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Nuclear pores as versatile reference standards for quantitative superresolution microscopy

Nature Methods volume 16, pages 1045–1053 (2019)Cite this article

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A Publisher Correction to this article was published on 25 October 2019

This article has been updated

Abstract

Quantitative fluorescence and superresolution microscopy are often limited by insufficient data quality or artifacts. In this context, it is essential to have biologically relevant control samples to benchmark and optimize the quality of microscopes, labels and imaging conditions. Here, we exploit the stereotypic arrangement of proteins in the nuclear pore complex as in situ reference structures to characterize the performance of a variety of microscopy modalities. We created four genome edited cell lines in which we endogenously labeled the nucleoporin Nup96 with mEGFP, SNAP-tag, HaloTag or the photoconvertible fluorescent protein mMaple. We demonstrate their use (1) as three-dimensional resolution standards for calibration and quality control, (2) to quantify absolute labeling efficiencies and (3) as precise reference standards for molecular counting. These cell lines will enable the broader community to assess the quality of their microscopes and labels, and to perform quantitative, absolute measurements.

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Fig. 1: Nup96 cell lines.
Fig. 2: Nuclear pores as calibration reference standards.
Fig. 3: Effective labeling efficiencies.
Fig. 4: Counting of protein copy numbers in complexes.

Data availability

All processed data (lists of localizations) and for each condition at least one example file of raw data (camera frames of blinking fluorophores) are deposited on BioStudies (https://www.ebi.ac.uk/biostudies/BioImages/studies/S-BIAD8).

Change history

  • 25 October 2019

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

References

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Xu, K., Zhong, G. & Zhuang, X. Actin, spectrin, and associated proteins form a periodic cytoskeletal structure in axons. Science 339, 452–456 (2013).

    Article  CAS  PubMed  Google Scholar 

  4. Mund, M. et al. Systematic nanoscale analysis of endocytosis links efficient vesicle formation to patterned actin nucleation. Cell 174, 884–896.e17 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Szymborska, A. et al. Nuclear pore scaffold structure analyzed by super-resolution microscopy and particle averaging. Science 341, 655–658 (2013).

    Article  CAS  PubMed  Google Scholar 

  6. Ball, G. et al. SIMcheck: a toolbox for successful super-resolution structured illumination microscopy. Sci. Rep. 5, 15915 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Sage, D. et al. Super-resolution fight club: assessment of 2D and 3D single-molecule localization microscopy software. Nat. Methods 16, 387–395 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Steinhauer, C., Jungmann, R., Sobey, T. L., Simmel, F. C. & Tinnefeld, P. DNA origami as a nanoscopic ruler for super-resolution microscopy. Angew. Chem. Int. Ed. 48, 8870–8873 (2009).

    Article  CAS  Google Scholar 

  10. Iinuma, R. et al. Polyhedra self-assembled from DNA tripods and characterized with 3D DNA-PAINT. Science 344, 65–69 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Huang, B., Jones, S. A., Brandenburg, B. & Zhuang, X. Whole-cell 3D STORM reveals interactions between cellular structures with nanometer-scale resolution. Nat. Methods 5, 1047–1052 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Huang, B., Wang, W., Bates, M. & Zhuang, X. Three-dimensional super-resolution imaging by stochastic optical reconstruction microscopy. Science 319, 810–813 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. von Appen, A. et al. In situ structural analysis of the human nuclear pore complex. Nature 526, 140–143 (2015).

    Article  CAS  Google Scholar 

  14. Durisic, N., Laparra-Cuervo, L., Sandoval Álvarez, Á., Borbely, J. S. & Lakadamyali, M. Single-molecule evaluation of fluorescent protein photoactivation efficiency using an in vivo nanotemplate. Nat. Methods 11, 156–162 (2014).

    Article  CAS  PubMed  Google Scholar 

  15. Zanacchi, F. C. et al. A DNA origami platform for quantifying protein copy number in super-resolution. Nat. Methods 14, 789–792 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Finan, K., Raulf, A. & Heilemann, M. A set of homo-oligomeric standards allows accurate protein counting. Angew. Chem. Int. Ed. 54, 12049–12052 (2015).

    Article  CAS  Google Scholar 

  17. Zanacchi, F. C., Manzo, C., Magrassi, R., Derr, N. D. & Lakadamyali, M. Quantifying protein copy number in super-resolution using an imaging-invariant calibration. Biophys. J. 116, 2195–2203 (2019).

    Article  CAS  Google Scholar 

  18. Löschberger, A. et al. Super-resolution imaging visualizes the eightfold symmetry of gp210 proteins around the nuclear pore complex and resolves the central channel with nanometer resolution. J. Cell Sci. 125, 570–575 (2012).

    Article  PubMed  CAS  Google Scholar 

  19. McEvoy, A. L. et al. mMaple: a photoconvertible fluorescent protein for use in multiple imaging modalities. PLoS ONE 7, e51314 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Keppler, A., Pick, H., Arrivoli, C., Vogel, H. & Johnsson, K. Labeling of fusion proteins with synthetic fluorophores in live cells. Proc. Natl Acad. Sci. USA 101, 9955–9959 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Los, G. V. et al. HaloTag: a novel protein labeling technology for cell imaging and protein analysis. ACS Chem. Biol. 3, 373–382 (2008).

    Article  CAS  PubMed  Google Scholar 

  22. Klar, T. A., Jakobs, S., Dyba, M., Egner, A. & Hell, S. W. Fluorescence microscopy with diffraction resolution barrier broken by stimulated emission. Proc. Natl Acad. Sci. USA 97, 8206–8210 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Chen, F., Tillberg, P. W. & Boyden, E. S. Expansion microscopy. Science 347, 543–548 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Pesce, L., Cozzolino, M., Lanzanò, L., Diaspro, A. & Bianchini, P. Measuring expansion from macro- to nanoscale using NPC as intrinsic reporter. J. Biophotonics 12, e201900018 (2019).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  26. Gambarotto, D. et al. Imaging cellular ultrastructures using expansion microscopy (U-ExM). Nat. Methods 16, 71 (2019).

    Article  CAS  PubMed  Google Scholar 

  27. Demmerle, J., Wegel, E., Schermelleh, L. & Dobbie, I. M. Assessing resolution in super-resolution imaging. Methods 88, 3–10 (2015).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Schlichthaerle, T. et al. Direct visualization of single nuclear pore complex proteins using genetically-encoded probes for DNA-PAINT. Angew. Chem. Int. Ed. 58, 13004–13008 (2019).

    Article  CAS  PubMed  Google Scholar 

  30. Li, Y., Wu, Y.-L., Hoess, P., Mund, M. & Ries, J. Depth-dependent PSF calibration and aberration correction for 3D single-molecule localization. Biomed. Opt. Express 10, 2708–2718 (2019).

  31. Fricke, F., Beaudouin, J., Eils, R. & Heilemann, M. One, two or three? Probing the stoichiometry of membrane proteins by single-molecule localization microscopy. Sci. Rep. 5, 14072 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Ries, J., Kaplan, C., Platonova, E., Eghlidi, H. & Ewers, H. A simple, versatile method for GFP-based super-resolution microscopy via nanobodies. Nat. Methods 9, 582–584 (2012).

    Article  CAS  PubMed  Google Scholar 

  33. Grimm, J. B. et al. Bright photoactivatable fluorophores for single-molecule imaging. Nat. Methods 13, 985–988 (2016).

    Article  CAS  PubMed  Google Scholar 

  34. Dempsey, G. T., Vaughan, J. C., Chen, K. H., Bates, M. & Zhuang, X. Evaluation of fluorophores for optimal performance in localization-based super-resolution imaging. Nat. Methods 8, 1027–1036 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Hartwich, T. M. et al. A stable, high refractive index, switching buffer for super-resolution imaging. Preprint at bioRxiv https://doi.org/10.1101/465492 (2018).

  36. Klehs, K. et al. Increasing the brightness of cyanine fluorophores for single-molecule and superresolution imaging. Chem. Phys. Chem. 15, 637–641 (2014).

    Article  CAS  PubMed  Google Scholar 

  37. Ong, W. Q., Citron, Y. R., Schnitzbauer, J., Kamiyama, D. & Huang, B. Heavy water: a simple solution to increasing the brightness of fluorescent proteins in super-resolution imaging. Chem. Commun. 51, 13451–13453 (2015).

    Article  CAS  Google Scholar 

  38. Ori, A. et al. Cell type‐specific nuclear pores: a case in point for context‐dependent stoichiometry of molecular machines. Mol. Syst. Biol. 9, 648–648 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Otsuka, S. et al. Nuclear pore assembly proceeds by an inside-out extrusion of the nuclear envelope. eLife 5, e19071 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Lee, S.-H., Shin, J. Y., Lee, A. & Bustamante, C. Counting single photoactivatable fluorescent molecules by photoactivated localization microscopy (PALM). Proc. Natl Acad. Sci. USA 109, 17436–17441 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Rollins, G. C., Shin, J. Y., Bustamante, C. & Pressé, S. Stochastic approach to the molecular counting problem in superresolution microscopy. Proc. Natl Acad. Sci. USA 112, E110–E118 (2014).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  43. Puchner, E. M., Walter, J. M., Kasper, R., Huang, B. & Lim, W. A. Counting molecules in single organelles with superresolution microscopy allows tracking of the endosome maturation trajectory. Proc. Natl Acad. Sci. USA 110, 16015–16020 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Baldering, T. N. et al. Synthetic and genetic dimers as quantification ruler for single-molecule counting with PALM. Mol. Biol. Cell 30, 1369–1376 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Kim, S. J. et al. Integrative structure and functional anatomy of a nuclear pore complex. Nature 555, 475–482 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Rajoo, S., Vallotton, P., Onischenko, E. & Weis, K. Stoichiometry and compositional plasticity of the yeast nuclear pore complex revealed by quantitative fluorescence microscopy. Proc. Natl Acad. Sci. USA 115, E3969–E3977 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Wang, S., Moffitt, J. R., Dempsey, G. T., Xie, X. S. & Zhuang, X. Characterization and development of photoactivatable fluorescent proteins for single-molecule-based superresolution imaging. Proc. Natl Acad. Sci. USA 111, 8452–8457 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Beck, M., Lučić, V., Förster, F., Baumeister, W. & Medalia, O. Snapshots of nuclear pore complexes in action captured by cryo-electron tomography. Nature 449, 611–615 (2007).

    Article  CAS  PubMed  Google Scholar 

  49. Deschamps, J., Mund, M. & Ries, J. 3D superresolution microscopy by supercritical angle detection. Opt. Express 22, 29081–29091 (2014).

    Article  PubMed  CAS  Google Scholar 

  50. Erdmann, R. S. et al. Labeling strategies matter for super-resolution microscopy: a comparison between halotags and SNAP-tags. Cell Chem. Biol. 26, 584–592 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Pleiner, T. et al. Nanobodies: site-specific labeling for super-resolution imaging, rapid epitope-mapping and native protein complex isolation. eLife 4, e11349 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Göttfert, F. et al. Strong signal increase in STED fluorescence microscopy by imaging regions of subdiffraction extent. Proc. Natl Acad. Sci. USA 114, 2125–2130 (2017).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  53. Heilemann, M., Margeat, E., Kasper, R., Sauer, M. & Tinnefeld, P. Carbocyanine dyes as efficient reversible single-molecule optical switch. J. Am. Chem. Soc. 127, 3801–3806 (2005).

    Article  CAS  PubMed  Google Scholar 

  54. Bates, M., Blosser, T. & Zhuang, X. Short-range spectroscopic ruler based on a single-molecule optical switch. Phys. Rev. Lett. 94, 108101 (2005).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. Koch, B. et al. Generation and validation of homozygous fluorescent knock-in cells using CRISPR–Cas9 genome editing. Nat. Protoc. 13, 1465–1487 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Sun, X. et al. Development of SNAP-Tag fluorogenic probes for wash-free fluorescence imaging. Chem. Bio. Chem. 12, 2217–2226 (2011).

    Article  CAS  PubMed  Google Scholar 

  57. Tillberg, P. W. et al. Protein-retention expansion microscopy of cells and tissues labeled using standard fluorescent proteins and antibodies. Nat. Biotechnol. 34, 987–992 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Mund, M., Kaplan, C. & Ries, J. Localization microscopy in yeast. Meth. Cell Biol. 123, 253–271 (2014).

    Article  Google Scholar 

  59. Khmelinskii, A., Meurer, M., Duishoev, N., Delhomme, N. & Knop, M. Seamless gene tagging by endonuclease-driven homologous recombination. PLoS ONE 6, e23794 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Deschamps, J., Rowald, A. & Ries, J. Efficient homogeneous illumination and optical sectioning for quantitative single-molecule localization microscopy. Opt. Express 24, 28080–28090 (2016).

    Article  CAS  PubMed  Google Scholar 

  61. Li, Y. et al. Real-time 3D single-molecule localization using experimental point spread functions. Nat. Methods 15, 367–369 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Smith, C. S., Joseph, N., Rieger, B. & Lidke, K. A. Fast, single-molecule localization that achieves theoretically minimum uncertainty. Nat. Methods 7, 373–375 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Mortensen, K. I., Churchman, L. S., Spudich, J. A. & Flyvbjerg, H. Optimized localization analysis for single-molecule tracking and super-resolution microscopy. Nat. Methods 7, 377–381 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

J.V.T. and P.H. are candidates for joint PhD degrees from EMBL and Heidelberg University. PA-JF549 and Halo-Cy5 were a kind gift of L. Lavis, HHMI Janelia Research Campus. We thank the EMBL advanced light microscopy facility for their help. This work was supported by the European Research Council (grant no. ERC CoG-724489 to J.R., M.M., P.H. and J.V.T.), the National Institutes of Health Common Fund 4D Nucleome Program (grant no. U01 EB021223/U01 DA047728 to J.E. and J.R.), the Allen Distinguished Investigator Program through The Paul G. Allen Frontiers Group (J.E.), the UK Biotechnology and Biological Sciences Research Council (grant nos. BB/M022374/1, BB/P027431/1, BB/R000697/1 and BB/S507532/1 to R.H. and P.M.P.), the Wellcome Trust (203276/Z/16/Z, R.H. and P.M.P.), the EMBL Interdisciplinary Postdoc Programme (EIPOD) under Marie Curie Actions COFUND (Y.L.), the Human Frontier Science Program (RGY0065/2017 to J.R.) and the European Molecular Biology Laboratory (J.V.T., K.C., P.H., S.K.P., K.C.K., Y.L., Y.W., M.M., U.M., B.N., M.K., V.J.S., J.E. and J.R.). V.J.S. acknowledges support by the Boehringer Ingelheim Fonds.

Author information

Author notes

  1. Sudheer Kumar Peneti

    Present address: Centre for Bacterial Cell Biology, Institute for Cell and Molecular Biosciences, Newcastle University, Newcastle upon Tyne, UK

  2. Manuel Reitberger

    Present address: Division of Stem Cells and Cancer, German Cancer Research Center, Heidelberg, Germany

  3. Daniel Heid

    Present address: Department for Applied Tumor Biology, Heidelberg University Hospital, Heidelberg, Germany

  4. Krishna Chaitanya Kasuba

    Present address: Eidgenössische Technische Hochschule Zurich, Department of Biosystems Science and Engineering, Basel, Switzerland

  5. Sarah Janice Hoerner

    Present address: Institute of Molecular and Cell Biology, Mannheim University of Applied Sciences and Interdisciplinary Center for Neuroscience, Heidelberg University, Heidelberg, Germany

  6. Markus Mund

    Present address: Department of Biochemistry, University of Geneva, Science 2, Genève, Switzerland

  7. These authors contributed equally: Jervis Vermal Thevathasan, Maurice Kahnwald.

Authors and Affiliations

  1. EMBL, Cell Biology and Biophysics, Heidelberg, Germany

    Jervis Vermal Thevathasan, Maurice Kahnwald, Konstanty Cieśliński, Philipp Hoess, Sudheer Kumar Peneti, Manuel Reitberger, Daniel Heid, Krishna Chaitanya Kasuba, Sarah Janice Hoerner, Yiming Li, Yu-Le Wu, Markus Mund, Ulf Matti, Bianca Nijmeijer, Moritz Kueblbeck, Vilma Jimenez Sabinina, Jan Ellenberg & Jonas Ries

  2. Faculty of Biosciences, Heidelberg University, Heidelberg, Germany

    Jervis Vermal Thevathasan, Philipp Hoess & Yu-Le Wu

  3. MRC-Laboratory for Molecular Cell Biology, University College London, London, UK

    Pedro Matos Pereira & Ricardo Henriques

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  1. Jervis Vermal Thevathasan
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Contributions

J.R. conceived the approach. B.N., M.Ku., V.J.S., J.E., J.V.T. and U.M. generated the cell lines. J.V.T., M.Ka., K.C., P.H., S.K.P., M.R., D.H., K.C.K., S.J.H., Y.L., Y.W., M.M., U.M. and J.R. developed the methods, wrote the software, acquired and analyzed the data. R.H. and P.M.P. acquired the expansion microscopy data. J.V.T., M.Ka., P.H., M.M. and J.R. wrote the manuscript with input from all authors.

Corresponding author

Correspondence to Jonas Ries.

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The authors declare no competing interests.

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Integrated supplementary information

Supplementary Figure 1 Validation of cell line homozygosity.

(a-d) Southern blots of (a) Nup96-SNAP, (b) Nup96-Halo, (c) Nup96-mMaple, (d) Nup96-mEGFP. Blots on the left were generated from probes against Nup96-C-term and blots on the right were generated from probes against respective tags. The presence of a single band was indicative of a homozygous knock-in (in red). (e) Western blot of the homozygous cell lines probed with an anti-Nup98 antibody. Reduction of band intensity in Nup96-WT indicates the specificity of the antibody to Nup96. siRNA concentrations used: 0.6 µg, 1.2 µg and 1.8 µg. Representative images of one (a-d) or two (e) independent experiments are shown. Full scans of all blots are shown in Supplementary Fig. 11.

Supplementary Figure 2 SMLM with total internal reflection fluorescence (TIRF) excitation on Nup96-SNAP-AF647.

In the majority of cells, the lower nuclear envelope is sufficiently close to the coverslip to be efficiently excited with TIRF. Representative images from two independent experiments are shown. Scale bars 1 µm.

Supplementary Figure 3 Resolution.

(a) Logarithmic power spectrum for Fig. 1f–i. (b) Fourier Ring Correlation for Fig. 1m–q including resolution estimates. To calculate these curves, the localizations were divided into ten time windows, and the correlation was computed between even and odd time windows. A correction for spurious correlation due to re-activations was not applied. (c) Histogram of radii resulting from a ring fit to Fig. 1p. From the average measured radius <R> = 174 ± 25 nm and the known radius (Fig. 2) the expansion factor was estimated to be 3.2. Values depict mean ± SD.

Supplementary Figure 4 Live-cell SMLM on Nup96-mMaple.

Representative images from three independent experiments are shown. Scale bars 10 µm (upper panel) and 1 µm (lower panel).

Supplementary Figure 5 Dimensions of Nup96 with different labels.

(a) Nup96-SNAP-AF647. R = 53.7 ± 2.1 nm (N = 3, nC = 6 nNPC = 1856). (b) Nup96-Halo-Cy5. R = 54.5 ± 2.6 nm (N = 2, nC = 6 nNPC = 4959). (c) Nup96-mMaple. R = 55.4 ± 3.5 nm (N = 3, nC = 6 nNPC = 4276). (d) Nup96-GFP-Nanobody-Q-AF647. R = 55.0 ± 1.9 nm (N = 2, nC = 6 nNPC = 2913). (e) Nup96-GFP-Antibody-AF647. R = 64.3 ± 2.6 nm N = 2, nC = 6 nNPC = 3158). (f) Box plots of the radii for the different labels. Each data point corresponds to one cell. The center of the box plot shows the median, while lower and upper boundaries indicate the 25th and 75th percentiles, respectively. N denotes the number of biologically independent experiments, nC the number of imaged cells and nNPC the number of analyzed NPCs. Values show weighted mean ± SD, based on nNPC. Measurements were performed in GLOX/MEA blinking buffer (a,b,d,e) or 50 mM Tris in D2O (c).

Supplementary Figure 6 Depth induced aberrations lead to local deformations in z (related to Fig. 2j).

Distance between rings plotted vs the z-position a) before correction and b) after correction (N = 2, nC = 8, nNPC = 7234). Straight lines are the linear fit of the ring distance for each cell. Before correction these values are highly correlated (Pearson coefficient −0.27 ± 0.10), after correcting for the localization errors, the correlation is reduced (Pearson coefficient 0.04 ± 0.15). N denotes the number of biologically independent experiments, nC the number of imaged cells and nNPC the number of analyzed NPCs. Values depict mean ± SD.

Supplementary Figure 7 Simulations for determining the ELE.

Error in determining ELE (inferred ELE – true ELE) in dependence on (a) the brightness of the fluorophores, (b) the labeling efficiency, (c) the number of re-activations for bright (5000 photons) fluorophores and for (d) dim (500 photons) fluorophores and (e) in dependence on the number of nuclear pores analyzed. (f) Statistical accuracy (SEM) in determining the ELE in dependence on the number of nuclear pores. Unless otherwise indicated the simulation parameters were: labeling efficiency 0.5 (a,e,f) and 0.3 (c,d), number of photons = 5000, on average 1 re-activation, background 20 photons, 900 nuclear pores. Error bars denote mean ± SD.

Supplementary Figure 8 Fixed and labeled samples are stable.

ELE of Nup107-SNAP labeled with BG-AF647 and stored at 4 °C imaged (a) on the day of sample preparation, (b) two months after sample preparation and (c) two years after sample preparation. Representative images from one independent experiment are shown. Scale bars 1 µm.

Supplementary Figure 9 Example images for data in Table 1 and Fig. 3.

Representative images of two (a-f,I,k,m,n,p), three (g,j,o,r), four (h), five (l) or six (q) independent experiments are shown. Scale bars 100 nm.

Supplementary Figure 10 Characterization of stable knock-in HEK293T mMaple-Nup107 cell line.

(a) Lanes 1 & 2: 2 different cell line clones under tetracycline (tet) induction. Lanes 3–7: clone no. 10 under tet induction for 4-0 days. Lane 8: blank. Lane 9: HEK293T wildtype. Lane 10: ladder. GAPDH is used as loading control. Clone 10 was used for all subsequent experiments and 2 days of tet induction was sufficient to knock down endogenous Nup107 and induce expressions of mMaple-Nup107. (b) Dose titration of tet concentration to induce mMaple-Nup107 expression. 10 ng/mL was sufficient to knock down endogenous Nup107 and induce expressions of mMaple-Nup107. Increasing tet concentration resulted in increasing expression of mMaple-Nup107. A tet concentration of 1 mg/mL was used for all subsequent experiments. (c) Recovery of endogenous expression of Nup107 after removal of tet from media. Expression of endogenous Nup107 can be observed after 1 day off tet induction with an increasing trend the longer tet is removed. The opposite trend is observed for expression levels of mMaple-Nup107. Representative images of one independent experiment are shown. Full scans of all blots are shown in Supplementary Fig. 12.

Supplementary Figure 11 Full scans of blots for Supplementary Fig. 1.

(a-e) Original Southern blots for U2OS cell line validation with the probes indicated above each blot. The clones chosen for all subsequent experiments are marked with a red asterisk. (f) Original Western blots for U2OS cell line validation decorated for Nup96 and GAPDH, respectively.

Supplementary Figure 12 Full scans of blots for Supplementary Fig. 10.

(a-c) Original Western blots for HEK293T cell validation and tetracycline titration decorated for Nup107 and GAPDH, respectively.

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Thevathasan, J.V., Kahnwald, M., Cieśliński, K. et al. Nuclear pores as versatile reference standards for quantitative superresolution microscopy. Nat Methods 16, 1045–1053 (2019). https://doi.org/10.1038/s41592-019-0574-9

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