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.

  • Article
  • Published:

Non-specific interactions govern cytosolic diffusion of nanosized objects in mammalian cells

Nature Materials volume 17, pages 740–746 (2018)Cite this article

Subjects

A Publisher Correction to this article was published on 19 September 2018

This article has been updated

Abstract

The diffusivity of macromolecules in the cytoplasm of eukaryotic cells varies over orders of magnitude and dictates the kinetics of cellular processes. However, a general description that associates the Brownian or anomalous nature of intracellular diffusion to the architectural and biochemical properties of the cytoplasm has not been achieved. Here we measure the mobility of individual fluorescent nanoparticles in living mammalian cells to obtain a comprehensive analysis of cytoplasmic diffusion. We identify a correlation between tracer size, its biochemical nature and its mobility. Inert particles with size equal or below 50 nm behave as Brownian particles diffusing in a medium of low viscosity with negligible effects of molecular crowding. Increasing the strength of non-specific interactions of the nanoparticles within the cytoplasm gradually reduces their mobility and leads to subdiffusive behaviour. These experimental observations and the transition from Brownian to subdiffusive motion can be captured in a minimal phenomenological model.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: NPs of similar size have different diffusive signatures.
Fig. 2: Cytoplasmic mobility is strongly reduced for NPs above 50 nm in size.
Fig. 3: QDs-PEG-NH2 shows intrinsic subdiffusive behaviour and ergodicity breaking.
Fig. 4: A minimal CTRW model captures all the different NPs diffusive behaviours observed.
Fig. 5: NP diffusivity can be ranked by the strength of non-specific interactions.
Fig. 6: General model of cytosolic mobility.

Similar content being viewed by others

Change history

  • 19 September 2018

    In the version of this Article originally published, Supplementary Videos 3–5 were incorrectly labelled; 3 should have been 5, 4 should have been 3 and 5 should have been 4. This has now been corrected.

References

  1. Bénichou, O., Chevalier, C., Klafter, J., Meyer, B. & Voituriez, R. Geometry-controlled kinetics. Nat. Chem. 2, 472–477 (2010).

    Article  Google Scholar 

  2. Hellmann, M., Heermann, D. W. & Weiss, M. Enhancing phosphorylation cascades by anomalous diffusion. Europhys. Lett. 97, 58004 (2012).

    Article  Google Scholar 

  3. Izeddin, I. et al. Single-molecule tracking in live cells reveals distinct target-search strategies of transcription factors in the nucleus. eLife 3, e02230 (2014).

    Article  Google Scholar 

  4. Hou, L., Lanni, F. & Luby-Phelps, K. Tracer diffusion in F-actin and Ficoll mixtures. Toward a model for cytoplasm. Biophys. J. 58, 31–43 (1990).

    Article  CAS  Google Scholar 

  5. Moeendarbary, E. et al. The cytoplasm of living cells behaves as a poroelastic material. Nat. Mater. 12, 253–261 (2013).

    Article  CAS  Google Scholar 

  6. Luby-Phelps, K. Cytoarchitecture and physical properties of cytoplasm: volume, viscosity, diffusion, intracellular surface area. Int. Rev. Cytol. 192, 189–221 (2000).

    Article  CAS  Google Scholar 

  7. Wojcieszyn, J. W., Schlegel, R. A., Wu, E.-S. & Jacobson, K. A. Diffusion of injected macromolecules within the cytoplasm of living cells. Proc. Natl Acad. Sci. USA 78, 4407–4410 (1981).

    Article  CAS  Google Scholar 

  8. Tabaka, M., Kalwarczyk, T., Szymanski, J., Hou, S. & Holyst, R. The effect of macromolecular crowding on mobility of biomolecules, association kinetics, and gene expression in living cells. Front. Phys. 2, 54 (2014).

    Article  Google Scholar 

  9. Hofling, F. & Franosch, T. Anomalous transport in the crowded world of biological cells. Rep. Prog. Phys. 76, 046602 (2013).

    Article  Google Scholar 

  10. Luby-Phelps, K., Taylor, D. L. & Lanni, F. Probing the structure of cytoplasm. J. Cell Biol. 102, 2015–2022 (1986).

    Article  CAS  Google Scholar 

  11. Luby-Phelps, K., Castle, P. E., Taylor, D. L. & Lanni, F. Hindered diffusion of inert tracer particles in the cytoplasm of mouse 3T3cells. Proc. Natl Acad. Sci. USA 84, 4910–4913 (1987).

    Article  CAS  Google Scholar 

  12. Jacobson, K. & Wojcieszyn, J. The translational mobility of substances within the cytoplasmic matrix. Proc. Natl Acad. Sci. USA 81, 6747–6751 (1984).

    Article  CAS  Google Scholar 

  13. Yokoe, H. & Meyer, T. Spatial dynamics of GFP-tagged proteins investigated by local fluorescence enhancement.Nat. Biotechnol. 14, 1252–1256 (1996).

    Article  CAS  Google Scholar 

  14. Klann, M. T., Lapin, A. & Reuss, M. Stochastic simulation of signal transduction: impact of the cellular architecture on diffusion. Biophys. J. 96, 5122–5129 (2009).

    Article  CAS  Google Scholar 

  15. Banks, D. S. & Fradin, C. Anomalous diffusion of proteins due to molecular crowding. Biophys. J. 89, 2960–2971 (2005).

    Article  CAS  Google Scholar 

  16. Regner, B. M. et al. Anomalous diffusion of single particles in cytoplasm. Biophys. J. 104, 1652–1660 (2013).

    Article  CAS  Google Scholar 

  17. Nelson, S. R., Ali, M. Y., Trybus, K. M. & Warshaw, D. M. Random walk of processive, quantum dot-labeled myosin Va molecules within the actin cortex of COS-7 cells. Biophys. J. 97, 509–518 (2009).

    Article  CAS  Google Scholar 

  18. Dix, J. A. & Verkman, A. S. Crowding effects on diffusion in solutions and cells. Annu. Rev. Biophys. 37, 247–263 (2008).

    Article  CAS  Google Scholar 

  19. Weiss, M., Elsner, M., Kartberg, F. & Nilsson, T. Anomalous subdiffusion is a measure for cytoplasmic crowding in living cells. Biophys. J. 87, 3518–3524 (2004).

    Article  CAS  Google Scholar 

  20. Keren, K., Yam, P. T., Kinkhabwala, A., Mogilner, A. & Theriot, J. A. Intracellular fluid flow in rapidly moving cells. Nat. Cell Biol. 11, 1219–1224 (2009).

    Article  CAS  Google Scholar 

  21. Barkai, E., Garini, Y. & Metzler, R. Strange kinetics of single molecules in living cells. Phys. Today 65, 29–35 (August, 2012).

    Article  CAS  Google Scholar 

  22. Fulton, A. B. How crowded is the cytoplasm? Cell 30, 345–347 (1982).

    Article  CAS  Google Scholar 

  23. Berry, H. & Chaté, H. Anomalous diffusion due to hindering by mobile obstacles undergoing Brownian motion or Orstein–Ulhenbeck processes. Phys. Rev. E 89, 022708 (2014).

    Article  Google Scholar 

  24. Guigas, G., Kalla, C. & Weiss, M. The degree of macromolecular crowding in the cytoplasm and nucleoplasm of mammalian cells is conserved. FEBS Lett. 581, 5094–5098 (2007).

    Article  CAS  Google Scholar 

  25. Pinaud, F., Clarke, S., Sittner, A. & Dahan, M. Probing cellular events, one quantum dot at a time. Nat. Methods 7, 275–285 (2010).

    Article  CAS  Google Scholar 

  26. Clarke, S. et al. Covalent monofunctionalization of peptide-coated quantum dots for single-molecule assays. Nano Lett. 10, 2147–2154 (2010).

    Article  CAS  Google Scholar 

  27. Okada, C. Y. & Rechsteiner, M. Introduction of macromolecules into cultured mammalian cells by osmotic lysis of pinocytic vesicles. Cell 29, 33–41 (1982).

    Article  CAS  Google Scholar 

  28. Nandi, A., Heinrich, D. & Lindner, B. Distributions of diffusion measures from a local mean-square displacement analysis. Phys. Rev. E 86, 021926 (2012).

    Article  Google Scholar 

  29. Luby-Phelps, K. et al. A novel fluorescence ratiometric method confirms the low solvent viscosity of the cytoplasm. Biophys. J. 65, 236–242 (1993).

    Article  CAS  Google Scholar 

  30. Fushimi, K. & Verkman, A. S. Low viscosity in the aqueous domain of cell cytoplasm measured by picosecond polarization microfluorimetry. J. Cell Biol. 112, 719–725 (1991).

    Article  CAS  Google Scholar 

  31. Récamier, V. et al. Single cell correlation fractal dimension of chromatin. A framework to interpret 3D single molecule super-resolution. Nucleus 5, 75–84 (2014).

    Article  Google Scholar 

  32. Metzler, R., Jeon, J.-H., Cherstvy, A. G. & Barkai, E. Anomalous diffusion models and their properties: non-stationarity, non-ergodicity, and ageing at the centenary of single particle tracking. Phys. Chem. Chem. Phys. 16, 24128–24164 (2014).

    Article  CAS  Google Scholar 

  33. Meroz, Y. & Sokolov, I. M. A toolbox for determining subdiffusive mechanisms. Phys. Rep. 573, 1–29 (2015).

    Article  Google Scholar 

  34. Weigel, A. V., Simon, B., Tamkun, M. M. & Krapf, D. Ergodic and nonergodic processes coexist in the plasma membrane as observed by single-molecule tracking. Proc. Natl Acad. Sci. USA 108, 6438–6443 (2011).

    Article  CAS  Google Scholar 

  35. Schulz, J. H. P., Barkai, E. & Metzler, R. Aging effects and population splitting in single-particle trajectory averages. Phys. Rev. Lett. 110, 020602 (2013).

    Article  Google Scholar 

  36. Liße, D. et al. Monofunctional stealth nanoparticle for unbiased single molecule tracking inside living cells. Nano Lett. 14, 2189–2195 (2014).

    Article  Google Scholar 

  37. Saxton, M. J. Anomalous diffusion due to obstacles: a Monte Carlo study. Biophys. J. 66, 394–401 (1994).

    Article  CAS  Google Scholar 

  38. Saxton, M. J. Anomalous diffusion due to binding: a Monte Carlo study. Biophys. J. 70, 1250–1262 (1996).

    Article  CAS  Google Scholar 

  39. Di Rienzo, C., Piazza, V., Gratton, E., Beltram, F. & Cardarelli, F. Probing short-range protein Brownian motion in the cytoplasm of living cells. Nat. Commun. 5, 5891 (2014).

    Article  Google Scholar 

  40. Del Pino, P. et al. Protein corona formation around nanoparticles—from the past to the future. Mater. Horiz. 1, 301–313 (2014).

    Article  Google Scholar 

  41. Tabei, S. M. A. et al. Intracellular transport of insulin granules is a subordinated random walk. Proc. Natl Acad. Sci. USA 110, 4911–4916 (2013).

    Article  Google Scholar 

  42. Janson, L. W., Ragsdale, K. & Luby-Phelps, K. Mechanism and size cutoff for steric exclusion from actin-rich cytoplasmic domains. Biophys. J. 71, 1228–1234 (1996).

    Article  CAS  Google Scholar 

  43. Guo, M. et al. Probing the stochastic, motor-driven properties of the cytoplasm using force spectrum microscopy. Cell 158, 822–832 (2014).

    Article  CAS  Google Scholar 

  44. Tseng, Y., Kole, T. P. & Wirtz, D. Micromechanical mapping of live cells by multiple-particle-tracking microrheology. Biophys. J. 83, 3162–3176 (2002).

    Article  CAS  Google Scholar 

  45. Hu, J. et al. Size- and speed-dependent mechanical behavior in living mammalian cytoplasm. Proc. Natl Acad. Sci. USA 114, 9529–9534 (2017).

    Article  CAS  Google Scholar 

  46. Delehanty, J. B., Mattoussi, H. & Medintz, I. L. Delivering quantum dots into cells: strategies, progress and remaining issues. Anal. Bioanal. Chem. 393, 1091–1105 (2009).

    Article  CAS  Google Scholar 

  47. Etoc, F. et al. Subcellular control of Rac-GTPase signalling by magnetogenetic manipulation inside living cells.Nat. Nanotech. 8, 193–198 (2013).

    Article  CAS  Google Scholar 

  48. Clarke, S., Tamang, S., Reiss, P. & Dahan, M. A simple and general route for monofunctionalization of fluorescent and magnetic nanoparticles using peptides. Nanotechnology 22, 175103 (2011).

    Article  Google Scholar 

  49. Normanno, D. et al. Probing the target search of DNA-binding proteins in mammalian cells using TetR as model searcher. Nat. Commun. 6, 7357 (2015).

    Article  Google Scholar 

  50. Tokunaga, M., Imamoto, N. & Sakata-Sogawa, K. Highly inclined thin illumination enables clear single-molecule imaging in cells. Nat. Methods 5, 159–161 (2008).

    Article  CAS  Google Scholar 

  51. Sergé, A., Bertaux, N., Rigneault, H. & Marguet, D. Dynamic multiple-target tracing to probe spatiotemporal cartography of cell membranes. Nat. Methods 5, 687–694 (2008).

    Article  Google Scholar 

  52. Michalet, X. Mean square displacement analysis of single-particle trajectories with localization error: Brownian motion in an isotropic medium. Phys. Rev. E 82, 041914 (2010).

    Article  Google Scholar 

  53. Backlund, M. P., Joyner, R. & Moerner, W. E. Chromosomal locus tracking with proper accounting of static and dynamic errors. Phys. Rev. E 91, 062716 (2015).

    Article  Google Scholar 

Download references

Acknowledgements

We acknowledge M. Coppey-Moisan, B. Goud and S. Letard and P. Dubreuil for supplying various cells (see text). E.B. acknowledges the Ecole Doctorale Frontières du Vivant (FdV)—Programme Bettencourt for financial support. M.D. and M.C. acknowledge financial support from the French National Research Agency (ANR) Paris-Science-Lettres Program (ANR-10-IDEX-0001-02 PSL), Labex CelTisPhyBio (ANR-10-LBX-0038), the Human Frontier Science Program (grant no. RGP0005/2007) and the France-BioImaging infrastructure supported by ANR Grant ANR-10-INSB-04 (Investments for the Future). This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement no. 686841 (MAGNEURON).

Author information

Author notes

  1. Fred Etoc

    Present address: Center for Studies in Physics and Biology, The Rockefeller University, New York, NY, USA

  2. Chiara Vicario

    Present address: Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Spain

  3. These authors contributed equally: Elie Balloul, Chiara Vicario.

Authors and Affiliations

  1. Laboratoire Physico-Chimie, Institut Curie, CNRS UMR168, PSL Research University, Université Pierre et Marie Curie-Paris, Paris, France

    Fred Etoc, Elie Balloul, Chiara Vicario, Davide Normanno, Maxime Dahan & Mathieu Coppey

  2. Centre de Recherche en Cancérologie de Marseille, CNRS UMR7258, Inserm U1068, Aix-Marseille Université UM105, Institut Paoli-Calmettes, Marseilles, France

    Davide Normanno

  3. Division of Biophysics, Department of Biology, Osnabrück University, Osnabrück, Germany

    Domenik Liße & Jacob Piehler

  4. Department of Infectious Diseases, Israel Institute for Biological Research, Ness Ziona, Israel

    Assa Sittner

Authors
  1. Fred Etoc
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Elie Balloul
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chiara Vicario
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Davide Normanno
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Domenik Liße
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Assa Sittner
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jacob Piehler
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Maxime Dahan
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Mathieu Coppey
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

F.E., M.D. and M.C. conceived the study. F.E. performed experiments and numerical simulations. F.E. and D.N. did the ferritin NP experiments in HeLa cells. A.S. executed the early experiments. C.V. conducted the NP-release validation experiments. E.B. performed the RPE-1 and hMSC experiments. D.N. collected HDFa data. D.L. and J.P. provided reagents. F.E., E.B. and M.C. analysed the data. All the authors discussed the results and commented on the manuscript. F.E., D.N., M.D. and M.C. wrote the manuscript.

Corresponding authors

Correspondence to Davide Normanno, Maxime Dahan or Mathieu Coppey.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

Supplementary information

Supplementary Information

Supplementary Video Legends 1–5, Supplementary Figures 1–17, Supplementary Tables 1–3

Reporting Summary

Life Sciences Reporting Summary

Supplementary Video 1

Representative video of 25 nm Rho-NPs diffusing in a HeLa cell after pinocytic loading. Three unburst vesicles can be seen at the top of the images: two very, close together, on the left and one, very bright, in the middle. These unburst vesicles are much larger than individual NPs and are basically immobile. Images were acquired every 10 ms, the video is encoded at 30 frames per second (fps). Scale bar represents 10 μm

Supplementary Video 2

Representative videos of 25 nm Rho-NPs diffusing in HeLa cells after pinocytic loading. Top row: crops of the field of view showing unburst vesicles. NPs encapsulated within these intact vesicles move very fast and cannot be tracked individually. Bottom row: examples of typical regions of interest chosen for NPs single-particle tracking analysis and selected in a way such that unburst vesicles were systematically excluded. The marked difference in the behaviour of free versus encapsulated NPs ensures a reliable discrimination between the two cases. Images were acquired every 10 ms, the video is encoded at 30 fps. Scale bars represent 1 μm

Supplementary Video 3

Representative video of QDs (QDs-PEG-NH2) diffusing in a HeLa cell after pinocytic loading. QDs clearly show on-off fluctuations (blinking) of the fluorescence signal (use Supplementary Fig. 4 as reference to more easily spot blinking particles). The blinking behaviour is a typical signature of individual emitters and indicates that QDs are mono-dispersed, confirming their escape from pinocytic vesicles and dismissing potential aggregations. Images were acquired every 30 ms, the video is encoded at 15 fps. The counter indicates the frame number. Scale bar represents 2 μm

Supplementary Video 4

Representative video of the simultaneous diffusion of two different types of QDs, QDs-605 emitting at 605 nm (green) and QDs-PEG-NH2 emitting at 655 nm (red), after concomitant pinocytic internalization. The images clearly illustrate that in the two HeLa cells visible in the video there is no co-localization of the two probes, on the contrary of what expect if NPs would have been still encapsulated inside unburst vesicles. Images were acquired every 30 ms, the video is encoded at 15 fps. Scale bar represents 5 μm

Supplementary Video 5

The video shows the concurrent detection of the two types of QDs displayed in Supplementary Video 4: QDs-605 (red circles) and QDs-PEG-NH2 (yellow circles). The images put well in evidence the absence of correlated dynamics in the mobility of the two probes, on the contrary of what expected if NPs would have been still encapsulated inside unburst vesicles. Images were acquired every 30 ms, the video is encoded at 10 fps. Scale bar represents 5 μm

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Etoc, F., Balloul, E., Vicario, C. et al. Non-specific interactions govern cytosolic diffusion of nanosized objects in mammalian cells. Nature Mater 17, 740–746 (2018). https://doi.org/10.1038/s41563-018-0120-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41563-018-0120-7

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing