Multiplexed single-cell proteomics using SCoPE2

Andrew Leduc, Nikolai Slavov, Aleksandra A. Petelski, Edward Emmott, R. Gray Huffman, Harrison Specht, David H. Perlman

Published: 2021-10-28 DOI: 10.1038/s41596-021-00616-z

Mass spectrometry

Single-cell proteomics

SCoPE2

Isobaric labeling

Multiplexed analysis

AI 解读

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Data integration（数据集成）

Heterogeneity（异质性）

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References

Many biological systems are composed of diverse single cells. This diversity necessitates functional and molecular single-cell analysis. Single-cell protein analysis has long relied on affinity reagents, but emerging mass-spectrometry methods (either labe
Introduction
Materials
Procedure
Troubleshooting
Timings
- Preparatory work
- Single-cell work
Anticipated results
- References
- References
References
ACKNOWLEDGMENTS
ACKNOWLEDGMENTS

Huffman, G., Chen, A. T., Specht, H. & Slavov, N. DO-MS: data-driven optimization of mass spectrometry methods. J. Proteome Res. http://doi.org/10.1021/acs.jproteome.9b00039 (2019).
Symmons, O. & Raj, A. What’s luck got to do with it: single cells, multiple fates, and biological nondeterminism. Mol. Cell 62, 788–802 (2016).
Paul, I., White, C., Turcinovic, I. & Emili, A. Imaging the future: the emerging era of single-cell spatial proteomics. FEBS J. 2020).
Levy, E. & Slavov, N. Single cell protein analysis for systems biology. Essays Biochem. http://doi.org/10.1042/EBC20180014 (2018).
Regev, A. et al. Science forum: the human cell atlas. eLife 6, e27041 (2017).
Ziegenhain, C. et al. Comparative analysis of single-cell RNA sequencing methods. Mol. Cell 65, 631–643 (2017).
Engblom, C. et al. Osteoblasts remotely supply lung tumors with cancer-promoting SiglecFhigh neutrophils. Science 358 2017).
Shaffer, S. M. et al. Rare cell variability and drug-induced reprogramming as a mode of cancer drug resistance. Nature 546, 431–435 (2017).
Franks, A., Airoldi, E. & Slavov, N. Post-transcriptional regulation across human tissues. PLoS Comput. Biol. 13, e1005535 (2017).
Slavov, N. Unpicking the proteome in single cells. Science 367, 512–513 (2020).
Washburn, M. P., Wolters, D. & Yates, J. R. Large-scale analysis of the yeast proteome by multidimensional protein identification technology. Nat. Biotechnol. 19, 242–247 (2001).
Altelaar, A. F. M., Munoz, J. & Heck, A. J. R. Next-generation proteomics: towards an integrative view of proteome dynamics. Nat. Rev. Genet. 14, 35–48 (2013).
Cravatt, B. F., Simon, G. M. & Yates Iii, J. R. The biological impact of mass-spectrometry-based proteomics. Nature 450, 991 (2007).
Boersema, P. J., Raijmakers, R., Lemeer, S., Mohammed, S. & Heck, A. J. Multiplex peptide stable isotope dimethyl labeling for quantitative proteomics. Nat. Protoc. 4, 484–494 (2009).
Muntel, J. et al. Comparison of protein quantification in a complex background by DIA and TMT workflows with fixed instrument time. J. Proteome Res. 18, 1340–1351 (2019). PMID: 30726097.
Slavov, N. Single-cell protein analysis by mass spectrometry. Curr. Opin. Chem. Biol. 60, 1–9 (2020).
Kelly, R. T. Single-cell proteomics: progress and prospects. Mol. Cell. Proteomics 19, 1739–1748 (2020).
Budnik, B., Levy, E., Harmange, G. & Slavov, N. SCoPE-MS: mass-spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation. Genome Biology 19, 161 (2018).
Specht, H. et al. Single-cell proteomic and transcriptomic analysis of macrophage heterogeneity using SCoPE2. Genome Biol. http://doi.org/10.1186/s13059-021-02267-5 (2021).
Specht, H. & Slavov, N. Optimizing accuracy and depth of protein quantification in experiments using isobaric carriers. J. Proteome Res. 20, a8802021–887 (2021).
Cheung, T. K. et al. Defining the carrier proteome limit for single-cell proteomics. Nat. Methods 18, 76–83 (2021).
Specht, H. et al. Automated sample preparation for high-throughput single-cell proteomics. Preprint at bioRxiv http://doi.org/10.1101/399774 (2018).
Marx, V. A dream of single-cell proteomics. Nat. Methods 16, 809–812 (2019).
Leduc, A., Huffman, R. G. & Slavov, N. Droplet sample preparation for single-cell proteomics applied to the cell cycle. Preprint at bioRxiv http://doi.org/10.1101/2021.04.24.441211 (2021).
Chen, A. T., Franks, A. & Slavov, N. DART-ID increases single-cell proteome coverage. PLOS Comput. Biol. 15, 1–30 (2019).
Bendall, S. C. et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332, 687–696 (2011).
Albayrak, C. et al. Digital quantification of proteins and mRNA in single mammalian cells. Mol. Cell 61, 914–924 (2016).
Darmanis, S. et al. Simultaneous multiplexed measurement of RNA and proteins in single cells. Cell Rep. 14, 380–389 (2016).
Hughes, A. J. et al. Single-cell western blotting. Nat. Methods 11, 749–755 (2014).
Specht, H., Emmott, E., Perlman, D. H., Koller, A. & Slavov, N. High-throughput single-cell proteomics quantifies the emergence of macrophage heterogeneity. Preprint at bioRxiv. http://doi.org/10.1101/665307 (2019).
Virant-Klun, I., Leicht, S., Hughes, C. & Krijgsveld, J. Identification of maturation-specific proteins by single-cell proteomics of human oocytes. Mol. Cell. Proteomics 15, 2616–2627 (2016).
Lombard-Banek, C., Moody, S. A. & Nemes, P. Single-cell mass spectrometry for discovery proteomics: quantifying translational cell heterogeneity in the 16-cell frog (Xenopus) embryo. Angew. Chem. Int. Ed. 55, 2454–2458 (2016).
Cong, Y. et al. Improved single-cell proteome coverage using narrow-bore packed nanoLC columns and ultrasensitive mass spectrometry. Anal. Che. 92, 2665–2671 (2020).
Cong, Y. et al. Ultrasensitive single-cell proteomics workflow identifies >1000 protein groups per mammalian cell. Chem. Sci. 12, 1001–1006 (2020).
Brunner, A.-D. et al. Ultra-high sensitivity mass spectrometry quantifies single-cell proteome changes upon perturbation. Preprint at bioRxiv http://doi.org/10.1101/2020.12.22.423933 (2020).
Zhu, Y. et al. Nanodroplet processing platform for deep and quantitative proteome profiling of 10–100 mammalian cells. Nat. Commun. 9, 882 (2018).
Dou, M. et al. High-throughput single cell proteomics enabled by multiplex isobaric labeling in a nanodroplet sample preparation platform. Anal. Chem. 91, 13119–13127 (2019).
Li, Z.-Y. et al. Nanoliter-scale oil-air-droplet chip-based single cell proteomic analysis. Anal. Chem. 90, 5430–5438 (2018).
Liang, Y. et al. Fully automated sample processing and analysis workflow for low-input proteome profiling. Anal. Chem. 93, 1658–1666 (2021).
Lafzi, A., Moutinho, C., Picelli, S. & Heyn, H. Tutorial: guidelines for the experimental design of single-cell RNA sequencing studies. Nat. Protoc. 13, 2742–2757 (2018).
Barkas, N. et al. Joint analysis of heterogeneous single-cell RNA-seq dataset collections. Nat. Methods 16, 695–698 (2019).
Marx, V. A dream of single-cell proteomics. Nat. Methods. http://doi.org/10.1038/s41592-019-0540-6 (2019).
Grün, D. & van Oudenaarden, A. Design and analysis of single-cell sequencing experi- ments. Cell 163, 799–810 (2015).
Meier, F. et al. Online parallel accumulation-serial fragmentation (PASEF) with a novel trapped ion mobility mass spectrometer. Mol. Cell. Proteomics 17, 2534–2545 (2018).
Specht, H. & Slavov, N. Transformative opportunities for single-cell proteomics. J. Proteome Res. 17, 2563–2916 (2018).
Slavov, N. Driving single cell proteomics forward with innovation. J. Proteome Res. http://doi.org/10.1021/acs.jproteome.1c00639 (2021).
Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized ppb- range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).
Tyanova, S., Temu, T. & Cox, J. The MaxQuant computational platform for mass spectrometry-based shotgun proteomics. Nat. Protoc. 11, 2301 (2016).
Yu, S.-H., Kyriakidou, P. & Cox, J. Isobaric matching between runs and novel PSM-level normalization in MaxQuant strongly improve reporter ion-based quantification. J. Proteome Res. 19, 3945–3954 (2020).
Fondrie, W. E. & Noble, W.S. mokapot: fast and flexible semisupervised learning for peptide detection. J. Proteome Res. 20, 1966–1971 (2021).
Milo, R., Jorgensen, P., Moran, U., Weber, G. & Springer, M. BioNumbers—the database of key numbers in molecular and cell biology. Nucleic Acids Res. 38, D750–D753 (2010).
Specht, H. et al. Single-cell proteomic and transcriptomic analysis of macrophage heterogeneity. GitHub github.com/SlavovLab/SCoPE2 (2019).
Specht, H. et al. Single-cell proteomic and transcriptomic analysis of macrophage heterogeneity using SCoPE2. Zenodo http://doi.org/10.5281/zenodo.4339954 (2020).
Vanderaa, C. & Gatto, L. Replication of single-cell proteomics data reveals important computational challenges. Preprint at bioRxiv. http://doi.org/10.1101/2021.04.12.439408 (2021).
Vanderaa, C. & Gatto, L. Mass spectrometry-based single-cell proteomics data analysis. Bioconductor http://www.bioconductor.org/packages/release/bioc/html/scp.html (2020).
SCP2019 Workshop on single-cell proteomics Aug. 2019. http://workshop2019.single-cell.net

Multiplexed single-cell proteomics using SCoPE2

Extended

Extended Data Fig. 1 The ABIRD may suppress contaminant ions and enhance peptide identification.

Supplementary information

Supplementary Data 1

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