Spatial single-cell mass spectrometry defines zonation of the hepatocyte proteome
Open accessAbstract
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.
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Compartmentalization, cooperation, and communication: The 3Cs of Hepatocyte zonation
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Proteome-Scale Tissue Mapping Using Mass Spectrometry Based on Label-Free and Multiplexed Workflows
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SPARCS, a platform for genome-scale CRISPR screening for spatial cellular phenotypesPreprint
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Single‐Cell and Spatial Omics: Methods and Applications
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iDIA-QC: AI-empowered data-independent acquisition mass spectrometry-based quality control
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Proteomics sets up single-cell and single-molecule solutions
Marx, V. (2023) · Nature Methods · doi:10.1038/s41592-023-01781-7 · Cited by 13 · see citing works
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Are We There Yet? Assessing the Readiness of Single-Cell Proteomics to Answer Biological Hypotheses
Nitz, A. A., Chavez, J. H. G., Eliason, Z. G., & Payne, S. (2024) · Journal of Proteome Research · doi:10.1021/acs.jproteome.4c00091 · Cited by 12 · see citing works
Proteomic Insight Into Alzheimer's Disease Pathogenesis Pathways
Ryu, T., Kim, K., Asiimwe, N., & Na, C. H. (2025) · PROTEOMICS · doi:10.1002/pmic.202400298 · Cited by 12 · see citing works
Robust collection and processing for label-free single voxel proteomics
Kitata, R. B., Veličković, M., Xu, Z., Zhao, R., Scholten, D., Chu, R., Orton, D., Chrisler, W., Zhang, T., Mathews, J. A., et al. (2025) · Nature Communications · doi:10.1038/s41467-024-54643-x · Cited by 12 · see citing works
Mass Spectrometry-based Solutions for Single-cell Proteomics
Li, S., Liu, S., Liu, S., & Ren, Y. (2025) · Genomics Proteomics & Bioinformatics · doi:10.1093/gpbjnl/qzaf012 · Cited by 12 · see citing works
A critical evaluation of ultrasensitive single-cell proteomics strategies
Nalehua, M. R., & Zaia, J. (2024) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-024-05171-6 · Cited by 10 · see citing works
Making space for spatial biology in the clinic
(2025) · Nature Biotechnology · doi:10.1038/s41587-024-02543-2 · Cited by 12 · see citing works
Guedes, J., Woldmar, N., Szasz, A. M., Wieslander, E., Pawłowski, K., Horvatovich, P., Malm, J., Szadai, L., Németh, I. B., Marko-Varga, G., et al. (2025) · Journal of Proteomics · doi:10.1016/j.jprot.2025.105493 · Cited by 11 · see citing works
Unveiling the power of proteomics in advancing tropical animal health and production
Adnane, M., de Almeida, A. M., & Chapwanya, A. (2024) · Tropical Animal Health and Production · doi:10.1007/s11250-024-04037-4 · Cited by 13 · see citing works
Review and Practical Guide for Getting Started With Single‐Cell Proteomics
Lin, H. L., Webber, K. G. I., Nwosu, A. J., & Kelly, R. (2024) · PROTEOMICS · doi:10.1002/pmic.202400021 · Cited by 11 · see citing works
Entering the era of deep single-cell proteomics
Sinn, L. R., & Demichev, V. (2025) · Nature Methods · doi:10.1038/s41592-025-02620-7 · Cited by 11 · see citing works
Hepatocyte dedifferentiation in 2D culture reveals extensive transcriptomic and proteomic rewiring
Dall, M., Stocks, B., Cervone, D. T., Deshmukh, A. S., & Treebak, J. T. (2025) · Hepatology Communications · doi:10.1097/hc9.0000000000000795 · Cited by 10 · see citing works
Getting in the zone: Metabolite transport across liver zones
van de Graaf, S. V. D., Paulusma, C., & Panhuis, W. I. H. (2024) · Acta Physiologica · doi:10.1111/apha.14239 · Cited by 11 · see citing works
Application of machine learning for mass spectrometry-based multi-omics in thyroid diseases
Che, Y., Zhao, M., Gao, Y., Zhang, Z., & Zhang, X. (2024) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2024.1483326 · Cited by 10 · see citing works
Mass Spectrometry‐Based Proteomics for Next‐Generation Precision Oncology
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Seedat, F., Holden, K. L., Davis, S. A., Fischer, R., Bancroft, J. M., Drydale, E., Kandžija, N., Todd, J. A., Vatish, M., & Stefana, M. I. (2025) · Nature Communications · doi:10.1038/s41467-025-61852-5 · Cited by 14 · see citing works
nanoPhos enables ultra-sensitive and cell-type resolved spatial phosphoproteomicsPreprint
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Advances and Applications of Spatial Proteomics: From Organellar Maps to Clinical Translation
Bernardini, C., Däther, M., & Traube, F. R. (2026) · ChemBioChem · doi:10.1002/cbic.202500616 · Cited by 9 · see citing works
Greguš, M., Ivanov, A. R., & Wilson, S. R. (2023) · Journal of Separation Science · doi:10.1002/jssc.202300440 · Cited by 9 · see citing works
Mapping the Multiscale Proteomic Organization of Cellular and Disease Phenotypes
Cesnik, A. J., Schaffer, L. V., Gaur, I., Jain, M., Ideker, T., & Lundberg, E. K. (2024) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-102423-113534 · Cited by 9 · see citing works
Single-cell mass spectrometry imaging: emerging advancements and future insights
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Deciphering metabolic crosstalk in context: lessons from inflammatory diseases
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Pang, M., Jones, J. J., Wang, T., Quan, B., Kubat, N. J., Qiu, Y., Roukes, M. L., & Chou, T. (2024) · Journal of Proteome Research · doi:10.1021/acs.jproteome.4c00062 · Cited by 8 · see citing works
Spatial Proteomics of Single Hepatocytes with Multiplexed Data-Independent Acquisition (mDIA)
Thielert, M., Weiss, C. A. M., Mann, M., & Rosenberger, F. A. (2024) · Methods in molecular biology · doi:10.1007/978-1-0716-3934-4_9 · Cited by 7 · see citing works
StageTip: a little giant unveiling the potential of mass spectrometry-based proteomics
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An Ultrasensitive Spatial Tissue Proteomics Workflow Exceeding 100 Proteomes Per Day
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New Insights on Hepatitis B Virus Viral Transcription in Single Hepatocytes
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Cellular variability as a driver for bioprocess innovation and optimization
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High-Sensitive Spatial Proteomics for Pancreatic Cancer Progression Analysis
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Expansion omics: from expansion microscopy to spatial omics
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- Authors
- 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.
- Affiliations
- Max Planck Institute of Biochemistry (DE)
- University of Copenhagen (DK)
- Helmholtz Munich (DE)
- Corresponding author
- Mann, Matthias
- ORCID
- Venue
- Nature Methods
- Published
- 2023-10
- Volume
- 20
- Issue
- 10
- Pages
- 1530-1536
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC10555842
- OpenAlex
- W4387266998
- Cited by
- 176
- References
- 36
- Citations by year
- 2026: 49 · 2025: 62 · 2024: 48 · 2023: 17
- FWCI
- 16.23
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature Methods (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by)
- PuSH - Publication Server of Helmholtz Zentrum München (submittedVersion, cc-by)
- Funders
- Fund for Astrophysical Research
- European Respiratory Society (847462)
- Chan Zuckerberg Initiative (CZF2019-002448)
- European Commission (H2020, 825694, 874839, 847462)
- Boehringer Ingelheim Fonds
- Max-Planck-Gesellschaft
- Novo Nordisk (NNF14CC0001, NNF15CC0001)
- Novo Nordisk Fonden (NNF 14CC0001, NNF15CC0001)
- Horizon 2020 Framework Programme (874839, 825694, 847462)
- Topics
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Physical Sciences › Chemistry › Spectroscopy › Mass Spectrometry Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- spatial proteomics, single-cell proteomics, hepatocytes, liver zonation, central vein, laser microdissection, high-content imaging, machine learning
- MeSH terms
- Animals, Mass Spectrometry, Proteome, Proteomics, Mice, Laser Capture Microdissection
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