Skip to main content
SpringerLink
Log in

Single-molecule motions enable direct visualization of biomolecular interactions in solution

  • Brief Communication
  • Published:

From Nature Methods

View current issue Submit your manuscript

Abstract

Biomolecular interactions are generally accompanied by modifications in size and charge of biomolecules at the nanometer scale. Here we describe a single-molecule method to sense these changes in real time based on statistical learning of diffusive and electric field–induced motion parameters of a trapped molecule in solution. We demonstrate the approach by resolving a monomer-trimer mixture along a protein dissociation pathway and visualizing the binding-unbinding kinetics of a single DNA molecule.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1: Measurement principle and transport-based single-molecule identification.
Figure 2: Resolving monomers and trimers of APC in solution along the dissociation pathway.
Figure 3: DNA hybridization kinetics visualized by dynamic changes in transport coefficients in solution.

Similar content being viewed by others

References

  1. van Oijen, A.M. Curr. Opin. Biotechnol. 22, 75–80 (2011).

    Article  CAS  Google Scholar 

  2. Cisse, I.I., Kim, H. & Ha, T. Nat. Struct. Mol. Biol. 19, 623–627 (2012).

    Article  CAS  Google Scholar 

  3. Jungmann, R. et al. Nano Lett. 10, 4756–4761 (2010).

    Article  CAS  Google Scholar 

  4. Ulbrich, M.H. & Isacoff, E.Y. Nat. Methods 4, 319–321 (2007).

    Article  CAS  Google Scholar 

  5. Ha, T. & Tinnefeld, P. Annu. Rev. Phys. Chem. 63, 595–617 (2012).

    Article  CAS  Google Scholar 

  6. Magde, D., Webb, W.W. & Elson, E.L. Biopolymers 17, 361–367 (1978).

    Article  CAS  Google Scholar 

  7. Muller, C.B. et al. Eur. Phys. Lett. 83, 46001 (2008).

    Article  Google Scholar 

  8. Cohen, A.E. & Moerner, W.E. Proc. Natl. Acad. Sci. USA 103, 4362–4365 (2006).

    Article  CAS  Google Scholar 

  9. Wang, Q. & Moerner, W.E. ACS Nano 5, 5792–5799 (2011).

    Article  CAS  Google Scholar 

  10. Fields, A.P. & Cohen, A.E. Proc. Natl. Acad. Sci. USA 108, 8937–8942 (2011).

    Article  CAS  Google Scholar 

  11. Wang, Q. & Moerner, W.E. J. Phys. Chem. B 117, 4641–4648 (2012).

    Article  Google Scholar 

  12. Enderlein, J. Appl. Phys. B 71, 773–777 (2000).

    Article  CAS  Google Scholar 

  13. Qi, Y. Extending Expectation Propagation for Graphical Models. PhD Thesis, Massachusetts Institute of Technology (2004).

  14. Shumway, R.H. & Stoffer, D.S. J. Time Ser. Anal. 3, 253–264 (1982).

    Article  Google Scholar 

  15. Huang, C., Berns, D.S. & MacColl, R. Biochemistry 26, 243–245 (1987).

    Article  CAS  Google Scholar 

  16. Cohen-Bazire, G., Beguin, S., Rimon, S., Glazer, A.N. & Brown, D.M. Arch. Microbiol. 111, 225–238 (1977).

    Article  CAS  Google Scholar 

  17. Markham, N.R. & Zuker, M. Nucleic Acids Res. 33, W577–W581 (2005).

    Article  CAS  Google Scholar 

  18. Goldsmith, R.H. & Moerner, W.E. Nat. Chem. 2, 179–186 (2010).

    Article  CAS  Google Scholar 

  19. Holzmeister, P., Acuna, G.P., Grohmann, D. & Tinnefeld, P. Chem. Soc. Rev. 10.1039/C3CS60207A (10 September 2013).

  20. Tyagi, S. et al. Nat. Methods 10.1038/nmeth.2809 (19 January 2014).

  21. Cohen, A.E. & Moerner, W.E. Opt. Express 16, 6941–6956 (2008).

    Article  CAS  Google Scholar 

  22. Kartalov, E., Unger, M. & Quake, S.R. BioTechniques 34, 505–510 (2003).

    Article  CAS  Google Scholar 

  23. Horvath, J. & Dolnik, V. Electrophoresis 22, 644–655 (2001).

    Article  CAS  Google Scholar 

  24. Jordan, M.I. Stat. Sci. 19, 140–155 (2004).

    Article  Google Scholar 

  25. Qi, Y. & Minka, T.P. IEEE Trans. Wirel. Comm. 6, 348–355 (2007).

    Article  Google Scholar 

  26. Botev, Z.I., Grotowski, J.F. & Kroese, D.P. Ann. Stat. 38, 2916–2957 (2010).

    Article  Google Scholar 

  27. Murphy, K.P. Machine Learning: a Probabilistic Perspective (The MIT Press, 2012).

  28. Rasnik, I., McKinney, S.A. & Ha, T. Nat. Methods 3, 891–893 (2006).

    Article  CAS  Google Scholar 

  29. Aitken, C.E., Marshall, R.A. & Puglisi, J.D. Biophys. J. 94, 1826–1835 (2008).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank Y. Jiang for help with high-performance liquid chromatography purification, and C. Calderon, G. Schlau-Cohen, H.-Y. Yang, S. Bockenhauer and S.J. Sahl for discussion. This work is funded in part by the Division of Chemical Sciences, Geosciences and Biosciences, Office of Basic Energy Sciences of the US Department of Energy through grant DE-FG02-07ER15892.

Author information

Authors and Affiliations

  1. Department of Chemistry, Stanford University, Stanford, California, USA

    Quan Wang & W E Moerner

  2. Department of Electrical Engineering, Stanford University, Stanford, California, USA

    Quan Wang

Authors
  1. Quan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. W E Moerner
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Q.W. and W.E.M. conceived the project, discussed the results and wrote the manuscript. Q.W. designed and performed the experiments and data analysis.

Corresponding author

Correspondence to W E Moerner.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–19, Supplementary Tables 1–5 and Supplementary Notes 1–7 (PDF 7165 kb)

Real-time estimation of single-molecule transport coefficients in an anti-Brownian electrokinetic trap.

Screen recording during an experiment trapping single Alexa647 labeled 10nt-ssDNA molecules without complementary strand. Intensity (I) is photon counts every 10 ms, diffusion coefficient (D) and electrokinetic mobility (μx and μy) are estimated every 5,000 photons using the real-time EM algorithm. Occasional intensity spikes are indicative of transient co-occupancy of two objects in the trap. Time axis units: 10 ms (AVI 4611 kb)

Real-time visualization of single-DNA binding-unbinding dynamics.

Screen recording during an experiment trapping single 10nt-ssDNA in presence of unlabeled complementary strand (2 μM of 24nt-10comp). Frequent anti-correlated dynamics in diffusion coefficient (D) and electrokinetic mobility (μx and μy) visualize transitions between ssDNA (blue band) and dsDNA (red band). Time axis units: 10 ms (AVI 10418 kb)

Supplementary Software

C program implementation of the parameter estimation algorithm. (TXT 17 kb)

Source data

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wang, Q., Moerner, W. Single-molecule motions enable direct visualization of biomolecular interactions in solution. Nat Methods 11, 555–558 (2014). https://doi.org/10.1038/nmeth.2882

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nmeth.2882

  • Springer Nature America, Inc.

This article is cited by

Search

Navigation