Key Laboratory of Mass Spectrometry Imaging and Metabolomics (Minzu University of China), National Ethnic Affairs Commission, Beijing 100081, P. R. China.
Center for Imaging and Systems Biology, College of Life and Environmental Sciences, Minzu University of China, 27 Zhongguancun South Avenue, Beijing 100081, P. R. China.
Anal Chem. 2023 Jun 20;95(24):9164-9172. doi: 10.1021/acs.analchem.2c05047. Epub 2023 Jun 2.
Zebrafish () represent an effective model biological material for human disease research, even for personalized precision medicine. Thus, it is necessary to fully characterize their molecular information in order to obtain a global metabolic profile. Here, a spatially resolved metabolomics method for whole-body zebrafish analysis was established based on an air-flow-assisted desorption electrospray ionization-mass spectrometry imaging (AFADESI-MSI) system. Using the optimized experimental conditions, the method provided high-quality visual distribution information for >1000 functional metabolites, thereby organ-specific metabolites characterizing nine regions were obtained comprehensively, including the eyes, brain, gill, heart, liver, kidney, intestine, muscle, and spinal cord. Then, combined with metabolic pathway analysis, a global metabolic network with information on zebrafish was mapped for the first time. We also tried to use the recently published MSI database to annotate the metabolites in this study; however, the annotation rate was only 33.7 and 10.4% in positive and negative modes, respectively. This further demonstrated the necessity of establishing a suitable AFADESI-MSI method for zebrafish samples. These results offer comprehensive and in-depth molecular information about zebrafish at the metabolic level, which facilitates the use of zebrafish models to understand metabolic reprogramming in human diseases and the development of zebrafish disease models.
斑马鱼(Zebrafish)是人类疾病研究,甚至是个性化精准医学的有效模式生物材料。因此,有必要充分描述其分子信息,以获得全面的代谢图谱。在这里,我们建立了一种基于气流辅助解吸电喷雾电离质谱成像(AFADESI-MSI)系统的用于全鱼分析的空间分辨代谢组学方法。利用优化的实验条件,该方法为 >1000 种功能代谢物提供了高质量的可视化分布信息,从而全面获得了 9 个区域的器官特异性代谢物特征,包括眼睛、大脑、鳃、心脏、肝脏、肾脏、肠、肌肉和脊髓。然后,结合代谢途径分析,首次绘制了具有斑马鱼代谢组学信息的全局代谢网络。我们还尝试使用最近发表的 MSI 数据库对本研究中的代谢物进行注释,但在正、负离子模式下的注释率分别仅为 33.7%和 10.4%。这进一步证明了为斑马鱼样本建立合适的 AFADESI-MSI 方法的必要性。这些结果提供了关于斑马鱼代谢水平的全面而深入的分子信息,有助于利用斑马鱼模型来理解人类疾病中的代谢重编程和斑马鱼疾病模型的开发。