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

Rosenberger, Florian A.; Thielert, Marvin; Strauss, Maximilian T.; Schweizer, Lisa; Ammar, Constantin; Mädler, Sophia C.; Metousis, Andreas; Skowronek, Patricia; Wahle, Maria; Madden, Katherine; Gote-Schniering, Janine; Semenova, Anna; Schiller, Herbert B.; Rodriguez, Edwin; Nordmann, Thierry M.; Mund, Andreas; Mann, Matthias

doi:10.1038/s41592-023-02007-6

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

MPS-Authors

/persons/resource/persons292643

Rosenberger, Florian A.
Mann, Matthias / Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Max Planck Society;

/persons/resource/persons273052

Thielert, Marvin
Mann, Matthias / Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Max Planck Society;
IMPRS-ML: Martinsried, Max Planck Institute of Biochemistry, Max Planck Society;

/persons/resource/persons247786

Schweizer, Lisa
Mann, Matthias / Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Max Planck Society;
IMPRS-ML: Martinsried, Max Planck Institute of Biochemistry, Max Planck Society;

/persons/resource/persons273054

Ammar, Constantin
Mann, Matthias / Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Max Planck Society;

/persons/resource/persons271031

Mädler, Sophia C.
Mann, Matthias / Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Max Planck Society;
IMPRS-ML: Martinsried, Max Planck Institute of Biochemistry, Max Planck Society;

/persons/resource/persons294065

Metousis, Andreas
Mann, Matthias / Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Max Planck Society;
IMPRS-ML: Martinsried, Max Planck Institute of Biochemistry, Max Planck Society;

/persons/resource/persons281940

Skowronek, Patricia
Mann, Matthias / Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Max Planck Society;
IMPRS-ML: Martinsried, Max Planck Institute of Biochemistry, Max Planck Society;

/persons/resource/persons284696

Wahle, Maria
Mann, Matthias / Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Max Planck Society;
IMPRS-ML: Martinsried, Max Planck Institute of Biochemistry, Max Planck Society;

/persons/resource/persons294067

Madden, Katherine
Mann, Matthias / Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Max Planck Society;

/persons/resource/persons294069

Rodriguez, Edwin
Mann, Matthias / Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Max Planck Society;

/persons/resource/persons291638

Nordmann, Thierry M.
Mann, Matthias / Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Max Planck Society;

/persons/resource/persons78356

Mann, Matthias
Mann, Matthias / Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Max Planck Society;

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s41592-023-02007-6.pdf
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引用

Rosenberger, F. A., Thielert, M., Strauss, M. T., Schweizer, L., Ammar, C., Mädler, S. C., Metousis, A., Skowronek, P., Wahle, M., Madden, K., Gote-Schniering, J., Semenova, A., Schiller, H. B., Rodriguez, E., Nordmann, T. M., Mund, A., & Mann, M. (2023). Spatial single-cell mass spectrometry defines zonation of the hepatocyte proteome. Nature Methods, 20(10), 1530-1536. doi:10.1038/s41592-023-02007-6.

引用: https://hdl.handle.net/21.11116/0000-000E-00DF-7

要旨

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.