Spatially resolved multi-omics highlights cell-specific metabolic remodeling and interactions in gastric cancer.
Chenglong SunAnqiang WangYanhe ZhouPanpan ChenXiangyi WangJianpeng HuangJiamin GaoXiao WangLiebo ShuJiawei LuWentao DaiZhaode BuJia Fu JiJiuming HePublished in: Nature communications (2023)
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.
Keyphrases
- single cell
- high resolution
- mass spectrometry
- gene expression
- transcription factor
- papillary thyroid
- squamous cell carcinoma
- ms ms
- genome wide
- photodynamic therapy
- liquid chromatography
- lymph node metastasis
- big data
- childhood cancer
- deep learning
- high performance liquid chromatography
- mesenchymal stem cells
- fluorescence imaging
- artificial intelligence
- squamous cell