SEAM is a spatial single nuclear metabolomics method for dissecting tissue microenvironment

• Published in: Nature methods Vol. 18; no. 10; pp. 1223 - 1232
• Main Authors: Yuan, Zhiyuan, Zhou, Qiming, Cai, Lesi, Pan, Lin, Sun, Weiliang, Qumu, Shiwei, Yu, Si, Feng, Jiaxin, Zhao, Hansen, Zheng, Yongchang, Shi, Minglei, Li, Shao, Chen, Yang, Zhang, Xinrong, Zhang, Michael Q.
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.10.2021; Nature Publishing Group
• Subjects: 631/114/2391; 631/1647/1407/1555; 631/61/320; Algorithms; Animals; Bioinformatics; Biological Microscopy; Biological research; Biological Techniques; Biology, Experimental; Biomedical and Life Sciences; Biomedical Engineering/Biotechnology; Cellular Microenvironment; Clustering; Composition; Computational Biology - methods; Computer applications; Fibrosis; Hepatocytes; Hepatocytes - physiology; Heterogeneity; Histology; Humans; Life Sciences; Liver; Liver - cytology; Liver - physiology; Liver Cirrhosis - metabolism; Mass spectrometry; Mass spectroscopy; Metabolism; Metabolomics; Metabolomics - methods; Methods; Mice; Microenvironments; Proteomics; Reproducibility of Results; Scientific imaging; Secondary ion mass spectrometry; Single Molecule Imaging; Spectroscopy; Subpopulations; Tissues; Transcriptome; Zonation; China
• ISSN: 1548-7091; 1548-7105; 1548-7105
• DOI: 10.1038/s41592-021-01276-3

Spatial metabolomics can reveal intercellular heterogeneity and tissue organization. Here we report on the spatial single nuclear metabolomics (SEAM) method, a flexible platform combining high-spatial-resolution imaging mass spectrometry and a set of computational algorithms that can display multiscale and multicolor tissue tomography together with identification and clustering of single nuclei by their in situ metabolic fingerprints. We first applied SEAM to a range of wild-type mouse tissues, then delineated a consistent pattern of metabolic zonation in mouse liver. We further studied the spatial metabolic profile in the human fibrotic liver. We discovered subpopulations of hepatocytes with special metabolic features associated with their proximity to the fibrotic niche, and validated this finding by spatial transcriptomics with Geo-seq. These demonstrations highlighted SEAM’s ability to explore the spatial metabolic profile and tissue histology at the single-cell level, leading to a deeper understanding of tissue metabolic organization. SEAM is a platform for the analysis of high-resolution secondary ion mass spectrometry imaging that allows spatially resolved nuclear metabolomic profiling at the single-cell level.