State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100050, China.
Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100730, China.
Anal Chim Acta. 2019 Oct 24;1077:183-190. doi: 10.1016/j.aca.2019.05.068. Epub 2019 Jun 4.
Spatially resolved metabolomics is an excellent tool for elucidating in situ molecular events, but its use remains challenging due to the complexity of the endogenous metabolites in bio-tissue and tissue heterogeneity. In this study, a data processing pipeline for spatially resolved metabolomics analysis of tumor microregion heterogeneity was developed and built into a graphical interface with MSI software. Biological tissue sections were analysed by ambient air-flow assisted desorption electrospray ionization mass spectrometry imaging. Histology-driven and characterized ion images overlay combined with metabolic feature-based spatial segmentation were developed to accurately extract the metabolic profile from the tissue microregion of interest. In addition, appropriate data pretreatment methods were investigated to evaluate their ability to identify biological variations from the complicated spatially resolved metabolomics data. Diverse graphical metabolic feature extraction and various data pretreatment methods enable not only the achievement of the best multivariate statistical results in an intuitive and simple way but also the discovery of low-abundance but reliable biomarkers. The results from a papillary thyroid cancer tissue study demonstrated that this data processing pipeline is a powerful and easy-to-use tool for investigating the spatial molecular events in tumor microenvironments and to therefore thoroughly understand their metabolic heterogeneity.
基于空间分辨的代谢组学是揭示原位分子事件的有力工具，但由于生物组织内源性代谢物的复杂性和组织异质性，其应用仍然具有挑战性。本研究开发了一种用于肿瘤微区异质性的基于空间分辨代谢组学分析的数据处理流程，并将其构建成一个带有 MSI 软件的图形界面。采用环境气流辅助解吸电喷雾电离质谱成像技术对生物组织切片进行分析。开发了基于组织学驱动和特征离子图像叠加以及基于代谢特征的空间分割的方法，以从感兴趣的组织微区中准确提取代谢轮廓。此外，还研究了适当的数据预处理方法，以评估它们从复杂的基于空间分辨的代谢组学数据中识别生物学变化的能力。不同的图形代谢特征提取和各种数据预处理方法不仅可以直观、简单地实现最佳的多变量统计结果，还可以发现低丰度但可靠的生物标志物。来自甲状腺乳头状癌组织研究的结果表明，该数据处理流程是一种强大且易于使用的工具，可用于研究肿瘤微环境中的空间分子事件，从而深入了解其代谢异质性。
