Xu Binjie, Chen Lirun, Lv Fengqi, Pan Yuan, Fu Xing, Pei Zhaoqing
Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine; China Resources Sanjiu (Ya'an) Pharmaceutical Co., Ltd.
School of Medical Technology, Chengdu University of Traditional Chinese Medicine.
J Vis Exp. 2022 Dec 16(190). doi: 10.3791/64912.
The medicinal use of traditional Chinese medicine is mainly due to its secondary metabolites. Visualization of the distribution of these metabolites has become a crucial topic in plant science. Mass spectrometry imaging can extract huge volumes of data and provide spatial distribution information about these by analyzing tissue slices. With the advantage of high throughput and higher accuracy, desorption electrospray ionization mass spectrometry imaging (DESI-MSI) is often used in biological research and in the study of traditional Chinese medicine. However, the procedures used in this research are complicated and not affordable. In this study, we optimized sectioning and DESI imaging procedures and developed a more cost-effective method to identify the distribution of metabolites and categorize these compounds in plant tissues, with a special focus on traditional Chinese medicines. The study will promote the utilization of DESI in metabolite analysis and standardization of traditional Chinese medicine/ethnic medicine for research-related technologies.
中药的药用主要归因于其次生代谢产物。这些代谢产物分布的可视化已成为植物科学中的一个关键课题。质谱成像可以通过分析组织切片提取大量数据,并提供有关这些代谢产物的空间分布信息。解吸电喷雾电离质谱成像(DESI-MSI)具有高通量和更高准确性的优势,常用于生物学研究和中药研究。然而,该研究中使用的程序复杂且成本高昂。在本研究中,我们优化了切片和DESI成像程序,开发了一种更具成本效益的方法来识别代谢产物在植物组织中的分布并对这些化合物进行分类,特别关注中药。该研究将促进DESI在代谢产物分析中的应用以及中药/民族药研究相关技术的标准化。