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Quantitative super-resolution imaging with qPAINT

Nature Methods volume 13, pages 439–442 (2016)Cite this article

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Abstract

Counting molecules in complexes is challenging, even with super-resolution microscopy. Here, we use the programmable and specific binding of dye-labeled DNA probes to count integer numbers of targets. This method, called quantitative points accumulation in nanoscale topography (qPAINT), works independently of dye photophysics for robust counting with high precision and accuracy over a wide dynamic range. qPAINT was benchmarked on DNA nanostructures and demonstrated for cellular applications by quantifying proteins in situ and the number of single-molecule FISH probes bound to an mRNA target.

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Figure 1: qPAINT principle.
Figure 2: qPAINT in vitro benchmarking.
Figure 3: qPAINT in situ.

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Acknowledgements

We thank M.T. Strauss and J. Lara for help with DNA origami design and J. Werbin (Department of Systems Biology, Harvard Medical School) for the donation of CHO cells and fruitful discussions. We also thank A. Raj for providing the smFISH sequences, F. Schueder for initial Brp imaging experiments, and H. Soundararajan and J. Paulson for fruitful discussions. This work is supported by a National Institutes of Health (NIH) Director's New Innovator Award (1DP2OD007292), an NIH Transformative Research Award (1R01EB018659), an NIH grant (5R21HD072481), an Office of Naval Research (ONR) Young Investigator Program Award (N000141110914), ONR grants (N000141010827 and N000141310593), a National Science Foundation (NSF) Faculty Early Career Development Award (CCF1054898), an NSF grant (CCF1162459) and a Wyss Institute for Biologically Engineering Faculty Startup Fund to P.Y., and a Pew Scholar Award to A.R. The BRP antibodies were obtained from the Developmental Studies Hybridoma Bank, created by the NICHD of the NIH. R.J. acknowledges support from the Alexander von Humboldt Foundation through a Feodor-Lynen Fellowship. M.S.A. and M.D. acknowledge support from HHMI International Student Research Fellowships.

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Author notes

  1. Ralf Jungmann, Johannes B Woehrstein & Sarit S Agasti

    Present address: Present addresses: Max Planck Institute of Biochemistry and Ludwig Maximilian University of Munich, Munich, Germany (R.J. and J.B.W.); Jawaharlal Nehru Centre for Advanced Scientific Research (JNCASR), Jakkur, Bangalore, India (S.S.A.).,

  2. Ralf Jungmann and Maier S Avendaño: These authors contributed equally to this work.

Authors and Affiliations

  1. Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts, USA

    Ralf Jungmann, Maier S Avendaño, Mingjie Dai, Johannes B Woehrstein, Sarit S Agasti & Peng Yin

  2. Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, USA

    Ralf Jungmann, Maier S Avendaño & Peng Yin

  3. Harvard Program in Biophysics, Harvard University, Boston, Massachusetts, USA

    Mingjie Dai

  4. Department of Biology, Rosenstiel Basic Medical Sciences Research Center, Brandeis University, Waltham, Massachusetts, USA

    Zachary Feiger & Avital Rodal

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Contributions

R.J. and M.S.A. conceived of the study, designed and performed the experiments, analyzed the data, developed software, and wrote the manuscript. M.D. developed software and wrote the manuscript. J.B.W. helped with in vitro experimental design and wrote the manuscript. S.S.A. helped with DNA-dye conjugation and developed the antibody labeling protocol. Z.F. and A.R. helped with Brp experiments. P.Y. conceived of and supervised the study, interpreted the data and wrote the manuscript. All authors reviewed and approved the manuscript.

Corresponding author

Correspondence to Peng Yin.

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Competing interests

R.J., M.S.A., M.D., J.B.W. and P.Y. declare conflict of interests and have filed a patent application. P.Y. and R.J. are co-founders of Ultivue, Inc., a startup company with interest to commercialize the reported technology.

Integrated supplementary information

Supplementary Figure 1 Mean dark time value calculation

Mean dark time values are calculated from the cumulative distribution function (cdf)

Supplementary Figure 2 Binding frequency vs. number of binding sites

(a) Stochastic simulations of DNA-PAINT binding events. Plotted is the binding frequency vs. number of simulated binding sites (Black, mean ± stdev; Red: Linear fit obtained from origin and first data point). (b) Zoom-in (blue area in a) shows deviations from linear behavior already for as little as ten binding sites.

Simulation conditions: 500 runs per site, 100 ms integration time, 15000 frames, ξ=0.01 s−1.

Supplementary Figure 3 Counting error time and influx rate dependency

Counting error can be reduced by increasing image acquisition time (squares and circles: experimental data; lines: data fitted with the function A(1/sqrt(x)+B). qPAINT analysis was performed on datasets of 20 nm grid DNA origami structures, by only analyzing structures showing 11 binding sites. After 166 min of imaging, for a probe influx rate of 0.02 s−1 the mean number of binding sites was 11.09 with a standard deviation of 0.72. For a probe influx rate of 0.03 s−1 the mean was 11.08 with a standard deviation of 0.49.

Imaging conditions: The imaging buffer contained 10 nM (ξ=0.02 s−1) or 15 nM (ξ=0.03 s−1) Cy3b-labeled imager strands in buffer B+. Imaging chambers were sealed with epoxy before imaging. Image acquisition was carried out with a CCD readout bandwidth of 3 MHz at 14 bit and 5.1 pre-amp gain. No EM gain was used. Imaging was performed using TIR illumination with an excitation intensity of ~5 mW using the 561 nm laser line. 50,000 frames at 5 Hz frame rate were acquired.

Supplementary Figure 4 Strand diagram and schematics for DNA origami structures

(a) Detailed DNA origami strand diagram for the 20 nm grid structure designed to carry 12 DNA-PAINT docking sites (zoom in to see details. (b) Schematic representation for the structure in a. Hexagons represent 3’ ends of staples. (c) DNA origami with 12 DNA-PAINT docking sites. (d) DNA origami with 48 DNA-PAINT docking sites. (e) DNA origami with 150 docking sites. (f) 20 nm grid structure with 12 fixed Cy3 dyes (hybridized via handle/anti-handle strands). (g) DNA origami with 44 DNA-PAINT docking and 15 fixed Cy3 dyes (hybridized via handle/anti-handle strands).

Strands are color-coded to denote strand extensions (see Supplementary Tables S1, S2, S3, S4, S5, and S6). Color code: Blue: DNA scaffold; Gray: staple strands; Red: staples with a 3′-handle extension for DNA-PAINT (docking sites); Purple: 5′-biotinylated strands; Green: 3′-Cy3-modified staples.

Supplementary Figure 5 Overview of 20 nm grid DNA origami

(a) DNA-PAINT super-resolution and diffraction-limited image of 20 nm grid DNA origami structures are superimposed. Scale bar: 2 µm. (b) Zoom-in of the highlighted region in a. Scale bar: 500 nm.

Imaging conditions: See conditions for Fig. 2b.

Supplementary Figure 6 Binding sites distributions for visual counting, in silico qPAINT, and in vitro qPAINT

(a) Distribution of the number of sites by direct counting the single spots in each DNA origami grid from Fig. 2b. Red line shows the Gaussian fit to the data, yielding a strand incorporation efficiency of ˜87.5 % for the 12 binding sites 20 nm grid structure. (b) qPAINT stochastic simulations (in silico) and in vitro data from the structures analyzed visually in a. In silico simulations were performed using a normal distribution of binding sites of 10.5 ± 1.2 to represent the exact experimental conditions. qPAINT in silico (yielding 10.5 ± 1.6, mean ± stdv) and qPAINT experimental in vitro (yielding 10.4 ± 1.6, mean ± stdv) data is in good agreement.

Imaging conditions: See conditions for Fig. 2b.

Simulation conditions: qPAINT simulations in b were performed using the exact binding site distribution (obtained from visual counting) from a as model input in combination with association and dissociation rates obtained from the experimental data. 15,000 frames at an “integration time” of 0.1 s were simulated.

Supplementary Figure 7 Distinguishability of binding sites based on Tukey’s post hoc analysis

We found significant differences for the number of binding sites at 0.01 level (Fig. 2d). A post Tukey test showed that all distributions differed at 0.01 level of significance (if an interval does not contain 0, the corresponding mean values are significantly different).

Supplementary Figure 8 qPAINT dynamic range

(a) Binding site distribution for DNA origami structures designed to carry 12, 48, and 150 binding sites for a probe influx rate of ξ=0.03 s−1 (n = 1215). qPAINT is able to operate effictively over more than an order of magnitude difference in binding sites per diffraction-limited area. Imaging was performed with Atto 655-labeled imager strands. (b) Corresponding in silico evaluation confirms the in vitro findings.

Supplementary Figure 9 qPAINT in situ benchmarking using DNA origami

(a) CHO cells were transfected to transiently express EGF receptors (EGFR). EGFR labeling is performed with pre-assembled antibody-DNA origami conjugates (44 fixed Cy3 labels, 12 DNA-PAINT docking sites for Atto 655-labeled imager strands). (b) Diffraction-limited (green) and super-resolved DNA-PAINT image (red) of DNA origami on a cell membrane. Transverse profile of two origami structures on a cell membrane in the boxed region are spaced ˜93 nm apart. (c) qPAINT analysis (n = 239) yields the binding site distribution of DNA origami structures on cell membranes. The measured number of binding sites (10.7) matches well with the expected number of binding sites (10.4 for a monomer origami with 12 designed sites and 87% incorporation efficiency of the binding sites). (d) DNA origami (carrying 44 fixed Cy3 labels and 44 DNA-PAINT docking sites for Atto 655-labeled imager strands) were microinjected into fixed HeLa cells. Injections were targeted to nuclear and cytoplasmic regions. (e) Diffraction-limited image (green), super-resolved DNA-PAINT image (red), and bright field image (gray) showing origami structure inside the nucleus and in the cytoplasm. (f) qPAINT analysis of structures inside (n = 105) and outside (n = 24) the nuclear boundary revealed similar binding site distributions, indicating that hybridization kinetics for DNA-PAINT probes are comparable in nuclear and cytoplasmic regions (mean-to-mean difference: ˜1.4 binding sites). (g) Large view of diffraction-limited (gray) and super-resolved DNA-PAINT image (red) of DNA origami on a cell membrane (bright individual dots). (h) Zoom-in of the highlighted area in g. (i) For qPAINT analysis, origami structures in the cytoplasm were separated from structures in the nucleus by using DAPI signal for nuclear segmentation. DNA-PAINT super-resolution reconstruction (red), diffraction-limited image (green), and nuclear staining (blue) are superimposed. Scale bars: 1 µm (b, g, h, and i), 2 µm (e).

Supplementary Figure 10 Symmetric arrangement of Nup98 protein clusters in NPCs

Representative images of NPC structures displaying 1–7 Nup98 protein clusters. Distance between clusters was determined by measuring the interval between means of fitted Gaussians of linearized circular intensity projection (see Online Methods for details). Scale bar: 50 nm.

Supplementary Figure 11 Overview image of Brp proteins in fixed Drosophila NMJs

(a) Epi-fluorescence image of anti-HRP-Alexa488 showing synaptic boutons in neuromuscular junctions (NMJ). (b) Diffraction-limited and (c) DNA-PAINT image of Brp proteins. Fillets were imaged using 10 nM Atto655-labeled imager strands (15000 frames, 10 Hz frame rate). (i), (ii) zoomed in view of the highlighted areas in c. Scale bars: 5 µm (a-c), 500 nm (i, ii).

Supplementary Figure 12 Overview image of mRNA detection inside fixed HeLa cells

Diffraction-limited (green) and DNA-PAINT image (red) of SUZ12 mRNA show co-localization. Scale bar: 1 µm.

Imaging conditions: See conditions for Fig. 3f.

Supplementary Figure 13 Constant influx rate of imaging probes

The number of single-molecule localization events (y-axis), and thus the influx rate of imaging probes remains constant over time (x-axis) during DNA-PAINT imaging, demonstrating that qPAINT analysis is not compromised, as “photobleaching” does not occur. Black: Localizations per frame; Red: zero-slope (constant) linear curve as a guide to the eye.

Data obtained from images used for Supplementary Fig. 9e.

Supplementary Figure 14 Influx rate variability in Nup98 experiments

Using the same concentration of imager probes, the mean dark times (y-axis), and thus the probe influx rate remains constant for different Nup98 qPAINT datasets that were acquired at different days (x-axis). This underlines qPAINT’s robustness and repeatability for different samples and different days. Furthermore, a single calibration can be used for subsequent experiments.

Data obtained from images used for Fig. 3a.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–14 and Supplementary Tables 1–9 (PDF 5901 kb)

Confocal imaging of DNA origami microinjected in fixed cells

Confocal image of HeLa cells (blue) after microinjection with Cy3-labeled DNA origami structures (green spots). Cell membrane delimitation was possible by using whole cell blue staining for 10 min (MP4 8719 kb)

De-convoluted wide-field image stack of DNA origami microinjected in fixed cells

A 3D representation of HeLa cells after microinjection of Cy3-labeled origami structures was obtained by de-convolution of a wide-field image stack using Huygens Professional image processing software. The Cy3 signal (yellow) is overlaid with the spots detected through the software (green). The 3D image shows the homogeneous distribution of structures inside the nucleus and cytoplasm in the whole cell. Different viewing perspectives of the 3D image are presented by rotating the image. (MP4 4424 kb)

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Jungmann, R., Avendaño, M., Dai, M. et al. Quantitative super-resolution imaging with qPAINT. Nat Methods 13, 439–442 (2016). https://doi.org/10.1038/nmeth.3804

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