Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Parallel cryo electron tomography on in situ lamellae

Abstract

In situ cryo electron tomography of cryo focused ion beam milled samples has emerged in recent years as a powerful technique for structural studies of macromolecular complexes in their native cellular environment. However, the possibilities for recording tomographic tilt series in a high-throughput manner are limited, in part by the lamella-shaped samples. Here we utilize a geometrical sample model and optical image shift to record tens of tilt series in parallel, thereby saving time and gaining access to sample areas conventionally used for tracking specimen movement. The parallel cryo electron tomography (PACE-tomo) method achieves a throughput faster than 5 min per tilt series and allows for the collection of sample areas that were previously unreachable, thus maximizing the amount of data from each lamella. Performance testing with ribosomes in vitro and in situ on state-of-the-art and general-purpose microscopes demonstrated the high throughput and quality of PACE-tomo.

This is a preview of subscription content, access via your institution

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: Lamella pretilt and sample geometry must be considered for accurate prediction of sample movement.
Fig. 2: PACE-tomo keeps specimen shift errors within ±50 nm.
Fig. 3: PACE-tomo performs well on a side-entry holder.
Fig. 4: PACE-tomo datasets yield high-resolution subtomogram averages.

Data availability

Subtomogram averages were deposited in the Electron Microscopy Data Bank under accession codes EMD-33834, EMD-33115, EMD-33116, EMD-33117, EMD-33118 and EMD-33833. Raw tilt series frames were deposited in the Electron Microscopy Public Image Archive under accession codes EMPIAR-11111, EMPIAR-10985, EMPIAR-10986 and EMPIAR-10987.

Code availability

SerialEM python scripts for auxiliary functions, target selection and PACE-tomo are available at https://github.com/eisfabian/PACEtomo.

References

  1. Hylton, R. K. & Swulius, M. T. Challenges and triumphs in cryo-electron tomography. iScience 24, 102959 (2021).

    Article  Google Scholar 

  2. Assaiya, A., Burada, A. P., Dhingra, S. & Kumar, J. An overview of the recent advances in cryo-electron microscopy for life sciences. Emerg. Top. Life Sci. 5, 151–168 (2021).

    Article  CAS  Google Scholar 

  3. Danev, R. et al. Routine sub-2.5 Å cryo-EM structure determination of GPCRs. Nat. Commun. 12, 4333 (2021).

    Article  CAS  Google Scholar 

  4. Hagen, W. J. H., Wan, W. & Briggs, J. A. G. Implementation of a cryo-electron tomography tilt-scheme optimized for high resolution subtomogram averaging. J. Struct. Biol. 197, 191–198 (2017).

    Article  Google Scholar 

  5. 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).

    Article  CAS  Google Scholar 

  6. Tegunov, D., Xue, L., Dienemann, C., Cramer, P. & Mahamid, J. Multi-particle cryo-EM refinement with M visualizes ribosome-antibiotic complex at 3.5 Å in cells. Nat. Methods 18, 186–193 (2021).

    Article  CAS  Google Scholar 

  7. Chreifi, G., Chen, S., Metskas, L. A., Kaplan, M. & Jensen, G. J. Rapid tilt-series acquisition for electron cryotomography. J. Struct. Biol. 205, 163–169 (2019).

    Article  Google Scholar 

  8. Eisenstein, F., Danev, R. & Pilhofer, M. Improved applicability and robustness of fast cryo-electron tomography data acquisition. J. Struct. Biol. 208, 107–114 (2019).

    Article  CAS  Google Scholar 

  9. Cheng, A. et al. High resolution single particle cryo-electron microscopy using beam-image shift. J. Struct. Biol. 204, 270–275 (2018).

    Article  CAS  Google Scholar 

  10. Weis, F. & Hagen, W. J. H. Combining high throughput and high quality for cryo-electron microscopy data collection. Acta Crystallogr. D Struct. Biol. 76, 724–728 (2020).

    Article  CAS  Google Scholar 

  11. Wu, C., Huang, X., Cheng, J., Zhu, D. & Zhang, X. High-quality, high-throughput cryo-electron microscopy data collection via beam tilt and astigmatism-free beam-image shift. J. Struct. Biol. 208, 107396 (2019).

    Article  CAS  Google Scholar 

  12. Danev, R., Yanagisawa, H. & Kikkawa, M. Cryo-EM performance testing of hardware and data acquisition strategies. Microscopy 70, 487–497 (2021).

    Article  Google Scholar 

  13. Zivanov, J. et al. New tools for automated high-resolution cryo-EM structure determination in RELION-3. eLife 7, e42166 (2018).

    Article  Google Scholar 

  14. Bouvette, J. et al. Beam image-shift accelerated data acquisition for near-atomic resolution single-particle cryo-electron tomography. Nat. Commun. 12, 1957 (2021).

    Article  CAS  Google Scholar 

  15. Zheng, Q. S., Braunfeld, M. B., Sedat, J. W. & Agard, D. A. An improved strategy for automated electron microscopic tomography. J. Struct. Biol. 147, 91–101 (2004).

    Article  Google Scholar 

  16. Albert, S. et al. Direct visualization of degradation microcompartments at the ER membrane. Proc. Natl Acad. Sci. USA 117, 1069–1080 (2020).

    Article  CAS  Google Scholar 

  17. Gupta, T. K. et al. Structural basis for VIPP1 oligomerization and maintenance of thylakoid membrane integrity. Cell 184, 3643–3659 (2021).

    Article  CAS  Google Scholar 

  18. Mahamid, J. et al. Visualizing the molecular sociology at the HeLa cell nuclear periphery. Science 351, 969–972 (2016).

    Article  CAS  Google Scholar 

  19. Schaffer, M. et al. A cryo-FIB lift-out technique enables molecular-resolution cryo-ET within native Caenorhabditis elegans tissue. Nat. Methods 16, 757–762 (2019).

    Article  CAS  Google Scholar 

  20. Weiss, G. L. et al. Structure of a thylakoid-anchored contractile injection system in multicellular cyanobacteria. Nat. Microbiol. 7, 386–396 (2022).

    Article  CAS  Google Scholar 

  21. Marko, M., Hsieh, C., Schalek, R., Frank, J. & Mannella, C. Focused-ion-beam thinning of frozen-hydrated biological specimens for cryo-electron microscopy. Nat. Methods 4, 215–217 (2007).

    Article  CAS  Google Scholar 

  22. Medeiros, J. M. et al. Robust workflow and instrumentation for cryo-focused ion beam milling of samples for electron cryotomography. Ultramicroscopy 190, 1–11 (2018).

    Article  CAS  Google Scholar 

  23. Schaffer, M. et al. Optimized cryo-focused ion beam sample preparation aimed at in situ structural studies of membrane proteins. J. Struct. Biol. 197, 73–82 (2017).

    Article  CAS  Google Scholar 

  24. Buckley, G. et al. Automated cryo-lamella preparation for high-throughput in-situ structural biology. J. Struct. Biol. 210, 107488 (2020).

    Article  CAS  Google Scholar 

  25. Klumpe, S. et al. A modular platform for automated cryo-FIB workflows. eLife 10, e70506 (2021).

    Article  CAS  Google Scholar 

  26. Tacke, S. et al. A streamlined workflow for automated cryo focused ion beam milling. J. Struct. Biol. 213, 107743 (2021).

    Article  CAS  Google Scholar 

  27. Zachs, T. et al. Fully automated, sequential focused ion beam milling for cryo-electron tomography. eLife 9, e52286 (2020).

    Article  Google Scholar 

  28. Khavnekar, S. et al. Multishot tomography for high-resolution in situ subtomogram averaging. Preprint at bioRxiv https://doi.org/10.1101/2022.04.10.487763 (2022).

  29. Mastronarde, D. N. Automated electron microscope tomography using robust prediction of specimen movements. J. Struct. Biol. 152, 36–51 (2005).

    Article  Google Scholar 

  30. Schorb, M., Haberbosch, I., Hagen, W. J. H., Schwab, Y. & Mastronarde, D. N. Software tools for automated transmission electron microscopy. Nat. Methods 16, 471 (2019).

    Article  CAS  Google Scholar 

  31. Harris, C. R. et al. Array programming with NumPy. Nature 585, 357–362 (2020).

    Article  CAS  Google Scholar 

  32. Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  34. Zivanov, J. et al. A Bayesian approach to single-particle electron cryo-tomography in RELION-4.0. Preprint at bioRxiv https://doi.org/10.1101/2022.02.28.482229 (2022).

  35. Khavnekar, S. et al. Optimizing cryo-FIB lamellas for sub-5Å in situ structural biology. Preprint at bioRxiv https://doi.org/10.1101/2022.06.16.496417 (2022).

  36. Wang, Z. et al. Structures from intact myofibrils reveal mechanism of thin filament regulation through nebulin. Science 375, eabn1934 (2022).

    Article  CAS  Google Scholar 

  37. Peck, A. et al. Montage electron tomography of vitrified specimens. J. Struct. Biol. 214, 107860 (2022).

    Article  CAS  Google Scholar 

  38. Yang, J. E. et al. Correlative cryogenic montage electron tomography for comprehensive in-situ whole-cell structural studies. Preprint at bioRxiv https://doi.org/10.1101/2021.12.31.474669 (2022).

  39. Mastronarde, D. N. Correction for non-perpendicularity of beam and tilt axis in tomographic reconstructions with the IMOD package. J. Microsc. 230, 212–217 (2008).

    Article  CAS  Google Scholar 

  40. Zheng, S. et al. AreTomo: an integrated software package for automated marker-free, motion-corrected cryo-electron tomographic alignment and reconstruction. J. Struct. Biol. X 6, 100068 (2022).

    CAS  Google Scholar 

  41. Ni, T. et al. High-resolution in situ structure determination by cryo-electron tomography and subtomogram averaging using emClarity. Nat. Protoc. 17, 421–444 (2022).

    Article  CAS  Google Scholar 

  42. Chen, M. et al. A complete data processing workflow for cryo-ET and subtomogram averaging. Nat. Methods 16, 1161–1168 (2019).

    Article  CAS  Google Scholar 

  43. Burt, A., Gaifas, L., Dendooven, T. & Gutsche, I. A flexible framework for multi-particle refinement in cryo-electron tomography. PLoS Biol. 19, e3001319 (2021).

    Article  CAS  Google Scholar 

  44. Ecken, J., von der, Heissler, S. M., Pathan-Chhatbar, S., Manstein, D. J. & Raunser, S. Cryo-EM structure of a human cytoplasmic actomyosin complex at near-atomic resolution. Nature 534, 724–728 (2016).

    Article  CAS  Google Scholar 

  45. Fialka, I. et al. The estrogen-dependent c-JunER protein causes a reversible loss of mammary epithelial cell polarity involving a destabilization of adherens junctions. J. Cell Biol. 132, 1115–1132 (1996).

    Article  CAS  Google Scholar 

  46. Rigort, A. et al. Focused ion beam micromachining of eukaryotic cells for cryoelectron tomography. Proc. Natl Acad. Sci. USA 109, 4449–4454 (2012).

    Article  CAS  Google Scholar 

  47. Wolff, G. et al. Mind the gap: micro-expansion joints drastically decrease the bending of FIB-milled cryo-lamellae. J. Struct. Biol. 208, 107389 (2019).

    Article  Google Scholar 

  48. Ederth, J., Mandava, C. S., Dasgupta, S. & Sanyal, S. A single-step method for purification of active His-tagged ribosomes from a genetically engineered Escherichia coli. Nucleic Acids Res. 37, e15 (2009).

    Article  Google Scholar 

  49. 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).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  51. Zhang, K. Gctf: real-time CTF determination and correction. J. Struct. Biol. 193, 1–12 (2016).

    Article  CAS  Google Scholar 

  52. Wagner, T. et al. SPHIRE-crYOLO is a fast and accurate fully automated particle picker for cryo-EM. Commun. Biol. 2, 1–13 (2019).

    Article  Google Scholar 

  53. Castaño-Díez, D., Kudryashev, M., Arheit, M. & Stahlberg, H. Dynamo: a flexible, user-friendly development tool for subtomogram averaging of cryo-EM data in high-performance computing environments. J. Struct. Biol. 178, 139–151 (2012).

    Article  Google Scholar 

  54. Rigort, A. et al. Automated segmentation of electron tomograms for a quantitative description of actin filament networks. J. Struct. Biol. 177, 135–144 (2012).

    Article  CAS  Google Scholar 

  55. 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).

    Article  Google Scholar 

  56. Nicastro, D. et al. The molecular architecture of axonemes revealed by cryoelectron tomography. Science 313, 944–948 (2006).

    Article  CAS  Google Scholar 

  57. Goddard, T. D. et al. UCSF ChimeraX: meeting modern challenges in visualization and analysis. Protein Sci. 27, 14–25 (2018).

    Article  CAS  Google Scholar 

  58. Buchholz, T.-O. et al. Content-aware image restoration for electron microscopy. Methods Cell Biol. 152, 277–289 (2019).

    Article  Google Scholar 

Download references

Acknowledgements

We are grateful to Y. Fujiyoshi and H. Suzuki for stimulating discussions and valuable feedback. We thank K. Nakamura and Y. Sakamaki for the management and support of the Graduate School of Medicine cryoEM facility. We are grateful for support of cryoFIB milling and instrument maintenance by Y. Fukuda and for comments on the manuscript by I. Selvam. This research was partially supported by Platform Project for Supporting Drug Discovery and Life Science Research (Basis for Supporting Innovative Drug Discovery and Life Science Research (BINDS)) from AMED under grant number JP21am0101115j0005. F.E. is an International Research Fellow of the Japan Society for the Promotion of Science (JSPS, #P20764) and received a Grant-in-Aid for Scientific Research (KAKENHI, 21F20764). H.Y. and M.K. were supported by Grant-in-Aid for Transformative Research Areas A (JSPS, 21H05248). S.T. received funding from Grant-in-Aid for Specially Promoted Research (JP19H05468). R.D. was supported by Takeda Science Foundation 2019 Medical Research Grant and Japan Science and Technology Agency PRESTO (18069571).

Author information

Authors and Affiliations

Authors

Contributions

F.E. developed PACE-tomo and conducted all experiments. H.Y. purified ribosomes and processed several datasets. H.K. prepared the eukaryotic cell cultures. F.E., H.Y., H.K., M.K., S.T. and R.D. designed the experiments. F.E. wrote the manuscript with comments from all authors.

Corresponding author

Correspondence to Radostin Danev.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Methods thanks Petr Chlanda, Wim Hagen, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling editor: Rita Strack, in collaboration with the Nature Methods team. Peer reviewer reports are available.

Additional information

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

Extended data

Extended Data Fig. 1 A linear defocus ramp can compensate for errors in tilt axis offset.

a. Shown are defocus values estimated by CTF fitting for two tilt series collected on the same lamella using PACE-tomo. One tilt series was recorded without applying a defocus ramp (dashed line). The other tilt series (solid line) was recorded while applying a defocus ramp of 0.03 µm/degree. Outliers at extreme tilt angles were not considered for the linear fit of the defocus slope. b. Shown are defocus values estimated by CTF fitting for two tilt series collected on amorphous carbon support using PACE-tomo. One tilt series was recorded using the tilt axis offset determined by the fine eucentricity routine of SerialEM (dashed line). The other tilt series (solid line) was recorded using the tilt axis offset determined using the PACEtomo_measureOffset.py script.

Extended Data Fig. 2 Side entry holder behavior throughout dose-symmetric tilt series.

a. Specimen shifts in µm along x and y (parallel and perpendicular to the tilt axis respectively). The behavior of the negative tilt angle branch differs from the positive tilt angle branch. b. Defocus throughout the tilt series measured by beam tilt. While defocus remains relatively constant for the positive tilt angle branch, it shows a significant slope for the negative tilt angle branch.

Extended Data Fig. 3 PACE-tomo tilt series collected using a cryo side-entry holder.

a. An overview image of a holey carbon sample area indicating the positions at which tilt series were collected. Targets are numbered according to acquisition order. Red frame indicates the position of the tracking tilt series. The tilt axis is along Y = 0. Collection time was 118 minutes for 25 tilt series with an angular range of ± 30°. b, c. Measured specimen shift errors by cross correlation-based alignment to first tilt image at 0° in each tilt series in x- (b) and y-direction (c), parallel and perpendicular to the tilt axis, respectively. Tilt series 1 is the tracking tilt series and points are coloured grey. d. Defocus values for each tilt series estimated by CTF fitting. e. Defocus values for each tilt series estimated by CTF fitting of the 0° tilt image plotted against the target coordinates in X and Y. The 3D plot shows that targets are approximately on a plane indicating significant tilt of the sample support film.

Extended Data Fig. 4 Image shift influence on resolution.

a. Tilt series of dataset 1 were divided into 5 groups parallel (red groups) and perpendicular (blue groups) to the tilt axis, respectively, and reconstructed independently. Resolutions were plotted against the relative distance to the central group. Neither parallel nor perpendicular groups to the tilt axis show a significant resolution dependency on the distance from the centre. b. Acquisition pattern for dataset 2 – PACE-tomo with 81 targets in a regular 9 by 9 pattern. Targets are numbered according to acquisition order. The tracking tilt series (red frame) is at the origin and all other targets are acquired by relative image shift along the tilt axis (x-axis) and perpendicular to the tilt axis (y-axis). Collection time was 6 hours. c. Vectors representing beam tilt determined by RELION Tomo CTF refinement using 1 optics group per tilt series. Origins of vectors are at their respective image shifts relative to the tracking tilt series at 0° tilt angle. Magnitude scale of vectors is indicated by the scale bar (top right). d. Tilt series of dataset 2 were divided into 5 groups parallel (red groups) and perpendicular (blue groups) to the tilt axis, respectively, and reconstructed independently. Resolutions were plotted against the relative distance to the central group. Parallel groups to the tilt axis show a resolution dependency on the distance from the centre while perpendicular groups do not.

Extended Data Fig. 5 PACE-tomo facilitates data collection in challenging areas.

a. Shown is a 0.66 nm thick slice through one of the 21 cryo electron tomograms (acquisition area 3) collected on the lamella shown in Fig. 4b. The subtomogram average was mapped into the tomogram using the refined coordinates and orientations. Membrane associated and cytoplasmic ribosomes were coloured red and blue, respectively. Scale bar: 100 nm. b. Shown is a 0.66 nm thick slice through one of 7 cryo electron tomograms collected on the bottom lamella shown in c. The subtomogram average of actin was mapped into the tomogram using the refined coordinates and orientations. Scale bar: 100 nm. c. Overview montages of two (out of 41) cryoFIB-milled lamellae indicating the positions at which tilt series were collected. Red frame indicates the position that was used for the tracking tilt series. White arrowheads mark targets that would not have been accessible by conventional cryoET collection schemes. Collection times were 23 minutes for 6 tilt series (top) and 30 minutes for 7 tilt series (bottom, used in dataset 5).

Extended Data Fig. 6 Representative sample used for cryoFIB milling.

a. Scanning electron microscopy image of a representative sample of EpH4 cells grown on a 150-mesh gold grid. A total of eight grids were used to prepare 41 lamellae. Scale bar: 1 mm. b. CryoFIB image after premilling trenches at 38° that mark targets for lamella milling and limit lamella length. Shown are four examples of the 41 prepared lamellae. Scale bar: 100 µm. c. Scanning electron microscopy image of finished lamella with micro-expansion joints that was used for a PACE-tomo acquisition (Extended Data Fig. 5c top). Shown is one of the 41 prepared lamellae. Scale bar: 10 µm.

Extended Data Fig. 7 Simplified schematic of PACE-tomo.

Shown are the essential steps of the PACE-tomo data acquisition scheme.

Extended Data Fig. 8 Processing workflows.

Shown are the processing workflows for datasets 1–5 using RELION-4.0-beta. Blue and red boxes indicate steps done in dynamo and PEET, respectively. Final maps were filtered and coloured according to the local resolution distribution.

Supplementary information

Supplementary Information

Supplementary Table 1, and captions for Supplementary Videos 1 and 2.

Reporting Summary

Peer Review File

Supplementary Video 1

Slices through tomogram from dataset 4 collected on the cryoFIB-milled lamella shown in Fig. 4b (tilt series 3). The subtomogram average was mapped into the tomogram using the refined coordinates and orientations. Membrane-associated and cytoplasmic ribosomes were coloured red and blue, respectively.

Supplementary Video 2

Cross-correlation aligned tilt series from dataset 4 collected on the cryoFIB-milled lamella shown in Fig. 4b (tilt series 3). Scale bar, 100 nm.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Eisenstein, F., Yanagisawa, H., Kashihara, H. et al. Parallel cryo electron tomography on in situ lamellae. Nat Methods 20, 131–138 (2023). https://doi.org/10.1038/s41592-022-01690-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41592-022-01690-1

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing