SCoPE-MS: mass spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation
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Abstract
Some exciting biological questions require quantifying thousands of proteins in single cells. To achieve this goal, we develop Single Cell ProtEomics by Mass Spectrometry (SCoPE-MS) and validate its ability to identify distinct human cancer cell types based on their proteomes. We use SCoPE-MS to quantify over a thousand proteins in differentiating mouse embryonic stem cells. The single-cell proteomes enable us to deconstruct cell populations and infer protein abundance relationships. Comparison between single-cell proteomes and transcriptomes indicates coordinated mRNA and protein covariation, yet many genes exhibit functionally concerted and distinct regulatory patterns at the mRNA and the protein level.
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Single-cell proteomics still faces limitations such as lower proteome coverage and higher per-sample cost.
“However, single-cell proteomics still faces limitations such as lower proteome coverage and higher per-sample cost184,185.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [184]
The Background and Discussion support lower proteome coverage: bulk LC-MS/MS quantifies thousands of proteins, whereas this single-cell implementation quantified mostly abundant proteins. The Discussion reports a cost of $15–30 per cell, but gives no comparative per-sample cost, so the claim that it is higher is not established by this paper. (LLM, conf=0.84)
Experimental — accuracy not yet measuredSCoPE-MS and SCoPE2 use mass spectrometry and have no available integration method with other omics.
“SCoPE-MS and SCoPE2 are such methods using mass spectrometry, but they have no available integration method with other omics, leaving room for future development72,73.”
From The technological landscape and applications of single-cell multi-omics (2023) · cites this paper as [72]
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Cites
Cox, J., & Mann, M. (2008) · Nature Biotechnology · doi:10.1038/nbt.1511 · Cited by 14,855 · see citing works
Mass spectrometry-based proteomics
Aebersold, R., & Mann, M. (2003) · Nature · doi:10.1038/nature01511 · Cited by 6,955 · see citing works
Eng, J. K., McCormack, A. L., & Yates, J. R. (1994) · Journal of the American Society for Mass Spectrometry · doi:10.1016/1044-0305(94)80016-2 · Cited by 6,484 · see citing works
Andromeda: A Peptide Search Engine Integrated into the MaxQuant Environment
Cox, J., Neuhauser, N., Michalski, A., Scheltema, R. A., Olsen, J. V., & Mann, M. (2011) · Journal of Proteome Research · doi:10.1021/pr101065j · Cited by 5,349 · see citing works
The MaxQuant computational platform for mass spectrometry-based shotgun proteomics
Tyanova, S., Temu, T., & Cox, J. (2016) · Nature Protocols · doi:10.1038/nprot.2016.136 · Cited by 4,691 · see citing works
Ross, P. L., Huang, Y. N., Marchese, J. N., Williamson, B., Parker, K., Hattan, S., Khainovski, N., Pillai, S., Dey, S., Daniels, S., et al. (2004) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m400129-mcp200 · Cited by 4,401 · see citing works
Tumour stem cells and drug resistance
Dean, M., Fojo, T., & Bates, S. (2005) · Nature reviews. Cancer · doi:10.1038/nrc1590 · Cited by 3,668 · see citing works
Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells
Klein, A. M., Mazutis, L., Akartuna, I., Tallapragada, N., Veres, A., Li, V., Peshkin, L., Weitz, D. A., & Kirschner, M. W. (2015) · Cell · doi:10.1016/j.cell.2015.04.044 · Cited by 3,356 · see citing works
Thompson, A., Schäfer, J., Kuhn, K., Kienle, S., Schwarz, J., Schmidt, G., Neumann, T., & Hamon, C. (2003) · Analytical Chemistry · doi:10.1021/ac0262560 · Cited by 2,386 · see citing works
Bendall, S. C., Simonds, E. F., Qiu, P., Amir, E.-A. D., Krutzik, P. O., Finck, R., Bruggner, R. V., Melamed, R., Trejo, A., Ornatsky, O. I., et al. (2011) · Science · doi:10.1126/science.1198704 · Cited by 2,279 · see citing works
Mass-spectrometry-based draft of the human proteome
Wilhelm, M., Schlegl, J., Hahne, H., Gholami, A. M., Lieberenz, M., Savitski, M. M., Ziegler, E., Butzmann, L., Gessulat, S., Marx, H., et al. (2014) · Nature · doi:10.1038/nature13319 · Cited by 1,820 · see citing works
Kulak, N. A., Pichler, G., Paron, I., Nagaraj, N., & Mann, M. (2014) · Nature Methods · doi:10.1038/nmeth.2834 · Cited by 1,719 · see citing works
Bandura, D. R., Baranov, V. I., Ornatsky, O. I., Antonov, A., Kinach, R., Lou, X., Pavlov, S., Vorobiev, S., Dick, J. E., & Tanner, S. D. (2009) · Analytical Chemistry · doi:10.1021/ac901049w · Cited by 1,295 · see citing works
Ultrasensitive proteome analysis using paramagnetic bead technology
Hughes, C. S., Föehr, S., Garfield, D. A., Furlong, E. E. M., Steinmetz, L. M., & Krijgsveld, J. (2014) · Molecular Systems Biology · doi:10.15252/msb.20145625 · Cited by 1,194 · see citing works
BioNumbers—the database of key numbers in molecular and cell biology
Milo, R., Jorgensen, P., Moran, U., Weber, G., & Springer, M. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkp889 · Cited by 1,055 · see citing works
Random matrix approach to cross correlations in financial data
Plerou, V., Gopikrishnan, P., Rosenow, B., Amaral, L. A. N., Guhr, T., & Stanley, E. (2002) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.65.066126 · Cited by 938 · see citing works
Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast
de Godoy, L. M. F., Olsen, J. V., Cox, J., Nielsen, M. L., Hubner, N. C., Fröhlich, F., Walther, T. C., & Mann, M. (2008) · Nature · doi:10.1038/nature07341 · Cited by 873 · see citing works
Michalski, A., Damoc, E., Hauschild, J.-P., Lange, O., Wieghaus, A., Makarov, A., Nagaraj, N., Cox, J., Mann, M., & Horning, S. (2011) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m111.011015 · Cited by 832 · see citing works
Proteogenomics: concepts, applications and computational strategies
Nesvizhskii, A. I. (2014) · Nature Methods · doi:10.1038/nmeth.3144 · Cited by 763 · see citing works
Zubarev, R. A., & Makarov, A. (2013) · Analytical Chemistry · doi:10.1021/ac4001223 · Cited by 662 · see citing works
Extraribosomal functions of ribosomal proteins
Wool, I. G. (1996) · Trends in Biochemical Sciences · doi:10.1016/s0968-0004(96)20011-8 · Cited by 620 · see citing works
Dynamic Proteomics of Individual Cancer Cells in Response to a Drug
Cohen, A., Geva‐Zatorsky, N., Eden, E., Frenkel‐Morgenstern, M., Issaeva, I., Sigal, A., Milo, R., Cohen‐Saidon, C., Liron, Y., Kam, Z., et al. (2008) · Science · doi:10.1126/science.1160165 · Cited by 594 · see citing works
Meier, F., Beck, S., Graßl, N., Lubeck, M., Park, M. A., Raether, O., & Mann, M. (2015) · Journal of Proteome Research · doi:10.1021/acs.jproteome.5b00932 · Cited by 422 · see citing works
Hughes, A. J., Spelke, D. P., Xu, Z., Kang, C., Schaffer, D. V., & Herr, A. E. (2014) · Nature Methods · doi:10.1038/nmeth.2992 · Cited by 415 · see citing works
Sielaff, M., Kuharev, J., Bohn, T., Hahlbrock, J., Bopp, T., Tenzer, S., & Distler, U. (2017) · Journal of Proteome Research · doi:10.1021/acs.jproteome.7b00433 · Cited by 321 · see citing works
Segregation of molecules at cell division reveals native protein localization
Landgraf, D., Okumuş, B., Chien, P., Baker, T. A., & Paulsson, J. (2012) · Nature Methods · doi:10.1038/nmeth.1955 · Cited by 301 · see citing works
Measuring and Managing Ratio Compression for Accurate iTRAQ/TMT Quantification
Savitski, M. M., Mathieson, T., Zinn, N., Sweetman, G. M. A., Doce, C., Becher, I., Pachl, F., Küster, B., & Bantscheff, M. (2013) · Journal of Proteome Research · doi:10.1021/pr400098r · Cited by 278 · see citing works
The ribosome filter hypothesis
Mauro, V. P., & Edelman, G. M. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.192442499 · Cited by 260 · see citing works
Correction: Corrigendum: Global quantification of mammalian gene expression control
Schwanhäußer, B., Busse, D., Li, N., Dittmar, G. A. G., Schuchhardt, J., Wolf, J., Chen, W., & Selbach, M. (2013) · Nature · doi:10.1038/nature11848 · Cited by 249 · see citing works
Simultaneous Multiplexed Measurement of RNA and Proteins in Single Cells
Darmanis, S., Gallant, C. J., Marinescu, V. D., Niklasson, M., Segerman, A., Flamourakis, G., Fredriksson, S., Assarsson, E., Lundberg, M., Nelander, S., et al. (2016) · Cell Reports · doi:10.1016/j.celrep.2015.12.021 · Cited by 219 · see citing works
Post-transcriptional regulation across human tissues
Franks, A., Airoldi, E., & Slavov, N. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005535 · Cited by 219 · see citing works
Self-citationLombard‐Banek, C., Moody, S. A., & Nemes, P. (2016) · Angewandte Chemie International Edition · doi:10.1002/anie.201510411 · Cited by 214 · see citing works
Identification of Maturation-Specific Proteins by Single-Cell Proteomics of Human Oocytes
Virant‐Klun, I., Leicht, S., Hughes, C., & Krijgsveld, J. (2016) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m115.056887 · Cited by 202 · see citing works
Differential Stoichiometry among Core Ribosomal Proteins
Slavov, N., Semrau, S., Airoldi, E. M., Budnik, B., & van Oudenaarden, A. (2015) · Cell Reports · doi:10.1016/j.celrep.2015.09.056 · Cited by 188 · see citing works
Self-citationWhat’s Luck Got to Do with It: Single Cells, Multiple Fates, and Biological Nondeterminism
Symmons, O., & Raj, A. (2016) · Molecular Cell · doi:10.1016/j.molcel.2016.05.023 · Cited by 180 · see citing works
Li, S., Plouffe, B. D., Belov, A. M., Ray, S., Wang, X., Murthy, S. K., Karger, B. L., & Ivanov, A. R. (2015) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m114.045724 · Cited by 147 · see citing works
Computational Methods for Understanding Mass Spectrometry–Based Shotgun Proteomics Data
Sinitcyn, P. G., Rudolph, J. D., & Cox, J. (2018) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-080917-013516 · Cited by 138 · see citing works
Thompson, A., Schäfer, J., Kuhn, K., et al. (2003) · Analytical Chemistry · doi:10.1021/ac030267r · Cited by 132 · see citing works
Transformative Opportunities for Single-Cell Proteomics
Specht, H., & Slavov, N. (2018) · Journal of Proteome Research · doi:10.1021/acs.jproteome.8b00257 · Cited by 127 · see citing works
Self-citationMarcon, E., Jain, H., Bhattacharya, A., Guo, H., Phanse, S., Pu, S., Byram, G., Collins, B. C., Dowdell, E., Fenner, M., et al. (2015) · Nature Methods · doi:10.1038/nmeth.3472 · Cited by 127 · see citing works
Proteogenomic Database Construction Driven from Large Scale RNA-seq Data
Woo, S., Cha, S. W., Merrihew, G. E., He, Y., Castellana, N. E., Guest, C. C., MacCoss, M. J., & Bafna, V. (2013) · Journal of Proteome Research · doi:10.1021/pr400294c · Cited by 121 · see citing works
Constant Growth Rate Can Be Supported by Decreasing Energy Flux and Increasing Aerobic Glycolysis
Slavov, N., Budnik, B., Schwab, D. J., Airoldi, E. M., & van Oudenaarden, A. (2014) · Cell Reports · doi:10.1016/j.celrep.2014.03.057 · Cited by 113 · see citing works
Self-citationSilverman, S. J., Petti, A. A., Slavov, N., Parsons, L. R., Briehof, R., Thiberge, S. Y., Zenklusen, D., Gandhi, S. J., Larson, D. R., Singer, R. H., et al. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1002422107 · Cited by 96 · see citing works
Self-citationRibosome Stoichiometry: From Form to Function
Emmott, E., Jovanović, M., & Slavov, N. (2018) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2018.10.009 · Cited by 95 · see citing works
Self-citationSingle cell protein analysis for systems biology
Levy, E., & Slavov, N. (2018) · Essays in Biochemistry · doi:10.1042/ebc20180014 · Cited by 89 · see citing works
Self-citationAutomated sample preparation for high-throughput single-cell proteomicsPreprint
Specht, H., Harmange, G., Perlman, D. H., Emmott, E., Niziolek, Z., Budnik, B., & Slavov, N. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/399774 · Cited by 77 · see citing works
Self-citationDART-ID increases single-cell proteome coverage
Chen, A. T., Franks, A. M., & Slavov, N. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007082 · Cited by 71 · see citing works
Self-citationCorrelation signature of the macroscopic states of the gene regulatory network in cancer
Slavov, N., & Dawson, K. A. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0810803106 · Cited by 49 · see citing works
Self-citationStudying Lineage Decision-Making In Vitro: Emerging Concepts and Novel Tools
Semrau, S., & van Oudenaarden, A. (2015) · Annual Review of Cell and Developmental Biology · doi:10.1146/annurev-cellbio-100814-125300 · Cited by 35 · see citing works
A High-Efficiency Cellular Extraction System for Biological Proteomics
Dhabaria, A., Cifani, P., Reed, C., Steen, H., & Kentsis, A. (2015) · Journal of Proteome Research · doi:10.1021/acs.jproteome.5b00547 · Cited by 30 · see citing works
All Ribosomes Are Created Equal. Really?
Preiß, T. (2015) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2015.11.009 · Cited by 21 · see citing works
Dynamic post-transcriptional regulation during embryonic stem cell differentiationPreprint
van den Berg, P. R., Budnik, B., Slavov, N., & Semrau, S. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/123497 · Cited by 16 · see citing works
Self-citationInnovations in Proteomics: The Drive to Single Cells
Yates, J. R. (2018) · Journal of Proteome Research · doi:10.1021/acs.jproteome.8b00484 · Cited by 14 · see citing works
Zlotorynski, E. (2014) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm3833 · Cited by 11 · see citing works
DART-ID increases single-cell proteome coveragePreprint
Chen, A. T., Franks, A. M., & Slavov, N. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/399121 · Cited by 11 · see citing works
Self-citationSingle cell protein analysis for systems biologyPreprint
Levy, E., & Slavov, N. (2018) · doi:10.7287/peerj.preprints.26965v1 · Cited by 6 · see citing works
Self-citationRibosome stoichiometry: from form to function.Preprint
Emmott, E., Jovanović, M., & Slavov, N. (2018) · doi:10.7287/peerj.preprints.26991v1 · Cited by 4 · see citing works
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- [38]Budnik B Levy E Harmange G Slavov N Mass-spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation 2018 PMC6196420 30343672
- [39]Budnik B Levy E Harmange G Slavov N Mass-spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation 2018 PMC6196420 30343672
- [41]Hicks SC Teng M Irizarry RA On the widespread and critical impact of systematic bias and batch effects in single-cell RNA-Seq data bioRxiv 2015 1 025528
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Single-cell technologies in hepatology: new insights into liver biology and disease pathogenesis
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Unbiased spatial proteomics with single-cell resolution in tissues
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- Authors
- Budnik, B., Levy, E., Harmange, G., & Slavov, N.
- Affiliations
- Harvard University (US)
- Northeastern University (US)
- Venue
- Genome Biology
- Published
- 2018-10-22
- Volume
- 19
- Issue
- 1
- Pages
- 161-161
- ISSN
- 1465-6906 (ISSN-L)
- 1474-760X
- PMC
- PMC6196420
- OpenAlex
- W2951821937
- Cited by
- 832
- References
- 57
- Citations by year
- 2026: 87 · 2025: 95 · 2024: 122 · 2023: 124 · 2022: 110 · 2021: 125 · 2020: 88 · 2019: 53 · 2018: 22 · 2017: 5 · 2015: 1
- FWCI
- 31.09
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Genome biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-sa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University)
- Funders
- National Institute of General Medical Sciences (1DP2GM123497-01)
- 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
- single-cell proteomics, mass spectrometry, cell differentiation, mouse embryonic stem cells, transcriptome comparison, protein abundance
- MeSH terms
- Mouse Embryonic Stem Cells, Animals, Cell Differentiation, Cell Line, Humans, Mammals, RNA, Messenger, Mass Spectrometry, Proteome, Principal Component Analysis, Proteomics, Mice, Single-Cell Analysis
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