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

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

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.

In: Nature Methods, Vol. 20, No. 10, 2023, p. 1530-1536.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Rosenberger, FA, Thielert, M, Strauss, MT, Schweizer, L, Ammar, C, Mädler, SC, Metousis, A, Skowronek, P, Wahle, M, Madden, K, Gote-Schniering, J, Semenova, A, Schiller, HB, Rodriguez, E, Nordmann, TM, Mund, A & Mann, M 2023, 'Spatial single-cell mass spectrometry defines zonation of the hepatocyte proteome', Nature Methods, vol. 20, no. 10, pp. 1530-1536. https://doi.org/10.1038/s41592-023-02007-6

APA

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. https://doi.org/10.1038/s41592-023-02007-6

Vancouver

Rosenberger FA, Thielert M, Strauss MT, Schweizer L, Ammar C, Mädler SC et al. Spatial single-cell mass spectrometry defines zonation of the hepatocyte proteome. Nature Methods. 2023;20(10):1530-1536. https://doi.org/10.1038/s41592-023-02007-6

Author

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. / Spatial single-cell mass spectrometry defines zonation of the hepatocyte proteome. In: Nature Methods. 2023 ; Vol. 20, No. 10. pp. 1530-1536.

Bibtex

@article{f0e7e5f539cf4b45a85689d2e46be602,
title = "Spatial single-cell mass spectrometry defines zonation of the hepatocyte proteome",
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.",
author = "Rosenberger, {Florian A.} and Marvin Thielert and Strauss, {Maximilian T.} and Lisa Schweizer and Constantin Ammar and M{\"a}dler, {Sophia C.} and Andreas Metousis and Patricia Skowronek and Maria Wahle and Katherine Madden and Janine Gote-Schniering and Anna Semenova and Schiller, {Herbert B.} and Edwin Rodriguez and Nordmann, {Thierry M.} and Andreas Mund and Matthias Mann",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s).",
year = "2023",
doi = "10.1038/s41592-023-02007-6",
language = "English",
volume = "20",
pages = "1530--1536",
journal = "Nature Methods",
issn = "1548-7091",
publisher = "nature publishing group",
number = "10",

}

RIS

TY - JOUR

T1 - Spatial single-cell mass spectrometry defines zonation of the hepatocyte proteome

AU - Rosenberger, Florian A.

AU - Thielert, Marvin

AU - Strauss, Maximilian T.

AU - Schweizer, Lisa

AU - Ammar, Constantin

AU - Mädler, Sophia C.

AU - Metousis, Andreas

AU - Skowronek, Patricia

AU - Wahle, Maria

AU - Madden, Katherine

AU - Gote-Schniering, Janine

AU - Semenova, Anna

AU - Schiller, Herbert B.

AU - Rodriguez, Edwin

AU - Nordmann, Thierry M.

AU - Mund, Andreas

AU - Mann, Matthias

N1 - Publisher Copyright: © 2023, The Author(s).

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

U2 - 10.1038/s41592-023-02007-6

DO - 10.1038/s41592-023-02007-6

M3 - Journal article

C2 - 37783884

AN - SCOPUS:85173076127

VL - 20

SP - 1530

EP - 1536

JO - Nature Methods

JF - Nature Methods

SN - 1548-7091

IS - 10

ER -

ID: 370119502