Abstract

Single-molecule localization microscopy techniques have proven to be essential tools for quantitatively monitoring biological processes at unprecedented spatial resolution. However, these techniques are very low throughput and are not yet compatible with fully automated, multiparametric cellular assays. This shortcoming is primarily due to the huge amount of data generated during imaging and the lack of software for automation and dedicated data mining. We describe an automated quantitative single-molecule-based super-resolution methodology that operates in standard multiwell plates and uses analysis based on high-content screening and data-mining software. The workflow is compatible with fixed- and live-cell imaging and allows extraction of quantitative data like fluorophore photophysics, protein clustering or dynamic behavior of biomolecules. We demonstrate that the method is compatible with high-content screening using 3D dSTORM and DNA-PAINT based super-resolution microscopy as well as single-particle tracking.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

References

  1. 1.

    et al. High-throughput RNAi screening by time-lapse imaging of live human cells. Nat. Methods 3, 385–390 (2006).

  2. 2.

    et al. High-throughput fluorescence correlation spectroscopy enables analysis of proteome dynamics in living cells. Nat. Biotechnol. 33, 384–389 (2015).

  3. 3.

    et al. Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes. Nat. Protoc. 11, 1757–1774 (2016).

  4. 4.

    , & Imaging live-cell dynamics and structure at the single-molecule level. Mol. Cell 58, 644–659 (2015).

  5. 5.

    , & Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM). Nat. Methods 3, 793–795 (2006).

  6. 6.

    , & Subdiffraction-resolution fluorescence imaging of proteins in the mitochondrial inner membrane with photoswitchable fluorophores. J. Struct. Biol. 164, 250–254 (2008).

  7. 7.

    et al. Direct stochastic optical reconstruction microscopy with standard fluorescent probes. Nat. Protoc. 6, 991–1009 (2011).

  8. 8.

    et al. Expression-enhanced fluorescent proteins based on enhanced green fluorescent protein for super-resolution microscopy. ACS Nano 9, 9528–9541 (2015).

  9. 9.

    & Wide-field subdiffraction imaging by accumulated binding of diffusing probes. Proc. Natl. Acad. Sci. USA 103, 18911–18916 (2006).

  10. 10.

    et al. Dynamic superresolution imaging of endogenous proteins on living cells at ultra-high density. Biophys. J. 99, 1303–1310 (2010).

  11. 11.

    et al. Multiplexed 3D cellular super-resolution imaging with DNA-PAINT and Exchange-PAINT. Nat. Methods 11, 313–318 (2014).

  12. 12.

    , & Ultra-high resolution imaging by fluorescence photoactivation localization microscopy. Biophys. J. 91, 4258–4272 (2006).

  13. 13.

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

  14. 14.

    et al. High-density mapping of single-molecule trajectories with photoactivated localization microscopy. Nat. Methods 5, 155–157 (2008).

  15. 15.

    High-density single-particle tracking: quantifying molecule organization and dynamics at the nanoscale. Histochem. Cell Biol. 141, 587–595 (2014).

  16. 16.

    , & Photometry unlocks 3D information from 2D localization microscopy data. Nat. Methods 14, 41–44 (2016).

  17. 17.

    et al. 3D high-density localization microscopy using hybrid astigmatic/ biplane imaging and sparse image reconstruction. Biomed. Opt. Express 5, 3935–3948 (2014).

  18. 18.

    , , & Dual-color 3D superresolution microscopy by combined spectral-demixing and biplane imaging. Biophys. J. 109, 3–6 (2015).

  19. 19.

    Ovesný, M., Pavel, K., Švindrych, Z. & Hagen, G. M. High density 3D localization microscopy using sparse support recovery. Opt. Express 22, 31263–31276 (2014).

  20. 20.

    , & A high-density 3D localization algorithm for stochastic optical reconstruction microscopy. Opt. Nanoscopy 1, 6 (2012).

  21. 21.

    Microscopic imaging techniques for drug discovery. Nat. Rev. Drug Discov. 7, 54–67 (2008).

  22. 22.

    et al. Correlative light microscopy for high-content screening. Biotechniques 55, 243–252 (2013).

  23. 23.

    , , , & Integrated and correlative high-throughput and super-resolution microscopy. Histochem. Cell Biol. 141, 597–603 (2014).

  24. 24.

    et al. High throughput 3D super-resolution microscopy reveals Caulobacter crescentus in vivo Z-ring organization. Proc. Natl. Acad. Sci. USA 111, 4566–4571 (2014).

  25. 25.

    , & High-content 3D multicolor super-resolution localization microscopy. In Methods in Cell Biology Vol 125 (ed. Paluch, E.K.) Ch. 7 (Elsevier, 2015).

  26. 26.

    et al. CellProfiler Analyst: data exploration and analysis software for complex image-based screens. BMC Bioinformatics 9, 482 (2008).

  27. 27.

    , , , & ThunderSTORM: a comprehensive ImageJ plug-in for PALM and STORM data analysis and super-resolution imaging. Bioinformatics 30, 2389–2390 (2014).

  28. 28.

    , , & Three-dimensional super-resolution imaging by stochastic optical reconstruction microscopy. Science 319, 810–813 (2008).

  29. 29.

    , , & Super-resolution imaging with small organic fluorophores. Angew. Chem. Int. Edn Engl. 48, 6903–6908 (2009).

  30. 30.

    et al. Chemically induced photoswitching of fluorescent probes—a general concept for super-resolution microscopy. Molecules 16, 3106–3118 (2011).

  31. 31.

    et al. Enzymatic oxygen scavenging for photostability without pH drop in single-molecule experiments. ACS Nano 6, 6364–6369 (2012).

  32. 32.

    , , & Resolution doubling in 3D-STORM imaging through improved buffers. PLoS One 8, e69004 (2013).

  33. 33.

    et al. Single-molecule kinetics and super-resolution microscopy by fluorescence imaging of transient binding on DNA origami. Nano Lett. 10, 4756–4761 (2010).

  34. 34.

    , & Precise nanometer localization analysis for individual fluorescent probes. Biophys. J. 82, 2775–2783 (2002).

  35. 35.

    Zhang, X. et al. Highly photostable, reversibly photoswitchable fluorescent protein with high contrast ratio for live-cell superresolution microscopy. Proc. Natl. Acad. Sci. USA 113, 10364–10369 (2016).

  36. 36.

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

  37. 37.

    , , , & Evaluation of fluorophores for optimal performance in localization-based super-resolution imaging. Nat. Methods 8, 1027–1036 (2011).

  38. 38.

    et al. Make them blink: probes for super-resolution microscopy. Chemphyschem 11, 2475–2490 (2010).

  39. 39.

    et al. Nanoscale segregation of actin nucleation and elongation factors determines dendritic spine protrusion. EMBO J. 33, 2745–2764 (2014).

  40. 40.

    et al. Rational design of true monomeric and bright photoactivatable fluorescent proteins. Nat. Methods 9, 727–729 (2012).

  41. 41.

    & New concepts in synaptic biology derived from single-molecule imaging. Neuron 59, 359–374 (2008).

  42. 42.

    et al. Hippocampal LTP and contextual learning require surface diffusion of AMPA receptors. Nature 549, 384–388 (2017).

  43. 43.

    et al. Surface mobility of postsynaptic AMPARs tunes synaptic transmission. Science 320, 201–205 (2008).

  44. 44.

    Zhang, H. et al. Regulation of AMPA receptor surface trafficking and synaptic plasticity by a cognitive enhancer and antidepressant molecule. Mol. Psychiatry 18, 471–484 (2013).

  45. 45.

    et al. SR-Tesseler: a method to segment and quantify localization-based super-resolution microscopy data. Nat. Methods 12, 1065–1071 (2015).

  46. 46.

    et al. Quantitative super-resolution imaging with qPAINT. Nat. Methods 13, 439–442 (2016).

  47. 47.

    et al. FALCON: fast and unbiased reconstruction of high-density super-resolution microscopy data. Sci. Rep. 4, 4577 (2014).

  48. 48.

    , , , & Super-resolution imaging of multiple cells by optimised flat-field epi-illumination. Nat. Photonics 10, 705–708 (2016).

  49. 49.

    et al. Scoring diverse cellular morphologies in image-based screens with iterative feedback and machine learning. Proc. Natl. Acad. Sci. USA 106, 1826–1831 (2009).

  50. 50.

    , , & Automating single subunit counting of membrane proteins in mammalian cells. J. Biol. Chem. 287, 35912–35921 (2012).

  51. 51.

    , , , & Real-time analysis and visualization for single-molecule based super-resolution microscopy. PLoS One 8, e62918 (2013).

  52. 52.

    , , , & Super-resolution microscopy with DNA-PAINT. Nat. Protoc. 12, 1198–1228 (2017).

  53. 53.

    et al. Super-resolution imaging reveals that AMPA receptors inside synapses are dynamically organized in nanodomains regulated by PSD95. J. Neurosci. 33, 13204–13224 (2013).

Download references

Acknowledgements

This work was supported by the advanced ERC grant ADOS to D.C., the regional council of Aquitaine grant Dynascreen to D.C. and J.-B.S., the FranceBioImaging infrastructure ANR-10-INBS-04 to J.-B.S., the LabEx BRAIN and the IdEx Bordeaux to J.-B.S., and the Fondation pour la Recherche Médicale DEI20151234402 and ANR-Integractome to G.G.

Author information

Author notes

    • Anne Beghin
    •  & Adel Kechkar

    These authors contributed equally to this work.

Affiliations

  1. Université de Bordeaux, Institut interdisciplinaire de Neurosciences, Bordeaux, France.

    • Anne Beghin
    • , Corey Butler
    • , Florian Levet
    • , Marine Cabillic
    • , Olivier Rossier
    • , Gregory Giannone
    • , Rémi Galland
    • , Daniel Choquet
    •  & Jean-Baptiste Sibarita
  2. CNRS UMR 5297, Institut interdisciplinaire de Neurosciences, Bordeaux, France.

    • Anne Beghin
    • , Corey Butler
    • , Florian Levet
    • , Marine Cabillic
    • , Olivier Rossier
    • , Gregory Giannone
    • , Rémi Galland
    • , Daniel Choquet
    •  & Jean-Baptiste Sibarita
  3. Ecole Nationale Supérieure de Biotechnologie, Constantine, Algeria

    • Adel Kechkar
  4. Imagine Optic, Orsay, France.

    • Corey Butler
  5. Bordeaux Imaging Center, CNRS, Université de Bordeaux, UMS 3420, INSERM US4, Bordeaux, France.

    • Florian Levet
    •  & Daniel Choquet

Authors

  1. Search for Anne Beghin in:

  2. Search for Adel Kechkar in:

  3. Search for Corey Butler in:

  4. Search for Florian Levet in:

  5. Search for Marine Cabillic in:

  6. Search for Olivier Rossier in:

  7. Search for Gregory Giannone in:

  8. Search for Rémi Galland in:

  9. Search for Daniel Choquet in:

  10. Search for Jean-Baptiste Sibarita in:

Contributions

A.B. performed all the experiments with the help of R.G., C.B. and M.C. A.K. developed the software for online processing and Single Molecule Profiler. F.L. and C.B. performed quantitative data analysis. M.C. developed the HCS-SMLM acquisition interface. O.R. and G.G. designed some biological control experiments. All the authors contributed to the manuscript. J.-B.S. and D.C. came up with the original idea. J.-B.S. supervised this work.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Jean-Baptiste Sibarita.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–12, Supplementary Table 1 and Supplementary Notes 1–4

  2. 2.

    Life Sciences Reporting Summary

    Life Sciences Reporting Summary

Zip files

  1. 1.

    Supplementary Software 1

    Single Molecule Profiler

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/nmeth.4486

Further reading