Unbiased spatial proteomics with single-cell resolution in tissues

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Unbiased spatial proteomics with single-cell resolution in tissues. / Mund, Andreas; Brunner, Andreas-David; Mann, Matthias.

In: Molecular Cell, Vol. 82, No. 12, 2022, p. 2335-2349.

Research output: Contribution to journal › Review › Research › peer-review

Harvard

Mund, A, Brunner, A-D & Mann, M 2022, 'Unbiased spatial proteomics with single-cell resolution in tissues', Molecular Cell, vol. 82, no. 12, pp. 2335-2349. https://doi.org/10.1016/j.molcel.2022.05.022

APA

Mund, A., Brunner, A-D., & Mann, M. (2022). Unbiased spatial proteomics with single-cell resolution in tissues. Molecular Cell, 82(12), 2335-2349. https://doi.org/10.1016/j.molcel.2022.05.022

Vancouver

Mund A, Brunner A-D, Mann M. Unbiased spatial proteomics with single-cell resolution in tissues. Molecular Cell. 2022;82(12):2335-2349. https://doi.org/10.1016/j.molcel.2022.05.022

Author

Mund, Andreas ; Brunner, Andreas-David ; Mann, Matthias. / Unbiased spatial proteomics with single-cell resolution in tissues. In: Molecular Cell. 2022 ; Vol. 82, No. 12. pp. 2335-2349.

Bibtex

@article{aecf5696075f4e7db074f57194e2a561,
title = "Unbiased spatial proteomics with single-cell resolution in tissues",
abstract = "Mass spectrometry (MS)-based proteomics has become a powerful technology to quantify the entire complement of proteins in cells or tissues. Here, we review challenges and recent advances in the LC-MS-based analysis of minute protein amounts, down to the level of single cells. Application of this technology revealed that single-cell transcriptomes are dominated by stochastic noise due to the very low number of transcripts per cell, whereas the single-cell proteome appears to be complete. The spatial organization of cells in tissues can be studied by emerging technologies, including multiplexed imaging and spatial transcriptomics, which can now be combined with ultra-sensitive proteomics. Combined with high-content imaging, artificial intelligence and single-cell laser microdissection, MS-based proteomics provides an unbiased molecular readout close to the functional level. Potential applications range from basic biological questions to precision medicine.",
keywords = "Artificial Intelligence, Mass Spectrometry/methods, Proteome/metabolism, Proteomics/methods",
author = "Andreas Mund and Andreas-David Brunner and Matthias Mann",
note = "Copyright {\textcopyright} 2022 Elsevier Inc. All rights reserved.",
year = "2022",
doi = "10.1016/j.molcel.2022.05.022",
language = "English",
volume = "82",
pages = "2335--2349",
journal = "Molecular Cell",
issn = "1097-2765",
publisher = "Cell Press",
number = "12",

}

RIS

TY - JOUR

T1 - Unbiased spatial proteomics with single-cell resolution in tissues

AU - Mund, Andreas

AU - Brunner, Andreas-David

AU - Mann, Matthias

N1 - Copyright © 2022 Elsevier Inc. All rights reserved.

PY - 2022

Y1 - 2022

N2 - Mass spectrometry (MS)-based proteomics has become a powerful technology to quantify the entire complement of proteins in cells or tissues. Here, we review challenges and recent advances in the LC-MS-based analysis of minute protein amounts, down to the level of single cells. Application of this technology revealed that single-cell transcriptomes are dominated by stochastic noise due to the very low number of transcripts per cell, whereas the single-cell proteome appears to be complete. The spatial organization of cells in tissues can be studied by emerging technologies, including multiplexed imaging and spatial transcriptomics, which can now be combined with ultra-sensitive proteomics. Combined with high-content imaging, artificial intelligence and single-cell laser microdissection, MS-based proteomics provides an unbiased molecular readout close to the functional level. Potential applications range from basic biological questions to precision medicine.

AB - Mass spectrometry (MS)-based proteomics has become a powerful technology to quantify the entire complement of proteins in cells or tissues. Here, we review challenges and recent advances in the LC-MS-based analysis of minute protein amounts, down to the level of single cells. Application of this technology revealed that single-cell transcriptomes are dominated by stochastic noise due to the very low number of transcripts per cell, whereas the single-cell proteome appears to be complete. The spatial organization of cells in tissues can be studied by emerging technologies, including multiplexed imaging and spatial transcriptomics, which can now be combined with ultra-sensitive proteomics. Combined with high-content imaging, artificial intelligence and single-cell laser microdissection, MS-based proteomics provides an unbiased molecular readout close to the functional level. Potential applications range from basic biological questions to precision medicine.

KW - Artificial Intelligence

KW - Mass Spectrometry/methods

KW - Proteome/metabolism

KW - Proteomics/methods

U2 - 10.1016/j.molcel.2022.05.022

DO - 10.1016/j.molcel.2022.05.022

M3 - Review

C2 - 35714588

VL - 82

SP - 2335

EP - 2349

JO - Molecular Cell

JF - Molecular Cell

SN - 1097-2765

IS - 12

ER -

ID: 311126004