Macromolecular crowding creates heterogeneous environments of gene expression in picolitre droplets


Understanding the dynamics of complex enzymatic reactions in highly crowded small volumes is crucial for the development of synthetic minimal cells. Compartmentalized biochemical reactions in cell-sized containers exhibit a degree of randomness due to the small number of molecules involved. However, it is unknown how the physical environment contributes to the stochastic nature of multistep enzymatic processes. Here, we present a robust method to quantify gene expression noise in vitro using droplet microfluidics. We study the changes in stochasticity in the cell-free gene expression of two genes compartmentalized within droplets as a function of DNA copy number and macromolecular crowding. We find that decreased diffusion caused by a crowded environment leads to the spontaneous formation of heterogeneous microenvironments of mRNA as local production rates exceed the diffusion rates of macromolecules. This heterogeneity leads to a higher probability of the molecular machinery staying in the same microenvironment, directly increasing the system's stochasticity.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1: Correlated versus uncorrelated noise.
Figure 2: Effect of decreased copy number on inherent stochasticity of gene expression in pico-reactors.
Figure 3: Enhancement of uncorrelated noise in the presence of Ficoll.
Figure 4: Protein expression rates and ribosomal diffusion coefficients.
Figure 5: Inhomogeneous distribution of mRNA over one droplet at high Ficoll concentrations.
Figure 6: Theoretical modelling of gene expression noise.


  1. 1

    Maamar, H., Raj, A. & Dubnau, D. Noise in gene expression determines cell fate in Bacillus subtilis. Science 317, 526–529 (2007).

  2. 2

    Chang, H. H., Hemberg, M., Barahona, M., Ingber, D. E. & Huang, S. Transcriptome-wide noise controls lineage choice in mammalian progenitor cells. Nature 453, 544–547 (2008).

  3. 3

    Graf, T. & Stadtfeld, M. Heterogeneity of embryonic and adult stem cells. Cell Stem Cell 3, 480–483 (2008).

  4. 4

    Gupta, P. B. et al. Stochastic state transitions give rise to phenotypic equilibrium in populations of cancer cells. Cell 146, 633–644 (2011).

  5. 5

    Weinberger, L. S., Burnett, J. C., Toettcher, J. E., Arkin, A. P. & Schaffer, D. V. Stochastic gene expression in a lentiviral positive-feedback loop: HIV-1 Tat fluctuations drive phenotypic diversity. Cell 122, 169–182 (2005).

  6. 6

    Yu, J., Xiao, J., Ren, X., Lao, K. & Xie, X. S. Probing gene expression in live cells, one protein molecule at a time. Science 311, 1600–1603 (2006).

  7. 7

    Hensel, Z. et al. Stochastic expression dynamics of a transcription factor revealed by single-molecule noise analysis. Nature Struct. Mol. Biol. 19, 797–802 (2012).

  8. 8

    Munsky, B., Neuert, G. & van Oudenaarden, A. Using gene expression noise to understand gene regulation. Science 336, 183–187 (2012).

  9. 9

    Pedraza, J. M. & Paulsson, J. Effects of molecular memory and bursting on fluctuations in gene expression. Science 319, 339–343 (2008).

  10. 10

    Mettetal, J. T., Muzzey, D., Pedraza, J. M., Ozbudak, E. M. & van Oudenaarden, A. Predicting stochastic gene expression dynamics in single cells. Proc. Natl Acad. Sci. USA 103, 7304–7309 (2006).

  11. 11

    Elowitz, M. B., Levine, A. J., Siggia, E. D. & Swain, P. S. Stochastic gene expression in a single cell. Science 297, 1183–1186 (2002).

  12. 12

    Hilfinger, A. & Paulsson, J. Separating intrinsic from extrinsic fluctuations in dynamic biological systems. Proc. Natl Acad. Sci. USA 108, 12167–12172 (2011).

  13. 13

    Raser, J. M. & O'Shea, E. K. Control of stochasticity in eukaryotic gene expression. Science 304, 1811–1814 (2004).

  14. 14

    Nishimura, K., Tsuru, S., Suzuki, H. & Yomo, T. Stochasticity in gene expression in a cell-sized compartment. ACS Synth. Biol. 4, 566–576 (2015).

  15. 15

    Shahrezaei, V. & Swain, P. S. The stochastic nature of biochemical networks. Curr. Opin. Biotechnol. 19, 369–374 (2008).

  16. 16

    Weitz, M. et al. Diversity in the dynamical behaviour of a compartmentalized programmable biochemical oscillator. Nature Chem. 6, 295–302 (2014).

  17. 17

    Bratsun, D., Volfson, D., Tsimring, L. S. & Hasty, J. Delay-induced stochastic oscillations in gene regulation. Proc. Natl Acad. Sci. USA 102, 14593–14598 (2005).

  18. 18

    Elowitz, M. B. & Leibler, S. A synthetic oscillatory network of transcriptional regulators. Nature 403, 335–338 (2000).

  19. 19

    Zimmerman, S. B. & Harrison, B. Macromolecular crowding increases binding of DNA polymerase to DNA: an adaptive effect. Proc. Natl Acad. Sci. USA 84, 1871–1875 (1987).

  20. 20

    Minton, A. P. How can biochemical reactions within cells differ from those in test tubes? J. Cell Sci. 119, 2863–2869 (2006).

  21. 21

    Montero Llopis, P. et al. Spatial organization of the flow of genetic information in bacteria. Nature 466, 77–81 (2010).

  22. 22

    Klumpp, S., Scott, M., Pedersen, S. & Hwa, T. Molecular crowding limits translation and cell growth. Proc. Natl Acad. Sci. USA 110, 16754–16759 (2013).

  23. 23

    Brangwynne, C. P. et al. Germline P granules are liquid droplets that localize by controlled dissolution/condensation. Science 324, 1729–1732 (2009).

  24. 24

    Parry, B. R. et al. The bacterial cytoplasm has glass-like properties and is fluidized by metabolic activity. Cell 156, 183–194 (2014).

  25. 25

    Swain, P. S., Elowitz, M. B. & Siggia, E. D. Intrinsic and extrinsic contributions to stochasticity in gene expression. Proc. Natl Acad. Sci. USA 99, 12795–12800 (2002).

  26. 26

    Acar, M., Mettetal, J. T. & van Oudenaarden, A. Stochastic switching as a survival strategy in fluctuating environments. Nature Genet. 40, 471–475 (2008).

  27. 27

    van Zon, J. S., Morelli, M. J., Tănase-Nicola, S. & ten Wolde, P. R. Diffusion of transcription factors can drastically enhance the noise in gene expression. Biophys. J. 91, 4350–4367 (2006).

  28. 28

    Courtois, F. et al. An integrated device for monitoring time-dependent in vitro expression from single genes in picolitre droplets. ChemBioChem 9, 439–446 (2008).

  29. 29

    Karig, D. K., Jung, S.-Y., Srijanto, B., Collier, C. P. & Simpson, M. L. Probing cell-free gene expression noise in femtoliter volumes. ACS Synth. Biol. 2, 497–505 (2013).

  30. 30

    Sokolova, E. et al. Enhanced transcription rates in membrane-free protocells formed by coacervation of cell lysate. Proc. Natl Acad. Sci. USA 110, 11692–11697 (2013).

  31. 31

    Shim, J.-u. et al. Simultaneous determination of gene expression and enzymatic activity in individual bacterial cells in microdroplet compartments. J. Am. Chem. Soc. 131, 15251–15256 (2009).

  32. 32

    Paulsson, J. Summing up the noise in gene networks. Nature 427, 415–418 (2004).

  33. 33

    Dunlop, M. J., Cox, R. S., Levine, J. H., Murray, R. M. & Elowitz, M. B. Regulatory activity revealed by dynamic correlations in gene expression noise. Nature Genet. 40, 1493–1498 (2008).

  34. 34

    Ellis, R. J. Macromolecular crowding: obvious but underappreciated. Trends Biochem. Sci. 26, 597–604 (2001).

  35. 35

    Ge, X., Luo, D. & Xu, J. Cell-free protein expression under macromolecular crowding conditions. PLoS ONE 6, e28707 (2011).

  36. 36

    Gillespie, D. T., Petzold, L. R. & Seitaridou, E. Validity conditions for stochastic chemical kinetics in diffusion-limited systems. J. Chem. Phys. 140, 054111 (2014).

  37. 37

    Vargas, D. Y., Raj, A., Marras, S. A. E., Kramer, F. R. & Tyagi, S. Mechanism of mRNA transport in the nucleus. Proc. Natl Acad. Sci. USA 102, 17008–17013 (2005).

  38. 38

    Kim, S., Mlodzianoski, M., Bewersdorf, J. & Jacobs-Wagner, C. Probing spatial organization of mRNA in bacterial cells using 3D super-resolution microscopy. Biophys. J. 102, 278a (2012).

  39. 39

    Stogbauer, T., Windhager, L., Zimmer, R. & Radler, J. O. Experiment and mathematical modeling of gene expression dynamics in a cell-free system. Integr. Biol. 4, 494–501 (2012).

  40. 40

    Ozbudak, E. M., Thattai, M., Kurtser, I., Grossman, A. D. & van Oudenaarden, A. Regulation of noise in the expression of a single gene. Nature Genet. 31, 69–73 (2002).

Download references


The authors thank R.Y. Tsien for kindly donating the genes encoding for YFP and CFP, F.H.T. Nelissen and D. Foschepoth for assisting with cloning work, E. Dubuc for designing the molecular beacon, and J. Thiele for designing the masters for the fluidic devices. This work was supported by a European Research Council (ERC) Advanced Grant (246812 Intercom), a VICI grant from the Netherlands Organization for Scientific Research (NWO), and funding from the Ministry of Education, Culture and Science (Gravity programme, 024.001.035).

Author information

M.H., L.M. and W.H. conceived and designed the experiments. M.H., L.M., R.M. and M.V.R. performed the experiments. M.H. and L.M. analysed the data. E.S., J.G. and H.H. contributed materials/analysis tools. M.H. and W.H. co-wrote the paper.

Correspondence to Wilhelm T. S. Huck.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary information

Supplementary information (PDF 2792 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Hansen, M., Meijer, L., Spruijt, E. et al. Macromolecular crowding creates heterogeneous environments of gene expression in picolitre droplets. Nature Nanotech 11, 191–197 (2016).

Download citation

Further reading