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Precision and accuracy of single-molecule FRET measurements—a multi-laboratory benchmark study

Nature Methodsvolume 15pages669676 (2018) | Download Citation

Abstract

Single-molecule Förster resonance energy transfer (smFRET) is increasingly being used to determine distances, structures, and dynamics of biomolecules in vitro and in vivo. However, generalized protocols and FRET standards to ensure the reproducibility and accuracy of measurements of FRET efficiencies are currently lacking. Here we report the results of a comparative blind study in which 20 labs determined the FRET efficiencies (E) of several dye-labeled DNA duplexes. Using a unified, straightforward method, we obtained FRET efficiencies with s.d. between ±0.02 and ±0.05. We suggest experimental and computational procedures for converting FRET efficiencies into accurate distances, and discuss potential uncertainties in the experiment and the modeling. Our quantitative assessment of the reproducibility of intensity-based smFRET measurements and a unified correction procedure represents an important step toward the validation of distance networks, with the ultimate aim of achieving reliable structural models of biomolecular systems by smFRET-based hybrid methods.

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Acknowledgements

We thank the Eaton lab for early measurements that helped us design this study. We thank T. Peulen, M. Dimura, and R. McDonald for stimulating discussions on FRET measurements, data analysis, and modeling, and B. Bulat for measuring fluorescence quantum yields of Atto 550 and 1-mid (Atto 550). We also thank the company Atto-Tec for providing a reference sample of the dye Atto 550 for fluorescence characterization. The authors acknowledge networking support by the Dr. Wilhelm Heinrich und Else Heraeus Foundation and COST Action CM1306 “Understanding Movement and Mechanism in Molecular Machines.” The idea of a worldwide benchmark study of standard FRET rulers emerged at the 512th WE Heraeus Seminar “Single molecule kinetics” (Bad Honnef, Germany, 2012) and evolved further during the international COST symposium “Integrating spectroscopic and theoretical methods to analyse molecular machines” (Castle of Ringberg, Germany, 2014).

This work was supported by the European Research Council (ERC; grant agreement nos. 261227 (to A.N.K.), 646451 (to E.L.), 638536 (to T.C.), 671208 (to C.A.M.S.), and 681891 (to T. Hugel)), the Deutsche Forschungsgemeinschaft (DFG) (grant MI 749/4-1 to J.M., grant TI 329/10-1 to P.T., and grant SCHL 1896/3-1 to M.S.), the Swiss National Science Foundation (to B.S.), the German Federal Ministry of Education and Research (BMBF; 03Z2EN11 to M.S.), Research Foundation Flanders (FWO; grant G0B4915N to J. Hendrix), the Agency for Innovation by Science and Technology (IWT Flanders; doctoral scholarship to N.V.), the Danish Council for Independent Research (Sapere Aude grant 0602-01670B to V.B.), the Novo Nordisk Foundation (NNF15OC0017956 to V.B.), the UK BBSRC (grant BB/H01795X/1 to A.N.K.), the National Institute of Mental Health (grant MH081923 to M.E.B.), Clemson University (start-up funds to H. Sanabria, S.R.A., and I.S.Y.-O.), the NIH (grants GM109832 and GM118508 to K.R.W.; grant GM112659 to T. Ha), the NSF (Career Award MCS1749778 to H. Sanabria), the Carl-Zeiss-Stiftung (doctoral fellowship to E.K.), the Stipendienstiftung Rheinland-Pfalz (doctoral scholarship to G.K.), the Braunschweig International Graduate School of Metrology (B-IGSM; to B.W.), the DFG Research Training Group (GrK1952/1 “Metrology for Complex Nanosystems” to B.W.), the University of Sheffield (start-up funds to T.D.C.), and the National Research Foundation of Korea funded by the Ministry of Science and ICT (NRF-2017R1A2B3010309 to N.K.L.).

Author information

Author notes

  1. These authors contributed equally: Björn Hellenkamp, Sonja Schmid.

Affiliations

  1. Institute of Physical Chemistry, University of Freiburg, Freiburg im Breisgau, Germany

    • Björn Hellenkamp
    • , Sonja Schmid
    • , Markus Götz
    • , Johann Thurn
    •  & Thorsten Hugel
  2. Engineering and Applied Sciences, Columbia University, New York, NY, USA

    • Björn Hellenkamp
  3. Department of Bionanoscience, Kavli Institute of Nanoscience Delft, Delft University of Technology, Delft, the Netherlands

    • Sonja Schmid
  4. Molecular Physical Chemistry, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany

    • Olga Doroshenko
    • , Oleg Opanasyuk
    • , Ralf Kühnemuth
    • , Christian A. Hanke
    •  & Claus A. M. Seidel
  5. Department of Physics and Astronomy, Clemson University, Clemson, SC, USA

    • Soheila Rezaei Adariani
    • , Hugo Sanabria
    •  & Inna S. Yanez-Orozco
  6. Department of Chemistry, University of Sheffield, Sheffield, UK

    • Benjamin Ambrose
    •  & Timothy D. Craggs
  7. Interdisciplinary Nanoscience Center (iNANO) and Department of Chemistry, Aarhus University, Aarhus, Denmark

    • Mikayel Aznauryan
    • , Victoria Birkedal
    •  & Lasse L. Hildebrandt
  8. Physical Chemistry, Department of Chemistry, Nanosystems Initiative Munich (NIM), Center for Integrated Protein Science Munich (CiPSM) and Center for Nanoscience (CeNS), Ludwig-Maximilians-Universität München, Munich, Germany

    • Anders Barth
    • , Don C. Lamb
    •  & Nikolaus Naredi-Rainer
  9. Department of Physiology & Biophysics, Stony Brook University, Stony Brook, NY, USA

    • Mark E. Bowen
    • , Brié Levesque
    •  & James J. McCann
  10. Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, USA

    • Hongtao Chen
    •  & Enrico Gratton
  11. Molecular Microscopy Research Group, Zernike Institute for Advanced Materials, University of Groningen, Groningen, the Netherlands

    • Thorben Cordes
    •  & Giorgos Gouridis
  12. Physical and Synthetic Biology, Faculty of Biology, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Germany

    • Thorben Cordes
    • , Christian Gebhardt
    •  & Giorgos Gouridis
  13. Institute for Biophysics, Ulm University, Ulm, Germany

    • Tobias Eilert
    • , Eleni Kallis
    • , Carlheinz Röcker
    • , Lisa Streit
    •  & Jens Michaelis
  14. Laboratory of Biophysics, Wageningen University & Research, Wageningen, the Netherlands

    • Carel Fijen
    •  & Johannes Hohlbein
  15. Department of Biomedical Engineering, John Hopkins University, Baltimore, MD, USA

    • Taekjip Ha
    • , Boyang Hua
    •  & Thuy Ngo
  16. Department of Physics, North Carolina State University, Raleigh, NC, USA

    • Pengyu Hao
    • , Ruoyi Qiu
    •  & Keith R. Weninger
  17. B CUBE—Center for Molecular Bioengineering, TU Dresden, Dresden, Germany

    • Andreas Hartmann
    • , Georg Krainer
    •  & Michael Schlierf
  18. Laboratory for Photochemistry and Spectroscopy, Department of Chemistry, University of Leuven, Leuven, Belgium

    • Jelle Hendrix
    •  & Niels Vandenberk
  19. Dynamic Bioimaging Lab, Advanced Optical Microscopy Center and Biomedical Research Institute, Hasselt University, Hasselt, Belgium

    • Jelle Hendrix
  20. Institute of Physics, University of Lübeck, Lübeck, Germany

    • Verena Hirschfeld
    • , Christian G. Hübner
    •  & Henning Seidel
  21. Microspectroscopy Research Facility Wageningen, Wageningen University & Research, Wageningen, the Netherlands

    • Johannes Hohlbein
  22. Gene Machines Group, Clarendon Laboratory, Department of Physics, University of Oxford, Oxford, UK

    • Achillefs N. Kapanidis
    • , Nicole C. Robb
    •  & Timothy D. Craggs
  23. School of Chemistry, Seoul National University, Seoul, South Korea

    • Jae-Yeol Kim
    •  & Nam Ki Lee
  24. Molecular Biophysics, Technische Universität Kaiserslautern (TUK), Kaiserslautern, Germany

    • Georg Krainer
  25. Departments of Biology and Chemistry, Pharmacy and Geosciences, Johannes Gutenberg-University Mainz, Mainz, Germany

    • Edward A. Lemke
  26. Institute of Molecular Biology (IMB), Mainz, Germany

    • Edward A. Lemke
  27. Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany

    • Edward A. Lemke
    •  & Swati Tyagi
  28. School of Molecular Sciences and The Biodesign Institute, Arizona State University, Tempe, AZ, USA

    • Marcia Levitus
  29. Department of Biochemistry, University of Zurich, Zurich, Switzerland

    • Daniel Nettels
    •  & Benjamin Schuler
  30. Department of Chemistry, Ludwig-Maximilians-Universität München, München, Germany

    • Tim Schröder
    • , Philip Tinnefeld
    •  & Andrés Manuel Vera
  31. Institute of Physical & Theoretical Chemistry, Braunschweig Integrated Centre of Systems Biology (BRICS), and Laboratory for Emerging Nanometrology (LENA), Braunschweig University of Technology, Braunschweig, Germany

    • Philip Tinnefeld
    •  & Bettina Wünsch
  32. BIOSS Centre for Biological Signalling Studies, University of Freiburg, Freiburg im Breisgau, Germany

    • Thorsten Hugel

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Contributions

B. Hellenkamp, T. Hugel, J.M., and C.A.M.S. designed the research; B. Hellenkamp, S.S., O.D., O.O., R.K., S.R.A., B.A., M.A., A.B., H.C., T.E., C.F., C.G., G.G., P.H., C.A.H., A.H., J. Hendrix, L.L.H., V.H., J. Hohlbein, B. Hua, E.K., J.-Y.K., G.K., B.L., J.J.M., N.N.-R., D.N., T.N., R.Q., N.C.R., C.R., T.S., H.S., L.S., J.T., S.T., N.V., A.M.V., B.W., I.S.Y.-O., and T.D.C. performed measurements; B. Hellenkamp, S.S., and T. Hugel compared the measurements; all of the aforementioned authors and V.B., M.E.B., T.C., M.G., E.G., T. Ha, C.G.H., A.N.K., D.C.L., N.K.L., E.A.L., M.L., H. Sanabria, H. Seidel, M.S., B.S., P.T., K.R.W., J.M., and C.S. contributed to the analysis of the data and commented on the manuscript; B. Hellenkamp, S.S., T.D.C., J.M., C.A.M.S., and T. Hugel wrote the manuscript in consultation with O.D. and O.O.; and O.D. performed the calculations of the model distances.

Competing interests

The authors declare no competing interests.

Corresponding authors

Correspondence to Jens Michaelis or Claus A. M. Seidel or Timothy D. Craggs or Thorsten Hugel.

Integrated supplementary information

  1. Supplementary Figure 1 DNA sample and utilized dyes.

    Left: DNA model with dye accessible volumes of the donor (blue) and acceptor (red) that were used in this study, indicating lo-, mid- and hi-FRET samples. Right: Structural formula of the dyes used in this study. Based on dyes from Molecular Probes / Thermo Fisher Scientific (Waltham, USA) and Atto-tec (Siegen, D).

  2. Supplementary Figure 2 FRET efficiencies of all labs for all measured samples as indicated.

    FRET efficiencies of all labs for all measured samples as indicated. Sample 1 to 4 (see Supplementary Table 1 and Supplementary Note 1) are color coded (red, blue, green, yellow) for all data points from intensity-based techniques. For a table of R E and R MP and sample size for these measurements see Supplementary Table 4. Ensemble lifetime, single molecule lifetime and phasor approach derived data is shown in black. The FRET efficiencies (means and s.d.) for these measurements (depicted in black, sample size n) are: E 1a  = 0.21±0.05 (n = 6); E 1b  = 0.51±0.08 (n = 6); E 2a  = 0.25±0.06 (n = 4); E 2b  = 0.59±0.07 (n = 4); E 3a  = 0.10±0.04 (n = 3); E 3b  = 0.26±0.03 (n = 3); E 4a  = 0.12±0.10 (n = 3); E 1a  = 0.42±0.02 (n = 3). The left figure depicts all measurements from the main study, the right figure depicts all measurements from the later measurements of two additional samples (1-hi, 2-hi).

  3. Supplementary Figure 3 Schematics of a typical confocal setup with alternating laser excitation and pulsed interleaved excitation.

    Schematics of a typical confocal setup with alternating laser excitation / pulsed interleaved excitation and color-sensitive detection. The most important elements are specified: Objective (O), dichroic mirror (DM), pinhole (P), spectral filter (F), avalanche photo diode (APD) and electronic micro- or picosecond synchronization of laser pulses and single photon counting (Sync). Elements used for the correction factors in Table 2 (main text) were: F34-641 Laser clean-up filter z 640/10 (right after Laser 640 nm); DM1: F43-537 laser beam splitter z 532 RDC ; DM2: F53-534 Dual Line beam splitter z 532/633; DM3: F33-647 laser- laser beam splitter 640 DCXR; FG: F37-582 Brightline HC 582/75; FR: F47-700 ET Bandpass 700/75; Objective: Cfi plan apo VC 60xWI, NA1.2; Detectors: MPD Picoquant (green), tau-SPAD, Picoquant (red); Pinholes: 100 µm; ; Laser power at sample: ≈ 100 µW; Beam diameter ≈ 2 mm; Diffusion time of Atto550 and Atto647N around 0.42 ms and 0.50 ms, respectively. For details on all used setups and analysis software, see Supplementary Note 8.

  4. Supplementary Figure 4 Schematic designs of an objective-type and a prism-type TIRF setup.

    a, Objective. b, Prism. Green and red lasers are used to excite donor and acceptor dyes, respectively. M, mirror. L, lens. DM, dichroic mirror. Obj, objective. AD, achromatic doublet lens. Sl, tunable slit. F, filters. Det, detector (e.g. electron multiplying charge-coupled device camera, EMCCD). The inset shows a side view of the objective with the out-of-plane (45°) mirror below. SC, sample chamber. Ir, iris. St, translation stage, Pr, prism. The dashed black line in (a) indicates the on-axis path to the objective, in contrast to the displayed off-axis path for TIR illumination. Elements used for the correction factors in Table 2 (main text) were: Dichroic before objective: F53-534 (AHF), Dichroics in detection: F33-726 and F33-644 (AHF). Band pass filters in detection: BP F39-572 and BP F37-677 (AHF). SI: SP40 (Owis), Objective: CFI Apo TIRF 100x, NA 1.49 (Nikon). Camera: EMCCD, iXonUltra, Andor. Lasers: 532nm, Compass 215M (Coherent) and 635nm, Lasiris (Stoker Yale). Note that we have used a Dichroic in the fluorescence excitation and emission path that reflects the higher wavelength, but this does not have any effect on the FRET efficiency measurement and related determination of correction factors. For details on all used setups and analysis software, see Supplementary Note 8.

  5. Supplementary Figure 5 Correcting for differences in the excitation intensity in TIRF microscopy.

    Accounting for the differences in the excitation intensity profiles of the green and red laser across the field of view. The individual excitation profiles are determined as the mean image of a stack of images recorded while moving across a dense layer of dyes. In contrast to the uncorrected case (“before”), a position specific normalization creates narrower and more symmetric SE-populations (“after”). The standard corrections described in the main text are performed subsequently.

  6. Supplementary Figure 6 Computation of the spectral overlap integral J.

    Computation of the spectral overlap integral J for the FRET pair Atto550-Atto647N in sample 1. Normalized donor fluorescence and acceptor absorption spectra normalized to the maximum (left scale). Spectral overlap density j(λ)(right scale) to compute the spectral overlap integral J[cm−1M−1nm4]with $$J = \mathop {\smallint }\limits_0^\infty {\mathrm{j}}\left( \lambda \right){\mathrm{d}}\lambda$$ and $$j(\lambda ) = \bar F_D\left( \lambda \right)\varepsilon _A\left( \lambda \right)\lambda ^4$$ . The extinction coefficient ε A of Atto647N was assumed to be 150000M−1cm−1 at the maximum as provided by the manufacturer. The donor fluorescence and the acceptor absorption spectra were recorded in two laboratories in at least three independent experiments. Spectra with a flat baseline were selected. The computation was performed once.

  7. Supplementary Figure 7 Time-resolved anisotropies and FRET.

    The time-resolved anisotropies of dyes bound to a larger object (e.g. DNA or protein) normally consist of a fast decay from rotational relaxation of the dipole (left) and of a slow decay from translational relaxation (right). τ ET  = 1/k FRET : time of energy transfer; r A,∞: residual anisotropy of dye A. (Figure from ref. 1). The data exemplarily shown is from a single measurement.1 Hellenkamp, B., Wortmann, P., Kandzia, F., Zacharias, M. & Hugel, T. Multidomain Structure and Correlated Dynamics Determined by Self-Consistent FRET Networks. Nat. Meth. 14, 174-180 (2017).

  8. Supplementary Figure 8 Visualizations of different averages for efficiencies according to different fluorophore dynamics.

    (a) Dynamic average, which applies in the case of the fluorophore movements being faster than the rate of energy transfer. There the rate of energy transfer has to be calculated taking into account the average over all possible distances and orientations. (b) Intermediate case, called the isotropic average, where the orientational variation of the fluorophores is faster than the rate of energy transfer while the positional variation is slower (c) Static case, where the fluorophore movements are much slower than the rate of energy transfer. In this case each distance and respective fluorophore orientation has to be taken into account with its individual transfer efficiency. These efficiencies then are averaged by the measurement process. (Figure from ref. 2). 2 Wozniak, A. K., Schröder, G. F., Grubmüller, H., Seidel, C. A. M. & Oesterhelt, F. Single-Molecule FRET Measures Bends and Kinks in DNA. Proc. Natl Acad. Sci. USA 105, 18337-18342 (2008).

Supplementary information

  1. Supplementary Text and Figures

    Supplementary Figures 1–8, Supplementary Tables 1–6, and Supplementary Notes 1–8

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https://doi.org/10.1038/s41592-018-0085-0