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

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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.

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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.

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.


  1. 1.

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

  2. 2.

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

  3. 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).

  4. 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).

  5. 5.

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

  6. 6.

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

  7. 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).

  8. 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).

  9. 9.

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

  10. 10.

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

  11. 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).

  12. 12.

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

  13. 13.

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

  14. 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).

  15. 15.

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

  16. 16.

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

  17. 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).

  18. 18.

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

  19. 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).

  20. 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).

  21. 21.

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

  22. 22.

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

  23. 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).

  24. 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).

  25. 25.

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

  26. 26.

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

  27. 27.

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

  28. 28.

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

  29. 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).

  30. 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).

  31. 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).

  32. 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).

  33. 33.

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

  34. 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).

  35. 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).

  36. 36.

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

  37. 37.

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

  38. 38.

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

  39. 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).

  40. 40.

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

  41. 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).

  42. 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).

  43. 43.

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

  44. 44.

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

  45. 45.

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

  46. 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).

  47. 47.

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

  48. 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).

  49. 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).

  50. 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).

  51. 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).

  52. 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).

  53. 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).

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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

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.

Correspondence to Davide Normanno or Maxime Dahan or Mathieu Coppey.

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Supplementary information

Supplementary Information

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

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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

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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) doi:10.1038/s41563-018-0120-7

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