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A DNA origami platform for quantifying protein copy number in super-resolution

Nature Methods volume 14, pages 789–792 (2017)Cite this article

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Abstract

Single-molecule-based super-resolution microscopy offers researchers a unique opportunity to quantify protein copy number with nanoscale resolution. However, while fluorescent proteins have been characterized for quantitative imaging using calibration standards, similar calibration tools for immunofluorescence with small organic fluorophores are lacking. Here we show that DNA origami, in combination with GFP antibodies, is a versatile platform for calibrating fluorophore and antibody labeling efficiency to quantify protein copy number in cellular contexts using super-resolution microscopy.

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Figure 1: DNA origami calibration.
Figure 2: Validation of stoichiometry determination.
Figure 3: Quantification of Nup133 complexes in U2OS cells.

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Acknowledgements

We thank P. Gomez (ICFO, Barcelona) for helpful discussions. The siRNA-resistant Nup133-GFP and Nup107-GFP plasmids were a kind gift from J. Ellenberg (EMBL, Heidelberg). M.L. acknowledges funding from the Fundació Cellex Barcelona, European Union Seventh Framework Programme under the European Research Council grants 337191-MOTORS and Spanish Ministry of Economy and Competitiveness and the Fondo Europeo de Desarrollo Regional (FEDER) grant FIS2015-63550-R (MINECO/FEDER). F.C.Z. acknowledges funding from the 'Severo Ochoa' Programme for Centres of Excellence in R&D (SEV-2015-0522). C.M. acknowledges funding from the Spanish Ministry of Economy and Competitiveness and the European Social Fund (ESF) through the Ramón y Cajal program 2015 (RYC-2015-17896).

Author information

Author notes

  1. Francesca Cella Zanacchi

    Present address: Department of Physiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA

  2. Melike Lakadamyali

    Present address: Nanoscopy and NIC@IIT, Istituto italiano di tecnologia (IIT), Genova, Italy

  3. Carlo Manzo and Angel S Alvarez: These authors contributed equally to this work.

Authors and Affiliations

  1. ICFO-Institut de Ciencies Fotoniques, The Barcelona Institute of Science and Technology, Castelldefels, Spain

    Francesca Cella Zanacchi, Carlo Manzo, Angel S Alvarez, Maria F Garcia-Parajo & Melike Lakadamyali

  2. Universitat de Vic – Universitat Central de Catalunya (UVic-UCC), Vic, Spain

    Carlo Manzo

  3. Department of Biological Sciences, Smith College, Northampton, Massachusetts, USA

    Nathan D Derr

  4. Center for Microscopy and Imaging, Smith College, Northampton, Massachusetts, USA

    Nathan D Derr

  5. ICREA, Barcelona, Spain

    Maria F Garcia-Parajo

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  1. Francesca Cella Zanacchi
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Contributions

F.C.Z. performed experiments and analyzed data. C.M. wrote software and analyzed data. A.S.A. performed the dynein purification and gave support with sample preparation. N.D.D. provided DNA origami materials. M.L. conceived the idea, and M.L. and M.F.G.-P. supervised the research. M.L. and F.C.Z. wrote the manuscript. All authors provided feedback on the manuscript.

Corresponding authors

Correspondence to Francesca Cella Zanacchi or Melike Lakadamyali.

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Competing interests

F.C.Z, C.M., N.D. and M.L declare conflict of interests and have filed a patent application.

Integrated supplementary information

Supplementary Figure 1 DNA origami characterization

(a) The number of counted steps from the stepwise photobleaching experiments (red) fit to a binomial distribution giving a handle/anti-handle attachment probability of 48% (black) (N=3 independent experiments, N1step= 206, N2steps= 192, N3steps= 61, Reduced ChiSquare=0.0018). (b) After clustering analysis of DNA origami images functionalized with AlexaFluor 647, the mean value of the nearest neighbour distance between clusters (Mean distance 85nm, standard deviation=7nm for distances from handle 1 to handle 7 and from handle 7 and 13) and the distance between the two furthest clusters (Mean distance 157nm, standard deviation=17nm, for distances between handle 1 and 13) were calculated by the distribution of center-to-center distances between each cluster identified (N=2 independent experiments, N= 28 images analysed for handles 1-7/7-13, N= 10 images analysed for handles 1-13). (c) The counted number of single, double and triple clusters detected in STORM images of chassis functionalized with Dynein (red) (labelled with AlexaFluor 405/AlexaFluor 647 through GFP immunostaining) gave a distribution that fit well to a binomial with a labelling probability of 38% (black) (N=3 independent experiments, N1cluster= 1030, N2clusters= 630, N3clusters= 160 Reduced ChiSquare=0.0006).

Supplementary Figure 2 Agarose gel electrophoresis of DNA origami and purified Dynein

(a) Folding of the 12 helix chassis was assessed by 2% agarose gel electrophoresis showing the difference between the folded chassis (second lane) compared to the initial scaffold used for the reaction (first lane). (b) Dynein from Saccharomyces cerevisiae was purified as previously described obtaining a final concentration of 330nM (white box).

Supplementary Figure 3 Comparison between DNA origami on different substrates

The number of localizations per cluster was calculated for chassis functionalized with one motor protein (the handle at position 7 was functionalized with Dynein) and immobilized on glass (red) or on BS-C-1 cells (black) through Biotin-Streptavidin attachment. The median value of the number of localizations per cluster distribution for DNA chassis on glass and on cells was (66±56) and (60±89) localizations, respectively (N= 5 independent experiments, N=3077 clusters analysed and N= 4 independent experiments, N=258 clusters analysed on glass and on cells, respectively)

Supplementary Figure 4 Correlation between estimated and actual values at varying statistics and stoichiometry.

(a-b)Pearson correlation coefficient R for known synthetic distributions of localizations corresponding to known fractions of single, double and triple motors (Rmax=0.99) (a) and a mixture of 1,4,8,16 motors Rmax=0.75 (b). The distribution used in (a) follows a distribution similar to the one used for 1,2,3 motors shown in Fig2a-c and the values of the fractions are distributed according to an exponential function (y=7.98*exp(-0.47*x). The fractions in (b) are uniformly distributed as the one used for 1,4,8,16 motors in Fig.2d-f. To estimate the mixture of 1,4,8,16 motors the analysis was stopped when the minimum value of the objective function was reached (Nmax=20) (c) and fitting the data to a convolution of more than 20 functions did not change the results (inset). (d) Pearson correlation coefficient R calculated on synthetic distributions of localizations obtained by randomly combining the values measured for single, double and triple motors in order to provide a maximum stoichiometry ranging from 3 to 16 (d). The value of each fraction was calculated according to an exponential function (y=7.98*exp(-0.47*x). (e) Pearson correlation coefficient R calculated on synthetic distributions of localizations obtained by randomly combining the values measured for 1,4,8,16 motors in order to provide increasing stoichiometries ranging from 16 to 32 motors (e). All the fractions have a uniform value.

Supplementary Figure 5 Objective function F values and the stoichiometry (Nmax) at which F is minimized for NUP 133.

NPC images were sorted depending upon the number of Nup133 clusters: 1 Cluster (a), 2 Clusters (b), 3 Clusters (c), 4 Clusters (d), 5 Clusters (e) and whole cell (f).

Supplementary Figure 6 NUP133 distribution obtained by fitting to the convolution of an increasing number of calibration functions.

The minimum of the objective function is obtained for Nmax=37 and fitting to a convolution of more than 30 functions did not significantly change the results while fitting to fewer than 30 functions gave rise to isolated peaks at the tail of the stoichiometry distribution.

Supplementary Figure 7 Quantification of Nup107 complexes in U2OS cells

(a, c) STORM image showing Nup107 in siRNA resistant Nup107-GFP expressing U20S cell in which the endogenous copy of Nup107 was knocked down by siRNA: whole nucleus (a) and zoomed region (c). (b, d, inset) Corresponding clustering analysis of the STORM image. (e) Distribution of the number of localizations per NPC (red) and the corresponding fit to a linear combination of calibration functions (black line) (N=1 independent experiment, N= 855 NPC rings analysed). (f) GFP copy-number distribution for Nup107 extracted from the fit minimizing the objective function. Errors bars: lower bound to the standard errors based on the Fisher Information Matrix. Scale bars: 2um (a, b) 200 nm (c, d).

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Supplementary Figures 1–7, Supplementary Note 1–3 and Supplementary Table 1 (PDF 1777 kb)

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Zanacchi, F., Manzo, C., Alvarez, A. et al. A DNA origami platform for quantifying protein copy number in super-resolution. Nat Methods 14, 789–792 (2017). https://doi.org/10.1038/nmeth.4342

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