iMS2Net: A multiscale networking methodology to decipher metabolic synergy of organism
Jiyang Dong,
Qianwen Peng,
Lingli Deng,
Jianjun Liu,
Wei Huang,
Xin Zhou,
Chao Zhao,
Zongwei Cai
Affiliations
Jiyang Dong
Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
Qianwen Peng
Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
Lingli Deng
Department of Information Engineering, East China University of Technology, China
Jianjun Liu
Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline of Health Toxicology (2020-2024), Shenzhen Center for Disease Control and Prevention, Shenzhen, China
Wei Huang
School of Environment, Guangdong Key Laboratory of Environmental Pollution and Health, Jinan University, China
Xin Zhou
Bionic Sensing and Intelligence Center, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline of Health Toxicology (2020-2024), Shenzhen Center for Disease Control and Prevention, Shenzhen, China
Chao Zhao
Bionic Sensing and Intelligence Center, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong SAR, China; Corresponding author
Zongwei Cai
State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong SAR, China; Corresponding author
Summary: 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.
