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


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.

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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.

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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.

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Authors and Affiliations



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.

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Correspondence to Jin Zhang.

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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 μm.

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 μM 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 μM). (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.

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

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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).

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