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DNA-based communication in populations of synthetic protocells


Developing molecular communication platforms based on orthogonal communication channels is a crucial step towards engineering artificial multicellular systems. Here, we present a general and scalable platform entitled ‘biomolecular implementation of protocellular communication’ (BIO-PC) to engineer distributed multichannel molecular communication between populations of non-lipid semipermeable microcapsules. Our method leverages the modularity and scalability of enzyme-free DNA strand-displacement circuits to develop protocellular consortia that can sense, process and respond to DNA-based messages. We engineer a rich variety of biochemical communication devices capable of cascaded amplification, bidirectional communication and distributed computational operations. Encapsulating DNA strand-displacement circuits further allows their use in concentrated serum where non-compartmentalized DNA circuits cannot operate. BIO-PC enables reliable execution of distributed DNA-based molecular programs in biologically relevant environments and opens new directions in DNA computing and minimal cell technology.

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The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Journal peer review information Nature Nanotechnology thanks Friedrich Simmel and other anonymous reviewer(s) for their contribution to the peer review of this work.

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  1. 1.

    Waters, C. M. & Bassler, B. L. Quorum sensing: cell-to-cell communication in bacteria. Annu. Rev. Cell Dev. Biol. 21, 319–346 (2005).

  2. 2.

    Doganer, B. A., Yan, L. K. & Youk, H. Autocrine signaling and quorum sensing: extreme ends of a common spectrum. Trends Cell Biol. 26, 262–271 (2016).

  3. 3.

    Barcena Menendez, D., Senthivel, V. R. & Isalan, M. Sender–receiver systems and applying information theory for quantitative synthetic biology. Curr. Opin. Biotechnol. 31, 101–107 (2015).

  4. 4.

    Hart, Y., Antebi, Y. E., Mayo, A. E., Friedman, N. & Alon, U. Design principles of cell circuits with paradoxical components. Proc. Natl Acad. Sci. USA 109, 8346–8351 (2012).

  5. 5.

    You, L., Cox, R. S. III, Weiss, R. & Arnold, F. H. Programmed population control by cell–cell communication and regulated killing. Nature 428, 868–871 (2004).

  6. 6.

    Basu, S., Gerchman, Y., Collins, C. H., Arnold, F. H. & Weiss, R. A synthetic multicellular system for programmed pattern formation. Nature 434, 1130–1134 (2005).

  7. 7.

    Tabor, J. J. et al. A synthetic genetic edge detection program. Cell 137, 1272–1281 (2009).

  8. 8.

    Ortiz, M. E. & Endy, D. Engineered cell–cell communication via DNA messaging. J. Biol. Eng. 6, 16 (2012).

  9. 9.

    Youk, H. & Lim, W. Secreting and sensing the same molecule allows cells to achieve versatile social behaviors. Science 343, 1242782 (2014).

  10. 10.

    Balagaddé et al. A synthetic Escherichia coli predator–prey ecosystem. Mol. Syst. Biol. 4, 187 (2008).

  11. 11.

    Bacchus, W. et al. Synthetic two-way communication between mammalian cells. Nat. Biotechnol. 30, 991–996 (2012).

  12. 12.

    Danino, T., Mondragon-Palomino, O., Tsimring, L. & Hasty, J. A synchronized quorum of genetic clocks. Naure 463, 326–330 (2010).

  13. 13.

    Tamsir, A., Tabor, J. J. & Voigt, C. A. Robust multicellular computing using genetically encoded NOR gates and chemical ‘wires’. Nature 469, 212–215 (2011).

  14. 14.

    Regot et al. Distributed biological computation with multicellular engineered networks. Nature 469, 207–211 (2011).

  15. 15.

    Huang, S. et al. Coupling spatial segregation with synthetic circuits to control bacterial survival. Mol. Syst. Biol. 12, 859 (2016).

  16. 16.

    Cardinale, S. & Arkin, A. P. Contextualizing context for synthetic biology—identifying causes of failure of synthetic biological systems. Biotechnol. J. 7, 856–866 (2012).

  17. 17.

    Lentini, R., Yeh Martín, N. & Mansy, S. S. Communicating artificial cells. Curr. Opin. Chem. Biol. 34, 53–61 (2016).

  18. 18.

    Buddingh, B. & van Hest, J. C. M. Artificial cells: synthetic compartments with life-like functionality and adaptivity. Acc. Chem. Res. 50, 769–777 (2017).

  19. 19.

    Salehi-Reyhani, A., Ces, O. & Elani, Y. Artificial cell mimics as simplified models for the study of cell biology. Exp. Biol. Med. 242, 1309–1317 (2017).

  20. 20.

    Lim, W. A., Lee, C. M. & Tang, C. Design principles of regulatory networks: searching for the molecular algorithms of the cell. Mol. Cell 49, 202–212 (2013).

  21. 21.

    Points, L. J., Taylor, J. W., Grizou, J., Donkers, K. & Cronin, L. Artificial intelligence exploration of unstable protocells leads to predictable properties and discovery of collective behavior. Proc. Natl Acad. Sci. USA 115, 885–890 (2018).

  22. 22.

    Adamala, K. P., Martin-Alarcon, D. A., Guthrie-Honea, K. R. & Boyden, E. S. Engineering genetic circuit interactions within and between synthetic minimal cells. Nat. Chem. 9, 431–439 (2017).

  23. 23.

    Qiao, Y., Li, M., Booth, R. & Mann, S. Predatory behaviour in synthetic protocell communities. Nat. Chem. 9, 110–119 (2016).

  24. 24.

    Tang, T.-Y. D. et al. Gene-mediated chemical communication in synthetic protocell communities. ACS Synth. Biol. 7, 339–346 (2018).

  25. 25.

    Sun, S. et al. Chemical signaling and functional activation in colloidosome-based protocells. Small 12, 1920–1927 (2016).

  26. 26.

    Huang, X. et al. Interfacial assembly of protein–polymer nano-conjugates into stimulus-responsive biomimetic protocells. Nat. Commun. 4, 2239 (2013).

  27. 27.

    Zhang, D. Y. & Seelig, G. Dynamic DNA nanotechnology using strand-displacement reactions. Nat. Chem. 3, 103–113 (2011).

  28. 28.

    Yurke, B. et al. A DNA-fuelled molecular machine made of DNA. Nature 406, 605–608 (2000).

  29. 29.

    Seelig, G., Soloveichik, D., Zhang, D. Y. & Winfree, E. Enzyme-free nucleic acid logic circuits. Science 314, 1585–1588 (2006).

  30. 30.

    Qian, L. & Winfree, E. Scaling up digital circuit computation with DNA strand displacement cascades. Science 332, 1196–1201 (2011).

  31. 31.

    Qian, L., Winfree, E. & Bruck, J. Neural network computation with DNA strand displacement cascades. Nature 475, 368–372 (2011).

  32. 32.

    Chen, Y.-J. et al. Programmable chemical controllers made from DNA. ‎Nat. Nanotechnol. 8, 755–762 (2013).

  33. 33.

    Srinivas, N., Parkin, J., Seelig, G., Winfree, E. & Soloveichik, D. Enzyme-free nucleic acid dynamical systems. Science 358, eaal2052 (2017).

  34. 34.

    Frezza, B. M., Cockroft, S. L. & Ghadiri, M. R. Modular multi-level circuits from immobilized DNA-based logic gates. J. Am. Chem. Soc. 129, 14875–14879 (2007).

  35. 35.

    Yashin, R., Rudchenko, S. & Stojanovic, M. N. Networking particles over distance using oligonucleotide-based devices. J. Am. Chem. Soc. 129, 15581–15584 (2007).

  36. 36.

    Gines, G. et al. Microscopic agents programmed by DNA circuits. Nat. Nanotechnol. 12, 351–359 (2017).

  37. 37.

    Zhang, D. Y. & Winfree, E. Control of DNA strand displacement kinetics using toehold exchange. J. Am. Chem. Soc. 131, 17303–17314 (2009).

  38. 38.

    Dunn, K. E., Trefzer, M. A., Johnson, S. & Tyrrell, A. M. Investigating the dynamics of surface-immobilized DNA nanomachines. Sci. Rep. 6, 29581 (2016).

  39. 39.

    Teichmann, M., Kopperger, E. & Simmel, F. C. Robustness of localized DNA strand displacement cascades. ACS Nano 8, 8487–8496 (2014).

  40. 40.

    Chatterjee, G., Dalchau, N., Muscat, R. A., Phillips, A. & Seelig, G. A spatially localized architecture for fast and modular DNA computing. Nat. Nanotechnol. 12, 920–927 (2017).

  41. 41.

    Zhang, D. Y., Turberfield, A. J., Yurke, B. & Winfree, E. Engineering entropy-driven reactions and networks catalyzed by DNA. Science 318, 1121–1125 (2007).

  42. 42.

    Lakin, M. R., Youssef, S., Polo, F., Emmott, S. & Phillips, A. Visual DSD: a design and analysis tool for DNA strand displacement systems. Bioinformatics 27, 3211–3213 (2011).

  43. 43.

    Chen, X., Briggs, N., McLain, J. R. & Ellington, A. D. Stacking nonenzymatic circuits for high signal gain. Proc. Natl Acad. Sci. USA 110, 5386–5391 (2013).

  44. 44.

    Freeman, M. Feedback control of intercellular signalling in development. Nature 408, 313–319 (2000).

  45. 45.

    Vertosick, F. T. & Kelly, R. H. Immune network theory: a role for parallel distributed processing? Immunology 66, 1–7 (1989).

  46. 46.

    Chen, Y.-J., Groves, B., Muscat, R. A. & Seelig, G. DNA nanotechnology from the test tube to the cell. Nat. Nanotechnol. 10, 748–760 (2015).

  47. 47.

    Fern, J. & Schulman, R. Design and characterization of DNA strand-displacement circuits in serum-supplemented cell medium. ACS Synth. Biol. 6, 1774–1783 (2017).

  48. 48.

    Cherry, K. M. & Qian, L. Scaling up molecular pattern recognition with DNA-based winner-take-all neural networks. Nature 559, 370–376 (2018).

  49. 49.

    Lopez, R., Wang, R. & Seelig, G. A molecular multi-gene classifier for disease diagnostics. Nat. Chem. 10, 746–754 (2018).

  50. 50.

    Ugrinic, M. et al. Microfluidic formation of proteinosomes. Chem. Commun. 54, 287–290 (2018).

  51. 51.

    Liu, L. et al. Construction of biological hybrid microcapsules with defined permeability towards programmed release of biomacromolecules. Chem. Commun. 53, 11678–11681 (2017).

  52. 52.

    Fern, J. et al. DNA strand-displacement timer circuits. ACS Synth. Biol. 6, 190–193 (2017).

  53. 53.

    Zadeh, J. N. et al. NUPACK: analysis and design of nucleic acid systems. J. Comput. Chem. 32, 170–173 (2011).

  54. 54.

    Unger, M. A., Chou, H.-P., Thorsen, T., Scherer, A. & Quake, S. R. Monolithic microfabricated valves and pumps by multilayer soft lithography. Science 288, 113–116 (2000).

  55. 55.

    Padirac, A., Fujii, T. & Rondelez, Y. Quencher-free multiplexed monitoring of DNA reaction circuits. Nucleic Acids Res. 40, e118 (2012).

  56. 56.

    Dalchau, N., Seelig, G. & Phillips, A. Computational Design of Reaction–Diffusion Patterns Using DNA-Based Chemical Reaction Networks, Vol. 8727, 84–99 (Lecture Notes in Computer Science Series, Springer, 2014).

  57. 57.

    Lukacs, G. L. et al. Size-dependent DNA mobility in cytoplasm and nucleus. J. Biol. Chem. 275, 1625–1629 (2000).

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The authors thank G. Seelig, W. Mulder and L. Cronin for helpful discussions. This work was supported by the European Research Council (ERC project no. 677313 BioCircuit), the ERC Advanced Grant Scheme (EC-2016-ADG 740235) and a Marie Curie Individual Fellowship, an NWO-VIDI grant from the Netherlands Organisation for Scientific Research (NWO, 723.016.003), funding from the Ministry of Education, Culture and Science (Gravity programmes 024.001.035 and 024.003.013) and BrisSynBio (BB/L01386X/1).

Author information

A.J. designed the study, performed experiments, analysed the data and wrote the manuscript. S.Y., B.B. and P.P. performed experiments and analysed the data. A.v.d.L. and A.J. designed and fabricated the microfluidic chip. N.D. and A.P. performed computational experiments. B.V.V.S.P.K. and S.M. provided key reagents and provided critical input for the initial experiments. T.F.A.d.G. conceived, designed and supervised the study, analysed the data and wrote the manuscript. S.M. revised the manuscript. All authors discussed the results and commented on the manuscript.

Competing interests

The authors declare no competing interests.

Correspondence to Tom F. A. de Greef.

Supplementary information

Supplementary information

Supplementary Methods, Figures 1–14, Notes, Tables 1–6 and References.

Supplementary Video 1

Diffusion of a fluorescently labelled input DNA strand into protocells and activation of a fluorescent DNA gate complex.

Supplementary Video 2

Activation of the two-layer signalling cascade.

Supplementary Video 3

Negative feedback loop in two populations.

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Fig. 1: Design elements for BIO-PC.
Fig. 2: Signalling cascade between protocell populations using non-enzymatic DNA signal amplification.
Fig. 3: Three-layer amplified signalling cascade.
Fig. 4: Protocellular negative feedback loop.
Fig. 5: Compartmentalized DNA-based Boolean logic circuits.
Fig. 6: Compartmentalized DNA reaction networks in 50% FBS.