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:

Universality of dissipative self-assembly from quantum dots to human cells

Nature Physics volume 16, pages 795–801 (2020)Cite this article

Subjects

Abstract

An important goal of self-assembly research is to develop a general methodology applicable to almost any material, from the smallest to the largest scales, whereby qualitatively identical results are obtained independently of initial conditions, size, shape and function of the constituents. Here, we introduce a dissipative self-assembly methodology demonstrated on a diverse spectrum of materials, from simple, passive, identical quantum dots (a few hundred atoms) that experience extreme Brownian motion, to complex, active, non-identical human cells (~1017 atoms) with sophisticated internal dynamics. Autocatalytic growth curves of the self-assembled aggregates are shown to scale identically, and interface fluctuations of growing aggregates obey the universal Tracy–Widom law. Example applications for nanoscience and biotechnology are further provided.

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: Universal dissipative self-assembly methodology.
Fig. 2: Universal dynamics.
Fig. 3: Spatiotemporal control over the aggregates.
Fig. 4: Proof-of-principle demonstrations on living organisms.

Similar content being viewed by others

Data availability

The data represented in Fig. 2, Supplementary Fig. 2 and Extended Data Figs. 3, 4, 5, 7, 8 and 10 are available as Source Data. Additional data may be requested from the corresponding author.

Code availability

MATLAB codes used to compute simulations of the Hammersley process and the length of the longest increasing subsequence for a given permutation, Tracy–Widom GUE simulations, image processing, and holographic algoritms are available from the authors on reasonable request.

References

  1. Heinen, L. & Walther, A. Celebrating Soft Matter’s 10th anniversary: approaches to program the time domain of self-assemblies. Soft Matter 11, 7857–7866 (2015).

    ADS  Google Scholar 

  2. Showalter, K. & Epstein, I. R. From chemical systems to systems chemistry: patterns in space and time. Chaos 25, 097613 (2015).

    ADS  Google Scholar 

  3. Wojciechowski, J. P., Martin, A. D. & Thordarson, P. Kinetically controlled lifetimes in redox-responsive transient supramolecular hydrogels. J. Am. Chem. Soc. 140, 2869–2874 (2018).

    Google Scholar 

  4. Hu, K. & Sheiko, S. S. Time programmable hydrogels: regulating the onset time of network dissociation by a reaction relay. Chem. Commun. 54, 5899 (2018).

    Google Scholar 

  5. Che, H., Cao, S. & Van Hest, J. C. M. Feedback-induced temporal control of ‘breathing’ polymersomes to create self-adaptive nanoreactors. J. Am. Chem. Soc. 140, 5356–5359 (2018).

    Google Scholar 

  6. Dhiman, S., Jain, A., Kumar, M. & George, S. J. Adenosine-phosphate-fueled, temporally programmed supramolecular polymers with multiple transient states. J. Am. Chem. Soc. 139, 16568–16575 (2017).

    Google Scholar 

  7. Burute, M. & Kapitein, L. C. Cellular logistics: unraveling the interplay between microtubule organization and intracellular transport. Annu. Rev. Cell Dev. Biol. 35, 22.1–22.26 (2019).

    Google Scholar 

  8. Cheeseman, I. M. & Desai, A. Molecular architecture of the kinetochore-microtubule interface. Nat. Rev. Mol. Cell Biol. 9, 33–46 (2008).

    Google Scholar 

  9. Bansagi, T., Vanag, V. K. & Epstein, I. R. Tomography of reaction-diffusion microemulsions reveals three-dimensional Turing patterns. Science 331, 1309–1312 (2011).

    ADS  MathSciNet  MATH  Google Scholar 

  10. Yan, J., Bloom, M., Bae, S. C., Luijten, E. & Granick, S. Linking synchronization to self-assembly using magnetic Janus colloids. Nature 491, 578–581 (2012).

    ADS  Google Scholar 

  11. Palacci, J., Sacanna, S., Steinberg, A. P., Pine, D. J. & Chaikin, P. M. Living crystals of light-activated colloidal surfers. Science 339, 936–940 (2013).

    ADS  Google Scholar 

  12. Aubret, A., Youssef, M., Sacanna, S. & Palacci, J. Targeted assembly and synchronization of self-spinning microgears. Nat. Phys. 14, 1114–1118 (2018).

    Google Scholar 

  13. Alapan, Y., Yigit, B., Beker, O., Demirörs, A. F. & Sitti, M. Shape-encoded dynamic assembly of mobile micromachines. Nat. Mater. 18, 1244–1251 (2019).

  14. Ilday, S. et al. Rich complex behaviour of self-assembled nanoparticles far from equilibrium. Nat. Commun. 8, 14942 (2017).

    ADS  Google Scholar 

  15. Bisket, G. & England, J. L. Nonequilibrium associative retrieval of multiple stored self-assembly targets. Proc. Natl Acad. Sci. USA 115, 45 (2018).

    Google Scholar 

  16. Prost, J., Julicher, F. & Joanny, J.-F. Active gel physics. Nat. Phys. 11, 111–117 (2015).

    Google Scholar 

  17. Tafoyaa, S., Larged, S. J., Liue, S., Bustamante, C. & Sivak, D. A. Using a system’s equilibrium behavior to reduce its energy dissipation in nonequilibrium processes. Proc. Natl Acad. Sci. USA 116, 5920–5924 (2019).

    Google Scholar 

  18. Jarzynski, C. Diverse phenomena, common themes. Nat. Phys. 11, 105–107 (2015).

    Google Scholar 

  19. Woods, D. Intrinsic universality and the computational power of self-assembly. Phil. Trans. R. Soc. A 373, 20140214 (2015).

    ADS  Google Scholar 

  20. Woods, D. et al. Diverse and robust molecular algorithms using reprogrammable DNA self-assembly. Nature 567, 366–372 (2019).

    ADS  Google Scholar 

  21. Lin, M. Y. et al. Universality in colloid aggregation. Nature 339, 360–362 (1989).

    ADS  Google Scholar 

  22. Barabasi, A.-L. & Stanley, H. E. Fractal Concepts in Surface Growth (Cambridge University Press, 1995).

  23. Tracy, C. A. & Widom, H. Level spacing distributions and the Airy kernel. Commun. Math. Phys. 159, 151–174 (1994).

    ADS  MathSciNet  MATH  Google Scholar 

  24. Tracy, C. A. & Widom, H. On orthogonal and symplectic matrix ensembles. Commun. Math. Phys. 177, 727–754 (1996).

    ADS  MathSciNet  MATH  Google Scholar 

  25. Majumdar, S. N. & Schehr, G. Top eigenvalue of a random matrix: large deviations and third order phase transition. J. Stat. Mech. 2014, P01012 (2014).

  26. Baik, J. & Rains, E. M. Limiting distributions for a polynuclear growth model with external sources. J. Stat. Phys. 100, 523–541 (2000).

    MathSciNet  MATH  Google Scholar 

  27. Prahofer, M. & Spohn, H. Universal distributions for growth processes in 1+1 dimensions and random matrices. Phys. Rev. Lett. 84, 4882–4885 (2000).

    ADS  Google Scholar 

  28. Amir, G., Corwin, I. & Quastel, J. Probability distribution of the free energy of the continuum directed random polymer in 1 + 1 dimensions. Commun. Pure Appl. Math. 64, 466–537 (2011).

  29. Biroli, G., Bouchaud, J.-P. & Potters, M. On the top eigenvalue of heavy-tailed random matrices. Eur. Phys. Lett. 78, 10001 (2007).

    ADS  MathSciNet  MATH  Google Scholar 

  30. Halpin-Healy, T. & Zhang, Y. C. Kinetic roughening phenomena, stochastic growth, directed polymers and all that. Phys. Rep. 254, 215–414 (1995).

    ADS  Google Scholar 

  31. Takeuchi, K. A. & Sano, M. Universal fluctuations of growing interfaces: evidence in turbulent liquid crystals. Phys. Rev. Lett. 104, 230601 (2010).

    ADS  Google Scholar 

  32. Miettinen, L., Myllys, M., Merikoski, J. & Timonen, J. Experimental determination of KPZ height-fluctuation distributions. Eur. Phys. J. B 46, 55–60 (2005).

    ADS  Google Scholar 

  33. Fridman, M., Pugatch, R., Nixon, M., Friesem, A. A. & Davidson, N. Measuring maximal eigenvalue distribution of Wishart random matrices with coupled lasers. Phys. Rev. E 85, 020101(R) (2012).

    ADS  Google Scholar 

  34. Vogel, A., Linz, N., Freidank, S. & Paltauf, G. Femtosecond-laser-induced nanocavitation in water: implications for optical breakdown threshold and cell surgery. Phys. Rev. Lett. 100, 038102 (2008).

    ADS  Google Scholar 

  35. Haken, H. Synergetics, an Introduction: Nonequilibrium Phase Transitions and Self-Organization in Physics, Chemistry, and Biology 3rd edn (Springer, 1983).

  36. Darcy, H. Les Fontaines Publiques de la Ville de Dijon (Dalmont, 1856).

  37. Vallejo, D. M., Juarez-Carreño, S., Bolivar, J., Morante, J. & Dominguez, M. A brain circuit that synchronizes growth and maturation revealed through Dilp8 binding to Lgr3. Science 350, aac6767 (2015).

    ADS  Google Scholar 

  38. Lindsay, R. J., Pawlowska, B. J. & Gudelj, I. Privatization of public goods can cause population decline. Nat. Ecol. Evol. 3, 1206–1216 (2019).

    Google Scholar 

  39. Chen, Y.-G. Logistic models of fractal dimension growth of urban morphology. Fractals 26, 1850033 (2018).

    ADS  Google Scholar 

  40. Kardar, M., Parisi, G. & Zhang, Y.-C. Dynamic scaling of growing interfaces. Phys. Rev. Lett. 56, 889–892 (1986).

    ADS  MATH  Google Scholar 

  41. Aldous, D. & Diaconis, P. Hammersley’s interacting particle process and longest increasing subsequences. Probab. Theory Relat. Fields 103, 199–213 (1995).

    MathSciNet  MATH  Google Scholar 

  42. Chiani, M. Distribution of the largest eigenvalue for real Wishart and Gaussian random matrices and a simple approximation for the Tracy–Widom distribution. J. Multivar. Anal. 129, 69–81 (2014).

    MathSciNet  MATH  Google Scholar 

  43. Baik, J., Deift, P. & Johansson, K. On the distribution of the length of the longest increasing subsequence of random permutations. J. Am. Math. Soc. 12, 1119–1178 (1999).

    MathSciNet  MATH  Google Scholar 

  44. Aldous, D. & Diaconis, P. Longest increasing subsequences: from patience sorting to the Baik-Deift-Johansson theorem. Bull. Am. Math. Soc. 36, 413–432 (1999).

    MathSciNet  MATH  Google Scholar 

  45. von Smoluchowski, M. Versucheiner mathematischen theorie der koagulations kinetic kolloider lousungen. Z. Phys. Chem. 92, 129–168 (1917).

    Google Scholar 

  46. Deegan, R. D. et al. Capillary flow as the cause of ring stains from dried liquid drops. Nature 389, 827–829 (1997).

    ADS  Google Scholar 

  47. Vella, D. & Mahadevan, L. The "Cheerios effect". Am. J. Phys. 72, 817–825 (2005).

  48. Meadows, P. S. The attachment of bacteria to solid surfaces. Arch. Mikrobiol. 75, 374–381 (1971).

    Google Scholar 

  49. Jones, J. F. & Velegol, D. Laser trap studies of end-on E. coli adhesion to glass. Colloids Surf. B 50, 66–71 (2006).

    Google Scholar 

  50. Fang, X. & Gomelsky, M. A post-translational, c-di-GMP-dependent mechanism regulating flagellar motility. Mol. Microbiol. 76, 1295–1305 (2010).

    Google Scholar 

  51. Ozel, T. et al. Anisotropic emission from multilayered plasmon resonator nanocomposites of isotropic semiconductor quantum dots. ACS Nano 5, 1328–1334 (2011).

    Google Scholar 

  52. Rogach, A. L. et al. Aqueous synthesis of thiol-capped CdTe nanocrystals: state-of-the-art. J. Phys. Chem. C 111, 14628–14637 (2007).

    Google Scholar 

  53. Parkinson, J. S. & Houts, S. E. Isolation and behavior of Escherichia coli deletion mutants lacking chemotaxis functions. J. Bacteriol. 151, 106–113 (1982).

    Google Scholar 

  54. Fleming, A. On a remarkable bacteriolytic element found in tissues and secretions. Proc. R. Soc. Lond. B 93, 306–317 (1922).

    ADS  Google Scholar 

  55. Atlas, R. M. in Handbook of Microbiological Media 4th edn, 934 (Taylor and Francis, 2010).

  56. Atlas, R. M. in Handbook of Microbiological Media 4th edn, 1949 (Taylor and Francis, 2010).

  57. Olivares-Marin, I. K., González-Hernández, J. C., Regalado-Gonzalez, C. & Madrigal-Perez, L. A. Saccharomyces cerevisiae exponential growth kinetics in batch culture to analyze respiratory and fermentative metabolism. J. Vis. Exp. (139), e58192 (2018).

  58. Swiecicki, J.-M., Sliusarenko, O. & Weibel, D. B. From swimming to swarming: Escherichia coli cell motility in two-dimensions. Integr. Biol. 5, 1490–1494 (2013).

    Google Scholar 

  59. Hasan, N. M., Adams, G. E. & Joiner, M. C. Effect of serum starvation on expression and phosphorylation of PKC-alpha and p53 in V79 cells: implications for cell death. Int. J. Cancer 80, 400–405 (1999).

    Google Scholar 

  60. Masamha, C. P. & Benbrook, D. M. Cyclin D1 degradation is sufficient to induce G1 cell cycle arrest despite constitutive expression of cyclin E2 in ovarian cancer cells. Cancer Res. 69, 6565–6572 (2009).

    Google Scholar 

  61. Rezaei, P. F., Fouladdel, S., Ghaffari, S. M., Amin, G. & Azizi, E. Induction of G1 cell cycle arrest and cyclin D1 down-regulation in response to pericarp extract of Baneh in human breast cancer T47D cells. Daru 20, 101 (2012).

    Google Scholar 

  62. Gonzalez, R. C., Woods, R. E. & Eddins, S. L. Digital Image Processing using MATLAB (McGraw-Hill, 2013).

  63. Najarian, K. & Splinter, R. Biomedical Signal and Image Processing (CRC Press, 2016).

  64. Marques, O. Practical Image and Video Processing Using MATLAB (Wiley, 2011).

  65. King, A. & Aljabar, P. MATLAB Programming for Biomedical Engineers and Scientists (Academic, 2017).

  66. Erdős, P. & Szekeres, G. A combinatorial theorem in geometry. Compositio Math. 2, 463–470 (1935).

    MathSciNet  MATH  Google Scholar 

  67. Logan, B. F. & Shepp, L. A. A variational problem for random Young tableaux. Adv. Math. 26, 206–222 (1977).

    MathSciNet  MATH  Google Scholar 

  68. Seppalainen, T. A microscopic model for the Burgers equation and longest increasing subsequences. Electron. J. Probab. 1, 1–51 (1994).

    MathSciNet  MATH  Google Scholar 

  69. Vershik, A. M. & Kerov, S. V. Asymptotics of the Plancherel measure of the symmetric group and the limiting form of Young tableaux. Sov. Math. Dokl. 18, 527–531 (1977).

    MATH  Google Scholar 

  70. Hammersley, J. M. A few seedlings of research. In Proc. 6th Berkeley Symposium on Mathematical Statistics and Probability Vol. 1, 345–394 (University of California Press, 1972).

Download references

Acknowledgements

This work received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement 853387), TÜBİTAK under projects 115F110 and 117F352, and a L’Oréal–UNESCO FWIS award. F.Ö.I., G.M. and H.V.D. gratefully acknowledge funding from the ERC Consolidator Grant ERC-617521 NLL, TÜBİTAK under project 117E823, and NRF-NRF 1-2016-08 and TÜBA, respectively.

Author information

Authors and Affiliations

  1. UNAM-National Nanotechnology Research Center and Institute of Materials Science and Nanotechnology, Bilkent University, Ankara, Turkey

    Ghaith Makey, Sezin Galioglu, Roujin Ghaffari, Kıvanç Güngör, Didem Dede, H. Volkan Demir, F. Ömer Ilday & Serim Ilday

  2. Department of Physics, Bilkent University, Ankara, Turkey

    Ghaith Makey, H. Volkan Demir & F. Ömer Ilday

  3. Institute of Biotechnology, Ankara University, Ankara, Turkey

    E. Doruk Engin

  4. Department of Mathematics, Bilkent University, Ankara, Turkey

    Gökhan Yıldırım

  5. Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey

    Özgün Yavuz, H. Volkan Demir & F. Ömer Ilday

  6. Department of Physics, Middle East Technical University, Ankara, Turkey

    Onurcan Bektaş

  7. Department of Mathematics, Middle East Technical University, Ankara, Turkey

    Onurcan Bektaş

  8. Department of Physics, Boğaziçi University, İstanbul, Turkey

    Ü. Seleme Nizam

  9. Department of Molecular Biology and Genetics, Bilkent University, Ankara, Turkey

    Özge Akbulut & Özgür Şahin

  10. Department of Drug Discovery and Biomedical Sciences, University of South Carolina, Columbia, SC, USA

    Özgür Şahin

  11. LUMINOUS! Center of Excellence for Semiconductor Lighting and Displays, School of Electrical and Electronic Engineering, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, Singapore

    H. Volkan Demir

Authors
  1. Ghaith Makey
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sezin Galioglu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Roujin Ghaffari
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. E. Doruk Engin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Gökhan Yıldırım
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Özgün Yavuz
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Onurcan Bektaş
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Ü. Seleme Nizam
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Özge Akbulut
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Özgür Şahin
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Kıvanç Güngör
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Didem Dede
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. H. Volkan Demir
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. F. Ömer Ilday
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Serim Ilday
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

S.I. designed the research and wrote the paper. S.I., S.G., R.G., G.M., Ö.Y. and O.B. performed the experiments. G.M. carried out image processing. G.M., O.B. and F.Ö.I. performed statistical analysis. Ü.S.N. carried out the numerical simulations of fluid dynamics. G.Y. provided the MATLAB code for the mathematical models. E.D.E. prepared the microorganisms. Ö.A. and Ö.Ş. prepared the human cells. K.G., D.D. and H.V.D. prepared the quantum dots.

Corresponding author

Correspondence to Serim Ilday.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Physics thanks Gili Bisker 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.

Extended data

Extended Data Fig. 1 Numerical simulation of the fluid flows.

Numerical simulation results show velocity field distribution of the fluid flows and corresponding streamlines (a) in the presence and (b) in the absence of a cavitation bubble. Red (dark blue) denotes the highest (lowest) flow speeds. The cavitation bubble is depicted by the white circle located at the centre of the computational area. The effect of the laser pulses is modelled as a boundary heat source at the lower right quarter of the bubble. A porous medium was introduced to model the aggregate positioned adjacent to the lower-right quarter of the bubble. In the absence of a bubble, we described the aggregate, also as a porous medium, located at the centre of the computational area (black circle). The beam profile of the laser is Gaussian, so we introduced a heat source with a Gaussian temperature profile to represent the effect of the laser. This source was located at the centre of the aggregate. The diameters of the porous medium and the laser beam were chosen to be 15 µm and 8 µm. The streamlines can be seen to penetrate the aggregate because it is modelled as a porous medium.

Extended Data Fig. 2 Microscopy images of aggregate collection and dissolution.

Microscopy images showing collection (laser on) and dissolution (laser off) of the aggregates of the particles and organisms. Red dot denotes position of the laser beam. Beam sizes are not drawn to scale; it is fixed to be ~8 µm in all experiments. A ×40 objective was used for imaging the CdTe quantum dots, polystyrene spheres, and S. cerevisiae yeast cells, whereas a ×100 objective was used for M. Luteus and E. Coli bacterial cells and ×10 for MCF10A human cells.

Extended Data Fig. 3 Growth curves at original time scales.

Graphs showing individual growth curves of particles and living organisms at their original time scales.

Source data

Extended Data Fig. 4 Growth curves in the presence or absence of a cavitation bubble.

Comparison of the growth curves of aggregates growing at and in the absence of a cavitation bubble.

Source data

Extended Data Fig. 5 Interface fluctuations of a virtual aggregate.

Illustration and microscopy image (inset) showing the control experiment. Arrows denote the direction of the fluid flow towards the open end of sample. Particles are assumed to be collected passing the virtual boundary. Interface fluctuations were calculated using average width (yn) and height (h(tframe, yn)) of the growing aggregates. Semi-log scale plot showing experimentally obtained probability distribution function of the interface fluctuations (green dots). A ×100 objective was used for the imaging.

Source data

Extended Data Fig. 6 Control over the aggregate movement.

Time-lapse microscopy images showing an aggregate of 0.5-µm polystyrene colloids following the movement of laser beam to form a line (top), and a wave-like pattern (bottom). The bright white dot is the laser beam. The transmission of a small portion of the infrared beam is allowed from the visible lowpass filter (with relatively low attenuation to infrared) to show its exact position. Illustrated red dots show the movement of the beam. ×60 objective was used for the imaging. .

Extended Data Fig. 7 CdTe characterization.

a, Transmission electron microscopy image and (b) photoluminescence spectrum of the aqueous CdTe quantum dots.

Source data

Extended Data Fig. 8 Filling ratios for different region of interests.

Filling ratio of different selected areas (region of interest: ROI) with E.coli and M. Luteus bacterial cells during their aggregation. Areas with different sizes (ROI 1, 2, and 3 of (a), (b), and (c)), shapes (a square, ROI 3, and a circle, ROI 4), and positions within the aggregate (c) do not affect the filling ratio.

Source data

Extended Data Fig. 9 Detection of CdTe photoluminescence signal.

Time-lapse microscopy images (top) and plots (bottom) showing detection of photoluminescence signal emitted from growing CdTe quantum dot aggregate. A ×40 objective was used for the imaging.

Extended Data Fig. 10 Tracy–Widom GUE temporal span analysis.

The evolution of the scaled normalized moments of temporal fluctuations with that of Tracy–Widom GUE with respect to temporal span.

Source data

Supplementary information

Supplementary Information

Supplementary text, methods, Figs. 1–3, Table 1 and references.

Reporting Summary

Supplementary Video 1

Aggregation of 0.5-µm polystyrene spheres adjacent to a cavitation bubble (left) or at the laser spot (right) in a quasi-2D setting through ultrafast laser-induced flows using a ×60 objective.

Supplementary Video 2

Collection (laser on) and dissolution (laser off) of the aggregates of particles and organisms. Red dot denotes the position of laser beam. Beam sizes are not drawn to scale, it is fixed to be ~8 µm in all experiments. A ×40 objective was used for the imaging of CdTe quantum dots, polystyrene spheres, and S. cerevisiae bacterial cells, whereas a ×100 objective was used for M. Luteus and E. Coli bacterial cells and ×10 for MCF10A human cells.

Supplementary Video 3

Three different experiments showing circularly growing aggregates (first column), calculated standard deviation maps (second column), and detected interfaces (third column) of growing aggregates using a ×100 objective.

Supplementary Video 4

Unidirectional fluid flow due to the pressure gradient, which drags particles towards the open end of the sample (right-hand-side) in the absence of a laser light using a ×100 objective.

Supplementary Video 5

Spatiotemporal control over the aggregates of polystyrene spheres using a ×60 objective. The bright white dot is the laser beam. The transmission of a small portion of the infrared beam is allowed from the visible low-pass filter (with relatively low attenuation to infrared) to show its exact position.

Supplementary Video 6

Aggregates of 0.5-µm polystyrene spheres can be patterned to write words and to give arbitrary geometrical shapes following the beam patterns (left) using a ×40 objective.

Supplementary Video 7

Spatiotemporal control over the aggregates of ~3 nm large quantum dots and ~5 µm large S. cerevisiae yeast cells using a ×40 objective.

Supplementary Video 8

Separation of M. luteus (gram positive) from E. coli (gram-negative) bacterial cells from an initially homogeneous mixture using a ×100 objective.

Supplementary Video 9

Formation of vertex flows that stirs S. cerevisiae yeast cells when the laser beam is moved using a ×40 objective.

Supplementary Video 10

Detection of E. coli and M. luteus bacterial cells using a ×100 objective in a selected rectangular area to calculate the filling ratio.

Source data

Source Data Fig. 2

Source Data Figure 2

Source Data Fig. S2

Source Data Figure S2

Source Data Extended Data Fig. 3

Source Data Extended Data Figure 3

Source Data Extended Data Fig. 4

Source Data Extended Data Figure 4

Source Data Extended Data Fig. 5

Source Data Extended Data Figure 5

Source Data Extended Data Fig. 7

Source Data Extended Data Figure 7

Source Data Extended Data Fig. 8

Source Data Extended Data Figure 8

Source Data Extended Data Fig. 10

Source Data Extended Data Figure 10

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Makey, G., Galioglu, S., Ghaffari, R. et al. Universality of dissipative self-assembly from quantum dots to human cells. Nat. Phys. 16, 795–801 (2020). https://doi.org/10.1038/s41567-020-0879-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41567-020-0879-8

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