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Mechanical regulation of photosynthesis in cyanobacteria

Nature Microbiology volume 5, pages 757–767 (2020)Cite this article

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Abstract

Photosynthetic organisms regulate their responses to many diverse stimuli in an effort to balance light harvesting with utilizable light energy for carbon fixation and growth (source–sink regulation). This balance is critical to prevent the formation of reactive oxygen species that can lead to cell death. However, investigating the molecular mechanisms that underlie the regulation of photosynthesis in cyanobacteria using ensemble-based measurements remains a challenge due to population heterogeneity. Here, to address this problem, we used long-term quantitative time-lapse fluorescence microscopy, transmission electron microscopy, mathematical modelling and genetic manipulation to visualize and analyse the growth and subcellular dynamics of individual wild-type and mutant cyanobacterial cells over multiple generations. We reveal that mechanical confinement of actively growing Synechococcus sp. PCC 7002 cells leads to the physical disassociation of phycobilisomes and energetic decoupling from the photosynthetic reaction centres. We suggest that the mechanical regulation of photosynthesis is a critical failsafe that prevents cell expansion when light and nutrients are plentiful, but when space is limiting. These results imply that cyanobacteria must convert a fraction of the available light energy into mechanical energy to overcome frictional forces in the environment, providing insight into the regulation of photosynthesis and how microorganisms navigate their physical environment.

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Fig. 1: Quantitative analysis of microcolony formation in Synechococcus sp. PCC 7002.
Fig. 2: Mechanically induced fluorescence emission during microcolony formation.
Fig. 3: The role of phycobilisomes in mechanically induced endogenous fluorescence emission.
Fig. 4: The role of known non-photochemical quenching and mechanical sensing and signalling mechanisms in microcolony formation and endogenous fluorescence emission.
Fig. 5: Modelling the effect of external forces on cell growth.
Fig. 6: Macroscale growth is affected by external forces and photosynthetic source–sink ratio.

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Data availability

Data and materials supporting the conclusions of the manuscript are available from the corresponding author on reasonable request. Source data for Figs. 1–3 and 5 are included with the paper.

Code availability

The code developed to quantify images as well as produce the biomechanical mathematical model was custom generated for this work and is available on GitHub (https://biof-git.colorado.edu/cameron-lab-public/mechanical-regulation-cyanobacteria).

References

  1. Walter, J. M. et al. Ecogenomics and taxonomy of cyanobacteria phylum. Front. Microbiol. 8, 2132 (2017).

    PubMed  PubMed Central  Google Scholar 

  2. Ludwig, M. & Bryant, D. A. Acclimation of the global transcriptome of the cyanobacterium Synechococcus sp. strain PCC 7002 to nutrient limitations and different nitrogen sources. Front. Microbiol. 3, 145 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Singh, A. K. et al. Integrative analysis of large scale expression profiles reveals core transcriptional response and coordination between multiple cellular processes in a cyanobacterium. BMC Syst. Biol. 4, 105 (2010).

    PubMed  PubMed Central  Google Scholar 

  4. Singh, A. K. et al. A systems-level analysis of the effects of light quality on the metabolism of a cyanobacterium. Plant Physiol. 151, 1596–1608 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Singh, A. K. et al. Integration of carbon and nitrogen metabolism with energy production is crucial to light acclimation in the cyanobacterium Synechocystis. Plant Physiol. 148, 467–478 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Singh, A. K. & Sherman, L. A. Pleiotropic effect of a histidine kinase on carbohydrate metabolism in Synechocystis sp. strain PCC 6803 and its requirement for heterotrophic growth. J. Bacteriol. 187, 2368–2376 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Cameron, J. C. & Pakrasi, H. B. Essential role of glutathione in acclimation to environmental and redox perturbations in the cyanobacterium Synechocystis sp. PCC 6803. Plant Physiol. 154, 1672–1685 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Cameron, J. C. & Pakrasi, H. B. Glutathione in Synechocystis 6803: a closer look into the physiology of a ∆gshB mutant. Plant Signal. Behav. 6, 89–92 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Cameron, J. C. & Pakrasi, H. B. Glutathione facilitates antibiotic resistance and photosystem I stability during exposure to gentamicin in cyanobacteria. Appl. Environ. Microbiol. 77, 3547–3550 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Bechtold, U., Karpinski, S. & Mullineaux, P. M. The influence of the light environment and photosynthesis on oxidative signalling responses in plant biotrophic pathogen interactions. Plant Cell Environ. 28, 1046–1055 (2005).

    CAS  Google Scholar 

  11. Mullineaux, C. W. Electron transport and light-harvesting switches in cyanobacteria. Front. Plant Sci. 5, 7 (2014).

    PubMed  PubMed Central  Google Scholar 

  12. Nogales, J., Gudmundsson, S., Knight, E. M., Palsson, B. O. & Thiele, I. Detailing the optimality of photosynthesis in cyanobacteria through systems biology analysis. Proc. Natl Acad. Sci. USA 109, 2678–2683 (2012).

    CAS  PubMed  Google Scholar 

  13. Bruce, D., Brimble, S. & Bryant, D. A. State transitions in a phycobilisome-less mutant of the cyanobacterium Synechococcus sp. PCC 7002. Biochim. Biophys. Acta 974, 66–73 (1989).

    CAS  PubMed  Google Scholar 

  14. Joshua, S. & Mullineaux, C. W. Phycobilisome diffusion is required for light-state transitions in cyanobacteria. Plant Physiol. 135, 2112–2119 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Richaud, C., Zabulon, G., Joder, A. & Thomas, J. C. Nitrogen or sulfur starvation differentially affects phycobilisome degradation and expression of the nblA gene in Synechocystis strain PCC 6803. J. Bacteriol. 183, 2989–2994 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Gwizdala, M., Wilson, A., Omairi-Nasser, A. & Kirilovsky, D. Characterization of the Synechocystis PCC 6803 fluorescence recovery protein involved in photoprotection. Biochim. Biophys. Acta 1827, 348–354 (2013).

    CAS  PubMed  Google Scholar 

  17. Joshua, S., Bailey, S., Mann, N. H. & Mullineaux, C. W. Involvement of phycobilisome diffusion in energy quenching in cyanobacteria. Plant Physiol. 138, 1577–1585 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Clark, R. L. et al. Light-optimized growth of cyanobacterial cultures: growth phases and productivity of biomass and secreted molecules in light-limited batch growth. Metab. Eng. 47, 230–242 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Martins, B. M. C., Tooke, A. K., Thomas, P. & Locke, J. C. W. Cell size control driven by the circadian clock and environment in cyanobacteria. Proc. Natl Acad. Sci. USA 115, E11415–E11424 (2018).

    CAS  PubMed  Google Scholar 

  20. Luke, C. S. et al. A microfluidic platform for long-term monitoring of algae in a dynamic environment. ACS Synth. Biol. 5, 8–14 (2015).

    PubMed  PubMed Central  Google Scholar 

  21. Häder, D. P. & Vogel, K. Interactive image analysis system to determine the motility and velocity of cyanobacterial filaments. J. Biochem. Biophys. Methods 22, 289–300 (1991).

    PubMed  Google Scholar 

  22. Yokoo, R., Hood, R. D. & Savage, D. F. Live-cell imaging of cyanobacteria. Photosynth. Res. 126, 33–46 (2014).

    PubMed  Google Scholar 

  23. Persat, A. et al. The mechanical world of bacteria. Cell 161, 988–997 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Janmey, P. A. & Miller, R. T. Mechanisms of mechanical signaling in development and disease. J. Cell Sci. 124, 9–18 (2011).

    CAS  PubMed  Google Scholar 

  25. Yang, D., Jennings, A. D., Borrego, E., Retterer, S. T. & Männik, J. Analysis of factors limiting bacterial growth in PDMS mother machine devices. Front. Microbiol. 9, 871 (2018).

    PubMed  PubMed Central  Google Scholar 

  26. Tuson, H. H. et al. Measuring the stiffness of bacterial cells from growth rates in hydrogels of tunable elasticity. Mol. Microbiol. 84, 874–891 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Warren, M. R. et al. Spatiotemporal establishment of dense bacterial colonies growing on hard agar. eLife 8, e41093 (2019).

    PubMed  PubMed Central  Google Scholar 

  28. Bhaya, D., Takahashi, A., Shahi, P. & Grossman, A. R. Novel motility mutants of Synechocystis strain PCC 6803 generated by in vitro transposon mutagenesis. J. Bacteriol. 183, 6140–6143 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Ng, W. O., Grossman, A. R. & Bhaya, D. Multiple light inputs control phototaxis in Synechocystis sp. strain PCC6803. J. Bacteriol. 185, 1599–1607 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Chau, R. M. W., Bhaya, D. & Huanga, K. C. Emergent phototactic responses of cyanobacteria under complex light regimes. mBio 8, e02330-16 (2017).

    PubMed  PubMed Central  Google Scholar 

  31. Fiedler, B., Börner, T. & Wilde, A. Phototaxis in the cyanobacterium Synechocystis sp. PCC 6803: role of different photoreceptors. Photochem. Photobiol. 81, 1481–1488 (2005).

    CAS  PubMed  Google Scholar 

  32. Wilde, A. & Mullineaux, C. W. Light-controlled motility in prokaryotes and the problem of directional light perception. FEMS Microbiol. Rev. 41, 900–922 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Nakane, D. & Nishizaka, T. Asymmetric distribution of type IV pili triggered by directional light in unicellular cyanobacteria. Proc. Natl Acad. Sci. USA 114, 6593–6598 (2017).

    CAS  PubMed  Google Scholar 

  34. Esteves-Ferreira, A. A. et al. A novel mechanism, linked to cell density, largely controls cell division in Synechocystis. Plant Physiol. 174, 2166–2182 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Tamary, E. et al. Structural and functional alterations of cyanobacterial phycobilisomes induced by high-light stress. Biochim. Biophys. Acta 1817, 319–327 (2012).

    CAS  PubMed  Google Scholar 

  36. Harris, D. et al. Orange carotenoid protein burrows into the phycobilisome to provide photoprotection. Proc. Natl Acad. Sci. USA 113, E1655–E1662 (2016).

    CAS  PubMed  Google Scholar 

  37. Gwizdala, M., Wilson, A. & Kirilovsky, D. In vitro reconstitution of the cyanobacterial photoprotective mechanism mediated by the orange carotenoid protein in Synechocystis PCC 6803. Plant Cell 23, 2631–2643 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Wilson, A. A soluble carotenoid protein involved in phycobilisome-related energy dissipation in cyanobacteria. Plant Cell 18, 992–1007 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Booth, I. R. & Blount, P. The MscS and MscL families of mechanosensitive channels act as microbial emergency release valves. J. Bacteriol. 194, 4802–4809 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Brand, J. J. & Becker, D. W. Evidence for direct roles of calcium in photosynthesis. J. Bioenerg. Biomembr. 16, 239–249 (1984).

    CAS  PubMed  Google Scholar 

  41. Shi, Y., Zhao, W., Zhang, W., Ye, Z. & Zhao, J. Regulation of intracellular free calcium concentration during heterocyst differentiation by HetR and NtcA in Anabaena sp. PCC 7120. Proc. Natl Acad. Sci. USA 103, 11334–11339 (2006).

    CAS  PubMed  Google Scholar 

  42. Bruni, G. N., Weekley, R. A., Dodd, B. J. T. & Kralj, J. M. Voltage-gated calcium flux mediates Escherichia coli mechanosensation. Proc. Natl Acad. Sci. USA 114, 9445–9450 (2017).

    CAS  PubMed  Google Scholar 

  43. Chen, T.-W. et al. Ultrasensitive fluorescent proteins for imaging neuronal activity. Nature 499, 295–300 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Su, P. T. et al. Bacterial colony from two-dimensional division to three-dimensional development. PLoS ONE 7, e48098 (2012).

  45. Allen, M. P. & Tildesley, D. J. Computer Simulation of Liquids Vol. 18, 385 (Oxford Univ. Press, 1987).

  46. Schwarz, R., Reinhold, L. & Kaplan, A. Low activation state of ribulose-1,5-bisphosphate carboxylase/oxygenase in carboxysome-defective Synechococcus mutants. Plant Physiol. 108, 183–190 (1995).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Salomon, E., Bar-Eyal, L., Sharon, S. & Keren, N. Balancing photosynthetic electron flow is critical for cyanobacterial acclimation to nitrogen limitation. Biochim. Biophys. Acta 1827, 340–347 (2013).

    CAS  PubMed  Google Scholar 

  48. Ludwig, M. & Bryant, D. A. Transcription profiling of the model cyanobacterium Synechococcus sp. strain PCC 7002 by next-gen (SOLiD™) sequencing of cDNA. Front. Microbiol. 2, 41 (2011).

  49. Ludwig, M. & Bryant, D. A. Synechococcus sp. strain PCC 7002 transcriptome: acclimation to temperature, salinity, oxidative stress, and mixotrophic growth conditions. Front. Microbiol. 3, 354 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Rae, B. D., Long, B. M., Badger, M. R. & Price, G. D. Functions, compositions, and evolution of the two types of carboxysomes: polyhedral microcompartments that facilitate CO2 fixation in cyanobacteria and some proteobacteria. Microbiol. Mol. Biol. Rev. 77, 357–379 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Kerfeld, Ca, Heinhorst, S. & Cannon, G. C. Bacterial microcompartments. Annu. Rev. Microbiol. 64, 391–408 (2010).

    CAS  PubMed  Google Scholar 

  52. Cameron, J. C., Wilson, S. C., Bernstein, S. L. & Kerfeld, C. A. Biogenesis of a bacterial organelle: the carboxysome assembly pathway. Cell 155, 1131–1140 (2013).

    CAS  PubMed  Google Scholar 

  53. Price, G. D., Badger, M. R., Woodger, F. J. & Long, B. M. Advances in understanding the cyanobacterial CO2-concentrating-mechanism (CCM): functional components, Ci transporters, diversity, genetic regulation and prospects for engineering into plants. J. Exp. Bot. 59, 1441–1461 (2008).

    CAS  PubMed  Google Scholar 

  54. Mahinthichaichan, P., Morris, D. M., Wang, Y., Jensen, G. J. & Tajkhorshid, E. Selective permeability of carboxysome shell pores to anionic molecules. J. Phys. Chem. B 122, 9110–9118 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Cai, F. et al. The pentameric vertex proteins are necessary for the icosahedral carboxysome shell to function as a CO2 leakage barrier. PLoS ONE 4, e7521 (2009).

  56. Clark, R. L., Cameron, J. C., Root, T. W. & Pfleger, B. F. Insights into the industrial growth of cyanobacteria from a model of the carbon-concentrating mechanism. AIChE J. 60, 1269–1277 (2014).

    CAS  Google Scholar 

  57. Schreiber, U. & Vidaver, W. Hydrostatic pressure: a reversible inhibitor of primary photosynthetic processes. Z. Naturforsch. C 28, 704–709 (1973).

    CAS  PubMed  Google Scholar 

  58. Foguel, D., Chaloub, R. M., Silva, J. L., Crofts, A. R. & Weber, G. Pressure and low temperature effects on the fluorescence emission spectra and lifetimes of the photosynthetic components of cyanobacteria. Biophys. J. 63, 1613–1622 (1992).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Adamec, F., Kaftan, D. & Nedbal, L. Stress-induced filament fragmentation of Calothrix elenkinii (Cyanobacteria) is facilitated by death of high-fluorescence cells. J. Phycol. 41, 835–839 (2005).

    Google Scholar 

  60. Kandemir, N., Vollmer, W., Jakubovics, N. S. & Chen, J. Mechanical interactions between bacteria and hydrogels. Sci. Rep. 8, 10893 (2018).

    PubMed  PubMed Central  Google Scholar 

  61. Stecchini, M. L. et al. Influence of structural properties and kinetic constraints on Bacillus cereus growth. Appl. Environ. Microbiol. 64, 1075–1078 (1998).

  62. Yoshihara, S. & Ikeuchi, M. Phototactic motility in the unicellular cyanobacterium Synechocystis sp. PCC 6803. Photochem. Photobiol. Sci. 3, 512–518 (2004).

    CAS  PubMed  Google Scholar 

  63. Schuergers, N., Mullineaux, C. W. & Wilde, A. Cyanobacteria in motion. Curr. Opin. Plant Biol. 37, 109–115 (2017).

    CAS  PubMed  Google Scholar 

  64. Schuergers, N. et al. Binding of the RNA chaperone Hfq to the type IV pilus base is crucial for its function in Synechocystis sp. PCC 6803. Mol. Microbiol. 92, 840–852 (2014).

    CAS  PubMed  Google Scholar 

  65. Yang, Y. et al. Phototaxis in a wild isolate of the cyanobacterium Synechococcus elongatus. Proc. Natl Acad. Sci. USA 115, E12378–E12387 (2018).

    CAS  PubMed  Google Scholar 

  66. Stevens, S. E. J., Patterson, C. O. & Myers, J. The production of hydrogen peroxide by blue-green algae: a survey. J. Phycol. 9, 427–430 (1973).

    CAS  Google Scholar 

  67. Gordon, G. C. et al. CRISPR interference as a titratable, trans-acting regulatory tool for metabolic engineering in the cyanobacterium Synechococcus sp. strain PCC 7002. Metab. Eng. 38, 170–179 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. Campbell, D. A., Hurry, V., Clarke, aK., Gustafsson, P. & Oquist, G. Chlorophyll fluorescence analysis of cyanobacterial photosynthesis and acclimation. Microbiol. Mol. Biol. Rev. 62, 667–683 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. Schuurmans, R. M., Van Alphen, P., Schuurmans, J. M., Matthijs, H. C. P. & Hellingwerf, K. J. Comparison of the photosynthetic yield of cyanobacteria and green algae: different methods give different answers. PLoS ONE 10, e0139061 (2015).

    PubMed  PubMed Central  Google Scholar 

  70. BioFormats v.1.1.0 (Open Microscopy Environment, 2017); https://www.openmicroscopy.org/bio-formats/

  71. Jaqaman, K. et al. Robust single-particle tracking in live-cell time-lapse sequences. Nat. Methods 5, 695–702 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. Jonker, R. & Volgenant, A. A shortest augmenting path algorithm for dense and sparse linear assignment problems. Computing 38, 325–340 (1987).

    Google Scholar 

  73. Campos, M. et al. A constant size extension drives bacterial cell size homeostasis. Cell 159, 1433–1446 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. O’Toole, E. T., Giddings, T. H. & Dutcher, S. K. Understanding microtubule organizing centers by comparing mutant and wild-type structures with electron tomography. Methods Cell Biol. 2007, 125–143 (2007).

    Google Scholar 

  75. Kremer, J. R., Mastronarde, D. N. & McIntosh, J. R. Computer visualization of three-dimensional image data using IMOD. J. Struct. Biol. 116, 71–76 (1996).

    CAS  Google Scholar 

  76. Mastronarde, D. N. Dual-axis tomography: an approach with alignment methods that preserve resolution. J. Struct. Biol. 120, 343–352 (1997).

    CAS  PubMed  Google Scholar 

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Acknowledgements

We thank A. Cahill for technical support during setup of the spectral imaging modality, J. Kralj for providing the GCaMP6f plasmid, N. Hill for providing the ∆ccm strain and members of the B. Pfleger laboratory for providing PCC 7002 cells; R. Clark for helpful discussions that improved the manuscript. This research was supported by a RASEI SEED grant (to D.M.B. and J.C.C.). This material is based on work supported by the NSF Postdoctoral Research Fellowship in Biology (grant no. 1711932, to K.A.M.) and in part by an appointment (to S.A.) with the NSF Mathematical Sciences Graduate Internship (MSGI) program sponsored by the National Science Foundation, Division of Mathematical Sciences (DMS). This program is administered by the Oak Ridge Institute for Science and Education (ORISE) through an interagency agreement between the US Department of Energy (DOE) and the NSF. ORISE is managed by ORAU under DOE contract number DE-SC0014664. All opinions expressed in this paper are the authors and do not necessarily reflect the policies and views of NSF, ORAU/ORISE or DOE.

Author information

Authors and Affiliations

  1. Renewable and Sustainable Energy Institute, University of Colorado, Boulder, CO, USA

    Kristin A. Moore, Jian W. Tay, Janet B. Meehl, Evan B. Johnson & Jeffrey C. Cameron

  2. Department of Applied Mathematics, University of Colorado, Boulder, CO, USA

    Sabina Altus & David M. Bortz

  3. Biofrontiers Institute, University of Colorado, Boulder, CO, USA

    Jian W. Tay & Janet B. Meehl

  4. Department of Biochemistry, University of Colorado, Boulder, CO, USA

    Janet B. Meehl, Evan B. Johnson & Jeffrey C. Cameron

  5. Biological Science Center, National Renewable Energy Laboratory, Golden, CO, USA

    Jeffrey C. Cameron

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Contributions

J.C.C. conceived the project. J.C.C. and K.A.M. designed the study and analysed the data. K.A.M. generated mutant lines and performed fluorescence microscopy experiments. E.B.J. and J.C.C. performed spot-plating experiments. S.A. and D.M.B. developed the mathematical model. J.C.C. and K.A.M. provided input on model development. J.B.M. performed the electron microscopy. J.W.T. developed the single-cell segmentation and tracking algorithms. J.C.C. and K.A.M. wrote the paper with input from all of the other authors.

Corresponding author

Correspondence to Jeffrey C. Cameron.

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Competing interests

K.A.M., S.A., J.W.T., J.B.M., E.B.J., D.M.B. and J.C.C. are inventors on a patent application filed by the University of Colorado on the basis of this work (patent application no. 16/036,645; filed on 16 July 2018). The authors declare they have no other competing interests.

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Extended data

Extended Data Fig. 1 Representative cell number and endogenous fluorescence traces.

Each panel represents number of cells (black) and mean endogenous fluorescence intensity (emission Cy5 705/72 nm following 640 nm excitation) of all cells (blue) during microcolony formation. Data is from four individual microcolonies, which started with either one or two cells. The red arrow indicates the beginning of fluorescence rise at the 4-cell colony stage.

Extended Data Fig. 2 Transmission electron micrographs of plunge-frozen cyanobacterial microcolonies.

Panels represent en face cross-sections through a Synechococcus sp. PCC 7002 microcolony grown on dialysis tubing on top of 1% agarose A + pads. a, Close-up of cell-cell interfaces in microcolony interior shown in Fig. 2f. b, Close up of microcolony edge. Mechanical deformation of cells is more apparent in the colony interior. Asterisk denotes a cell-lysis event of a highly deformed cell. c, and d, Larger field views of representative microcolonies with distinctive difference between interior and exterior cells. Similar results were observed in two independent experiments.

Extended Data Fig. 3 Analysis of ∆cpc mutant.

a, Absorbance spectra of WT and ∆cpc strains. Consistent results were observed in three independent experiments b, Schematics of WT and ∆cpc genomic regions and location of primers to confirm segregation (not to scale). c, PCR products confirming presence of ∆cpc insert and absence of WT product. Lanes: 1, 1 kb + MW ladder (Bands representing 0.5, 0.1, 1, and 3 kilobases are marked); 2, WT (primers: cpc upstream + GmR); 3, ∆cpc (primers: cpc upstream + GmR); 4, WT (primers: cpc upstream + cpc internal); 5, ∆cpc (primers: cpc upstream + cpc internal). Consistent results were observed in two independent experiments.

Extended Data Fig. 4 Ratio of 651 nm / 684 nm fluorescence emission from single cells during growth on 2% agarose.

Individual cell traces of 20 individual cells that exhibit increased fluorescence while growing on 2% agarose. Y-axis all represent values shown for top left graph. X-axis represent time elapsed from the initial frame of time-lapse imaging.

Extended Data Fig. 5 Ratio of 651 nm / 684 nm emission from single cells during growth on 1% Agarose.

Individual cell traces of 20 individual cells from the 4-cell colony stage. Y-axis all represent values shown for top left graph. X-axis represent time elapsed from the initial frame of time-lapse imaging.

Extended Data Fig. 6 Expanded image of chambers in microfluidic device.

Cells were grown for 15 hr within the microfluidic device. The white line represents the chamber boundary with cells above the line within the 1.1 µm chamber and cells below the line in the 1.3 µm chamber. Emission (Cy5; 705/72 nm) after 640 nm excitation shown. Scale bar = 5 µm. Calibration bar represents intensities between 50-3500 AU (purple to white, respectively) Similar results have been observed in two independent experiments.

Extended Data Fig. 7 Mean endogenous fluorescence emission intensity after nutrient deprivation.

a, Representative images of endogenous fluorescence (Cy5; 705/72 nm) after 640 nm excitation of WT cells grown for 16 hr in shaking flasks in (L to R) full A+ media, A+ media lacking phosphate, and A+ media with 5 % of the standard nitrate concentration. All images shown with the same look up table displaying intensities between 100-4800 AU (purple to white, respectively). Scale bar = 1 µm. Similar results were observed in two independent experiments. b, Box plot of endogenous fluorescence intensities for the population of cells in (a). n = 1961 cells – full media; n = 1180 cells - 0% P; n = 675 cells – 5% N. Boxplots represent same statistics described in Fig. 1e. The mean fluorescence intensity was greater in full media compared to 0% P (corrected P-value = 1.96 × 10-99) and 5% N (corrected P-value = 2.73 × 10-196). The populations are significantly different from each other based on a two sample Kolmogorov-Smirnov test with Bonferroni corrected p-values of < 0.0001 indicated ***.

Supplementary information

Supplementary Information

Supplementary Information, Tables 1 and 2, and references.

Reporting Summary

Suppmentary Video 1

Time-lapse imaging of cells expressing YFP. Left, YFP fluorescence. Middle, endogenous fluorescence with 640 nm excitation (Cy5 emission; 705/72 nm). Right, channel merge. Scale bar, 1 µm. Imaging frequency for Supplementary Videos 1–9 is 10 min. The calibration bar for Supplementary Videos 1 and 4–8 represents intensities of 75–3,500 AU (purple to white, respectively). Similar results were observed in two independent experiments.

Suppmentary Video 2

Time-lapse imaging of cell growth on 0.5% and 2.5% agarose. Left, cells grown on 0.5% agarose. Right, cells grown on 2.5% agarose. Endogenous fluorescence from 640 nm excitation (Cy5 emission; 705/72 nm) was pseudocoloured to compare differences in autofluorescence between conditions. The calibration bar represents intensities of 60–6,000 AU (purple to white, respectively). Scale bar for Supplementary Videos 2 and 4–9, 5 µm. Similar results were observed in two independent experiments.

Suppmentary Video 3

Time-lapse imaging of ∆cpc cells and control cells expressing YFP. Left, YFP and endogenous fluorescence from 640 nm excitation (Cy5 emission; 705/72 nm) merge. Right, endogenous fluorescence from 640 nm excitation (Cy5 emission; 705/72 nm) was pseudocoloured to compare differences in fluorescence between strains. Similar results were observed in two independent experiments.

Suppmentary Video 4

Time-lapse ratiometric imaging of WT cells grown on 2% agarose. WT cells grown on 2% agarose pads. Images were taken using spectral emission collection of 650–720 nm (2.5 nm bandwidth) with 561 nm excitation. Pseudocolouring represents a ratio of 651 nm:685 nm emission from the indicated cell. The calibration bar represents ratios from 0 (blue) to 4 (yellow). Similar results were observed in 20 independent microcolony lineages.

Suppmentary Video 5

Time-lapse imaging of ∆ocp and control cells expressing YFP. Left, YFP and endogenous fluorescence from 640 nm excitation (Cy5 emission; 705/72 nm) merge. Right, endogenous fluorescence from 640 nm excitation (Cy5 emission; 705/72 nm) was pseudocoloured to compare differences in fluorescence between strains. Similar results were observed in two independent experiments.

Suppmentary Video 6

Time-lapse imaging of frp CRISPRi and control cells expressing YFP. Left, YFP and endogenous fluorescence from 640 nm excitation (Cy5 emission; 705/72 nm) merge. Right, endogenous fluorescence from 640 nm excitation (Cy5 emission; 705/72 nm) was pseudocoloured to compare differences in fluorescence between strains. Similar results were observed in two independent experiments.

Suppmentary Video 7

Time-lapse imaging of the ∆mscL strain. Endogenous fluorescence from 640 nm excitation (Cy5 emission; 705/72 nm) was pseudocoloured to better visualize changes in fluorescence over time. Similar results were observed in two independent experiments.

Suppmentary Video 8

Time-lapse imaging of the ∆mscS strain. Endogenous fluorescence from 640 nm excitation (Cy5 emission; 705/72 nm) was pseudocoloured to compare differences in fluorescence between strains. Similar results were observed in two independent experiments.

Suppmentary Video 9

Time-lapse imaging of WT cells (left) and cpcB CRISPRi cells (right) expressing GCaMP, a Ca2+ signalling sensor. Ca2+ signalling was detected by increases in GCaMP6f intensity after excitation at 470 nm (pseudocoloured). The calibration bar represents intensities of 85–850 AU (purple to white, respectively). Similar results were observed in two independent experiments.

Suppmentary Video 10

Mathematical model of microcolony formation. The interior cell colours denote the predicted forces experienced by individual cells: black < 2, 2 < purple < 5, 5 < red < 7, 7 < orange < 10, yellow > 10. The scale bar (right) corresponds to the cell perimeter and denotes relative forces experienced through individual cell–cell interactions. Black = 0 (no force exerted); white = 1 (maximum force).

Suppmentary Video 11

Time-lapse imaging of ∆cpc cells grown on agarose of different percentages. Representative time-lapse-imaging series of endogenous fluorescence from 640 nm excitation (Cy5 emission; 705/72 nm) of ∆cpc cells grown on 0.5% agarose (left) and 1.0% agarose (right). The frame rate and length of time-lapse imaging are identical for both image sets. Similar results were observed in two independent experiments.

Source data

Source Data Fig. 1

Source data for the plots in Figs. 1d,e.

Source Data Fig. 2

Source data for the plot in Fig. 2d.

Source Data Fig. 3

Source data for the plots in Fig. 3a (inset) and 3c.

Source Data Fig. 5

Source data for the plot in Fig. 5c.

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Moore, K.A., Altus, S., Tay, J.W. et al. Mechanical regulation of photosynthesis in cyanobacteria. Nat Microbiol 5, 757–767 (2020). https://doi.org/10.1038/s41564-020-0684-2

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