Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Single-cell analysis targeting the proteome

Abstract

The existence of cellular heterogeneity and its central relevance to biological phenomena provides a strong rationale for a need for analytical methods that enable analysis at the single-cell level. Analysis of the genome and transcriptome is possible at the single-cell level, but the comprehensive interrogation of the proteome with this level of resolution remains challenging. Single-cell protein analysis tools are advancing rapidly, however, and providing insights into collections of proteins with great relevance to cell and disease biology. Here, we review single-cell protein analysis technologies and assess their advantages and limitations. The emerging technologies presented have the potential to reveal new insights into tumour heterogeneity and therapeutic resistance, elucidate mechanisms of immune response and immunotherapy, and accelerate drug discovery.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Classification of single-cell protein analysis methods based on the location of target protein.
Fig. 2: Single-cell analysis of proteins using fluorescent probes.
Fig. 3: Antibody-based microfluidic approaches for single-cell protein analysis.
Fig. 4: Mass-spectrometry approaches for single-cell analysis of proteins.
Fig. 5: Multiplexed analysis of mRNAs and proteins in single cells.

Similar content being viewed by others

References

  1. Kaern, M., Elston, T. C., Blake, W. J. & Collins, J. J. Stochasticity in gene expression: from theories to phenotypes. Nat. Rev. Genet. 6, 451–464 (2005).

    CAS  PubMed  Google Scholar 

  2. Toriello, N. M. et al. Integrated microfluidic bioprocessor for single-cell gene expression analysis. Proc. Natl Acad. Sci. USA 105, 20173–20178 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Altschuler, S. J. & Wu, L. F. Cellular heterogeneity: do differences make a difference? Cell 141, 559–563 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Uhlen, M. et al. Tissue-based map of the human proteome. Science 347, 1260419 (2015).

    PubMed  Google Scholar 

  5. Aebersold, R. et al. How many human proteoforms are there? Nat. Chem. Biol. 14, 206–214 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Cohen, P. The origins of protein phosphorylation. Nat. Cell Biol. 4, E127–E130 (2002).

    CAS  PubMed  Google Scholar 

  7. Gawad, C., Koh, W. & Quake, S. R. Single-cell genome sequencing: current state of the science. Nat. Rev. Genet. 17, 175–188 (2016).

    CAS  PubMed  Google Scholar 

  8. Schwartzman, O. & Tanay, A. Single-cell epigenomics: techniques and emerging applications. Nat. Rev. Genet. 16, 716–726 (2015).

    CAS  PubMed  Google Scholar 

  9. Stegle, O., Teichmann, S. A. & Marioni, J. C. Computational and analytical challenges in single-cell transcriptomics. Nat. Rev. Genet. 16, 133–145 (2015).

    CAS  PubMed  Google Scholar 

  10. Rubakhin, S. S., Romanova, E. V., Nemes, P. & Sweedler, J. V. Profiling metabolites and peptides in single cells. Nat. Methods 8, S20–S29 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Breker, M. & Schuldiner, M. The emergence of proteome-wide technologies: systematic analysis of proteins comes of age. Nat. Rev. Mol. Cell Biol. 15, 453–464 (2014).

    CAS  PubMed  Google Scholar 

  12. Levy, E. & Slavov, N. Single cell protein analysis for systems biology. Essays Biochem. 62, 595–605 (2018).

    PubMed  PubMed Central  Google Scholar 

  13. Ma, S. et al. Cell-type-specific brain methylomes profiled via ultralow-input microfluidics. Nat. Biomed. Eng. 2, 183–194 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Eberwine, J. et al. Analysis of gene expression in single live neurons. Proc. Natl Acad. Sci. USA 89, 3010–3014 (1992).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Tang, F. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–382 (2009).

    CAS  PubMed  Google Scholar 

  16. Munsky, B., Neuert, G. & van Oudenaarden, A. Using gene expression noise to understand gene regulation. Science 336, 183–187 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Smith, L. M. & Kelleher, N. L. Proteoform: a single term describing protein complexity. Nat. Methods 10, 186–187 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Deng, Q., Ramskold, D., Reinius, B. & Sandberg, R. Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells. Science 343, 193–196 (2014).

    CAS  PubMed  Google Scholar 

  19. Chalfie, M., Tu, Y., Euskirchen, G., Ward, W. W. & Prasher, D. C. Green fluorescent protein as a marker for gene expression. Science 263, 802–805 (1994).

    CAS  PubMed  Google Scholar 

  20. Elowitz, M. B., Levine, A. J., Siggia, E. D. & Swain, P. S. Stochastic gene expression in a single cell. Science 297, 1183–1186 (2002).

    CAS  PubMed  Google Scholar 

  21. Spencer, S. L. et al. The proliferation-quiescence decision is controlled by a bifurcation in CDK2 activity at mitotic exit. Cell 155, 369–383 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Spencer, S. L., Gaudet, S., Albeck, J. G., Burke, J. M. & Sorger, P. K. Non-genetic origins of cell-to-cell variability in TRAIL-induced apoptosis. Nature 459, 428–432 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Lahav, G. et al. Dynamics of the p53-Mdm2 feedback loop in individual cells. Nat. Genet. 36, 147–150 (2004).

    CAS  PubMed  Google Scholar 

  24. Purvis, J. E. et al. p53 dynamics control cell fate. Science 336, 1440–1444 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Cranfill, P. J. et al. Quantitative assessment of fluorescent proteins. Nat. Methods 13, 557–562 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Lippincott-Schwartz, J. & Patterson, G. H. Development and use of fluorescent protein markers in living cells. Science 300, 87–91 (2003).

    CAS  PubMed  Google Scholar 

  27. Wiedenmann, J. et al. EosFP, a fluorescent marker protein with UV-inducible green-to-red fluorescence conversion. Proc. Natl Acad. Sci. USA 101, 15905–15910 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Chudakov, D. M., Lukyanov, S. & Lukyanov, K. A. Tracking intracellular protein movements using photoswitchable fluorescent proteins PS-CFP2 and Dendra2. Nat. Protoc. 2, 2024–2032 (2007).

    CAS  PubMed  Google Scholar 

  29. Mainz, E. R., Wang, Q., Lawrence, D. S. & Allbritton, N. L. An integrated chemical cytometry method: shining a light on Akt activity in single cells. Angew. Chem. Int. Ed. 55, 13095–13098 (2016).

    CAS  Google Scholar 

  30. Phillips, R. M., Bair, E., Lawrence, D. S., Sims, C. E. & Allbritton, N. L. Measurement of protein tyrosine phosphatase activity in single cells by capillary electrophoresis. Anal. Chem. 85, 6136–6142 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Turner, A. H. et al. Rational design of a dephosphorylation-resistant reporter enables single-cell measurement of tyrosine kinase activity. ACS Chem. Biol. 11, 355–362 (2016).

    CAS  PubMed  Google Scholar 

  32. Gould, T. J., Verkhusha, V. V. & Hess, S. T. Imaging biological structures with fluorescence photoactivation localization microscopy. Nat. Protoc. 4, 291–308 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Nan, X. et al. Single-molecule superresolution imaging allows quantitative analysis of RAF multimer formation and signaling. Proc. Natl Acad. Sci. USA 110, 18519–18524 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Nan, X. et al. Ras-GTP dimers activate the mitogen-activated protein kinase (MAPK) pathway. Proc. Natl Acad. Sci. USA 112, 7996–8001 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Belyy, V. et al. PhotoGate microscopy to track single molecules in crowded environments. Nat. Commun. 8, 13978 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Hoyer, P. et al. Breaking the diffraction limit of light-sheet fluorescence microscopy by RESOLFT. Proc. Natl Acad. Sci. USA 113, 3442–3446 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Wang, J., Fei, B., Zhan, Y., Geahlen, R. L. & Lu, C. Kinetics of NF-κB nucleocytoplasmic transport probed by single-cell screening without imaging. Lab Chip 10, 2911–2916 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Miyawaki, A., Sawano, A. & Kogure, T. Lighting up cells: labelling proteins with fluorophores. Nat. Cell Biol. 5, S1–S7 (2003).

    Google Scholar 

  39. Miller, M. A. & Weissleder, R. Imaging of anticancer drug action in single cells. Nat. Rev. Cancer 17, 399–414 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Schubert, W. et al. Analyzing proteome topology and function by automated multidimensional fluorescence microscopy. Nat. Biotechnol. 24, 1270–1278 (2006).

    CAS  PubMed  Google Scholar 

  41. Zrazhevskiy, P. & Gao, X. Quantum dot imaging platform for single-cell molecular profiling. Nat. Commun. 4, 1619 (2013).

    PubMed  Google Scholar 

  42. Zrazhevskiy, P., True, L. D. & Gao, X. Multicolor multicycle molecular profiling with quantum dots for single-cell analysis. Nat. Protoc. 8, 1852–1869 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Junkin, M. et al. High-content quantification of single-cell immune dynamics. Cell Rep. 15, 411–422 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Zhang, C. et al. Ultra-multiplexed analysis of single-cell dynamics reveals logic rules in differentiation. Sci. Adv. 5, eaav7959 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Saka, S. K. et al. Immuno-SABER enables highly multiplexed and amplified protein imaging in tissues. Nat. Biotechnol. 37, 1080–1090 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Chattopadhyay, P. K. et al. Quantum dot semiconductor nanocrystals for immunophenotyping by polychromatic flow cytometry. Nat. Med. 12, 972–977 (2006).

    CAS  PubMed  Google Scholar 

  47. Qiu, P. et al. Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nat. Biotechnol. 29, 886–891 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Nguyen, L. V., Vanner, R., Dirks, P. & Eaves, C. J. Cancer stem cells: an evolving concept. Nat. Rev. Cancer 12, 133–143 (2012).

    CAS  PubMed  Google Scholar 

  49. Irish, J. M. et al. B-cell signaling networks reveal a negative prognostic human lymphoma cell subset that emerges during tumor progression. Proc. Natl Acad. Sci. USA 107, 12747–12754 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Irish, J. M. et al. Single cell profiling of potentiated phospho-protein networks in cancer cells. Cell 118, 217–228 (2004).

    CAS  PubMed  Google Scholar 

  51. Sachs, K., Perez, O., Pe’er, D., Lauffenburger, D. A. & Nolan, G. P. Causal protein-signaling networks derived from multiparameter single-cell data. Science 308, 523–529 (2005).

    CAS  PubMed  Google Scholar 

  52. Krutzik, P. O., Crane, J. M., Clutter, M. R. & Nolan, G. P. High-content single-cell drug screening with phosphospecific flow cytometry. Nat. Chem. Biol. 4, 132–142 (2008).

    CAS  PubMed  Google Scholar 

  53. Bendall, S. C. & Nolan, G. P. From single cells to deep phenotypes in cancer. Nat. Biotechnol. 30, 639–647 (2012).

    CAS  PubMed  Google Scholar 

  54. Bendall, S. C., Nolan, G. P., Roederer, M. & Chattopadhyay, P. K. A deep profiler’s guide to cytometry. Trends Immunol. 33, 323–332 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Bandura, D. R. et al. Mass cytometry: technique for real time single cell multitarget immunoassay based on inductively coupled plasma time-of-flight mass spectrometry. Anal. Chem. 81, 6813–6822 (2009).

    CAS  PubMed  Google Scholar 

  56. Angelo, M. et al. Multiplexed ion beam imaging of human breast tumors. Nat. Med. 20, 436–442 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Giesen, C. et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat. Methods 11, 417–422 (2014).

    CAS  PubMed  Google Scholar 

  58. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Porpiglia, E. et al. High-resolution myogenic lineage mapping by single-cell mass cytometry. Nat. Cell Biol. 19, 558–567 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Anchang, B. et al. DRUG-NEM: Optimizing drug combinations using single-cell perturbation response to account for intratumoral heterogeneity. Proc. Natl Acad. Sci. USA 115, E4294–E4303 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Cavrois, M. et al. Mass cytometric analysis of HIV entry, replication, and remodeling in tissue CD4+ T cells. Cell Rep. 20, 984–998 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Wroblewska, A. et al. Protein barcodes enable high-dimensional single-cell CRISPR screens. Cell 175, 1141–1155.e16 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Galli, E. et al. GM-CSF and CXCR4 define a T helper cell signature in multiple sclerosis. Nat. Med. 25, 1290–1300 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Bruggner, R. V., Bodenmiller, B., Dill, D. L., Tibshirani, R. J. & Nolan, G. P. Automated identification of stratifying signatures in cellular subpopulations. Proc. Natl Acad. Sci. USA 111, E2770–E2777 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Hu, Z. et al. MetaCyto: a tool for automated meta-analysis of mass and flow cytometry data. Cell Rep. 24, 1377–1388 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Spitzer, M. H. & Nolan, G. P. Mass cytometry: single cells, many features. Cell 165, 780–791 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Cohen, L. & Walt, D. R. Highly sensitive and multiplexed protein measurements. Chem. Rev. 119, 293–321 (2019).

    CAS  PubMed  Google Scholar 

  68. Brouzes, E. et al. Droplet microfluidic technology for single-cell high-throughput screening. Proc. Natl Acad. Sci. USA 106, 14195–14200 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. Mazutis, L. et al. Single-cell analysis and sorting using droplet-based microfluidics. Nat. Protoc. 8, 870–891 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. Eyer, K. et al. Single-cell deep phenotyping of IgG-secreting cells for high-resolution immune monitoring. Nat. Biotechnol. 35, 977–982 (2017).

    CAS  PubMed  Google Scholar 

  71. Segaliny, A. I. et al. Functional TCR T cell screening using single-cell droplet microfluidics. Lab Chip 18, 3733–3749 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. Dhar, M. et al. Functional profiling of circulating tumor cells with an integrated vortex capture and single-cell protease activity assay. Proc. Natl Acad. Sci. USA 115, 9986–9991 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. Love, J. C., Ronan, J. L., Grotenbreg, G. M., van der Veen, A. G. & Ploegh, H. L. A microengraving method for rapid selection of single cells producing antigen-specific antibodies. Nat. Biotechnol. 24, 703–707 (2006).

    CAS  PubMed  Google Scholar 

  74. Ogunniyi, A. O. et al. Profiling human antibody responses by integrated single-cell analysis. Vaccine 32, 2866–2873 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. Varadarajan, N. et al. Rapid, efficient functional characterization and recovery of HIV-specific human CD8+ T cells using microengraving. Proc. Natl Acad. Sci. USA 109, 3885–3890 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Shi, Q. et al. Single-cell proteomic chip for profiling intracellular signaling pathways in single tumor cells. Proc. Natl Acad. Sci. USA 109, 419–424 (2012).

    CAS  PubMed  Google Scholar 

  77. Bailey, R. C., Kwong, G. A., Radu, C. G., Witte, O. N. & Heath, J. R. DNA-encoded antibody libraries: a unified platform for multiplexed cell sorting and detection of genes and proteins. J. Am. Chem. Soc. 129, 1959–1967 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Lu, Y. et al. Highly multiplexed profiling of single-cell effector functions reveals deep functional heterogeneity in response to pathogenic ligands. Proc. Natl Acad. Sci. USA 112, E607–E615 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. Kravchenko-Balasha, N., Shin, Y. S., Sutherland, A., Levine, R. D. & Heath, J. R. Intercellular signaling through secreted proteins induces free-energy gradient-directed cell movement. Proc. Natl Acad. Sci. USA 113, 5520–5525 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  80. Hughes, A. J. et al. Single-cell western blotting. Nat. Methods 11, 749–755 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  81. Sinkala, E. et al. Profiling protein expression in circulating tumour cells using microfluidic western blotting. Nat. Commun. 8, 14622 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. Poudineh, M. et al. Tracking the dynamics of circulating tumour cell phenotypes using nanoparticle-mediated magnetic ranking. Nat. Nanotechnol. 12, 274–281 (2017).

    CAS  PubMed  Google Scholar 

  83. Labib, M. et al. Single-cell mRNA cytometry via sequence-specific nanoparticle clustering and trapping. Nat. Chem. 10, 489–495 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  84. Marcon, E. et al. Assessment of a method to characterize antibody selectivity and specificity for use in immunoprecipitation. Nat. Methods 12, 725–731 (2015).

    CAS  PubMed  Google Scholar 

  85. Tentori, A. M., Yamauchi, K. A. & Herr, A. E. Detection of isoforms differing by a single charge unit in individual cells. Angew. Chem. Int. Ed. 55, 12431–12435 (2016).

    CAS  Google Scholar 

  86. Ma, C. et al. A clinical microchip for evaluation of single immune cells reveals high functional heterogeneity in phenotypically similar T cells. Nat. Med. 17, 738–743 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  87. Han, Q. et al. Polyfunctional responses by human T cells result from sequential release of cytokines. Proc. Natl Acad. Sci. USA 109, 1607–1612 (2012).

    CAS  PubMed  Google Scholar 

  88. Aebersold, R. & Mann, M. Mass-spectrometric exploration of proteome structure and function. Nature 537, 347–355 (2016).

    CAS  PubMed  Google Scholar 

  89. Lombard-Banek, C., Portero, E. P., Onjiko, R. M. & Nemes, P. New-generation mass spectrometry expands the toolbox of cell and developmental biology. Genesis 55, e23012 (2017).

    Google Scholar 

  90. Sun, L., Zhu, G., Yan, X. & Dovichi, N. J. High sensitivity capillary zone electrophoresis-electrospray ionization-tandem mass spectrometry for the rapid analysis of complex proteomes. Curr. Opin. Chem. Biol. 17, 795–800 (2013).

    CAS  PubMed  Google Scholar 

  91. Angel, T. E. et al. Mass spectrometry-based proteomics: existing capabilities and future directions. Chem. Soc. Rev. 41, 3912–3928 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  92. Specht, H. & Slavov, N. Transformative opportunities for single-cell proteomics. J. Proteome Res. 17, 2565–2571 (2018).

    PubMed  PubMed Central  Google Scholar 

  93. Budnik, B., Levy, E., Harmange, G. & Slavov, N. SCoPE-MS: mass spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation. Genome Biol. 19, 161 (2018).

    PubMed  PubMed Central  Google Scholar 

  94. Kuster, S. K. et al. Interfacing droplet microfluidics with matrix-assisted laser desorption/ionization mass spectrometry: label-free content analysis of single droplets. Anal. Chem. 85, 1285–1289 (2013).

    CAS  PubMed  Google Scholar 

  95. Haidas, D. et al. Microfluidic platform for multimodal analysis of enzyme secretion in nanoliter droplet arrays. Anal. Chem. 91, 2066–2073 (2019).

    CAS  PubMed  Google Scholar 

  96. Zhu, Y. et al. Nanodroplet processing platform for deep and quantitative proteome profiling of 10–100 mammalian cells. Nat. Commun. 9, 882 (2018).

    PubMed  PubMed Central  Google Scholar 

  97. 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).

    CAS  Google Scholar 

  98. Onjiko, R. M., Portero, E. P., Moody, S. A. & Nemes, P. In situ microprobe single-cell capillary electrophoresis mass spectrometry: metabolic reorganization in single differentiating cells in the live vertebrate (Xenopus laevis) embryo. Anal. Chem. 89, 7069–7076 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  99. Lombard-Banek, C., Moody, S. A., Manzini, M. C. & Nemes, P. Microsampling capillary electrophoresis mass spectrometry enables single-cell proteomics in complex tissues: developing cell clones in live Xenopus laevis and zebrafish embryos. Anal. Chem. 91, 4797–4805 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  100. Lombard-Banek, C., Reddy, S., Moody, S. A. & Nemes, P. Label-free quantification of proteins in single embryonic cells with neural fate in the cleavage-stage frog (Xenopus laevis) embryo using capillary electrophoresis electrospray ionization high-resolution mass spectrometry (CE-ESI-HRMS). Mol. Cell. Proteomics 15, 2756–2768 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  101. Lombard-Banek, C., Moody, S. A. & Nemes, P. High-sensitivity mass spectrometry for probing gene translation in single embryonic cells in the early frog (Xenopus) embryo. Front. Cell. Dev. Biol. 4, 100 (2016).

    PubMed  PubMed Central  Google Scholar 

  102. Sun, L. et al. Single cell proteomics using frog (Xenopus laevis) blastomeres isolated from early stage embryos, which form a geometric progression in protein content. Anal. Chem. 88, 6653–6657 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  103. Yan, X., Sun, L., Zhu, G., Cox, O. F. & Dovichi, N. J. Over 4100 protein identifications from a Xenopus laevis fertilized egg digest using reversed-phase chromatographic prefractionation followed by capillary zone electrophoresis–electrospray ionization–tandem mass spectrometry analysis. Proteomics 16, 2945–2952 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  104. Stuart, T. & Satija, R. Integrative single-cell analysis. Nat. Rev. Genet. 20, 257–272 (2019).

    CAS  PubMed  Google Scholar 

  105. Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  106. Peterson, V. M. et al. Multiplexed quantification of proteins and transcripts in single cells. Nat. Biotechnol. 35, 936–939 (2017).

    CAS  PubMed  Google Scholar 

  107. La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018).

    PubMed  PubMed Central  Google Scholar 

  108. Cao, J. et al. Joint profiling of chromatin accessibility and gene expression in thousands of single cells. Science 361, 1380–1385 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  109. Nestorowa, S. et al. A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation. Blood 128, e20–e31 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  110. Darmanis, S. et al. Simultaneous multiplexed measurement of RNA and proteins in single cells. Cell Rep. 14, 380–389 (2016).

    CAS  PubMed  Google Scholar 

  111. Genshaft, A. S. et al. Multiplexed, targeted profiling of single-cell proteomes and transcriptomes in a single reaction. Genome Biol. 17, 188 (2016).

    PubMed  PubMed Central  Google Scholar 

  112. Soderberg, O. et al. Direct observation of individual endogenous protein complexes in situ by proximity ligation. Nat. Methods 3, 995–1000 (2006).

    PubMed  Google Scholar 

  113. Frei, A. P. et al. Highly multiplexed simultaneous detection of RNAs and proteins in single cells. Nat. Methods 13, 269–275 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  114. Lin, J. et al. Ultra-sensitive digital quantification of proteins and mRNA in single cells. Nat. Commun. 10, 3544 (2019).

    PubMed  PubMed Central  Google Scholar 

  115. Lundberg, M., Eriksson, A., Tran, B., Assarsson, E. & Fredriksson, S. Homogeneous antibody-based proximity extension assays provide sensitive and specific detection of low-abundant proteins in human blood. Nucleic Acids Res. 39, e102 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  116. Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  117. See, P. et al. Mapping the human DC lineage through the integration of high-dimensional techniques. Science 356, eaag3009 (2017).

    PubMed  PubMed Central  Google Scholar 

  118. Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  119. Polyak, K. Tumor heterogeneity confounds and illuminates: a case for Darwinian tumor evolution. Nat. Med. 20, 344–346 (2014).

    CAS  PubMed  Google Scholar 

  120. Kreso, A. & Dick, J. E. Evolution of the cancer stem cell model. Cell Stem Cell 14, 275–291 (2014).

    CAS  PubMed  Google Scholar 

  121. Quail, D. F. & Joyce, J. A. Microenvironmental regulation of tumor progression and metastasis. Nat. Med. 19, 1423–1437 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  122. Furnari, F. B., Cloughesy, T. F., Cavenee, W. K. & Mischel, P. S. Heterogeneity of epidermal growth factor receptor signalling networks in glioblastoma. Nat. Rev. Cancer 15, 302–310 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  123. Alix-Panabieres, C. & Pantel, K. Challenges in circulating tumour cell research. Nat. Rev. Cancer 14, 623–631 (2014).

    CAS  PubMed  Google Scholar 

  124. Keller, L. & Pantel, K. Unravelling tumour heterogeneity by single-cell profiling of circulating tumour cells. Nat. Rev. Cancer 19, 553–567 (2019).

    CAS  PubMed  Google Scholar 

  125. Nathanson, D. A. et al. Targeted therapy resistance mediated by dynamic regulation of extrachromosomal mutant EGFR DNA. Science 343, 72–76 (2014).

    CAS  PubMed  Google Scholar 

  126. Miyamoto, D. T. et al. RNA-seq of single prostate CTCs implicates noncanonical Wnt signaling in antiandrogen resistance. Science 349, 1351–1356 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  127. Shah, N. N. & Fry, T. J. Mechanisms of resistance to CAR T cell therapy. Nat. Rev. Clin. Oncol. 16, 372–385 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  128. Baldridge, M. T., King, K. Y., Boles, N. C., Weksberg, D. C. & Goodell, M. A. Quiescent haematopoietic stem cells are activated by IFN-gamma in response to chronic infection. Nature 465, 793–797 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  129. Zhao, J. L. et al. Conversion of danger signals into cytokine signals by hematopoietic stem and progenitor cells for regulation of stress-induced hematopoiesis. Cell Stem Cell 14, 445–459 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  130. Ledford, H. Cancer treatment: The killer within. Nature 508, 24–26 (2014).

    CAS  PubMed  Google Scholar 

  131. Palucka, K. & Banchereau, J. Cancer immunotherapy via dendritic cells. Nat. Rev. Cancer 12, 265–277 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  132. Rosenberg, S. A., Restifo, N. P., Yang, J. C., Morgan, R. A. & Dudley, M. E. Adoptive cell transfer: a clinical path to effective cancer immunotherapy. Nat. Rev. Cancer 8, 299–308 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  133. Perez-Ruiz, E. et al. Prophylactic TNF blockade uncouples efficacy and toxicity in dual CTLA-4 and PD-1 immunotherapy. Nature 569, 428–432 (2019).

    CAS  PubMed  Google Scholar 

  134. Scheetz, L. et al. Engineering patient-specific cancer immunotherapies. Nat. Biomed. Eng. 3, 768–782 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  135. Coulie, P. G., Van den Eynde, B. J., van der Bruggen, P. & Boon, T. Tumour antigens recognized by T lymphocytes: at the core of cancer immunotherapy. Nat. Rev. Cancer 14, 135–146 (2014).

    CAS  PubMed  Google Scholar 

  136. Perfetto, S. P., Chattopadhyay, P. K. & Roederer, M. Seventeen-colour flow cytometry: unravelling the immune system. Nat. Rev. Immunol. 4, 648–655 (2004).

    CAS  PubMed  Google Scholar 

  137. Amir, E. D. et al. viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat. Biotechnol. 31, 545–352 (2013).

    CAS  PubMed Central  Google Scholar 

  138. Ma, C. et al. Multifunctional T-cell analyses to study response and progression in adoptive cell transfer immunotherapy. Cancer Discov. 3, 418–429 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  139. Han, A., Glanville, J., Hansmann, L. & Davis, M. M. Linking T-cell receptor sequence to functional phenotype at the single-cell level. Nat. Biotechnol. 32, 684–692 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  140. Keskin, D. B. et al. Neoantigen vaccine generates intratumoral T cell responses in phase Ib glioblastoma trial. Nature 565, 234–239 (2019).

    CAS  PubMed  Google Scholar 

  141. Mariathasan, S. et al. TGFβ attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells. Nature 554, 544–548 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  142. Crompton, J. G., Sukumar, M. & Restifo, N. P. Uncoupling T-cell expansion from effector differentiation in cell-based immunotherapy. Immunol. Rev. 257, 264–276 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  143. Giladi, A. & Amit, I. Immunology, one cell at a time. Nature 547, 27–29 (2017).

    CAS  PubMed  Google Scholar 

  144. Davis, M. I. et al. Comprehensive analysis of kinase inhibitor selectivity. Nat. Biotechnol. 29, 1046–1051 (2011).

    CAS  PubMed  Google Scholar 

  145. Fan, H. C., Fu, G. K. & Fodor, S. P. A. Combinatorial labeling of single cells for gene expression cytometry. Science 347, 1258367 (2015).

    PubMed  Google Scholar 

  146. Bodenmiller, B. et al. Multiplexed mass cytometry profiling of cellular states perturbed by small-molecule regulators. Nat. Biotechnol. 30, 858–867 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors thank the Canadian Institutes of Health Research (grant no. FDN-148415), the Natural Sciences and Engineering Research Council of Canada (grant no. 2016-06090), the Province of Ontario through the Ministry of Research, Innovation and Science (grant no. RE05-009) and the National Cancer Institute of the National Institutes of Health (grant no. 1R33CA204574). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the other funding agencies.

Author information

Authors and Affiliations

Authors

Contributions

The authors contributed equally to all aspects of the article.

Corresponding author

Correspondence to Shana O. Kelley.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information

Nature Reviews Chemistry thanks U. Landegren and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Labib, M., Kelley, S.O. Single-cell analysis targeting the proteome. Nat Rev Chem 4, 143–158 (2020). https://doi.org/10.1038/s41570-020-0162-7

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41570-020-0162-7

This article is cited by

Search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research