Abstract
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.
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Data availability
Raw SIMS data for mouse liver, stomach, pancreas, kidney (Figs. 1–3), and human liver R1 (Fig. 4) are available at Github (https://github.com/yuanzhiyuan/SEAM/tree/master/SEAM/data/raw_tar). The rest of the raw SIMS data and processed SIMS data are available at figshare (https://doi.org/10.6084/m9.figshare.12622883.v1, https://doi.org/10.6084/m9.figshare.12622841.v1, https://doi.org/10.6084/m9.figshare.12622838.v1 and https://doi.org/10.6084/m9.figshare.12622922.v1). Geo-seq (Fig. 5) raw sequencing data and processed data have been deposited to NCBI GEO with accession number GSE153463. The MIBI–TOF19 data can be downloaded from https://mibi-share.ionpath.com. Single-cell metabolic regulome profiling43 data can be downloaded from https://doi.org/10.5281/zenodo.3951613. The seqFISH44 data can be downloaded from https://github.com/CaiGroup/seqFISH-PLUS.
Code availability
An open-source Python and MATLAB implementation of SEAM is available at Zenodo (https://doi.org/10.5281/zenodo.5025068).
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Acknowledgements
We acknowledge the Imaging Core Facility, Technology Center for Protein Sciences, Tsinghua University for assistance with using the LMD7000. We also thank Y. Chen from the Imaging Core Facility for her detailed instructions on the LMD7000. We thank the Center of Laboratory Animal Resources, Tsinghua University for maintenance of mice and providing the CM1900 Cryostat. We thank H. Li for computing resource support. We thank H. Zhang for help with ethics material preparation. We thank Y. Li, Z. Ye, R. Qi and all other members of our laboratory for valuable comments and discussions. We thank M. Qian for helpful advice on algorithm development. This work was supported by the National Basic Research Program of China (2018YFA0801402, 2017YFA0505503 (Y.C.)), National Nature Science Foundation of China (81890994, 31871343 (Y.C.), 21974078, 21727813 (X.Z.), 62050152 (M.S.), 81630103, 62061160369 (S.L.)), CAMS Innovation Fund for Medical Sciences (2020-RC310-009 (Y.C.)) and foundation of BNRist (BNR2019TD01020 (S.L.)). M.Q.Z. is supported by the Cecil H. and Ida Green Distinguished Chair.
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Y.C., M.Q.Z. and X.Z. conceived and designed the project. L.C. designed the IMS experiment and generated the IMS data. Q.Z. processed the mouse and human samples assisted by W.S., and generated immunohistochemistry and hematoxylin–eosin staining imaging data. Q.Z. and L.C. designed and conducted the cell culture and BrdU staining experiments. Q.Z. designed and conducted the modified Geo-seq experiment. Z.Y. developed and implemented the algorithms under the guidance of M.Q.Z. and Y.C., and was assisted by Q.Z. Z.Y. analyzed the SIMS data and Q.Z. analyzed the spatial transcriptome data. Y.Z. and S.Y. provided the clinical samples. L.P. and S.Q. guided the histological annotation. S.L. and M.S. gave suggestions on the application of the method. J.F. and H.Z. helped with the metabolite annotation. Z.Y., Q.Z. and L.C. completed the figures and writing of the paper with the guidance of Y.C., X.Z. and M.Q.Z.
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Peer review information Nature Methods thanks Benjamin Balluff and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Arunima Singh was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team
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Yuan, Z., Zhou, Q., Cai, L. et al. SEAM is a spatial single nuclear metabolomics method for dissecting tissue microenvironment. Nat Methods 18, 1223–1232 (2021). https://doi.org/10.1038/s41592-021-01276-3
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DOI: https://doi.org/10.1038/s41592-021-01276-3
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