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.

Genetically encoded biosensors for visualizing live-cell biochemical activity at super-resolution

Nature Methods volume 14, pages 427–434 (2017)Cite this article

Subjects

Abstract

Compartmentalized biochemical activities are essential to all cellular processes, but there is no generalizable method to visualize dynamic protein activities in living cells at a resolution commensurate with cellular compartmentalization. Here, we introduce a new class of fluorescent biosensors that detect biochemical activities in living cells at a resolution up to threefold better than the diffraction limit. These 'FLINC' biosensors use binding-induced changes in protein fluorescence dynamics to translate kinase activities or protein–protein interactions into changes in fluorescence fluctuations, which are quantifiable through stochastic optical fluctuation imaging. A protein kinase A (PKA) biosensor allowed us to resolve minute PKA activity microdomains on the plasma membranes of living cells and to uncover the role of clustered anchoring proteins in organizing these activity microdomains. Together, these findings suggest that biochemical activities of the cell are spatially organized into an activity architecture whose structural and functional characteristics can be revealed by these new biosensors.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Get just this article for as long as you need it

$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Figure 1: TagRFP-T (TT) red fluorescence fluctuations increase with Dronpa (Dp) proximity in a distance-dependent manner.
Figure 2: FLINC resolves PKA activity microdomains on the plasma membrane at super-resolution.
Figure 3: A kinase–anchoring proteins (AKAPs) are coclustered with PKA activity microdomains and are required for microdomain formation.
Figure 4: PKA activity gradient of a migrating cell at super-resolution.
Figure 5: FLINC-based design is generalizable.

References

  1. Avraham, R. & Yarden, Y. Feedback regulation of EGFR signalling: decision making by early and delayed loops. Nat. Rev. Mol. Cell Biol. 12, 104–117 (2011).

    CAS  PubMed  Google Scholar 

  2. Ganesan, A. & Zhang, J. How cells process information: quantification of spatiotemporal signaling dynamics. Protein Sci. 21, 918–928 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Wong, W. & Scott, J.D. AKAP signalling complexes: focal points in space and time. Nat. Rev. Mol. Cell Biol. 5, 959–970 (2004).

    CAS  PubMed  Google Scholar 

  4. Rizzuto, R. & Pozzan, T. Microdomains of intracellular Ca2+: molecular determinants and functional consequences. Physiol. Rev. 86, 369–408 (2006).

    CAS  PubMed  Google Scholar 

  5. Cambi, A. & Lidke, D.S. Nanoscale membrane organization: where biochemistry meets advanced microscopy. ACS Chem. Biol. 7, 139–149 (2012).

    CAS  PubMed  Google Scholar 

  6. Sengupta, P., van Engelenburg, S.B. & Lippincott-Schwartz, J. Superresolution imaging of biological systems using photoactivated localization microscopy. Chem. Rev. 114, 3189–3202 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Huang, B., Bates, M. & Zhuang, X. Super-resolution fluorescence microscopy. Annu. Rev. Biochem. 78, 993–1016 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Hell, S.W. Far-field optical nanoscopy. Science 316, 1153–1158 (2007).

    CAS  PubMed  Google Scholar 

  9. Betzig, E. et al. Imaging intracellular fluorescent proteins at nanometer resolution. Science 313, 1642–1645 (2006).

    Article  CAS  PubMed  Google Scholar 

  10. Rust, M.J., Bates, M. & Zhuang, X. Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM). Nat. Methods 3, 793–795 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Dedecker, P., Mo, G.C.H., Dertinger, T. & Zhang, J. Widely accessible method for superresolution fluorescence imaging of living systems. Proc. Natl. Acad. Sci. USA 109, 10909–10914 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Dertinger, T., Colyer, R., Iyer, G., Weiss, S. & Enderlein, J. Fast, background-free, 3D super-resolution optical fluctuation imaging (SOFI). Proc. Natl. Acad. Sci. USA 106, 22287–22292 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Rego, E.H. et al. Nonlinear structured-illumination microscopy with a photoswitchable protein reveals cellular structures at 50-nm resolution. Proc. Natl. Acad. Sci. USA 109, E135–E143 (2012).

    CAS  PubMed  Google Scholar 

  14. Liu, Z. et al. Super-resolution imaging and tracking of protein-protein interactions in sub-diffraction cellular space. Nat. Commun. 5, 4443 (2014).

    CAS  PubMed  Google Scholar 

  15. Nickerson, A., Huang, T., Lin, L.-J. & Nan, X. Photoactivated localization microscopy with bimolecular fluorescence complementation (BiFC-PALM) for nanoscale imaging of protein-protein interactions in cells. PLoS One 9, e100589 (2014).

    PubMed  PubMed Central  Google Scholar 

  16. Hertel, F., Mo, G.C.H., Duwé, S., Dedecker, P. & Zhang, J. RefSOFI for mapping nanoscale organization of protein-protein interactions in living cells. Cell Rep. 14, 390–400 (2016).

    CAS  PubMed  Google Scholar 

  17. Newman, R.H., Fosbrink, M.D. & Zhang, J. Genetically encodable fluorescent biosensors for tracking signaling dynamics in living cells. Chem. Rev. 111, 3614–3666 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Ando, R., Mizuno, H. & Miyawaki, A. Regulated fast nucleocytoplasmic shuttling observed by reversible protein highlighting. Science 306, 1370–1373 (2004).

    CAS  PubMed  Google Scholar 

  19. Shaner, N.C. et al. Improving the photostability of bright monomeric orange and red fluorescent proteins. Nat. Methods 5, 545–551 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Arai, R., Ueda, H., Kitayama, A., Kamiya, N. & Nagamune, T. Design of the linkers which effectively separate domains of a bifunctional fusion protein. Protein Eng. 14, 529–532 (2001).

    CAS  PubMed  Google Scholar 

  21. Dickson, R.M., Cubitt, A.B., Tsien, R.Y. & Moerner, W.E. On/off blinking and switching behaviour of single molecules of green fluorescent protein. Nature 388, 355–358 (1997).

    CAS  PubMed  Google Scholar 

  22. Bourgeois, D. & Adam, V. Reversible photoswitching in fluorescent proteins: a mechanistic view. IUBMB Life 64, 482–491 (2012).

    CAS  PubMed  Google Scholar 

  23. Geissbuehler, S. et al. Live-cell multiplane three-dimensional super-resolution optical fluctuation imaging. Nat. Commun. 5, 5830 (2014).

    PubMed  Google Scholar 

  24. Zhang, J., Hupfeld, C.J., Taylor, S.S., Olefsky, J.M. & Tsien, R.Y. Insulin disrupts beta-adrenergic signalling to protein kinase A in adipocytes. Nature 437, 569–573 (2005).

    CAS  PubMed  Google Scholar 

  25. Komatsu, N. et al. Development of an optimized backbone of FRET biosensors for kinases and GTPases. Mol. Biol. Cell 22, 4647–4656 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Bacskai, B.J. et al. Spatially resolved dynamics of cAMP and protein kinase A subunits in Aplysia sensory neurons. Science 260, 222–226 (1993).

    CAS  PubMed  Google Scholar 

  27. Chen, C., Nakamura, T. & Koutalos, Y. Cyclic AMP diffusion coefficient in frog olfactory cilia. Biophys. J. 76, 2861–2867 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Neves, S.R. et al. Cell shape and negative links in regulatory motifs together control spatial information flow in signaling networks. Cell 133, 666–680 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Nikolaev, V.O., Bünemann, M., Hein, L., Hannawacker, A. & Lohse, M.J. Novel single chain cAMP sensors for receptor-induced signal propagation. J. Biol. Chem. 279, 37215–37218 (2004).

    CAS  PubMed  Google Scholar 

  30. Saucerman, J.J., Greenwald, E.C. & Polanowska-Grabowska, R. Mechanisms of cyclic AMP compartmentation revealed by computational models. J. Gen. Physiol. 143, 39–48 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Harootunian, A.T. et al. Movement of the free catalytic subunit of cAMP-dependent protein kinase into and out of the nucleus can be explained by diffusion. Mol. Biol. Cell 4, 993–1002 (1993).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Wen, W. et al. Factors that influence the nuclear accessibility of the catalytic subunit of the cAMP-dependent protein-kinase. FASEB J. 8, A1226 (1994).

    Google Scholar 

  33. Dessauer, C.W. Adenylyl cyclase–A-kinase anchoring protein complexes: the next dimension in cAMP signaling. Mol. Pharmacol. 76, 935–941 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Dodge, K. & Scott, J.D. AKAP79 and the evolution of the AKAP model. FEBS Lett. 476, 58–61 (2000).

    CAS  PubMed  Google Scholar 

  35. Wang, Y. et al. Isoform-selective disruption of AKAP-localized PKA using hydrocarbon stapled peptides. ACS Chem. Biol. 9, 635–642 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Lim, C.J. et al. Integrin-mediated protein kinase A activation at the leading edge of migrating cells. Mol. Biol. Cell 19, 4930–4941 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Lim, C.J. et al. α4 Integrins are Type I cAMP-dependent protein kinase-anchoring proteins. Nat. Cell Biol. 9, 415–421 (2007).

    CAS  PubMed  Google Scholar 

  38. Inoue, T., Heo, W.D., Grimley, J.S., Wandless, T.J. & Meyer, T. An inducible translocation strategy to rapidly activate and inhibit small GTPase signaling pathways. Nat. Methods 2, 415–418 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Durocher, D. et al. The molecular basis of FHA domain:phosphopeptide binding specificity and implications for phospho-dependent signaling mechanisms. Mol. Cell 6, 1169–1182 (2000).

    CAS  PubMed  Google Scholar 

  40. Miyawaki, A. & Tsien, R.Y. Monitoring protein conformations and interactions by fluorescence resonance energy transfer between mutants of green fluorescent protein. Methods Enzymol. 327, 472–500 (2000).

    CAS  PubMed  Google Scholar 

  41. Lakowicz, J.R. Principles of Fluorescence Spectroscopy 3rd edn. (Springer, 2010).

  42. Landgraf, D., Okumus, B., Chien, P., Baker, T.A. & Paulsson, J. Segregation of molecules at cell division reveals native protein localization. Nat. Methods 9, 480–482 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Vandenberg, W. et al. Model-free uncertainty estimation in stochastical optical fluctuation imaging (SOFI) leads to a doubled temporal resolution. Biomed. Opt. Express 7, 467–480 (2016).

    PubMed  PubMed Central  Google Scholar 

  44. Zhang, J., Ma, Y., Taylor, S.S. & Tsien, R.Y. Genetically encoded reporters of protein kinase A activity reveal impact of substrate tethering. Proc. Natl. Acad. Sci. USA 98, 14997–15002 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Smith, F.D. et al. Intrinsic disorder within an AKAP-protein kinase A complex guides local substrate phosphorylation. eLife 2, e01319 (2013).

    PubMed  PubMed Central  Google Scholar 

  46. Gold, M.G. et al. Architecture and dynamics of an A-kinase anchoring protein 79 (AKAP79) signaling complex. Proc. Natl. Acad. Sci. USA 108, 6426–6431 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Sawano, A. & Miyawaki, A. Directed evolution of green fluorescent protein by a new versatile PCR strategy for site-directed and semi-random mutagenesis. Nucleic Acids Res. 28, E78 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Dedecker, P., Duwe, S., Neely, R.K. & Zhang, J. Localizer: fast, accurate, open-source, and modular software package for superresolution microscopy. J. Biomed. Opt. 17, 5 (2012).

    Google Scholar 

  49. Roy, R., Hohng, S. & Ha, T. A practical guide to single-molecule FRET. Nat. Methods 5, 507–516 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Ovesný, M., Křížek, P., Borkovec, J., Svindrych, Z. & Hagen, G.M. ThunderSTORM: a comprehensive ImageJ plug-in for PALM and STORM data analysis and super-resolution imaging. Bioinformatics 30, 2389–2390 (2014).

    PubMed  PubMed Central  Google Scholar 

  51. Dean, K.M. et al. Analysis of red-fluorescent proteins provides insight into dark-state conversion and photodegradation. Biophys. J. 101, 961–969 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Manna, P. & Jimenez, R. Time and frequency-domain measurement of ground-state recovery times in red fluorescent proteins. J. Phys. Chem. B 119, 4944–4954 (2015).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The authors acknowledge Q. Ni, L.M. Amzel, S. Mehta and P.A. Iglesias for critical reading of the manuscript, and X. Zhou for aid in protein purification. This work was supported by NIH DP1 CA174423, R35 CA197622 and R01 DK073368 (to J.Z.); R01 GM079440, T32 GM008403 and NSF MCB 141210B (to K.G.F.); R01CA74305 (to P.A.C.); NCI1K22CA154600 and 1R03A188439 (to E.J.K.); a Research-Foundation Flanders (FWO-Vlaanderen) postdoctoral fellowship, KU Leuven Research Professorship and European Research Council ERC Starting Grant 714688 (to P.D.); and a Graduate Research Fellowship from the NSF DGE-1232825 (to A.M.P.). J.Z. and G.M. acknowledge the UCSD Specialized Cancer Center Support Grant P30 2P30CA023100-28. R.J. acknowledges NIST and the NSF Physics Frontier Center at JILA for support. R.J. is a staff member in the Quantum Physics Division of the National Institute of Standards and Technology (NIST). Certain commercial equipment, instruments or materials are identified in this paper in order to specify the experimental procedure adequately. Such identification is not intended to imply recommendation or endorsement by the NIST, nor is it intended to imply that the materials or equipment identified are necessarily the best available for the purpose.

Author information

Authors and Affiliations

  1. Department of Pharmacology, University of California San Diego, La Jolla, California, USA

    Gary C H Mo, Brian Ross, Fabian Hertel, Eric Greenwald, Chris Booth & Jin Zhang

  2. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA

    Brian Ross

  3. JILA, University of Colorado and NIST, Boulder, Colorado, USA

    Premashis Manna & Ralph Jimenez

  4. Department of Chemistry and Biochemistry, University of Colorado, Boulder, Boulder, Colorado, USA

    Premashis Manna, Amy Palmer & Ralph Jimenez

  5. Department of Biophysics and Biophysical Chemistry, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA

    Xinxing Yang, Brian Tenner & Jie Xiao

  6. T. C. Jenkins Department of Biophysics, Johns Hopkins University, Baltimore, Maryland, USA

    Ashlee M Plummer & Karen G Fleming

  7. Department of Pharmacology and Molecular Sciences, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA

    Zan Chen, Philip A Cole & Jin Zhang

  8. Department of Pharmaceutical and Biomedical Sciences, University of Georgia, Athens, Georgia, USA

    Yuxiao Wang & Eileen J Kennedy

  9. BioFrontiers Institute, University of Colorado, Boulder, Boulder, Colorado, USA

    Amy Palmer

  10. Department of Chemistry, KU Leuven, Heverlee, Belgium

    Peter Dedecker

Authors
  1. Gary C H Mo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Brian Ross
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fabian Hertel
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Premashis Manna
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xinxing Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Eric Greenwald
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Chris Booth
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Ashlee M Plummer
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Brian Tenner
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Zan Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Yuxiao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Eileen J Kennedy
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Philip A Cole
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Karen G Fleming
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Amy Palmer
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Ralph Jimenez
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Jie Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Peter Dedecker
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Jin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

G.C.H.M. and J.Z. made the initial discovery and designed all experiments. P.D. was also involved in the initial proposal of the research direction. G.C.H.M., B.R., F.H., P.M., E.G., X.Y., C.B., B.T., A.M.P., Z.C. and K.G.F. performed experiments and analyzed data with input from J.Z., J.X., R.J., A.P., K.G.F. and P.A.C.; Y.W. and E.J.K. provided reagents. G.C.H.M., B.R., E.G. and P.D. developed and validated all postprocessing algorithms; P.D. formalized the normalization approach. G.C.H.M. and J.Z. wrote the paper.

Corresponding author

Correspondence to Jin Zhang.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Additional characterizations in the discovery of FLINC.

(A) Representative images from a single, 35 ms exposure using 561 nm laser on HeLa cells expressing DpTT and control constructs: TagRFP-T alone (TT), co-targeted Dronpa and TagRFP-T (Dp+TT), Dronpa-linker-TagRFP-T (DpTT), EGFP-linker-TagRFP-T (EGTT). Scale bar: 10 μm. (B) Averaged fluorescence intensity of HeLa cells expressing various constructs. n numbers are: TT (8), DpTT (7), Dp+TT (9), EGTT (8), and EGFP-linker-mCherry (EGmCh, n=9). (C) Representative images from a single, 35 ms exposure using 561 nm laser on HeLa cells expressing mutant DpTT constructs: Dp[S142D]-TT (S142D), Dp[C62G/Y63G]-TT (GGG), Dp[N102I]-TT (N102I), Dp[N102I/R149E]-TT (NIRE). Scale bar: 10 μm. (D) Averaged fluorescence intensity of HeLa cells expressing various mutant and control constructs; tandem model DpTT containing wild-type Dronpa (WT), singly-expressed TagRFP-T (TT-alone). n numbers are: TT-alone (8), WT (7), S142D (8), GGG (11), N102I (12), and NIRE (9). (E) Normalized skewness quantification demonstrates that Dronpa fluctuation is not significantly affected by co-transfection (Dp+TT) or fusion (DpTT) with TagRFP-T on the membrane of HeLa cells. (F) Normalized average fluorescence intensity of bacterial colonies in error-prone (K178R, N208K) and site-directed (residues N102 and R149) mutagenesis. Pair-wise t-test results in (B), (D), (E) are marked where data were compared with the construct marked “ref”; n numbers are marked in the corresponding columns. n.s.: not-significant; *: p<0.05; **: p<0.01; ***: p<0.001 where applicable. Center line and whiskers mark mean and s.e.m. values, respectively.

Supplementary Figure 2 In vitro characterization of FLINC.

(A) Size-exclusion chromatography permits a qualitative assessment of the interaction between Dronpa and TagRFP-T. Sizes of the protein oligomers are as indicated. A peak at 92 kDa was observed only in the mixed solution, indicating a trace amount of a 3-fluorescent-protein hetero-oligomer formed at protein concentrations of 60 μM each. Color codes: red, TagRFP-T; green, Dronpa; orange, algebraic sum of Dronpa/TagRFP-T chromatographs; blue, mixture. (B) SedAnal plot of ΔC as a function of cell radius for fitting of Dronpa+TagRFP-T mixed sample data from analytical ultracentrifugation. The experimental data are shown as black dots, while the calculated fitting curve for the A + B ↔ C model is shown as a solid blue line. Data from scans 55 to 100 are included in this fit; the input parameters include: S(Dronpa) = 2.15, S(TagRFP-T) = 3.30, and S(Dronpa+TagRFP-T) = 4.0. See Supplementary Information. (C) Absorption and (D) fluorescence spectroscopy of purified DpTT fractions compared to TagRFP-T or Dronpa. (E) Electrostatic surfaces of Dronpa and TagRFP-T calculated by the Adaptive Poisson-Boltzmann Solver (APBS) plug-in in PyMOL. The basic pocket in Dronpa and the acidic patch around the chromophore in TagRFP-T, both features believed to be important for FLINC, have been highlighted.

Supplementary Figure 3 The characteristic photophysical differences between DpTT and TT (TagRFP-T) are primarily observed in dark-state conversion.

(A) A simplified schematic depicting the 4-state model of the TagRFP-T chromophore transition. (B) Results of the Dark-State Conversion (DSC) measurement and fit. The bottom two figures summarize the difference in DSC time constant and Percent DSC. (C) Results of the Ground-State Recovery (GSR) measurement and fit. (D) Results of the fluorescence (em) life time measurement; IRF is instrument response function. Pair-wise t-test results in (B) are marked where data were compared with the construct marked “ref”; n numbers are marked in the corresponding columns. *: p<0.05. Center line and whiskers mark mean and s.e.m. values, respectively.

Supplementary Figure 4 Signal-to-noise estimation for pcSOFI imaging.

Jackknife resampling on a typical FLINC dataset demonstrates the accuracy of pcSOFI imaging in terms of signal-to-noise ratio (SNR). We obtain a high SNR of 6.7.

Supplementary Figure 5 Validation of the pcSOFi normalization algorithm.

(A) and (C) show average fluorescence images of plasma membrane targeted FLINC-AKAR1, where uneven distribution of the probe could be observed. (B) and (D) show normalized pcSOFI images of the same cells. (E) The effectiveness of the pcSOFI normalization scheme is demonstrated by performing normalization on HeLa cells expressing membrane targeted Dronpa. Normalization removes the clearly present concentration bias, as seen by the profile (averaged along the y-axis of the region indicated by the red box). All scalebars are 10 ÎĽm.

Supplementary Figure 6 Additional control data supporting the accuracy, biological relevance and function of FLINC biosensing.

(A) Treatment of Fsk/IBMX stimulated HeLa cells with 20 ÎĽM H89 over time can inhibit PKA activity to baseline levels. (B) FLINC-AKAR1 is sensitive to intermediate levels of PKA activity as demonstrated by representative response during 1) H89 inhibition (left column of B), and 2) submaximal forskolin (Fsk) dosage (right column of B). The response of the FRET-based biosensor is provided for comparison in each case. In (A), center line and whiskers mark mean and s.e.m. values, respectively.

Supplementary Figure 7 Resolution analyses of FLINC-based kinase-activity reporters (KAR).

In each row, the feature can be located in the mean fluorescence (Avg) images, and the enzyme activity detected from the feature is shown in the pcSOFI images, labeled by order of calculation. (A-B) Resolution analyses on lifeact-targeted FLINC-AKAR1, where the PKA activity detected on actin filament under different treatments is quantified by either 2nd or 3rd order pcSOFI. The profile of the filament at the red line was plotted in the graph to the right. The activity profile before and after Fsk/IBMX stimulation of PKA are shown to highlight the sensing of activity during superresolution imaging. After Gaussian fitting of many filament profile lines, the average FWHM the filaments were found to be 179±6 nm (n=7) and 116±6 nm (n=7) for (A) 2nd and (B) 3rd order pcSOFI images, respectively. (C-E) Resolution analyses using filopodia features. In each panel, the mean fluorescence (Avg) and the pcSOFI (2nd or 3rd order) image of the same region of interest are shown, while the profiles of the active feature marked by the red dashed line were plotted in the graph to the right. As demonstrated by a graph and a surface plot to aid the eye at the right end of rows C-E, the joints of two filopodial features can be clearly resolved across 3 pixels, allowing the spatial resolution to be estimated. We find a resolution of 160 nm for the 2nd order pcSOFI with FLINC-AKAR1 (C), a resolution of 107 nm for the 3rd order pcSOFI with FLINC-AKAR1 (D), and a resolution of 160 nm for 2nd order pcSOFI with FLINC-EKAR (E).

Supplementary Figure 8 STORM super-resolution imaging of PKA activity microdomains by using anti-pPKAsub antibody.

(A) A representative view of the p-PKAsub localization on the basal membrane of HeLa cells. Inset: a zoom-view illustrating the PKA microdomains observed. (B) Ripley’s K analysis of p-PKAsub localizations show clear clustering above random sampling. The average clustering length scale is 102 nm (n = 3). (C) and (D) Mean-shift clustering analysis reveals the average cluster diameter and number of clusters on the basal membrane, respectively, upon Fsk/IBMX stimulation (n=3 cells), H89 inhibition (n=4 cells), and without pretreatment (n=3 cells). Unless indicated otherwise, pair-wise t-test results are marked where data were compared with the construct marked “ref”. n.s.: not-significant; *: p<0.05; **: p<0.01; ***: p<0.001 where applicable. In (C) and (D), center line and whiskers mark mean and s.e.m. values, respectively.

Supplementary Figure 9 Representative data analysis for STORM imaging and STORM/FLINC imaging.

(A) and (B) Double exponential fit and parameters for the spot-linking or blinking-rejection utilized in post-processing of STORM dataset in Alexa568 and Alexa647 dyes, respectively. All data were treated by spot-linking prior to further analysis to avoid over-counting. (C) Representative processing from mean-shift clustering analysis. (C1) Identification and clustering of phospho-PKA substrates localizations; (C2) Identification and clustering of AKAP79 localizations; (C3) Convex-hull cluster pairs highlighting the AKAP79 clusters (blue) and their respective phospho-PKA substrate clusters (red). (C4) Histogram displaying the frequency of occurrence for centroid distances between AKAP79 clusters and their respective nearest p-PKAsub clusters; representative of n=5 cells. (C5) Histogram displaying the frequency of occurrence for centroid distances between AKAP79 and FLINC microdomain; representative of n=5 cells. (D) Representative processing from the Getis-Franklin co-clustering analysis. (D1) Self-clustering of AKAP79 by Getis-Franklin, which is a precursor to co-clustering analysis; red color indicates increasing tendency of clustering. (D2) Co-clustering analysis plot; red color indicates increasing tendency of clustering. (D3) Representative quadrant analysis from Getis-Franklin co-clustering, reported in Figure 3C.

Supplementary Figure 10 Normalized pcSOFI response to inhibition by STAD-2 and its scrambled-peptide control.

STAD-2 treatment (n=6) to dissociate PKA-RII subunit from A Kinase Anchoring Proteins (AKAP) inhibited membrane PKA activity while scrambled-control peptide (n=3) did not.

Supplementary Figure 11 Inhibition of global PKA activity with H-89 decreases PKA activity at the leading front.

Normalized pcSOFI images and their corresponding histograms for a migrating α4 CHO cell at t=0 min, 5.5 min, and 22 min after addition of 20 μM of H-89, a PKA inhibitor. All images are displayed using the same color scale. A shift to lower normalized pcSOFI values was observed for the leading region, but not the trailing region, of the cell. Scale bar: 10 μm.

Supplementary Figure 12 Detection of the weak protein–protein interaction between FHA1 and p-PKAsub.

(A) Schematic of the bi-molecular version of FLINC-AKAR1. It responses to PKA stimulation at the membrane by detecting the binding of phospho-substrate and FHA1, a weak protein-protein interaction (kD ~ 0.5 ÎĽM). (B) 11 min after Fsk/IBMX stimulation the wild-type (WT, n=3) biosensor shows significant response compare to the non-phosphorylatable mutant (TA, n=4). Pair-wise t-test result *: p<0.05; center line and whiskers mark mean and s.e.m. values, respectively.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–12, Supplementary Tables 1 and 2, and Supplementary Note (PDF 6485 kb)

Fluorescence fluctuations from FLINC and control constructs (MOV 3224 kb)

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mo, G., Ross, B., Hertel, F. et al. Genetically encoded biosensors for visualizing live-cell biochemical activity at super-resolution. Nat Methods 14, 427–434 (2017). https://doi.org/10.1038/nmeth.4221

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nmeth.4221

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