Spatially resolved multi-omics highlights cell-specific metabolic remodeling and interactions in gastric cancer

Chenglong Sun, Anqiang Wang, Yanhe Zhou, Panpan Chen, Xiangyi Wang, Jianpeng Huang, Jiamin Gao, Xiao Wang, Liebo Shu, Jiawei Lu, Wentao Dai (), Zhaode Bu (), Jiafu Ji () and Jiuming He ()
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Chenglong Sun: Chinese Academy of Medical Sciences and Peking Union Medical College
Anqiang Wang: Peking University Cancer Hospital and Institute
Yanhe Zhou: Chinese Academy of Medical Sciences and Peking Union Medical College
Panpan Chen: Qilu University of Technology (Shandong Academy of Sciences)
Xiangyi Wang: Chinese Academy of Medical Sciences and Peking Union Medical College
Jianpeng Huang: Chinese Academy of Medical Sciences and Peking Union Medical College
Jiamin Gao: Chinese Academy of Medical Sciences and Peking Union Medical College
Xiao Wang: Qilu University of Technology (Shandong Academy of Sciences)
Liebo Shu: Shanghai Luming Biological Technology co.Ltd
Jiawei Lu: Shanghai Luming Biological Technology co.Ltd
Wentao Dai: Fudan University
Zhaode Bu: Peking University Cancer Hospital and Institute
Jiafu Ji: Peking University Cancer Hospital and Institute
Jiuming He: Chinese Academy of Medical Sciences and Peking Union Medical College

Nature Communications, 2023, vol. 14, issue 1, 1-14

Abstract: Abstract Mapping tumor metabolic remodeling and their spatial crosstalk with surrounding non-tumor cells can fundamentally improve our understanding of tumor biology, facilitates the designing of advanced therapeutic strategies. Here, we present an integration of mass spectrometry imaging-based spatial metabolomics and lipidomics with microarray-based spatial transcriptomics to hierarchically visualize the intratumor metabolic heterogeneity and cell metabolic interactions in same gastric cancer sample. Tumor-associated metabolic reprogramming is imaged at metabolic-transcriptional levels, and maker metabolites, lipids, genes are connected in metabolic pathways and colocalized in the heterogeneous cancer tissues. Integrated data from spatial multi-omics approaches coherently identify cell types and distributions within the complex tumor microenvironment, and an immune cell-dominated “tumor-normal interface” region where tumor cells contact adjacent tissues are characterized with distinct transcriptional signatures and significant immunometabolic alterations. Our approach for mapping tissue molecular architecture provides highly integrated picture of intratumor heterogeneity, and transform the understanding of cancer metabolism at systemic level.

Date: 2023
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DOI: 10.1038/s41467-023-38360-5

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