Crossref journal-article
Springer Science and Business Media LLC
Nature Communications (297)
Abstract

AbstractCryo-FIB/SEM combined with cryo-ET has emerged from within the field of cryo-EM as the method for obtaining the highest resolution structural information of complex biological samples in-situ in native and non-native environments. However, challenges remain in conventional cryo-FIB/SEM workflows, including milling thick specimens with vitrification issues, specimens with preferred orientation, low-throughput when milling small and/or low concentration specimens, and specimens that distribute poorly across grid squares. Here we present a general approach called the ‘Waffle Method’ which leverages high-pressure freezing to address these challenges. We illustrate the mitigation of these challenges by applying the Waffle Method and cryo-ET to reveal the macrostructure of the polar tube in microsporidian spores in multiple complementary orientations, which was previously not possible due to preferred orientation. We demonstrate the broadness of the Waffle Method by applying it to three additional cellular samples and a single particle sample using a variety of cryo-FIB-milling hardware, with manual and automated approaches. We also present a unique and critical stress-relief gap designed specifically for waffled lamellae. We propose the Waffle Method as a way to achieve many advantages of cryo-liftout on the specimen grid while avoiding the long, challenging, and technically-demanding process required for cryo-liftout.

Bibliography

Kelley, K., Raczkowski, A. M., Klykov, O., Jaroenlak, P., Bobe, D., Kopylov, M., Eng, E. T., Bhabha, G., Potter, C. S., Carragher, B., & Noble, A. J. (2022). Waffle Method: A general and flexible approach for improving throughput in FIB-milling. Nature Communications, 13(1).

Authors 11
  1. Kotaro Kelley (first)
  2. Ashleigh M. Raczkowski (additional)
  3. Oleg Klykov (additional)
  4. Pattana Jaroenlak (additional)
  5. Daija Bobe (additional)
  6. Mykhailo Kopylov (additional)
  7. Edward T. Eng (additional)
  8. Gira Bhabha (additional)
  9. Clinton S. Potter (additional)
  10. Bridget Carragher (additional)
  11. Alex J. Noble (additional)
References 41 Referenced 100
  1. 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). (10.1038/nmeth1014) / Nat. Methods by M Marko (2007)
  2. Guo, Q. et al. In situ structure of neuronal C9orf72 Poly-GA aggregates reveals proteasome recruitment. Cell 172, 696–705.e12 (2018). (10.1016/j.cell.2017.12.030) / Cell by Q Guo (2018)
  3. Bykov, Y. S. et al. The structure of the COPI coat determined within the cell. eLife 6, e32493 (2017). (10.7554/eLife.32493) / eLife by YS Bykov (2017)
  4. Mahamid, J. et al. Visualizing the molecular sociology at the HeLa cell nuclear periphery. Science 351, 969–972 (2016). (10.1126/science.aad8857) / Science by J Mahamid (2016)
  5. Tacke, S. et al. A streamlined workflow for automated cryo focused ion beam milling. J. Struct. Biol. 107743 https://doi.org/10.1016/j.jsb.2021.107743 (2021). (10.1016/j.jsb.2021.107743)
  6. Toro-Nahuelpan, M. et al. Tailoring cryo-electron microscopy grids by photo-micropatterning for in-cell structural studies. Nat. Methods 17, 50–54 (2020). (10.1038/s41592-019-0630-5) / Nat. Methods by M Toro-Nahuelpan (2020)
  7. Engel, L. et al. Lattice micropatterning of electron microscopy grids for improved cellular cryo-electron tomography throughput. bioRxiv (2020) https://doi.org/10.1101/2020.08.30.272237 (2020). (10.1101/2020.08.30.272237)
  8. Sibert, B. S., Kim, J. Y., Yang, J. E. & Wright, E. R. Whole-cell cryo-electron tomography of cultured and primary eukaryotic cells on micropatterned TEM grids. bioRxiv https://doi.org/10.1101/2021.06.06.447251 (2021). (10.1101/2021.06.06.447251)
  9. 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). (10.1038/s41592-019-0497-5) / Nat. Methods by M Schaffer (2019)
  10. Hsieh, C., Schmelzer, T., Kishchenko, G., Wagenknecht, T. & Marko, M. Practical workflow for cryo focused-ion-beam milling of tissues and cells for cryo-TEM tomography. J. Struct. Biol. 185, 32–41 (2014). (10.1016/j.jsb.2013.10.019) / J. Struct. Biol. by C Hsieh (2014)
  11. Kuba, J. et al. Advanced cryo-tomography workflow developments – correlative microscopy, milling automation and cryo-lift-out. J. Microsc. 281, 112–124 (2021). (10.1111/jmi.12939) / J. Microsc. by J Kuba (2021)
  12. Kudo, R. Experiments on the extrusion of polar filaments of cnidosporidian spores. J. Parasitol. 4, 88093421 (1918). (10.2307/3271239)
  13. Jaroenlak, P. et al. 3-Dimensional organization and dynamics of the microsporidian polar tube invasion machinery. PLOS Pathog 16, e1008738 (2020). (10.1371/journal.ppat.1008738) / PLOS Pathog by P Jaroenlak (2020)
  14. Noble, A. J. et al. Routine single particle CryoEM sample and grid characterization by tomography. eLife 7, e34257 (2018). (10.7554/eLife.34257) / eLife by AJ Noble (2018)
  15. Noble, A. J. et al. Reducing effects of particle adsorption to the air–water interface in cryo-EM. Nat. Methods 15, 793–795 (2018). (10.1038/s41592-018-0139-3) / Nat. Methods by AJ Noble (2018)
  16. D’Imprima, E. et al. Protein denaturation at the air-water interface and how to prevent it. eLife 8, e42747 (2019). (10.7554/eLife.42747) / eLife by E D’Imprima (2019)
  17. Lam, V. & Villa, E. Practical approaches for Cryo-FIB milling and applications for cellular cryo-electron tomography. in cryoEM (eds. Gonen, T. & Nannenga, B. L.) 2215 49–82 (Springer US, 2021). (10.1007/978-1-0716-0966-8_3)
  18. Harapin, J. et al. Structural analysis of multicellular organisms with cryo-electron tomography. Nat. Methods 12, 634–636 (2015). (10.1038/nmeth.3401) / Nat. Methods by J Harapin (2015)
  19. Dahl, R. & Staehelin, L. A. High-pressure freezing for the preservation of biological structure: theory and practice. J. Electron Microsc. Tech. 13, 165–174 (1989). (10.1002/jemt.1060130305) / J. Electron Microsc. Tech. by R Dahl (1989)
  20. 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 1–8 https://doi.org/10.1038/s41592-020-01054-7 (2021). (10.1038/s41592-020-01054-7)
  21. Himes, B. A. & Zhang, P. emClarity: software for high-resolution cryo-electron tomography and subtomogram averaging. Nat. Methods 15, 955–961 (2018). (10.1038/s41592-018-0167-z) / Nat. Methods by BA Himes (2018)
  22. Chen, M. et al. A complete data processing workflow for cryo-ET and subtomogram averaging. Nat. Methods 16, 1161–1168 (2019). (10.1038/s41592-019-0591-8) / Nat. Methods by M Chen (2019)
  23. Song, K. et al. In situ structure determination at nanometer resolution using TYGRESS. Nat. Methods 17, 201–208 (2020). (10.1038/s41592-019-0651-0) / Nat. Methods by K Song (2020)
  24. Sanchez, R. M., Zhang, Y., Chen, W., Dietrich, L. & Kudryashev, M. Subnanometer-resolution structure determination in situ by hybrid subtomogram averaging - single particle cryo-EM. Nat. Commun. 11, 3709 (2020). (10.1038/s41467-020-17466-0) / Nat. Commun. by RM Sanchez (2020)
  25. Pyle, E. & Zanetti, G. Current data processing strategies for cryo-electron tomography and subtomogram averaging. Biochem. J. 478, 1827–1845 (2021). (10.1042/BCJ20200715) / Biochem. J. by E Pyle (2021)
  26. Wolff, G. et al. Mind the gap: micro-expansion joints drastically decrease the bending of FIB-milled cryo-lamellae. bioRxiv 656447 (2019) https://doi.org/10.1101/656447(2019). (10.1101/656447)
  27. Klumpe, S. et al. A Modular Platform for Streamlining Automated Cryo-FIB Workflows. https://doi.org/10.1101/2021.05.19.444745 (2021). (10.1101/2021.05.19.444745)
  28. Solter, L. F., Becnel, J. J. & Vávra, J. Research methods for entomopathogenic microsporidia and other protists. Man. Tech. Invertebr. Pathol. 329–371 (2012) https://doi.org/10.1016/B978-0-12-386899-2.00011-7(2012). (10.1016/B978-0-12-386899-2.00011-7)
  29. Suloway, C. et al. Fully automated, sequential tilt-series acquisition with Leginon. J. Struct. Biol. 167, 11–18 (2009). (10.1016/j.jsb.2009.03.019) / J. Struct. Biol. by C Suloway (2009)
  30. Zheng, S. Q. et al. MotionCor2: anisotropic correction of beam-induced motion for improved cryo-electron microscopy. Nat. Methods 14, 331–332 (2017). (10.1038/nmeth.4193) / Nat. Methods by SQ Zheng (2017)
  31. Noble, A. J. & Stagg, S. M. Automated batch fiducial-less tilt-series alignment in Appion using Protomo. J. Struct. Biol. 192, 270–278 (2015). (10.1016/j.jsb.2015.10.003) / J. Struct. Biol. by AJ Noble (2015)
  32. Winkler, H. & Taylor, K. A. Accurate marker-free alignment with simultaneous geometry determination and reconstruction of tilt series in electron tomography. Ultramicroscopy 106, 240–254 (2006). (10.1016/j.ultramic.2005.07.007) / Ultramicroscopy by H Winkler (2006)
  33. Lander, G. C. et al. Appion: An integrated, database-driven pipeline to facilitate EM image processing. J. Struct. Biol. 166, 95–102 (2009). (10.1016/j.jsb.2009.01.002) / J. Struct. Biol. by GC Lander (2009)
  34. 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). (10.7554/eLife.06980) / eLife by T Grant (2015)
  35. Agulleiro, J.-I. & Fernandez, J.-J. Tomo3D 2.0 – Exploitation of Advanced Vector eXtensions (AVX) for 3D reconstruction. J. Struct. Biol. 189, 147–152 (2015). (10.1016/j.jsb.2014.11.009) / J. Struct. Biol. by J-I Agulleiro (2015)
  36. Agulleiro, J. I. & Fernandez, J. J. Fast tomographic reconstruction on multicore computers. Bioinformatics 27, 582–583 (2011). (10.1093/bioinformatics/btq692) / Bioinformatics by JI Agulleiro (2011)
  37. Bepler, T., Kelley, K., Noble, A. J. & Berger, B. Topaz-Denoise: general deep denoising models for cryoEM and cryoET. Nat. Commun. 11, 5208 (2020). (10.1038/s41467-020-18952-1) / Nat. Commun. by T Bepler (2020)
  38. 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). (10.1006/jsbi.1996.0013) / J. Struct. Biol. by JR Kremer (1996)
  39. Zhao, L., Kopylov, M., Potter, C. S., Carragher, B. & Finn, M. G. Engineering the PP7 virus capsid as a peptide display platform. ACS Nano 13, 4443–4454 (2019). (10.1021/acsnano.8b09683) / ACS Nano by L Zhao (2019)
  40. Kelley, K. et al. Waffle method: A general and flexible approach for FIB-milling small and anisotropically oriented samples. https://doi.org/10.1101/2020.10.28.359372 (2020). (10.1101/2020.10.28.359372)
  41. Pettersen, E. F. et al. UCSF Chimera-a visualization system for exploratory research and analysis. J. Comput. Chem. 25, 1605–1612 (2004). (10.1002/jcc.20084) / J. Comput. Chem. by EF Pettersen (2004)
Dates
Type When
Created 3 years, 4 months ago (April 6, 2022, 6:08 a.m.)
Deposited 2 years, 8 months ago (Nov. 24, 2022, 11:44 a.m.)
Indexed 58 minutes ago (Aug. 20, 2025, 10:43 p.m.)
Issued 3 years, 4 months ago (April 6, 2022)
Published 3 years, 4 months ago (April 6, 2022)
Published Online 3 years, 4 months ago (April 6, 2022)
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@article{Kelley_2022, title={Waffle Method: A general and flexible approach for improving throughput in FIB-milling}, volume={13}, ISSN={2041-1723}, url={http://dx.doi.org/10.1038/s41467-022-29501-3}, DOI={10.1038/s41467-022-29501-3}, number={1}, journal={Nature Communications}, publisher={Springer Science and Business Media LLC}, author={Kelley, Kotaro and Raczkowski, Ashleigh M. and Klykov, Oleg and Jaroenlak, Pattana and Bobe, Daija and Kopylov, Mykhailo and Eng, Edward T. and Bhabha, Gira and Potter, Clinton S. and Carragher, Bridget and Noble, Alex J.}, year={2022}, month=apr }