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Fluorescence imaging-based methods for single-cell protein analysis

  • Siwen Wang
  • Fei Ji
  • Zhonghan Li
  • Min XueEmail author
Trends
  • 65 Downloads
Part of the following topical collections:
  1. Young Investigators in (Bio-)Analytical Chemistry

Abstract

The quantity and activity of proteins in many biological systems exhibit prominent heterogeneities. Single-cell analytical methods can resolve subpopulations and dissect their unique signatures from heterogeneous samples, enabling a clarifying view of the biological process. Over the last 5 years, technologies for single-cell protein analysis have significantly advanced. In this article, we highlight a branch of those technology developments involving fluorescence-based approaches, with a focus on the methods that increase the ability to multiplex and enable dynamic measurements. We also analyze the limitations of these techniques and discuss current challenges in the field, with the hope that more transformative platforms can soon emerge.

Keywords

Single-cell Fluorescence Protein analysis Multiplexed assays Dynamic measurements 

Notes

Acknowledgements

We thank Prof. Jin Zhang for valuable discussions.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflicts of interest.

References

  1. 1.
    Hunter T. Signaling--2000 and beyond. Cell. 2000;1:113–27.CrossRefGoogle Scholar
  2. 2.
    Massague J, Blain SW, Lo RS. TGFbeta signaling in growth control, cancer, and heritable disorders. Cell. 2000;2:295–309.CrossRefGoogle Scholar
  3. 3.
    Wullschleger S, Loewith R, Hall MN. TOR signaling in growth and metabolism. Cell. 2006;3:471–84.CrossRefGoogle Scholar
  4. 4.
    Wendel HG, De Stanchina E, Fridman JS, Malina A, Ray S, Kogan S, et al. Survival signalling by Akt and eIF4E in oncogenesis and cancer therapy. Nature. 2004;6980:332–7.CrossRefGoogle Scholar
  5. 5.
    Vetere A, Choudhary A, Burns SM, Wagner BK. Targeting the pancreatic beta-cell to treat diabetes. Nat Rev Drug Discov. 2014;4:278–89.CrossRefGoogle Scholar
  6. 6.
    Hanahan D, Weinberg RA. The hallmarks of cancer. Cell. 2000;1:57–70.CrossRefGoogle Scholar
  7. 7.
    Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;5:646–74.CrossRefGoogle Scholar
  8. 8.
    Taniguchi CM, Emanuelli B, Kahn CR. Critical nodes in signalling pathways: insights into insulin action. Nat Rev Mol Cell Biol. 2006;2:85–96.CrossRefGoogle Scholar
  9. 9.
    Rawlings DJ, Metzler G, Wray-Dutra M, Jackson SW. Altered B cell signalling in autoimmunity. Nat Rev Immunol. 2017;7:421–36.CrossRefGoogle Scholar
  10. 10.
    Gross S, Rahal R, Stransky N, Lengauer C, Hoeflich KP. Targeting cancer with kinase inhibitors. J Clin Invest. 2015;5:1780–9.CrossRefGoogle Scholar
  11. 11.
    Hughes T, Deininger M, Hochhaus A, Branford S, Radich J, Kaeda J, et al. Monitoring CML patients responding to treatment with tyrosine kinase inhibitors: review and recommendations for harmonizing current methodology for detecting BCR-ABL transcripts and kinase domain mutations and for expressing results. Blood. 2006;1:28–37.CrossRefGoogle Scholar
  12. 12.
    Wu P, Nielsen TE, Clausen MH. FDA-approved small-molecule kinase inhibitors. Trends Pharmacol Sci. 2015;7:422–39.CrossRefGoogle Scholar
  13. 13.
    Lim JS, Ibaseta A, Fischer MM, Cancilla B, O’Young G, Cristea S, et al. Intratumoural heterogeneity generated by notch signalling promotes small-cell lung cancer. Nature. 2017;7654:360–4.CrossRefGoogle Scholar
  14. 14.
    Welch DR. Tumor heterogeneity—a ‘contemporary concept’ founded on historical insights and predictions. Cancer Res. 2016;1:4–6.CrossRefGoogle Scholar
  15. 15.
    Ma C, Fan R, Ahmad H, Shi Q, Comin-Anduix B, Chodon T, et al. A clinical microchip for evaluation of single immune cells reveals high functional heterogeneity in phenotypically similar T cells. Nat Med. 2011;17:738–43.Google Scholar
  16. 16.
    Gawad C, Koh W, Quake SR. Single-cell genome sequencing: current state of the science. Nat Rev Genet. 2016;3:175–88.CrossRefGoogle Scholar
  17. 17.
    Prakadan SM, Shalek AK, Weitz DA. Scaling by shrinking: empowering single-cell ‘omics’ with microfluidic devices. Nat Rev Genet. 2017;6:345–61.CrossRefGoogle Scholar
  18. 18.
    Spitzer MH, Nolan GP. Mass cytometry: single cells, many features. Cell. 2016;4:780–91.CrossRefGoogle Scholar
  19. 19.
    Yu J, Zhou J, Sutherland A, Wei W, Shin YS, Xue M, et al. Microfluidics-based single-cell functional proteomics for fundamental and applied biomedical applications. Annu Rev Anal Chem. 2014;1:275–95.CrossRefGoogle Scholar
  20. 20.
    Su Y, Wei W, Robert L, Xue M, Tsoi J, Garcia-Diaz A, et al. Single-cell analysis resolves the cell state transition and signaling dynamics associated with melanoma drug-induced resistance. Proc Natl Acad Sci. 2017;52:13679–84.CrossRefGoogle Scholar
  21. 21.
    Ong SE, Mann M. Mass spectrometry-based proteomics turns quantitative. Nat Chem Biol. 2005;5:252–62.CrossRefGoogle Scholar
  22. 22.
    Altelaar AF, Munoz J, Heck AJ. Next-generation proteomics: towards an integrative view of proteome dynamics. Nat Rev Genet. 2013;1:35–48.CrossRefGoogle Scholar
  23. 23.
    Heath JR, Ribas A, Mischel PS. Single-cell analysis tools for drug discovery and development. Nat Rev Drug Discov. 2016;15:204–16.Google Scholar
  24. 24.
    Lu Y, Xue Q, Eisele MR, Sulistijo ES, Brower K, Han L, et al. Highly multiplexed profiling of single-cell effector functions reveals deep functional heterogeneity in response to pathogenic ligands. Proc Natl Acad Sci. 2015;7:E607–E15.CrossRefGoogle Scholar
  25. 25.
    Yang L, Wang Z, Deng Y, Li Y, Wei W, Shi Q. Single-cell, multiplexed protein detection of rare tumor cells based on a beads-on-barcode antibody microarray. Anal Chem. 2016;22:11077–83.CrossRefGoogle Scholar
  26. 26.
    Chen KH, Boettiger AN, Moffitt JR, Wang S, Zhuang X. Spatially resolved, highly multiplexed RNA profiling in single cells. Science. 2015;6233:aaa6090.CrossRefGoogle Scholar
  27. 27.
    Mondal M, Liao R, Xiao L, Eno T, Guo J. Highly multiplexed single-cell in situ protein analysis with cleavable fluorescent antibodies. Angew Chem Int Ed. 2017;10:2636–9.CrossRefGoogle Scholar
  28. 28.
    Scott JD, Pawson T. Cell signaling in space and time: where proteins come together and when they’re apart. Science. 2009;5957:1220–4.CrossRefGoogle Scholar
  29. 29.
    Shin YS, Remacle F, Fan R, Hwang K, Wei W, Ahmad H, et al. Protein signaling networks from single cell fluctuations and information theory profiling. Biophys J. 2011;10:2378–86.CrossRefGoogle Scholar
  30. 30.
    Karl FA. Free energy principle for biological systems. Entropy (Basel). 2012;11:2100–21.CrossRefGoogle Scholar
  31. 31.
    Komatsuzaki T, Baba A, Kawai S, Toda M, Straub JE, Berry RS. Ergodic problems for real complex systems in chemical physics. Advancing theory for kinetics and dynamics of complex, many-dimensional systems: clusters and proteins. Adv Chem Phys. 2011;145:171–220.Google Scholar
  32. 32.
    Ridden SJ, Chang HH, Zygalakis KC, MacArthur BD. Entropy, ergodicity, and stem cell multipotency. Phys Rev Lett. 2015;20:208103.CrossRefGoogle Scholar
  33. 33.
    Weigel AV, Simon B, Tamkun MM, Krapf D. Ergodic and nonergodic processes coexist in the plasma membrane as observed by single-molecule tracking. Proc Natl Acad Sci. 2011;16:6438–43.CrossRefGoogle Scholar
  34. 34.
    Han Q, Bagheri N, Bradshaw EM, Hafler DA, Lauffenburger DA, Love JC. Polyfunctional responses by human T cells result from sequential release of cytokines. Proc Natl Acad Sci. 2012;5:1607–12.CrossRefGoogle Scholar
  35. 35.
    Zotter A, Bäuerle F, Dey D, Kiss V, Schreiber G. Quantifying enzyme activity in living cells. J Biol Chem. 2017;38:15838–48.CrossRefGoogle Scholar
  36. 36.
    Rodriguez EA, Campbell RE, Lin JY, Lin MZ, Miyawaki A, Palmer AE, et al. The growing and glowing toolbox of fluorescent and photoactive proteins. Trends Biochem Sci. 2017;2:111–29.CrossRefGoogle Scholar
  37. 37.
    Ni Q, Mehta S, Zhang J. Live-cell imaging of cell signaling using genetically encoded fluorescent reporters. FEBS J. 2017;2:203–19.Google Scholar
  38. 38.
    Conlon P, Gelin-Licht R, Ganesan A, Zhang J, Levchenko A. Single-cell dynamics and variability of MAPK activity in a yeast differentiation pathway. Proc Natl Acad Sci. 2016;40:E5896–E905.CrossRefGoogle Scholar
  39. 39.
    Mehta S, Zhang Y, Roth RH, Zhang J-f, Mo A, Tenner B, et al. Single-fluorophore biosensors for sensitive and multiplexed detection of signalling activities. Nat Cell Biol. 2018;10:1215–25.CrossRefGoogle Scholar
  40. 40.
    Gross SM, Dane MA, Bucher E, Heiser LM. High resolution AKT signaling in individual cells. bioRxiv. 2018.  https://doi.org/10.1101/373993
  41. 41.
    Shao S, Li Z, Cheng H, Wang S, Perkins NG, Sarkar P, et al. Chemical approach for profiling intracellular AKT signaling dynamics from single cells. J Am Chem Soc. 2018;42:13586–9.CrossRefGoogle Scholar
  42. 42.
    Hasin Y, Seldin M, Lusis A. Multi-omics approaches to disease. Genome Biol. 2017;1:83.CrossRefGoogle Scholar
  43. 43.
    Huang S, Chaudhary K, Garmire LX. More is better: recent progress in multi-omics data integration methods. Front Genet. 2017;8:84.Google Scholar
  44. 44.
    Buescher JM, Driggers EM. Integration of omics: more than the sum of its parts. Cancer Metab. 2016;4:4.Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of ChemistryUniversity of California, RiversideRiversideUSA

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