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:

Quantitative imaging of RNA polymerase II activity in plants reveals the single-cell basis of tissue-wide transcriptional dynamics

Nature Plants volume 7, pages 1037–1049 (2021)Cite this article

Subjects

Abstract

The responses of plants to their environment are often dependent on the spatiotemporal dynamics of transcriptional regulation. While live-imaging tools have been used extensively to quantitatively capture rapid transcriptional dynamics in living animal cells, the lack of implementation of these technologies in plants has limited concomitant quantitative studies in this kingdom. Here, we applied the PP7 and MS2 RNA-labelling technologies for the quantitative imaging of RNA polymerase II activity dynamics in single cells of living plants as they respond to experimental treatments. Using this technology, we counted nascent RNA transcripts in real time in Nicotiana benthamiana (tobacco) and Arabidopsis thaliana. Examination of heat shock reporters revealed that plant tissues respond to external signals by modulating the proportion of cells that switch from an undetectable basal state to a high-transcription state, instead of modulating the rate of transcription across all cells in a graded fashion. This switch-like behaviour, combined with cell-to-cell variability in transcription rate, results in mRNA production variability spanning three orders of magnitude. We determined that cellular heterogeneity stems mainly from stochasticity intrinsic to individual alleles instead of variability in cellular composition. Together, our results demonstrate that it is now possible to quantitatively study the dynamics of transcriptional programs in single cells of living plants.

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: Fluorescence labelling of nascent RNA in tobacco and Arabidopsis reveals single-cell transcriptional dynamics in real time.
Fig. 2: Cross validation, absolute calibration and sensitivity of the PP7 reporter system.
Fig. 3: Single-cell control of transcriptional activity in response to heat shock in Arabidopsis.
Fig. 4: Single-cell regulatory strategies determining tissue-wide transcriptional dynamics.
Fig. 5: Allele-specific processes explain most of the cellular heterogeneity in produced mRNA in Arabidopsis.

Similar content being viewed by others

Data availability

Raw and analysed data are available upon request. All plasmids used in this study are listed in Supplementary Table 1 and were submitted to the AddGene public repository. Arabidopsis seeds are listed in Supplementary Table 3 and are available from the Arabidopsis Biological Resource Center stock centre and/or upon request from the Niyogi laboratory. Source data are provided with this paper.

Code availability

All code used to analyse raw data can be found in the public GitHub repositories https://github.com/GarciaLab/mRNADynamics and https://github.com/GarciaLab/PlantPP7.

References

  1. Suzuki, N. et al. Ultra-fast alterations in mRNA levels uncover multiple players in light stress acclimation in plants. Plant J. 84, 760–772 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Leivar, P. et al. Definition of early transcriptional circuitry involved in light-induced reversal of PIF-imposed repression of photomorphogenesis in young Arabidopsis seedlings. Plant Cell 21, 3535–53 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Krouk, G., Mirowski, P., LeCun, Y., Shasha, D. E. & Coruzzi, G. M. Predictive network modeling of the high-resolution dynamic plant transcriptome in response to nitrate. Genome Biol. 11, R123 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Zandalinas, S. I., Fritschi, F. B., Mittler, R. & Lawson, T. Signal transduction networks during stress combination. J. Exp. Bot. 71, 1734–1741 (2020).

    Article  CAS  PubMed  Google Scholar 

  5. Gould, P. D. et al. Coordination of robust single cell rhythms in the Arabidopsis circadian clock via spatial waves of gene expression. eLife 7, e31700 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Kollist, H. et al. Rapid responses to abiotic stress: priming the landscape for the signal transduction network. Trends Plant Sci. 24, 25–37 (2019).

    Article  CAS  PubMed  Google Scholar 

  7. Balleza, E., Kim, J. M. & Cluzel, P. Systematic characterization of maturation time of fluorescent proteins in living cells. Nat. Methods 15, 47–51 (2018).

    Article  CAS  PubMed  Google Scholar 

  8. Lucas, T. et al. Live imaging of bicoid-dependent transcription in Drosophila embryos. Curr. Biol. 23, 2135–2139 (2013).

    Article  CAS  PubMed  Google Scholar 

  9. Garcia, H. G., Tikhonov, M., Lin, A. & Gregor, T. Quantitative imaging of transcription in living Drosophila embryos links polymerase activity to patterning. Curr. Biol. 23, 2140–2145 (2013).

    Article  CAS  PubMed  Google Scholar 

  10. Lee, C. H., Shin, H. & Kimble, J. Dynamics of notch-dependent transcriptional bursting in its native context. Dev. Cell 50, 426–435 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Park, S. J. et al. Optimization of crop productivity in tomato using induced mutations in the florigen pathway. Nat. Genet. 46, 1337–42 (2014).

    Article  CAS  PubMed  Google Scholar 

  12. Hamada, S. et al. The transport of prolamine RNAs to prolamine protein bodies in living rice endosperm cells. Plant Cell 15, 2253–2264 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Zhang, F. Simon, A. E. A novel procedure for the localization of viral RNAs in protoplasts and whole plants. Plant J. 35, 665–673 (2003).

    Article  CAS  PubMed  Google Scholar 

  14. Schönberger, J., Hammes, U. Z. & Dresselhaus, T. In vivo visualization of RNA in plants cells using the λN22 system and a GATEWAY-compatible vector series for candidate RNAs. Plant J. 71, 173–181 (2012).

    Article  PubMed  Google Scholar 

  15. Lenstra, T. L., Rodriguez, J., Chen, H. & Larson, D. R. Transcription dynamics in living cells. Annu. Rev. Biophys. 45, 25–47 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Larson, D. R., Zenklusen, D., Wu, B., Chao, J. A. & Singer, R. H. Real-time observation of transcription initiation and elongation on an endogenous yeast gene. Science 332, 475–478 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Lammers, N. C. et al. Multimodal transcriptional control of pattern formation in embryonic development. Proc. Natl Acad. Sci. USA 117, 836–847 (2020).

    Article  CAS  PubMed  Google Scholar 

  18. Bindels, D. S. et al. mScarlet : a bright monomeric red fluorescent protein for cellular imaging. Nat. Methods 14, 53–56 (2016).

    Article  PubMed  Google Scholar 

  19. Mittler, R., Finka, A. & Goloubinoff, P. How do plants feel the heat? Trends Biochem. Sci. 37, 118–125 (2012).

    Article  CAS  PubMed  Google Scholar 

  20. Czechowski, T., Stitt, M., Altmann, T., Udvardi, M. K. & Scheible, W.-R. Genome-wide identification and testing of superior reference genes for transcript normalization in Arabidopsis. Plant Physiol. 139, 5–17 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Sung, D. Y., Vierling, E. & Guy, C. L. Comprehensive expression profile analysis of the Arabidopsis hsp70 gene family. Plant Physiol. 126, 789–800 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Hocine, S., Raymond, P., Zenklusen, D., Chao, J. A. & Singer, R. H. Single-molecule analysis of gene expression using two-color RNA labeling in live yeast. Nat. Methods 10, 119–121 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Coulon, A. et al. Kinetic competition during the transcription cycle results in stochastic rna processing. eLife 3, e03939 (2014).

    Article  PubMed Central  Google Scholar 

  24. Fukaya, T., Lim, B. & Levine, M. Enhancer control of transcriptional bursting. Cell 166, 358–368 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Bertrand, E. et al. Localization of ASH1 mRNA particles in living yeast. Mol. Cell 2, 437–445 (1998).

    Article  CAS  PubMed  Google Scholar 

  26. Liu, M., Zhu, J. & Dong, Z. Immediate transcriptional responses of Arabidopsis leaves to heat shock. Plant Biol. 63, 468–483 (2020).

    Google Scholar 

  27. Winter, D. et al. An ‘electronic fluorescent pictograph’ browser for exploring and analyzing large-scale biological data sets. PLoS ONE 2, 0000718 (2007).

    Article  Google Scholar 

  28. Hsia, Y. et al. Design of a hyperstable 60-subunit protein icosahedron. Nature 535, 136–139 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Akamatsu, M. et al. Principles of self-organization and load adaptation by the actin cytoskeleton during clathrin-mediated endocytosis. eLife 9, e49840 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Delarue, M. et al. mTORC1 controls phase separation and the biophysical properties of the cytoplasm by tuning crowding. Cell 174, 338–349 (2018).

    Article  CAS  PubMed  Google Scholar 

  31. Ardehali, M. B. & Lis, J. T. Tracking rates of transcription and splicing in vivo. Nat. Struct. Mol. Biol. 16, 1123–1124 (2009).

    Article  CAS  PubMed  Google Scholar 

  32. The Arabidopsis Genome Iniative. Analysis of the genome sequence of the flowering plant Arabidopsis thaliana. Nature 408, 796–815 (2000).

  33. Tornaletti, S., Reines, D. & Hanawalt, P. C. Structural characterization of RNA polymerase II complexes arrested by a cyclobutane pyrimidine dimer in the transcribed strand of template DNA. J. Biol. Chem. 274, 24124–24130 (1999).

    Article  CAS  PubMed  Google Scholar 

  34. Birnbaum, K. D. Power in numbers: single-cell RNA-seq strategies to dissect complex tissues. Annu. Rev. Genet. 52, 203–221 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Melaragno, J. E., Mehrotra, B. & Coleman, A. W. Relationship between endopolyploidy and cell size in epidermal tissue of Arabidopsis. Plant Cell 5, 1661–1668 (1993).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Robinson, D. O. et al. Ploidy and size at multiple scales in the Arabidopsis sepal. Plant Cell 30, 2308–2329 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Queitsch, C., Hong, S. W., Vierling, E. & Lindquist, S. Heat shock protein 101 plays a crucial role in thermotolerance in Arabidopsis. Plant Cell 12, 479–492 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Charng, Y.-Y. et al. A heat-inducible transcription factor, HsfA2 is required for extension of acquired thermotolerance. Plant Physiol. 143, 251–262 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Hafner, A. et al. Quantifying the central dogma in the p53 pathway in live single cells. Cell Syst. 10, 495–505 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. McLoughlin, F. et al. Class I and II small heat shock proteins together with HSP101 protect protein translation factors during heat stress. Plant Physiol. 172, 1221–1236 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Nicolas, D., Phillips, N. E. & Naef, F. What shapes eukaryotic transcriptional bursting? Mol. BioSyst. 13, 1280–1290 (2017).

    Article  CAS  PubMed  Google Scholar 

  42. Ietswaart, R., Rosa, S., Wu, Z., Dean, C. & Howard, M. Cell-size-dependent transcription of FLC and its antisense long non-coding RNA COOLAIR explain cell-to-cell expression variation. Cell Syst. 4, 622–635 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Shermoen, A. W. & Farrell, P. H. O. Progression of the cell cycle through mitosis leads to abortion of nascent transcripts. Cell 67, 303–310 (1991).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Ko, M. S. H. Induction mechanism of a single molecule: stochastic or deterministic? BioEssays 14, 341–346 (1992).

    Article  CAS  PubMed  Google Scholar 

  45. Fiering, S., Whitelaw, E. & Martin, D. I. K. To be or not to be active: the stochastic nature of enhancer action. BioEssays 22, 381–387 (2000).

    Article  CAS  PubMed  Google Scholar 

  46. Turco, G. M. et al. Molecular mechanisms driving switch behavior in xylem cell differentiation. Cell Rep. 28, 342–351.e4 (2019).

    Article  CAS  PubMed  Google Scholar 

  47. Angel, A., Song, J., Dean, C. & Howard, M. A Polycomb-based switch underlying quantitative epigenetic memory. Nature 476, 105–109 (2011).

    Article  CAS  PubMed  Google Scholar 

  48. Raj, A. & van Oudenaarden, A. Nature, nurture, or chance: stochastic gene expression and its consequences. Cell 135, 216–226 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Cortijo, S. & Locke, J. C. W. Does gene expression noise play a functional role in plants? Trends Plant Sci. 25, 1041–1051 (2020).

    Article  CAS  PubMed  Google Scholar 

  50. Meyer, H. M. et al. Fluctuations of the transcription factor ATML1 generate the pattern of giant cells in the Arabidopsis sepal. eLife 6, e19131 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Stapel, L. C., Zechner, C. & Vastenhouw, N. L. Uniform gene expression in embryos is achieved by temporal averaging of transcription noise. Genes Dev. 31, 1635–1640 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Raj, A., Peskin, C. S., Tranchina, D., Vargas, D. Y. & Tyagi, S. Stochastic mRNA synthesis in mammalian cells. PLoS Biol 4, e309 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Battich, N., Stoeger, T. & Pelkmans, L. Control of transcript variability in single mammalian cells. Cell 163, 1596–1610 (2015).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Yang, S. et al. Contribution of RNA polymerase concentration variation to protein expression noise. Nat. Commun. 5, 4761 (2014).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  57. Araújo, I. S. et al. Stochastic gene expression in Arabidopsis thaliana. Nat. Commun. 8, 2132 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Jupe, F. et al. The complex architecture and epigenomic impact of plant T-DNA insertions. PLoS Genet. 15, e1007819 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  59. McFaline-Figueroa, J. L., Trapnell, C. & Cuperus, J. T. The promise of single-cell genomics in plants. Curr. Op. Plant Biol. 54, 114–121 (2020).

    Article  CAS  Google Scholar 

  60. Duncan, S., Olsson, T. S. G., Hartley, M., Dean, C. & Rosa, S. A method for detecting single mRNA molecules in Arabidopsis thaliana. Plant Methods 12, 13 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Rodriguez, J. et al. Intrinsic dynamics of a human gene reveal the basis of expression heterogeneity. Cell 176, 213–226 (2019).

    Article  CAS  PubMed  Google Scholar 

  62. Cortijo, S. et al. Transcriptional regulation of the ambient temperature response by H2A.Z nucleosomes and HSF1 transcription factors in Arabidopsis. Mol. Plant 10, 1258–1273 (2017).

    Article  CAS  PubMed  Google Scholar 

  63. Novick, A. & Weiner, M. Enzyme induction as an all-or-none phenomenon. Proc. Natl Acad. Sci. USA 43, 553–566 (1957).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Little, S. C., Tikhonov, M. & Gregor, T. Precise developmental gene expression arises from globally stochastic transcriptional activity. Cell 154, 789–800 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Li, Z. et al. Gene duplicability of core genes is highly consistent across all angiosperms. Plant Cell 28, 326–344 (2015).

    Article  Google Scholar 

  66. Liu, T. L. et al. Observing the cell in its native state: Imaging subcellular dynamics in multicellular organisms. Science 360, eaaq1392 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Wu, Z. et al. Quantitative regulation of FLC via coordinated transcriptional initiation and elongation. Proc. Natl Acad. Sci. USA 113, 218–223 (2016).

    Article  CAS  PubMed  Google Scholar 

  68. Tantale, K. et al. A single-molecule view of transcription reveals convoys of RNA polymerases and multi-scale bursting. Nat. Commun. 7, 12248 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Iwatate, R. et al. Covalent self-labeling of tagged proteins with chemical fluorescent dyes in BY-2 cells and Arabidopsis seedlings. Plant Cell 32, 3081–3094 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Wu, D. et al. Structural basis of ultraviolet-B perception by UVR8. Nature 484, 214–219 (2012).

    Article  PubMed  Google Scholar 

  71. Daigle, N. & Ellenberg, J. λN-GFP: an RNA reporter system for live-cell imaging. Nat. Methods 4, 633–636 (2007).

    Article  CAS  PubMed  Google Scholar 

  72. Ronald, J. & Davis, S. J. Focusing on the nuclear and subnuclear dynamics of light and circadian signalling. Plant Cell Environ. 42, 2871–2884 (2019).

    Article  CAS  PubMed  Google Scholar 

  73. Phillips, R. et al. Figure 1 theory meets Figure 2 experiments in the study of gene expression. Annu. Rev. Biophys. 48, 121–163 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Yoshida, T. et al. Arabidopsis HsfA1 transcription factors function as the main positive regulators in heat shock-responsive gene expression. Mol. Genet. Genomics 286, 321–332 (2011).

    Article  CAS  PubMed  Google Scholar 

  75. Liu, H., Liu, B., Zhao, C., Pepper, M. & Lin, C. The action mechanisms of plant cryptochromes. Trends Plant Sci 16, 684–691 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Arganda-Carreras, I. et al. Trainable weka segmentation: a machine learning tool for microscopy pixel classification. Bioinformatics 33, 2424–2426 (2017).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank R. Phillips, A. Roeder, P. Quail, S. Wakao, C. Gee, J. Brunkard and A. Flamholz for comments on the manuscript; members of the Garcia laboratory: M. Turner and G. Martini for sharing their knowledge and materials related to the nanocages experiment and Y. J. Kim for discussing results, G. Martini in particular for setting up the microscope temperature chamber; C. Baker, S. Wakao and D. Westcott from the Niyogi laboratory for their RT–qPCR advice; A. Schwartz, J. O’Brien and F. Federici for sharing plasmids; A. Lin and J. Liu provided useful feedback regarding calculations; and M. Kobayashi for help and for making the Niyogi laboratory run smoothly. H.G.G. was supported by the Burroughs Wellcome Fund Career Award at the Scientific Interface, the Sloan Research Foundation, the Human Frontiers Science Program, the Searle Scholars Program, the Shurl and Kay Curci Foundation, the Hellman Foundation, the National Institutes for Health Director’s New Innovator Award (DP2 OD024541-01), and a National Science Foundation CAREER Award (1652236). K.K.N. is an investigator of the Howard Hughes Medical Institute. S.A. was supported by H.G.G. and K.K.N. A.R. was supported by H.G. and National Science Foundation Graduate Research Fellowships Program (DGE 1752814).

Author information

Authors and Affiliations

  1. Department of Plant and Microbial Biology, University of California Berkeley, Berkeley, CA, USA

    Simon Alamos & Krishna K. Niyogi

  2. Biophysics Graduate Group, University of California Berkeley, Berkeley, CA, USA

    Armando Reimer & Hernan G. Garcia

  3. Howard Hughes Medical Institute, University of California Berkeley, Berkeley, CA, USA

    Krishna K. Niyogi

  4. Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Krishna K. Niyogi

  5. Department of Molecular and Cell Biology, University of California Berkeley, Berkeley, CA, USA

    Hernan G. Garcia

  6. Department of Physics, University of California Berkeley, Berkeley, CA, USA

    Hernan G. Garcia

  7. Institute for Quantitative Biosciences-QB3, University of California Berkeley, Berkeley, CA, USA

    Hernan G. Garcia

Authors
  1. Simon Alamos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Armando Reimer
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Krishna K. Niyogi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hernan G. Garcia
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

S.A., H.G.G. and K.K.N. designed experiments. S.A. performed experiments and analysed the data. S.A., A.R. and H.G.G. wrote the analysis code. S.A., H.G.G. and K.K.N. wrote the paper.

Corresponding authors

Correspondence to Krishna K. Niyogi or Hernan G. Garcia.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Plants thanks Zhicheng Dong 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.

Supplementary information

Supplementary Information

Supplementary calculations, Figs. 1–26 and tables.

Reporting Summary

Supplementary Video 1

Constitutive GAPC2 reporter in tobacco.

Supplementary Video 2

Inducible HSP70 reporter in tobacco.

Supplementary Video 3

Inducible HSP101 reporter in Arabidopsis.

Supplementary Video 4

Inducible HsfA2 reporter in Arabidopsis.

Supplementary Video 5

Constitutive EF-Tu reporter in Arabidopsis.

Supplementary Video 6

Two spot inducible HSP101 reporter in Arabidopsis.

Source data

Source Data Fig. 2

Statistical source data

Source Data Fig. 3

Statistical source data

Source Data Fig. 4

Statistical source data

Source Data Fig. 5

Statistical source data

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Alamos, S., Reimer, A., Niyogi, K.K. et al. Quantitative imaging of RNA polymerase II activity in plants reveals the single-cell basis of tissue-wide transcriptional dynamics. Nat. Plants 7, 1037–1049 (2021). https://doi.org/10.1038/s41477-021-00976-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41477-021-00976-0

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