Spatial single-cell mass spectrometry defines zonation of the hepatocyte proteome

  • Nat Methods. 2023 Oct;20(10):1530-1536. doi: 10.1038/s41592-023-02007-6.
Florian A Rosenberger  1 ,  Marvin Thielert  1 ,  Maximilian T Strauss  2 ,  Lisa Schweizer  1 ,  Constantin Ammar  1 ,  Sophia C Mädler  1 ,  Andreas Metousis  1 ,  Patricia Skowronek  1 ,  Maria Wahle  1 ,  Katherine Madden  1 ,  Janine Gote-Schniering  3 ,  Anna Semenova  3 ,  Herbert B Schiller  3 ,  Edwin Rodriguez  1 ,  Thierry M Nordmann  1 ,  Andreas Mund  2 ,  Matthias Mann  4  5
Affiliations
  • 1. Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany.
  • 2. Proteomics Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
  • 3. Comprehensive Pneumology Center (CPC) / Institute of Lung Health and Immunity (LHI), Helmholtz Munich; Member of the German Center for Lung Research (DZL), Munich, Germany.
  • 4. Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany. [email protected].
  • 5. Proteomics Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark. [email protected].
PMID: 37783884 DOI: 10.1038/s41592-023-02007-6
Abstract

Single-cell proteomics by mass spectrometry is emerging as a powerful and unbiased method for the characterization of biological heterogeneity. So far, it has been limited to cultured cells, whereas an expansion of the method to complex tissues would greatly enhance biological insights. Here we describe single-cell Deep Visual Proteomics (scDVP), a technology that integrates high-content imaging, laser microdissection and multiplexed mass spectrometry. scDVP resolves the context-dependent, spatial proteome of murine hepatocytes at a current depth of 1,700 proteins from a cell slice. Half of the proteome was differentially regulated in a spatial manner, with protein levels changing dramatically in proximity to the central vein. We applied machine learning to proteome classes and images, which subsequently inferred the spatial proteome from imaging data alone. scDVP is applicable to healthy and diseased tissues and complements other spatial proteomics and spatial omics technologies.