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.

  • Article
  • Published:

Enhanced detection of fluorescence fluctuations for high-throughput super-resolution imaging

Nature Photonics volume 17, pages 806–813 (2023)Cite this article

Subjects

Abstract

High-throughput super-resolution (SR) imaging is attractive for rapid and high-precision profiling in a wide range of biomedical applications. However, current SR methods require sophisticated acquisition optics and long integration times to acquire a single field of view. By exploiting the natural photophysics of fluorescence, fluctuation-based microscopy techniques can routinely break the diffraction limit without requiring additional optical components. However, their long acquisition time still poses a challenge for high-throughput imaging and the visualization of transient cellular dynamics. Here we propose super-resolution imaging based on autocorrelation with two-step deconvolution (SACD). Our method notably reduces the number of frames required by maximizing the detectable fluorescence fluctuation behaviour in each measurement. SACD requires only 20 frames to achieve a twofold improvement in lateral and axial resolution, whereas current SR optical fluctuation imaging (SOFI) needs more than 1,000 frames. With an acquisition time of ~10 min, we record SR images with 128-nm resolution over a field of view of 2.0 mm × 1.4 mm, which includes more than 2,000 cells. By applying the continuity and sparsity joint constraint, sparse deconvolution-assisted SACD enables four-dimensional imaging of live cells and events such as mitochondrial fission and fusion. Overall, as an open-sourced module, we anticipate that SACD will improve accessibility to SR imaging, thus facilitating biological studies of cells and organisms with high throughput and low cost.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: Workflow and cross-validation of SACD.
Fig. 2: Validation for the 3D-resolution doubling of SACD and large FOV volumetric imaging.
Fig. 3: High-throughput SR imaging with SACD.
Fig. 4: 4D live-cell SR imaging using Sparse-SACD.

Similar content being viewed by others

Data availability

Several representative datasets are available at https://github.com/WeisongZhao/SACDj. All the data that support the findings of this study are available from the corresponding author on request.

Code availability

The SACD in the MATLAB library can be found at https://github.com/WeisongZhao/SACDm, and its corresponding ImageJ plugin is available at https://github.com/WeisongZhao/SACDj.

References

  1. Boutros, M., Heigwer, F. & Laufer, C. Microscopy-based high-content screening. Cell 163, 1314–1325 (2015).

    Article  Google Scholar 

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

    Article  ADS  Google Scholar 

  3. Rust, M., Bates, M. & Zhuang, X. Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM). Nat. Methods 3, 793–796 (2006).

    Article  Google Scholar 

  4. Beghin, A. et al. Localization-based super-resolution imaging meets high-content screening. Nat. Methods 14, 1184–1190 (2017).

    Article  Google Scholar 

  5. Shroff, H., Galbraith, C. G., Galbraith, J. A. & Betzig, E. Live-cell photoactivated localization microscopy of nanoscale adhesion dynamics. Nat. Methods 5, 417–423 (2008).

    Article  Google Scholar 

  6. Wombacher, R. et al. Live-cell super-resolution imaging with trimethoprim conjugates. Nat. Methods 7, 717–719 (2010).

    Article  Google Scholar 

  7. Jones, S. A., Shim, S.-H., He, J. & Zhuang, X. Fast, three-dimensional super-resolution imaging of live cells. Nat. Methods 8, 499–505 (2011).

    Article  Google Scholar 

  8. Mishin, A. & Lukyanov, K. Live-cell super-resolution fluorescence microscopy. Biochemistry 84, 19–31 (2019).

    Google Scholar 

  9. Godin, A. G., Lounis, B. & Cognet, L. Super-resolution microscopy approaches for live cell imaging. Biophys. J. 107, 1777–1784 (2014).

    Article  ADS  Google Scholar 

  10. Moran, U., Phillips, R. & Milo, R. SnapShot: key numbers in biology. Cell 141, 1262 (2010).

    Article  Google Scholar 

  11. Sage, D. et al. Quantitative evaluation of software packages for single-molecule localization microscopy. Nat. Methods 12, 717–724 (2015).

    Article  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).

    Article  ADS  Google Scholar 

  13. Gustafsson, N. et al. Fast live-cell conventional fluorophore nanoscopy with ImageJ through super-resolution radial fluctuations. Nat. Commun. 7, 12471 (2016).

    Article  ADS  Google Scholar 

  14. Agarwal, K. & Macháň, R. Multiple signal classification algorithm for super-resolution fluorescence microscopy. Nat. Commun. 7, 13752 (2016).

    Article  ADS  Google Scholar 

  15. Yahiatene, I., Hennig, S., Müller, M. & Huser, T. Entropy-based super-resolution imaging (ESI): from disorder to fine detail. ACS Photonics 2, 1049–1056 (2015).

    Article  Google Scholar 

  16. Moeyaert, B., Vandenberg, W. & Dedecker, P. SOFIevaluator: a strategy for the quantitative quality assessment of SOFI data. Biomed. Opt. Express 11, 636–648 (2020).

    Article  Google Scholar 

  17. Cevoli, D. et al. Design of experiments for the optimization of SOFI super-resolution microscopy imaging. Biomed. Opt. Express 12, 2617–2630 (2021).

    Article  Google Scholar 

  18. Deschout, H. et al. Complementarity of PALM and SOFI for super-resolution live-cell imaging of focal adhesions. Nat. Commun. 7, 13693 (2016).

    Article  ADS  Google Scholar 

  19. Zhang, X. et al. Development of a reversibly switchable fluorescent protein for super-resolution optical fluctuation imaging (SOFI). ACS Nano 9, 2659–2667 (2015).

    Article  ADS  Google Scholar 

  20. Yi, X., Son, S., Ando, R., Miyawaki, A. & Weiss, S. Moments reconstruction and local dynamic range compression of high order Superresolution Optical Fluctuation Imaging. Biomed. Opt. Express 10, 2430–2445 (2019).

    Article  Google Scholar 

  21. Nieuwenhuizen, R. P. et al. Measuring image resolution in optical nanoscopy. Nat. Methods 10, 557–562 (2013).

    Article  Google Scholar 

  22. Koho, S. et al. Fourier ring correlation simplifies image restoration in fluorescence microscopy. Nat. Commun. 10, 3103 (2019).

    Article  ADS  Google Scholar 

  23. Mahecic, D., Testa, I., Griffié, J. & Manley, S. Strategies for increasing the throughput of super-resolution microscopies. Curr. Opin. Chem. Biol. 51, 84–91 (2019).

    Article  Google Scholar 

  24. Weisong, Z. et al. Sparse deconvolution improves the resolution of live-cell super-resolution fluorescence microscopy. Nat. Biotechnol. 40, 606–617 (2022).

    Article  Google Scholar 

  25. Yi, X. & Weiss, S. Cusp-artifacts in high order superresolution optical fluctuation imaging. Biomed. Opt. Express 11, 554–570 (2020).

    Article  Google Scholar 

  26. Zeng, Z. et al. Fast super-resolution imaging with ultra-high labeling density achieved by joint tagging super-resolution optical fluctuation imaging. Sci. Rep. 5, 8359 (2015).

    Article  Google Scholar 

  27. Richardson, W. H. Bayesian-based iterative method of image restoration. J. Opt. Soc. Am. A 62, 55–59 (1972).

    Article  ADS  Google Scholar 

  28. Lucy, L. B. An iterative technique for the rectification of observed distributions. Astron. J 79, 745–754 (1974).

    Article  ADS  Google Scholar 

  29. Stein, S. C., Huss, A., Hähnel, D., Gregor, I. & Enderlein, J. Fourier interpolation stochastic optical fluctuation imaging. Opt. Express 23, 16154–16163 (2015).

    Article  ADS  Google Scholar 

  30. Geissbuehler, S. et al. Mapping molecular statistics with balanced super-resolution optical fluctuation imaging (bSOFI). Opt. Nanoscopy 1, 4 (2012).

    Article  Google Scholar 

  31. Dertinger, T., Colyer, R., Vogel, R., Enderlein, J. & Weiss, S. Achieving increased resolution and more pixels with Superresolution Optical Fluctuation Imaging (SOFI). Opt. Express 18, 18875–18885 (2010).

    Article  ADS  Google Scholar 

  32. Jiang, S. et al. Enhanced SOFI algorithm achieved with modified optical fluctuating signal extraction. Opt. Express 24, 3037–3045 (2016).

    Article  ADS  Google Scholar 

  33. Wang, X. et al. Enhanced temporal and spatial resolution in super‐resolution covariance imaging algorithm with deconvolution optimization. J. Biophotonics 14, e202000292 (2021).

    Article  Google Scholar 

  34. Huang, X. et al. Fast, long-term, super-resolution imaging with Hessian structured illumination microscopy. Nat. Biotechnol. 36, 451–459 (2018).

    Article  Google Scholar 

  35. Mahecic, D. et al. Homogeneous multifocal excitation for high-throughput super-resolution imaging. Nat. Methods 17, 726–733 (2020).

    Article  Google Scholar 

  36. Xu, J. et al. Labeling cytosolic targets in live cells with blinking probes. J. Phys. Chem. Lett. 4, 2138–2146 (2013).

    Article  Google Scholar 

  37. Lukeš, T. et al. Quantifying protein densities on cell membranes using super-resolution optical fluctuation imaging. Nat. Commun. 8, 1731 (2017).

    Article  ADS  Google Scholar 

  38. Biggs, D. S. & Andrews, M. Acceleration of iterative image restoration algorithms. Appl. Opt. 36, 1766–1775 (1997).

    Article  ADS  Google Scholar 

  39. Heel, M. V. & Schatz, M. Fourier shell correlation threshold criteria. J. Struct. Biol. 151, 250–262 (2005).

    Article  Google Scholar 

  40. Born, M. & Wolf, E. Principles of Optics 7th edn (Cambridge Univ. Press, 1999).

  41. Goldstein, T. & Osher, S. The split Bregman method for L1-regularized problems. SIAM J. Imag. Sci. 2, 323–343 (2009).

    Article  MathSciNet  MATH  Google Scholar 

  42. Pennacchietti, F. et al. Fast reversibly photoswitching red fluorescent proteins for live-cell RESOLFT nanoscopy. Nat. Methods 15, 601–604 (2018).

    Article  Google Scholar 

  43. Girsault, A. et al. SOFI simulation tool: a software package for simulating and testing super-resolution optical fluctuation imaging. PLoS ONE 11, e0161602 (2016).

    Article  Google Scholar 

  44. Wang, Z., Bovik, A. C., Sheikh, H. R. & Simoncelli, E. P. Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 600–612 (2004).

    Article  ADS  Google Scholar 

  45. Marsh, R. J. et al. Artifact-free high-density localization microscopy analysis. Nat. Methods 15, 689–692 (2018).

    Article  Google Scholar 

  46. Ovesný, M., Krízek, 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).

    Article  Google Scholar 

  47. Geissbuehler, M. & Lasser, T. How to display data by color schemes compatible with red-green color perception deficiencies. Opt. Express 21, 9862–9874 (2013).

    Article  ADS  Google Scholar 

  48. Culley, S. et al. Quantitative mapping and minimization of super-resolution optical imaging artifacts. Nat. Methods 15, 263–266 (2018).

    Article  Google Scholar 

Download references

Acknowledgements

We thank P. Xu at the Institute of Biophysics, Chinese Academy of Sciences for sharing nucleolar protein plasmids. We thank W. Chen at the University of California, Berkeley for the reading and critical comments on the manuscript. This work was supported by the National Natural Science Foundation of China (grants T2222009 (to H.L.), 32227802 (to L.C.), 81925022 (to L.C.), 92054301 (to L.C.), 32222022 (to Y.J.)), the National Key Research and Development Program of China (grant 2022YFC3400600 to L.C.), the Natural Science Foundation of Heilongjiang Province (grant YQ2021F013 to H.L.), the Beijing Natural Science Foundation (grant Z20J00059 to L.C.), the China Postdoctoral Science Foundation (grant 2022M720971 to W.Z.) and the Heilongjiang Provincial Postdoctoral Science Foundation (grant LBH-Z22027 to W.Z.). H.L. acknowledges support from the Laboratory for Space Environment and Physical Sciences. L.C. acknowledges support from the High-Performance Computing Platform of Peking University. S.Z. acknowledges support from the Boya Postdoctoral Fellowship of Peking University. G.H. acknowledges support from a start-up grant from Nanyang Technological University.

Author information

Author notes

  1. These authors contributed equally: Weisong Zhao, Shiqun Zhao, Zhenqian Han, Xiangyan Ding.

Authors and Affiliations

  1. Innovation Photonics and Imaging Center, School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, China

    Weisong Zhao, Zhenqian Han, Xiangyan Ding, Liying Qu, Yuanyuan Huang, Xinwei Wang, Xumin Ding & Haoyu Li

  2. Key Laboratory of Ultra-precision Intelligent Instrumentation of Ministry of Industry and Information Technology, Harbin Institute of Technology, Harbin, China

    Weisong Zhao, Jiubin Tan & Haoyu Li

  3. State Key Laboratory of Membrane Biology, Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Institute of Molecular Medicine, National Biomedical Imaging Center, School of Future Technology, Peking University, Beijing, China

    Shiqun Zhao, Liangyi Chen & Changliang Guo

  4. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Guangwei Hu

  5. School of Mathematical Sciences, Peking University, Beijing, China

    Heng Mao

  6. Unit of Cell Biology and Imaging Study of Pathogen Host Interaction, The Center for Microbes, Development and Health, Key Laboratory of Molecular Virology and Immunology, Institut Pasteur of Shanghai, Chinese Academy of Sciences, Shanghai, China

    Yaming Jiu

  7. School of Life Science and Technology, Harbin Institute of Technology, Harbin, China

    Ying Hu

  8. PKU-IDG/McGovern Institute for Brain Research, Beijing, China

    Liangyi Chen

  9. Jingjinji National Center of Technology Innovation, Beijing, China

    Changliang Guo

  10. Beijing Institute of Collaborative Innovation, Beijing, China

    Changliang Guo

Authors
  1. Weisong Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shiqun Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhenqian Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiangyan Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Guangwei Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Liying Qu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yuanyuan Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Xinwei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Heng Mao
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Yaming Jiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Ying Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Jiubin Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Xumin Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Liangyi Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Changliang Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Haoyu Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

H.L., C.G., L.C. and W.Z. supervised the project. W.Z. and H.L. initiated and conceived the research. W.Z. developed the algorithm and implemented the corresponding software. W.Z. and S.Z. designed the experiments. S.Z. performed the experiments and collected the data. W.Z. analysed the data and prepared the figures with contributions from Xiangyan Ding. W.Z. performed the simulations with contributions from Z.H., Xiangyan Ding, L.Q., Y.H. and X.W. Z.H. and W.Z. prepared the videos. H.M., Y.J., Y.Hu, Xumin Ding, G.H., C.G. and J.T. participated in discussions during the development of the manuscript. W.Z., H.L. and L.C. wrote the manuscript with input from all authors. All authors participated in the discussions and data interpretation.

Corresponding authors

Correspondence to Xumin Ding, Liangyi Chen, Changliang Guo or Haoyu Li.

Ethics declarations

Competing interests

L.C., H.L., W.Z. and S.Z. have a pending patent application on the presented framework.

Peer review

Peer review information

Nature Photonics thanks David Baddeley and Jörg Enderlein for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 The forward imaging model of fluctuation-based samples and reconstruction workflow of the FRC-assisted parameterless SACD.

(a) If it is in real experiments, the real fluorescent content we imaged includes not only in-focus fluorescent molecules (‘Desired fluctuation’), but also out-of-focus and cytosol fluorescence background. Then the fluorescence signal accompanied with baseline signal (‘Background’) is encoded by the microscopy PSF, and sampled by the sensor (‘Sampling & Noise’). The actual measurements will always deviate from the desired fluctuation model from SOFI (‘Unbiased fluctuation’, see also Methods). (b) Workflow and representative example (a COS-7 cell labelled with QD525) of the FRC-assisted parameterless SACD. Left insets of top panel: Fourier transforms, and the corresponding FRC resolutions of different stages are labelled on the top right; Right insets of top panel: Magnified views from the white box. In Step1, the image sequence for reconstruction is captured. In Step 1.5, the PSF of raw data for the pre-deconvolution is calculated by the theoretical model. In Step2, we perform RL deconvolution for each image and it will be stopped at half of the iteration of the maximal resolution (lowest FRC value). In Step3, we calculate 2nd order autocorrelation cumulant. Then in Step 3.5, we estimate the FRC resolution of the resulting image to calculate PSF for post-deconvolution. In Step4, we employ post-RL-deconvolution for the autocorrelation image and it will be stopped when reaching the maximum resolution. (c) Comparisons of the pre-deconvolution using different iteration times. Microtubule filaments in a COS-7 cell labelled with QD525 imaged by SD-confocal (averaged by 20 frames) and SACD under 9, 20, 50, and 100 iteration times, respectively, of pre-deconvolution configurations. Experiments were repeated ten times independently with similar results. Scale bars: (b, main panel and left inset) 5 μm; (b, right inset; c) 1 μm.

Extended Data Fig. 2 Characterizing properties of the pre-RL deconvolution.

(a) A representative image of a single molecule with a cosine intensity fluctuation convolved by a 220 nm Gaussian PSF before adding Poisson noise only. (b) Normalized intensity profiles of ground-truth (black), raw (blue), and RL deconvolution (red) for the temporal response indicated by the black point in (a). (c) A representative image of two blinking molecules convolved by a 220 nm Gaussian PSF before adding Poisson noise, ~2% Gaussian noise, and background noise. (d) Intensity profiles of ground-truth (black), raw (blue), and RL deconvolution (red, divided by 3.5804) for the temporal response indicated by the black point (left) and green point (right) in (c). (e) A single molecule convolved by a 155 nm Gaussian PSF as ground-truth (‘GT’; left), and convolved by a 220 nm Gaussian PSF before adding Poisson noise (‘Raw’; middle) followed by RL deconvolution (‘RL’; right). (f) Intensity profiles of ground-truth (black), raw (blue), and RL deconvolution (red). Black arrows indicate the pixel-level fluctuations in the raw capture (blue). (g) Representative images of two molecules with opposite cosine intensity fluctuations convolved by a 220 nm Gaussian PSF before adding Poisson noise, ~2% Gaussian noise, and background noise. From left to right: Ground-truth molecules in xy; Colored rapture in xy; Colored raw capture in xt; Uncolored raw capture in xy. (h) Normalized intensity profiles of ground-truth (black), raw (blue), and RL deconvolution (red) for the temporal response indicated by the magenta point (dashed lines) and green point (solid lines) in (g, 1st column). Details in Supplementary Note 1. Experiments were repeated ten times independently with similar results. Scale bars: (a, e, g) 100 nm; (c) 500 nm.

Extended Data Fig. 3 Characterizing properties of the pre-RL deconvolution.

Simulation validation of SACD and SOFI reconstructions under different imaging conditions. (a-d) From left to right: Wide-field image, 20-frame SACD reconstruction, 20-frame SOFI reconstruction followed by RL deconvolution, and 1000-frame SOFI reconstruction followed by RL deconvolution. Two molecules 160 nm apart were created at a 10 nm size pixel grid. We generated temporal blinking sequences of the fluorescence emitters (Methods), and convolved them with a 220 nm PSF subsequently. The resulting image stack was downsampled 6 times (pixel size 60 nm). The ‘Non-biased’ condition (b) represents that only Poisson noise was considered in the imaging process. The high (b), medium (c), and low (d) conditions denote the corresponding high, medium, and low SNRs and effective on/off contrast ratios (Methods). These conditions involved not only Poisson noise and Gaussian readout noise, but also the smooth fluctuating cytosol and out-of-focus background. In summary, with Poisson noise only, all SOFI (20-frame and 1000-frame) and SACD (20-frame) results achieved comparable performances in high quality. Upon decreases in SNR and contrast, qualities of 20-frame SOFI reconstructions continued to deteriorate while the 1000-frame one performed relatively stable. In comparison, SACD is insensitive to the SNR and contrast conditions and can effectively reconstruct high-fidelity SR images with minimum input. Experiments were repeated ten times independently with similar results. Scale bar: 100 nm.

Extended Data Fig. 4 Simulation comparisons of different SOFI methods and SACD under different SNRs using ring structures.

(a) The synthetic ring structures were convolved with a 110 nm PSF and down-sampled 3 times (pixel size 30 nm) as ground truth (GT, Methods). (b-f) Wide-field (WF, b) raw images and SOFI-wavelet (c), SOFI-RL (d), RD-Covar (e), and SACD (f) reconstructions using 20-frame under ‘High’ (right), ‘Medium’ (middle), and ‘Low’ (left) SNR conditions. To generate the raw WF image stacks, the structures were convolved with a 220 nm PSF, and down-sampled 3 times before adding mixture noise under three different levels, in which the mixture noise includes cytosol background, out-of-focus light, Poisson noise, Gaussian readout noise, and baseline background. Yellow numbers represent the minimum resolvable diameters of ring structures according to the RQR criterion. The sub-images at the upper right corner are the enlarged view from white box. The sub-images at the lower right corner are the intensity profiles and multiple Gaussian fitting from white line. (g) The SSIM curves of different methods under ‘High’ (right), ‘Medium’ (middle), and ‘Low’ (left) SNR conditions. (h) The calculation method of ring quality ratio (RQR) criterion. (i) The STD curves of different methods under SNR conditions. Details in Supplementary Note 2. Experiments were repeated ten times independently with similar results. Scale bars: (a) 300 nm; (a, inset) 150 nm.

Extended Data Fig. 5 Evaluating SACD performance in 2D QD525 labelled microtubule versus SOFI.

(a) A representative COS-7 cell labelled with QD525 under the SD-confocal and the SACD with 20 frames configurations was shown at the bottom and top sides. (b) Magnified views from the white box in (a) under different configurations. First row: SD-confocal, pre deconvolution followed by autocorrelation (AC) with 20 frames and full SACD reconstruction with 20 frames. Second row: pure AC with 20 frames, pure AC with 1000 frames, and AC with 1000 frames followed by RL deconvolution. Third row: pure cross-correlation (XC) with 20 frames, pure XC with 1000 frames, and XC with 1000 frames followed by RL deconvolution. (c) Fourier transforms of raw SD-confocal image, pure AC with 20 frames, AC with 1000 frames followed by RL deconvolution, and full SACD reconstruction with 20 frames. (d) Averaged intensity traces from pixels of the white circle in (b) for raw and raw after pre-deconvolution. (e) FRC analysis of the reconstructed images shows resolutions of 317 nm (raw SD-confocal), 206 nm (AC with 20 frames), 171 nm (AC with 1000 frames followed by RL deconvolution), 121 nm (SACD with 20 frames), and 118 nm (SACD with 1000 frames) respectively. (f) Intensity profiles and multiple Gaussian fitting of raw SD-confocal and 20-frame SACD for the structures of microtubule filaments indicated by the white arrows in (b). The numbers indicate the distance between peaks. Details in Supplementary Note 5. Experiments were repeated ten times independently with similar results. Scale bars: (a) 5 μm; (b) 500 nm; (c) 10 μm.

Extended Data Fig. 6 Experimental comparisons of different SOFI methods, SACD, and Sparse-SACD.

(a) Microtubule filaments in a COS-7 cell labelled with QD525 imaged by the wide-field microscopy (left) and 2D-SIM (right) (c.f., Fig. 1). (b, c) SACD without (w/o) the linearization step (b) and the autocorrelation step (c). As pointed out by the white arrows, SACD without the autocorrelation step failed to resolve fine structures. (d, e) Reconstruction results using 20 frames (d) and 1000 frames (e). (f, g) Reconstruction results of live COS-7 cell labelled with Skylan-S-TOM20 imaged at 37 °C imaged by SD-confocal (c.f., Fig. 4d) using 20 frames at time points 0 min 0 s (f) and 10 min 40 s (g). Details in Supplementary Note 9. Experiments were repeated ten times independently with similar results. Scale bars: 1 μm.

Extended Data Fig. 7 Representative 3D microtubule network reconstructed by SACD.

(a) Color-coded three-dimensional distributions of microtubule filaments in a COS-7 cell labelled with QD525 under SD-confocal (top left) and SACD (top right) images. The bottom panels show the xy slices (at 0 μm of z axial position) of SD-confocal (left) and SACD (right). (b) Corresponding vertical sections of SD-confocal (top) and SACD (bottom). (c) Axial resolution comparison using the enlarged regions of the white boxes indicated in (b). From left to right: cross-sections of SD-confocal, SOFI (20 frames), and SACD (20 frames) results, and the intensity profiles indicated by red arrows are displayed on the right. Experiments were repeated ten times independently with similar results. Scale bars: (a) 5 μm; (a inset, b) 2 μm; (c) 500 nm.

Extended Data Fig. 8 The flexibility of our SACD concept on other fluctuation-based SR techniques.

(a) A representative live COS-7 cell labelled with Skylan-S-TOM20 imaged by SD-confocal (bottom) and SACD using 20 frames (top) (Supplementary Video 3). (b) Comparison of results (enlarged regions enclosed by the white box in a) of SD-confocal (1st row, averaged by 20 frames), SOFI (2nd row, reconstructed by 20 frames), HAWK-SOFI (3rd row, reconstructed by 20 frames), and SACD (4th row, reconstructed by 20 frames). (c) Comparison results (enlarged regions enclosed by the white box in a) of SRRF with temporal radiality average (SRRF-TRC, top left), SACD assisted SRRF-TRC (bottom left), SRRF with temporal radiality autocorrelation (SRRF-TRAC, top middle), SACD assisted SRRF-TRAC (bottom middle), ESI with second-order (ESI-2nd, top right), and SACD assisted ESI-2nd (bottom right). (d) Highlighted regions from the white box in (b). Details in Supplementary Note 6. Experiments were repeated ten times independently with similar results. Scale bars: (a) 5 μm; (b, c) 3 μm; (d) 500 nm.

Extended Data Fig. 9 SACD imaging of the nucleolar components in live cells.

(a) A representative live COS-7 cell labelled by Nop56-Skylan-S imaged under wide-field microscopy (left), 2D-SIM (middle), and SACD using 20 frames (right). (b) Comparison of results (enlarged regions enclosed by the white box in a) of wide-field microscopy (1st column), SOFI (2nd column, reconstructed by 20 frames), 2D-SIM (3rd column), and SACD (4th row, reconstructed by 20 frames). (c) A representative live COS-7 cell labelled by B23-rsFusionRed3 imaged under wide-field microscopy (left), 2D-SIM (middle), and SACD using 20 frames (right). (d) Comparison of results (enlarged regions enclosed by the white box in c) of wide-field microscopy (1st column), SOFI (2nd column, reconstructed by 20 frames), 2D-SIM (3rd column), and SACD (4th row, reconstructed by 20 frames). In this experiment, we imaged two nucleolar proteins, the Nop56 and the B23, representing the dense fibrillar component and the granular component, respectively. With the SIM images as references, we found our SACD reliably improves the resolution while maintaining the linear intensity scale. Beyond that, for imaging such complex structures deep inside the nucleus, our SACD images exhibit better contrast than SIM by rejecting the out-of-focus emissions. These experimental tests all illustrate the broad applicability of SACD for different types of biological samples. Scale bars: (a, c) 5 μm; (b, d) 1 μm.

Extended Data Fig. 10 Sparse deconvolution assisted SACD for long-term live-cell SR imaging (c.f., Fig. 4d).

(a) A representative live COS-7 cell labelled with Skylan-S-TOM20 imaged by SD-confocal (left) and SD-confocal after background subtraction (right). (b) The estimated background. (c) SACD results at time points 0 s (left) and 10 min 40 s (right). (d) Sparse-SACD results at time points 0 s (left) and 10 min 40 s (right). Experiments were repeated five times independently with similar results. Scale bar: 5 μm.

Supplementary information

Supplementary Information

Supplementary Figs. 1–5 and Notes 1–9.

Reporting Summary

Supplementary Video 1

Large FOV SR imaging of 3D microtubule network. Microtubule filaments in COS-7 cells labelled with QD525 captured by SD-confocal and SACD-20f. (c.f. Fig. 2b). Part I shows the colour-coded projection of the microtubule filaments in the z direction as the depth increases gradually and the y–z orthoslices along the x axis. Part II shows the magnified views of different regions from the whole FOV.

Supplementary Video 2

SACD enables high-throughput SR imaging. Microtubule filaments in COS-7 cells labelled with QD525 captured by SD-confocal and SACD-20f. (c.f. Fig. 3a). Part I shows the microtubule filaments in a 2.0 mm × 1.4 mm area with 32 × 22 partly overlapping FOVs (66.6 μm × 66.6 μm each) in 20 min. Part II shows the magnified views of different regions from the whole FOV.

Supplementary Video 3

Comparisons of 20-frame SACD and 20-frame SOFI under live-cell conditions. Part I demonstrates a representative live COS-7 cell labelled with Skylan-S-TOM20 imaged at 37 °C by SD-confocal, SOFI-20f and SACD-20f (c.f. Extended Data Fig. 8). Part II compares the hollow outer mitochondrial membranes by SD-confocal, SOFI-20f, and SACD-20f for 20 time points at a 40-s interval.

Supplementary Video 4

Continuous live-cell SR imaging of microtubules. Part I demonstrates a representative live COS-7 cell labelled with MAP4-Skylan-S imaged at 37 °C by SD-confocal, SOFI-20f, SACD-20f and Sparse-SACD-20f (c.f. Fig. 4b). Part II compares the microtubule filaments by SD-confocal, SOFI-20f and SACD-20f for 50 time points at a 0.4-s acquisition time.

Supplementary Video 5

Long-term mitochondrial fission and fusion dissected by Sparse-SACD. Part I demonstrates a representative live COS-7 cell labelled with Skylan-S-TOM20 imaged at 37 °C by SD-confocal and Sparse-SACD-20f (c.f. Fig. 4d). Part II shows the fission and fusion events detected by Sparse-SACD-20f for 16 time points at a 40-s interval.

Supplementary Video 6

4D SR imaging of mitochondrial network within a whole live cell across 10 min. Part I shows the comparison of SD-confocal and Sparse-SACD using a live COS-7 cell labelled with Skylan-S-TOM20 at 37 °C (c.f. Fig. 4f). Part II shows the colour-coded projection of the hollow outer mitochondrial membranes in the z direction as the depth increases gradually and the y–z orthoslices along the y axis.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhao, W., Zhao, S., Han, Z. et al. Enhanced detection of fluorescence fluctuations for high-throughput super-resolution imaging. Nat. Photon. 17, 806–813 (2023). https://doi.org/10.1038/s41566-023-01234-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41566-023-01234-9

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