-2 cell model and the content of MDA in PC12 cells, demonstrating neuroprotective activity. These findings unraveled the underlying material and provided valuable insights into the quality control of D. nobile .
Keyphrases
- liquid chromatography
- tandem mass spectrometry
- high resolution mass spectrometry
- lps induced
- ultra high performance liquid chromatography
- mass spectrometry
- high performance liquid chromatography
- gas chromatography
- simultaneous determination
- inflammatory response
- network analysis
- quality control
- solid phase extraction
- single cell
- healthcare
- lipopolysaccharide induced
- mental health
- oxidative stress
- climate change
- cell therapy
- signaling pathway
- rna seq
- machine learning
- cell proliferation
- subarachnoid hemorrhage
- neuropathic pain
- big data
- cell death
- spinal cord injury
- risk assessment
- high speed
- blood brain barrier
- single molecule
- spinal cord
- data analysis
- artificial intelligence
- mesenchymal stem cells