Parameter-free image resolution estimation based on decorrelation analysis

Article metrics

Abstract

Super-resolution microscopy opened diverse new avenues of research by overcoming the resolution limit imposed by diffraction. Exploitation of the fluorescent emission of individual fluorophores made it possible to reveal structures beyond the diffraction limit. To accurately determine the resolution achieved during imaging is challenging with existing metrics. Here, we propose a method for assessing the resolution of individual super-resolved images based on image partial phase autocorrelation. The algorithm is model-free and does not require any user-defined parameters. We demonstrate its performance on a wide variety of imaging modalities, including diffraction-limited techniques. Finally, we show how our method can be used to optimize image acquisition and post-processing in super-resolution microscopy.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Image decorrelation analysis workflow.
Fig. 2: Confocal and STED.
Fig. 3: Widefield imaging and structured illumination microscopy.
Fig. 4: Localization microscopy.

Data availability

All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Results and Notes. Additional data related to this paper may be requested from the authors.

Code availability

The MATLAB soruce code, the ImageJ plugin and the source Java code are publicly available on https://github.com/Ades91/ImDecorr.git or may be requested from the authors.

References

  1. 1.

    Sahl, S. J., Hell, S. W. & Jakobs, S. Fluorescence nanoscopy in cell biology. Nat. Rev. Mol. Cell Biol. 18, 685–701 (2017).

  2. 2.

    Sigal, Y. M., Zhou, R. & Zhuang, X. Visualizing and discovering cellular structures with super-resolution microscopy. Science 361, 880–887 (2018).

  3. 3.

    Abbe, E. Beiträge zur Theorie des Mikroskops und der mikroskopischen Wahrnehmung. Arch. für. Mikrosk. Anat. 9, 413–418 (1873).

  4. 4.

    Sheppard, C. J. R. Resolution and super-resolution. Microsc. Res. Tech. 80, 590–598 (2017).

  5. 5.

    Power, R. M. & Huisken, J. Adaptable, illumination patterning light sheet microscopy. Sci. Rep. 8, 1–11 (2018).

  6. 6.

    Štefko, M., Ottino, B., Douglass, K. M. & Manley, S. Autonomous illumination control for localization microscopy. Opt. Express 26, 30882–30900 (2018).

  7. 7.

    Heel, M. Van Similarity measures between images. Ultramicroscopy 21, 95–100 (1987).

  8. 8.

    Saxton, W. & Baumeister, W. The correlation averaging of a regularly arranged bacterial cell envelope protein. J. Microsc. 127, 127–138 (1982).

  9. 9.

    Harauz, G. & van Heel, M. Exact filters for general geometry three dimensional reconstruction. Optik 78, 146–156 (1986).

  10. 10.

    Rosenthal, P. B. & Henderson, R. Optimal determination of particle orientation, absolute hand, and contrast loss in single-particle electron cryomicroscopy. J. Mol. Biol. 333, 721–745 (2003).

  11. 11.

    Orlova, E. V. et al. Structure of keyhole limpet hemocyanin type 1 (KLH1) at 15 Å resolution by electron cryomicroscopy and angular reconstitution. J. Mol. Biol. 271, 417–437 (1997).

  12. 12.

    Unser, M., Trus, B. L. & Steven, A. C. A new resolution criterion based on spectral signal-to-noise ratio. Ultramicroscopy 23, 39–52 (1987).

  13. 13.

    Banterle, N., Bui, K. H., Lemke, E. A. & Beck, M. Fourier ring correlation as a resolution criterion for super-resolution microscopy. J. Struct. Biol. 183, 363–367 (2013).

  14. 14.

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

  15. 15.

    Tortarolo, G., Castello, M., Diaspro, A., Koho, S. & Vicidomini, G. Evaluating image resolution in stimulated emission depletion microscopy. Optica 5, 32 (2018).

  16. 16.

    Raab, M. et al. Using DNA origami nanorulers as traceable distance measurement standards and nanoscopic benchmark structures. Sci. Rep. 8, 1780 (2018).

  17. 17.

    Abràmoff, M. D., Magalhães, P. J. & Ram, S. J. Image processing with ImageJ. Biophotonics Int. 11, 36–42 (2004).

  18. 18.

    Minsky, M. Memoir on inventing the confocal scanning microscope. Scanning 10, 128–138 (1988).

  19. 19.

    Hell, S. W. & Wichmann, J. Breaking the diffraction resolution limit by stimulated emission: stimulated-emission-depletion fluorescence microscopy. Opt. Lett. 19, 780 (1994).

  20. 20.

    Vicidomini, G., Bianchini, P. & Diaspro, A. STED super-resolved microscopy. Nat. Methods 15, 173–182 (2018).

  21. 21.

    Tortarolo, G., Sun, Y., Teng, W., Ishitsuka, Y. & Vicidomini, G. Photon-separation to enhance the spatial resolution of pulsed STED microscopy. Nanoscale 11, 1754–1761 (2019).

  22. 22.

    Westphal, V. & Hell, S. W. Nanoscale resolution in the focal plane of an optical microscope. Phys. Rev. Lett. 94, 1–4 (2005).

  23. 23.

    Heintzmann, R. & Cremer, C. G. Laterally modulated excitation microscopy: improvement of resolution by using a diffraction grating. (International Society for Optics and Photonics, 1999).

  24. 24.

    Frohn, J. T. Super-resolution fluorescence microscopy by structured light illumination. PhD thesis, ETH Zürich (2000).

  25. 25.

    Gustafsson, M. G. L. Surpassing the lateral resolution limit by a factor of two using structured illumination microscopy. J. Microsc. 198, 82–87 (2000).

  26. 26.

    Heintzmann, R. & Huser, T. Super-resolution structured illumination microscopy. Chem. Rev. 117, 13890–13908 (2017).

  27. 27.

    Demmerle, J. et al. Strategic and practical guidelines for successful structured illumination microscopy. Nat. Protoc. 12, 988–1010 (2017).

  28. 28.

    Müller, M., Mönkemöller, V., Hennig, S., Hübner, W. & Huser, T. Open-source image reconstruction of super-resolution structured illumination microscopy data in Image. J. Nat. Commun. 7, 1–6 (2016).

  29. 29.

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

  30. 30.

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

  31. 31.

    Sauer, M. & Heilemann, M. Single-molecule localization microscopy in eukaryotes. Chem. Rev. 117, 7478–7509 (2017).

  32. 32.

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

  33. 33.

    Legant, W. R. et al. High-density three-dimensional localization microscopy across large volumes. Nat. Methods 13, 359–365 (2016).

  34. 34.

    Fölling, J. et al. Photochromic rhodamines provide nanoscopy with optical sectioning **. Angew. Chem.ie - Int. Ed. Engl. 46, (6266–6270 (2007).

  35. 35.

    Bossi, M. et al. Multicolor far-field fluorescence nanoscopy through isolated detection of distinct molecular species. Nano Lett. 8, 2463–2468 (2008).

  36. 36.

    Lambert, T. J. & Waters, J. C. Navigating challenges in the application of superresolution microscopy. J. Cell Biol. 216, 53–63 (2016).

  37. 37.

    Mikhaylova, M. et al. Resolving bundled microtubules using anti-tubulin nanobodies. Nat. Commun. 6, 1–7 (2015).

  38. 38.

    Pleiner, T., Bates, M. & Görlich, D. A toolbox of anti-mouse and anti-rabbit IgG secondary nanobodies. J. Cell Biol. 217, 1143–1154 (2017).

  39. 39.

    Schmied, J. J. et al. Fluorescence and super-resolution standards based on DNA origami Flaws in evaluation schemes for pair- input computational predictions. Nat. Methods 9, 1133–1134 (2012).

  40. 40.

    Annibale, P., Vanni, S., Scarselli, M., Rothlisberger, U. & Radenovic, A. Quantitative photo activated localization microscopy: unraveling the effects of photoblinking. PLoS ONE 6, e22678 (2011).

  41. 41.

    Chazeau, A., Katrukha, E. A., Hoogenraad, C. C. & Kapitein, L. C. Studying neuronal microtubule organization and microtubule-associated proteins using single molecule localization microscopy. Methods Cell Biol. 131, 127–149 (2016).

Download references

Acknowledgements

The authors would like to thank T. Lukes and T. Laser for insightful discussions. We also thank H. Deschout, M. Muller, T. Huser and M. Sauer for sharing SOFI and SIM data. We also thank J. Schmied and P. Tinnefeld for sharing GATTAquant nanoruler data and N. Bantherle and A. Planchette for proofreading. This project has been funded in part by the Horizon 2020 research and innovation program of the European Union via grant 686271/SEFRI 16.0047. K.S.G. acknowledges support from the Horizon 2020 Framework Program of the European Union under the Marie Skłodowska-Curie grant agreement no. 750528 and thanks the NVIDIA Corporation for the donation of a Titan Xp GPU. A.D. and A.R. acknowledge support from the Zeiss IDEAS center. We would like to thank the EPFL BioImaging & Optics Core Facility (EPFL-BIOP) for access to confocal and STED microscopes.

Author information

A.D. proposed and developed the method, processed all of the presented data, and wrote the MATLAB and Java code. K.S.G. prepared all of the cells and performed measurements. A.R. supervised the research. A.D. wrote the manuscript with comments of all co-authors at all stages.

Correspondence to A. Descloux or A. Radenovic.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information: Rita Strack was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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

Supplementary information

Supplementary Information

Supplementary Tables 1–2, Supplementary Notes 1–5 and Supplementary Results 1–7.

Reporting Summary

Supplementary Software

Image J Plugin: Image Decorrelation Analysis plugin that provides image resolution estimate.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Descloux, A., Grußmayer, K.S. & Radenovic, A. Parameter-free image resolution estimation based on decorrelation analysis. Nat Methods 16, 918–924 (2019) doi:10.1038/s41592-019-0515-7

Download citation