Single-cell proteomics takes centre stage
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Single-cell mass spectrometry, including single-cell proteomics and single-cell metabolomics, provides protein and metabolite identification on a large scale.
“One method that has yet to be used to study senescence is single-cell mass spectrometry, including single-cell proteomics and single-cell metabolomics which provide protein (including post-translational modification) and metabolite identification on a large scale [37] and could have enormous implications for studying senescent cells.”
From The heterogeneity of cellular senescence: insights at the single-cell level (2023) · cites this paper as [37]
Cites
Deep proteome and transcriptome mapping of a human cancer cell line
Nagaraj, N., Wisniewski, J. R., Geiger, T., Cox, J., Kircher, M., Kelso, J., Pääbo, S., & Mann, M. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.81 · Cited by 979 · see citing works
Budnik, B., Levy, E., Harmange, G., & Slavov, N. (2018) · Genome Biology · doi:10.1186/s13059-018-1547-5 · Cited by 832 · see citing works
Zhu, Y., Piehowski, P. D., Zhao, R., Chen, J., Shen, Y., Moore, R. J., Shukla, A. K., Petyuk, V. A., Campbell-Thompson, M., Mathews, C. E., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03367-w · Cited by 587 · see citing works
Ultra‐high sensitivity mass spectrometry quantifies single‐cell proteome changes upon perturbation
Brunner, A., Thielert, M., Vasilopoulou, C., Ammar, C., Coscia, F., Mund, A., Hoerning, O. B., Bache, N., Apalategui, A., Lubeck, M., et al. (2022) · Molecular Systems Biology · doi:10.15252/msb.202110798 · Cited by 544 · see citing works
Single-cell proteomic and transcriptomic analysis of macrophage heterogeneity using SCoPE2
Specht, H., Emmott, E., Petelski, A. A., Huffman, R. G., Perlman, D. H., Serra, M., Kharchenko, P., Koller, A., & Slavov, N. (2021) · Genome Biology · doi:10.1186/s13059-021-02267-5 · Cited by 493 · see citing works
TMTpro-18plex: The Expanded and Complete Set of TMTpro Reagents for Sample Multiplexing
Li, J., Cai, Z., Bomgarden, R. D., Pike, I., Kuhn, K., Rogers, J. C., Roberts, T. M., Gygi, S. P., & Paulo, J. A. (2021) · Journal of Proteome Research · doi:10.1021/acs.jproteome.1c00168 · Cited by 274 · see citing works
Piehowski, P., Zhu, Y., Bramer, L., Stratton, K. G., Zhao, R., Orton, D., Moore, R. J., Yuan, J., Mitchell, H. D., Gao, Y., et al. (2020) · Nature Communications · doi:10.1038/s41467-019-13858-z · Cited by 264 · see citing works
Ultrasensitive single-cell proteomics workflow identifies >1000 protein groups per mammalian cell
Cong, Y., Motamedchaboki, K., Misal, S. A., Liang, Y., Guise, A. J., Truong, T., Huguet, R., Plowey, E. D., Zhu, Y., López‐Ferrer, D., et al. (2020) · Chemical Science · doi:10.1039/d0sc03636f · Cited by 229 · see citing works
Woo, J. J., Williams, S., Markillie, L. M., Feng, S., Tsai, C., Aguilera-Vazquez, V., Sontag, R. L., Moore, R. J., Hu, D., Mehta, H. S., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26514-2 · Cited by 198 · see citing works
Exploring functional protein covariation across single cells using nPOP
Leduc, A., Huffman, R. G., Cantlon-Bruce, J., Khan, S., & Slavov, N. (2022) · Genome biology · doi:10.1186/s13059-022-02817-5 · Cited by 161 · see citing works
Ultra-high sensitivity mass spectrometry quantifies single-cell proteome changes upon perturbationPreprint
Brunner, A., Thielert, M., Vasilopoulou, C. G., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.22.423933 · Cited by 123 · see citing works
Ctortecka, C., Hartlmayr, D., Seth, A., Mendjan, S., Tourniaire, G., & Mechtler, K. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.04.14.439828 · Cited by 82 · see citing works
Guo, G., Papanicolaou, M., Demarais, N. J., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-23461-w · Cited by 65 · see citing works
AI-driven Deep Visual Proteomics defines cell identity and heterogeneityPreprint
Mund, A., Coscia, F., Hollandi, R., Kovács, F., Kriston, A., Brunner, A., Bzorek, M., Naimy, S., Gjerdrum, L. M. R., Dyring‐Andersen, B., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.01.25.427969 · Cited by 46 · see citing works
Exploring functional protein covariation across single cells using nPOPPreprint
Leduc, A., Huffman, R. G., Cantlon-Bruce, J., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.04.24.441211 · Cited by 37 · see citing works
Woo, J. J., Williams, S., Aguilera-Vazquez, V., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.02.17.431689 · Cited by 29 · see citing works
The Human Proteoform Project: A Plan to Define the Human ProteomePreprint
Smith, L. M., Agar, J. N., Chamot‐Rooke, J., Danis, P. O., Ge, Y., Loo, J. A., Paša‐Tolić, L., Tsybin, Y. O., & Kelleher, N. L. (2020) · Preprints.org · doi:10.20944/preprints202010.0368.v1 · Cited by 16 · see citing works
Comparative proteome signatures of trace samples by multiplexed Data-Independent AcquisitionPreprint
Ctortecka, C., Krššáková, G., Stejskal, K., Penninger, J. M., Mendjan, S., Mechtler, K., & Stadlmann, J. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.02.11.430601 · Cited by 7 · see citing works
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CellChat for systematic analysis of cell–cell communication from single-cell transcriptomics
Jin, S., Plikus, M. V., & Nie, Q. (2025) · Nature Protocols · doi:10.1038/s41596-024-01045-4 · Cited by 1,448 · see citing works
The heterogeneity of cellular senescence: insights at the single-cell level
Cohn, R. L., Gasek, N. S., Kuchel, G. A., & Xu, M. (2023) · Trends in Cell Biology · doi:10.1016/j.tcb.2022.04.011 · Cited by 294 · see citing works
- Authors
- Perkel, J. M.
- Corresponding author
- Perkel, Jeffrey M.
- ORCID
- Perkel, Jeffrey M.
- Venue
- Nature
- Published
- 2021-09-20
- Volume
- 597
- Issue
- 7877
- Pages
- 580-582
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W3199418278
- Cited by
- 92
- References
- 18
- Citations by year
- 2026: 3 · 2025: 11 · 2024: 20 · 2023: 23 · 2022: 30 · 2021: 3 · 2020: 1
- FWCI
- 6.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Advanced Biosensing Techniques and Applications
- Keywords
- single-cell proteomics, proteome, single-cell analysis, protein expression
- MeSH terms
- Cell Cycle, Humans, Neurons, Mass Spectrometry, Protein Isoforms, Proteome, Proteomics, Single-Cell Analysis, Transcriptome
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