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

Nature Methods volume 18, pages 1223–1232 (2021)Cite this article

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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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Fig. 1: SEAM captures spatial metabolic heterogeneity at single nucleus resolution.
Fig. 2: Algorithm design and performance.
Fig. 3: SEAM detects zonation-like metabolic pattern in wild-type mouse liver.
Fig. 4: SEAM identifies hepatocyte subtypes with differential metabolic states associated with spatial localization.
Fig. 5: Spatial transcriptome-validated metabolism-associated gene expression alteration in heterogeneous hepatocyte subtypes identified by SEAM.

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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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Author notes

  1. Yang Chen

    Present address: The State Key Laboratory of Medical Molecular Biology, Department of Molecular Biology and Biochemistry, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing, China

  2. These authors contributed equally: Zhiyuan Yuan, Qiming Zhou, Lesi Cai.

  3. These authors jointly supervised this work: Yang Chen, Xinrong Zhang, Michael Q. Zhang.

Authors and Affiliations

  1. MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic and Systems Biology, BNRist, Institute of TCM-X, Department of Automation, Tsinghua University, Beijing, China

    Zhiyuan Yuan, Shao Li, Yang Chen & Michael Q. Zhang

  2. MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic and Systems Biology, BNRist, School of Life Sciences, Tsinghua University, Beijing, China

    Qiming Zhou

  3. Department of Chemistry, Tsinghua University, Beijing, China

    Lesi Cai, Jiaxin Feng, Hansen Zhao & Xinrong Zhang

  4. Institute of Clinical Medicine, China–Japan Friendship Hospital, National Clinical Research Center for Respiratory Diseases, Institute of Respiratory Medicine, Chinese Academy of Medical Science, Beijing, China

    Lin Pan & Weiliang Sun

  5. Department of Pulmonary and Critical Care Medicine, China–Japan Friend Hospital, National Clinical Research Center for Respiratory Diseases, Beijing, China

    Shiwei Qumu

  6. Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

    Si Yu & Yongchang Zheng

  7. MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic and Systems Biology, BNRist, Department of Basic Medical Sciences, School of Medicine, Tsinghua University, Beijing, China

    Minglei Shi & Michael Q. Zhang

  8. Department of Biological Sciences, Center for Systems Biology, The University of Texas, Richardson, TX, USA

    Michael Q. Zhang

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Contributions

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.

Corresponding authors

Correspondence to Yang Chen, Xinrong Zhang or Michael Q. Zhang.

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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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Supplementary Notes, Figs. 1–55 and Tables 1–10 (complete Supplementary Tables 2 and 5 are provided as another excel workbook).

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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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