emClarity: software for high-resolution cryo-electron tomography and subtomogram averaging

Article metrics

Abstract

Macromolecular complexes are intrinsically flexible and often challenging to purify for structure determination by single-particle cryo-electron microscopy (cryo-EM). Such complexes can be studied by cryo-electron tomography (cryo-ET) combined with subtomogram alignment and classification, which in exceptional cases achieves subnanometer resolution, yielding insight into structure–function relationships. However, it remains challenging to apply this approach to specimens that exhibit conformational or compositional heterogeneity or are present in low abundance. To address this, we developed emClarity (https://github.com/bHimes/emClarity/wiki), a GPU-accelerated image-processing package featuring an iterative tomographic tilt-series refinement algorithm that uses subtomograms as fiducial markers and a 3D-sampling-function-compensated, multi-scale principal component analysis classification method. We demonstrate that our approach offers substantial improvement in the resolution of maps and in the separation of different functional states of macromolecular complexes compared with current state-of-the-art software.

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: The emClarity workflow for subtomogram averaging and classification.
Fig. 2: Improvement in resolution of subtomogram averaging with emClarity.
Fig. 3: Classification of yeast 80S ribosome (EMPIAR-10045) with 3D-CTF-compensated missing wedge and multi-scale PCA.
Fig. 4: Classification of translating mammalian 80S ribosome with 3D-CTF-compensated missing wedge and multi-scale PCA.

Data availability

Cryo-EM structural data have been deposited in the Electron Microscopy Data Bank under accession codes EMD-8799 for the yeast 80S ribosome; EMD-8802, EMD-8803, EMD-8804, EMD-8805, and EMD-8806 for rabbit 80S ribosome classes I–V, respectively; and EMD-8986 for the HIV-1 Gag data.

References

  1. 1.

    Grant, T., Rohou, A. & Grigorieff, N. cisTEM, user-friendly software for single- particle image processing. eLife 7, e35383 (2018).

  2. 2.

    Scheres, S. H. W. RELION: implementation of a Bayesian approach to cryo-EM structure determination. J. Struct. Biol. 180, 519–530 (2012).

  3. 3.

    Glaeser, R. M. & Hall, R. J. Reaching the information limit in cryo-EM of biological macromolecules: experimental aspects. Biophys. J. 100, 2331–2337 (2011).

  4. 4.

    Cheng, Y., Grigorieff, N., Penczek, P. A. & Walz, T. A primer to single-particle cryo-electron microscopy. Cell 161, 438–449 (2015).

  5. 5.

    Oikonomou, C. M. & Jensen, G. J. Cellular electron cryotomography: toward structural biology in situ. Annu. Rev. Biochem. 86, 873–896 (2017).

  6. 6.

    Diebolder, C. A., Koster, A. J. & Koning, R. I. Pushing the resolution limits in cryo electron tomography of biological structures. J. Microsc. 248, 1–5 (2012).

  7. 7.

    Lučič, V., Rigort, A. & Baumeister, W. Cryo-electron tomography: the challenge of doing structural biology in situ. J. Cell Biol. 202, 407–419 (2013).

  8. 8.

    Frangakis, A. S. et al. Identification of macromolecular complexes in cryoelectron tomograms of phantom cells. Proc. Natl. Acad. Sci. USA 99, 14153–14158 (2002).

  9. 9.

    Bartesaghi, A. et al. Classification and 3D averaging with missing wedge correction in biological electron tomography. J. Struct. Biol. 162, 436–450 (2008).

  10. 10.

    Bharat, T. A. M., Russo, C. J., Löwe, J., Passmore, L. A. & Scheres, S. H. W. Advances in single-particle electron cryomicroscopy structure determination applied to sub-tomogram averaging. Structure 23, 1743–1753 (2015).

  11. 11.

    Cassidy, C. K. et al. CryoEM and computer simulations reveal a novel kinase conformational switch in bacterial chemotaxis signaling. eLife 4, e08419 (2015).

  12. 12.

    Zeev-Ben-Mordehai, T. et al. Two distinct trimeric conformations of natively membrane-anchored full-length herpes simplex virus 1 glycoprotein B. Proc. Natl. Acad. Sci. USA 113, 4176–4181 (2016).

  13. 13.

    Penczek, P. A., Frank, J. & Spahn, C. M. T. A method of focused classification, based on the bootstrap 3D variance analysis, and its application to EF-G-dependent translocation. J. Struct. Biol. 154, 184–194 (2006).

  14. 14.

    Liao, H. Y., Hashem, Y. & Frank, J. Efficient estimation of three-dimensional covariance and its application in the analysis of heterogeneous samples in cryo-electron microscopy. Structure 23, 1129–1137 (2015).

  15. 15.

    Trabuco, L. G., Villa, E., Schreiner, E., Harrison, C. B. & Schulten, K. Molecular dynamics flexible fitting: a practical guide to combine cryo-electron microscopy and X-ray crystallography. Methods 49, 174–180 (2009).

  16. 16.

    Schur, F. K. M., Hagen, W. J. H., de Marco, A. & Briggs, J. A. Determination of protein structure at 8.5Å resolution using cryo-electron tomography and sub-tomogram averaging. J. Struct. Biol. 184, 394–400 (2013).

  17. 17.

    Kudryashev, M., Castaño-Díez, D. & Stahlberg, H. Limiting factors in single particle cryo electron tomography. Comput. Struct. Biotechnol. J. 1, e201207002 (2012).

  18. 18.

    Kremer, J. R., Mastronarde, D. N. & McIntosh, J. R. Computer visualization of three-dimensional image data using IMOD. J. Struct. Biol. 116, 71–76 (1996).

  19. 19.

    Fernández, J. J., Li, S. & Crowther, R. A. CTF determination and correction in electron cryotomography. Ultramicroscopy 106, 587–596 (2006).

  20. 20.

    Rohou, A. & Grigorieff, N. CTFFIND4: fast and accurate defocus estimation from electron micrographs. J. Struct. Biol. 192, 216–221 (2015).

  21. 21.

    Hrabe, T. et al. PyTom: a Python-based toolbox for localization of macromolecules in cryo-electron tomograms and subtomogram analysis. J. Struct. Biol. 178, 177–188 (2012).

  22. 22.

    Jensen, G. J. & Kornberg, R. D. Defocus-gradient corrected back-projection. Ultramicroscopy 84, 57–64 (2000).

  23. 23.

    Turoňová, B., Schur, F. K. M., Wan, W. & Briggs, J. A. G. Efficient 3D-CTF correction for cryo-electron tomography using NovaCTF improves subtomogram averaging resolution to 3.4Å. J. Struct. Biol. 199, 187–195 (2017).

  24. 24.

    Kunz, M. & Frangakis, A. S. Three-dimensional CTF correction improves the resolution of electron tomograms. J. Struct. Biol. 197, 114–122 (2017).

  25. 25.

    Rickgauer, J. P., Grigorieff, N. & Denk, W. Single-protein detection in crowded molecular environments in cryo-EM images. eLife 6, 1–22 (2017).

  26. 26.

    Grant, T. & Grigorieff, N. Measuring the optimal exposure for single particle cryo-EM using a 2.6 Å reconstruction of rotavirus VP6. eLife 4, e06980 (2015).

  27. 27.

    Förster, F., Pruggnaller, S., Seybert, A. & Frangakis, A. S. Classification of cryo-electron sub-tomograms using constrained correlation. J. Struct. Biol. 161, 276–286 (2008).

  28. 28.

    Stewart, A. & Grigorieff, N. Noise bias in the refinement of structures derived from single particles. Ultramicroscopy 102, 67–84 (2004).

  29. 29.

    Henderson, R. et al. Outcome of the first electron microscopy validation task force meeting. Structure 20, 205–214 (2012).

  30. 30.

    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).

  31. 31.

    Sindelar, C. V. & Grigorieff, N. Optimal noise reduction in 3D reconstructions of single particles using a volume-normalized filter. J. Struct. Biol. 180, 26–38 (2012).

  32. 32.

    Scheres, S. H. Beam-induced motion correction for sub-megadalton cryo-EM particles. eLife 3, e03665 (2014).

  33. 33.

    Heumann, J. M., Hoenger, A. & Mastronarde, D. N. Clustering and variance maps for cryo-electron tomography using wedge-masked differences. J. Struct. Biol. 175, 288–299 (2011).

  34. 34.

    Alsberg, B. K. Multiscale cluster analysis. Anal. Chem. 71, 3092–3100 (1999).

  35. 35.

    Marabini, R. et al. The Electron Microscopy eXchange (EMX) initiative. J. Struct. Biol. 194, 156–163 (2016).

  36. 36.

    Bharat, T. A. M. & Scheres, S. H. W. Resolving macromolecular structures from electron cryo-tomography data using subtomogram averaging in RELION. Nat. Protoc. 11, 2054–2065 (2016).

  37. 37.

    Khoshouei, M., Pfeffer, S., Baumeister, W., Förster, F. & Danev, R. Subtomogram analysis using the Volta phase plate. J. Struct. Biol. 197, 94–101 (2017).

  38. 38.

    Bai, X. C., Fernandez, I. S., McMullan, G. & Scheres, S. H. W. Ribosome structures to near-atomic resolution from thirty thousand cryo-EM particles. eLife 2, e00461 (2013).

  39. 39.

    Schur, F. K. M. et al. An atomic model of HIV-1 capsid-SP1 reveals structures regulating assembly and maturation. Science 353, 506–508 (2016).

  40. 40.

    Adams, P. D. et al. PHENIX: a comprehensive Python-based system for macromolecular structure solution. Acta Crystallogr. D. Biol. Crystallogr. 66, 213–221 (2010).

  41. 41.

    Gutell, R. R., Weiser, B., Woese, C. R. & Noller, H. F. Comparative anatomy of 16-S-like ribosomal RNA. Prog. Nucleic. Acid. Res. Mol. Biol. 32, 155–216 (1985).

  42. 42.

    Beckmann, R. et al. Architecture of the protein-conducting channel associated with the translating 80S ribosome. Cell 107, 361–372 (2001).

  43. 43.

    Mohan, S. & Noller, H. F. Recurring RNA structural motifs underlie the mechanics of L1 stalk movement. Nat. Commun. 8, 14285 (2017).

  44. 44.

    Spahn, C. M. et al. Domain movements of elongation factor eEF2 and the eukaryotic 80S ribosome facilitate tRNA translocation. EMBO J. 23, 1008–1019 (2004).

  45. 45.

    Wilson, D. N. & Nierhaus, K. H. The E-site story: the importance of maintaining two tRNAs on the ribosome during protein synthesis. Cell. Mol. Life Sci. 63, 2725–2737 (2006).

  46. 46.

    Budkevich, T. V. et al. Regulation of the mammalian elongation cycle by subunit rolling: a eukaryotic-specific ribosome rearrangement. Cell 158, 121–131 (2014).

  47. 47.

    Abeyrathne, P. D., Koh, C. S., Grant, T., Grigorieff, N. & Korostelev, A. A. Ensemble cryo-EM uncovers inchworm-like translocation of a viral IRES through the ribosome. eLife 5, 1–31 (2016).

  48. 48.

    Gomez-Lorenzo, M. G. et al. Three-dimensional cryo-electron microscopy localization of EF2 in the Saccharomyces cerevisiae 80S ribosome at 17.5 A resolution. EMBO J. 19, 2710–2718 (2000).

  49. 49.

    Chakraborty, B., Mukherjee, R. & Sengupta, J. Structural insights into the mechanism of translational inhibition by the fungicide sordarin. J. Comput. Aided. Mol. Des. 27, 173–184 (2013).

  50. 50.

    Meyer, R. R., Kirkland, A. I. & Saxton, W. O. A new method for the determination of the wave aberration function for high-resolution TEM.; 2. Measurement of the antisymmetric aberrations. Ultramicroscopy 99, 115–123 (2004).

  51. 51.

    Anger, A. M. et al. Structures of the human and Drosophila 80S ribosome. Nature 497, 80–85 (2013).

  52. 52.

    Ning, J. et al. In vitro protease cleavage and computer simulations reveal the HIV-1 capsid maturation pathway. Nat. Commun. 7, 13689 (2016).

  53. 53.

    Fernando, K. V. & Fuller, S. D. Determination of astigmatism in TEM images. J. Struct. Biol. 157, 189–200 (2007).

  54. 54.

    Mastronarde, D. N. Fiducial marker and hybrid alignment methods for single- and double-axis tomography. In Electron Tomography: Methods for Three-dimensional Visualization of Structures in the Cell (ed Frank, J.)163–185 (Springer: New York, 2006).

  55. 55.

    Xiong, Q., Morphew, M. K., Schwartz, C. L., Hoenger, A. H. & Mastronarde, D. N. CTF determination and correction for low dose tomographic tilt series. J. Struct. Biol. 168, 378–387 (2009).

  56. 56.

    Frank, J. Three-dimensional Electron Microscopy of Macromolecular Assemblies 15-69 (Oxford University Press: New York, 2006).

  57. 57.

    Diebolder, C. A., Faas, F. G. A., Koster, A. J. & Koning, R. I. Conical Fourier shell correlation applied to electron tomograms. J. Struct. Biol. 190, 215–223 (2015).

  58. 58.

    Winkler, H. et al. Tomographic subvolume alignment and subvolume classification applied to myosin V and SIV envelope spikes. J. Struct. Biol. 165, 64–77 (2009).

Download references

Acknowledgements

We thank J. Frank and W. Li for very helpful discussions; D. Bevan for technical assistance with computer clusters; S. Loerch for help with Phenix real-space refinement and COOT; and T. Brosenitsch, F.J. Alvarez, and J.P. Rickgauer for reading the manuscript. We thank F. Schur and J. Briggs for the dataset on HIV-1 immature Gag, and X. Fu for testing the emClarity software. This work was supported by the National Institutes of Health (grants GM085043 and GM082251-6869 to P.Z.) and the UK Wellcome Trust Investigator Award (206422/Z/17/Z to P.Z.).

Author information

P.Z. and B.A.H. conceived and designed the study. B.A.H. developed and tested the code for emClarity. B.A.H. and P.Z. analyzed the results and wrote the paper.

Correspondence to Peijun Zhang.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

Integrated supplementary information

Supplementary Figure 1 Benchmarking of emClarity performance on GPU cards.

(a) Relative comparison of emClarity run times among GPU cards. The run times are normalized to a V100 card (13 h 40 min). The other system components on each machine are very similar to those listed in Supplementary Table 2. (b) Comparison of speed-up factor with four processes per GPU when disk i/o is a limiting factor. These trend with the Nvidia GPU architecture Volta > Pascal > Maxwell. Interestingly, the 1080 Ti, which has Pascal architecture, does not handle multiple processes as well as the Titan line of consumer cards. These experiments were repeated independently three times and the average result is presented.

Supplementary Figure 2 TomoCPR, constrained projection refinement.

(a) Schematic overview for reference generation in the tomoCPR. (b-c) Cartoons illustrating overlapping information in the projections arising from other particles, components in the specimen, and variable defocus as a function of tilt. (d-e) Examples of non-tilted (d) and tilted (e) projections used to generate references for the yeast 80S tomoCPR. Overlapping particles and features due to contaminants (white arrows) and the carbon edge (white chevron) are shown. Scale bars, 30 nm.

Supplementary Figure 3 Illustration of the effect and compensation of the missing wedge at multiple length scales.

Far left, the total average filtered by Gaussian kernels of variable width to correlate voxels over the given length scales. Center, eigen-images composed of the average plus the eigenvector, sorted from the most variance explained (1) to least (21). Black and white arrows show examples where es27 density is absent or present in the L1 position. Right three columns show 3D-variance maps in red overlaid with the average: without missing-wedge compensation (ac), with wedge-marked differences (WMDs) (df) and with 3D-sampling function in emClarity (gi). Positions for L1 (green arrow), es27 (orange arrow), e-Site tRNA, and the mRNA channel entrance (blue arrow) are labeled.

Supplementary Figure 4 Gold-standard FSC between half-sets and angular distribution of yeast 80S ribosomes from EMPIAR-10045.

(a) Plots show FSC calculated over 38 cones (dashed lines) with the overall average FSC in solid black. (b) Angular distribution plots for emClarity and (c) RELION. The microscope reference frame indicated with the y-axis corresponding to the tilt axis and in a right-handed convention.

Supplementary Figure 5 emClarity processing of HIV-1 immature Gag particle.

Gold-standard FSC between subtomogram averages of half-sets of HIV-1 Gag (EMPIAR-10164) by emClarity. Red, fsc “true” tight mask with correction via phase randomization. Blue, soft mask with solvent corrected fsc, with an estimated solvent:protein volume ratio of 0.31. Both approaches indicated a resolution of 3.1 Å at the 0.143 cutoff (dashed line).

Supplementary information

Supplementary Information

Supplementary Figures 1–5 and Supplementary Tables 1–4

Reporting Summary

Supplementary Software

emClarity software

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Himes, B.A., Zhang, P. emClarity: software for high-resolution cryo-electron tomography and subtomogram averaging. Nat Methods 15, 955–961 (2018) doi:10.1038/s41592-018-0167-z

Download citation

Further reading