Dong Jiyang, Peng Qianwen, Deng Lingli, Liu Jianjun, Huang Wei, Zhou Xin, Zhao Chao, Cai Zongwei
Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China.
Department of Information Engineering, East China University of Technology, China.
iScience. 2022 Aug 8;25(9):104896. doi: 10.1016/j.isci.2022.104896. eCollection 2022 Sep 16.
The metabolic responses of organism to external stimuli are characterized by the multicellular- and multiorgan-based synergistic regulation. Network analysis is a powerful tool to investigate this multiscale interaction. The imaging mass spectrometry (iMS)-based spatial omics provides multidimensional and multiscale information, thus offering the possibility of network analysis to investigate metabolic response of organism to environmental stimuli. We present iMS dataset-sourced multiscale network (iMS2Net) strategy to uncover prenatal environmental pollutant (PM2.5)-induced metabolic responses in the scales of cell and organ from metabolite abundances and metabolite-metabolite interaction using mouse fetal model, including metabotypic similarity, metabolic vulnerability, metabolic co-variability and metabolic diversity within and between organs. Furthermore, network-based analysis results confirm close associations between lipid metabolites and inflammatory cytokine release. This networking methodology elicits particular advantages for modeling the dynamic and adaptive processes of organism under environmental stresses or pathophysiology and provides molecular mechanism to guide the occurrence and development of systemic diseases.
生物体对外部刺激的代谢反应具有基于多细胞和多器官的协同调节特征。网络分析是研究这种多尺度相互作用的有力工具。基于成像质谱（iMS）的空间组学提供多维和多尺度信息，从而为通过网络分析研究生物体对环境刺激的代谢反应提供了可能性。我们提出了基于iMS数据集的多尺度网络（iMS2Net）策略，以利用小鼠胎儿模型，从代谢物丰度和代谢物-代谢物相互作用的角度，在细胞和器官尺度上揭示产前环境污染物（PM2.5）诱导的代谢反应，包括器官内和器官间的代谢型相似性、代谢脆弱性、代谢共变性和代谢多样性。此外，基于网络分析的结果证实了脂质代谢物与炎性细胞因子释放之间的密切关联。这种网络方法在模拟环境应激或病理生理条件下生物体的动态和适应性过程方面具有独特优势，并为指导全身性疾病的发生和发展提供分子机制。
