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Technical Advances in Cryo-Electron Microscopy

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Front. Mol. Biosci., 12 December 2018 | https://doi.org/10.3389/fmolb.2018.00114

The Evolution and the Advantages of MicroED

Brent L. Nannenga1, Guanhong Bu1 and Dan Shi2*
  • 1School for Engineering of Matter, Transport and Energy, Arizona State University, Tempe, AZ, United States
  • 2Structural Biophysics Laboratory, National Frederick Laboratory for Cancer Research, National Cancer Institute, Frederick, MD, United States

MicroED is a method which combines cryo-EM sample preparation and instrumentation, with electron and X-ray crystallography data analysis, and it has been employed to solve many protein crystal structures at high resolution. Initially, the main doubts of this method for structure determination were the dynamic scattering of electrons, which would cause severe inaccuracies in the measured intensities. In this paper, we will review the evolution of MicroED data collection and processing, the major differences of multiple scattering effects in protein crystals and inorganic material, and the advantages of continuous rotation data collection. Additionally, because of the periodic nature of the crystalline sample, radiation doses can be kept significantly lower than those used in single particle data collection. We review the work where this was used to assess the radiation damage of a high-energy electron beam on the protein molecules at much lower dose ranges compared to imaging.

Introduction

Cryo-EM has become one of the most powerful tools in structural biology after nearly four decades of improvements in electron optics, direct electron detectors, and software (Frank, 2016). The major break-through technologies that have enabled the wide-spread adoption of cryo-EM are the computerized and newly designed electron optic systems and the introduction of direct electron detectors (Ruskin et al., 2013; Cheng et al., 2015). These technologies have facilitated the automated collection of large single particle data sets, and the development of the new software to reduce the effects caused by specimen motion (Grigorieff, 2013; Li et al., 2013). With these advances, large structures can routinely be determined at resolutions that allow the modeling of amino acid side chains. In addition to single particle cryo-EM, electron crystallography has been used to determine 2D membrane crystal structure at high resolution (Gonen et al., 2005). 3D crystals were also analyzed by electron crystallography, however for many years these samples resisted structure determination (Dorset and Parsons, 1975; Unwin and Henderson, 1975; Shi et al., 1998; Jiang et al., 2009). In 2013, micro electron diffraction, or MicroED, was developed and used to determine the first structure of a protein from a thin 3D microcrystal (Shi et al., 2013). The MicroED technique is used to collect high-resolution electron diffraction movie data sets from sub-micrometer sized 3D protein crystals at extremely low-dose (Nannenga and Gonen, 2016). The new advantage of MicroED is that the electron diffraction movies collected using the continuous rotation method (Nannenga et al., 2014b) can then be processed by standard X-ray crystallographic programs. In recent years, electron diffraction methods become a valuable tool and has been used to determine biomolecular structures, in some cases at sub-angstrom resolution(Nannenga et al., 2014a; Rodriguez et al., 2015; Yonekura et al., 2015; Sawaya et al., 2016; Krotee et al., 2017; Gallagher-Jones et al., 2018; Guenther et al., 2018; Hughes et al., 2018; Liu and Gonen, 2018; Seidler et al., 2018; Xu et al., 2018), and applied to material science to novel structures (Mugnaioli et al., 2009; Simancas et al., 2016; Palatinus et al., 2017; Vergara et al., 2017; Yuan et al., 2018; Zhang et al., 2018). The general applicability of electron diffraction techniques to all these samples has been made possible by continued method development and optimization. The evolution of MicroED methods are briefly described in the following section.

The Evolution of Data Collection and Processing

In the first proof of concept MicroED study (Shi et al., 2013), still diffraction patterns were collected at discrete angles from multiple lysozyme crystals, and the data was processed and merged manually. Due to the nature of still diffraction patterns, most of the recorded intensities were only partially sampled. To collect full intensities, the continuous rotation (CR) method for MicroED was developed, in which the compustage of the cryo-TEM is continuously rotated at a constant speed. Initially, the tilting speed of the microscope was controlled by the force applied to the F20 (FEI/ThermoFisher) alpha tilt buttons. After few tests, a constant speed of 0.09 degrees per second was generated by a weight, as shown in the Supplementary Video. The rotation rate can be seen by the alpha value in the lower-right corner of the screen. This approach was the first used for the MicroED CR method, and it was applied to the same lysozyme crystals used in the first study (Nannenga et al., 2014b), as well as the study of catalase and α-synuclein peptide fragments (Nannenga et al., 2014a; Rodriguez et al., 2015). The next iteration of CR rotated the stage using a home designed device (Shi et al., 2016), and this was followed by the third generation of CR where the rotation is controlled via software embedded in ThermoFisher/FEI microscopes. Other related electron diffraction techniques have also made use of continuous rotation data collection to improve the resulting data quality (Nederlof et al., 2013; Gemmi et al., 2015). In the initial MicroED study, a combination of manual indexing and in-house developed programs based on previous algorithms (Shi et al., 1998) for integration were used to generate merged intensity of still diffraction data (Iadanza and Gonen, 2014). Since CR was developed, all diffraction movies from the rotating protein crystals can be easily processed through previously developed X-ray crystallographic software [e.g., Mosflm (Hattne et al., 2015), XDS (Rodriguez et al., 2015)], and DIALS (Clabbers et al., 2018). Detailed protocols on sample preparation, data collection, and processing have been published previously (Hattne et al., 2015).

Multiple Scattering and Dynamic Scattering in Protein Crystals and Inorganic Crystals

Because of the strong interaction between electrons and the sample, the dynamical scattering of electrons in a crystal has been a major hurdle in the recording of accurate intensities (Spence, 1993). To overcome this, the precession electron diffraction (PED) technique was developed for diffraction data collection (Vincent and Midgley, 1994), and this has been shown to dramatically decrease the effects of dynamic scattering relative to traditional electron diffraction patterns (Oleynikov et al., 2007). The rotation electron diffraction (RED) method was developed to determine the structures of inorganic crystals using fine step rotation and small-angle beam tilting (Wan et al., 2013), in which the diffraction data was off-zone axis patterns and might contain less overall dynamic scattering events. Similarly, the CR method developed for MicroED was shown to reduce the effects of dynamic scattering included on the zone axes of reciprocal space and yield more accurate structures (Nannenga et al., 2014b). Because the crystal is rotating as the data is being collected, the allowed secondary scattering events are reduced as the Bragg reflections are being integrated. Also, both the PED and the CR methods employ the relative movement between the Ewald sphere and the reciprocal lattice to scan reciprocal space, facilitating the collection of full diffraction intensities. The subtle difference between these two methods is that the crystal and its reciprocal spaces are continuously rotating while the scattered electron traveling in the crystal for the CR, as shown in Figure 1, and both the real and its reciprocal spaces are static to the scattered electrons in the PED method. In other words, the continuously rotation could generate an additional subtle tilting of the crystal to the dynamically scattered electrons. It would be interesting to compare the electron diffraction data from the same crystals using the PED and the CR methods on the major zone axis.

FIGURE 1
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Figure 1. Continuous rotation data collection in MicroED. The effects of multiple scattering (light blue) can be reduced using the CR method, as both the crystal and the reciprocal lattices are being rotated while the scattered electrons traveling in the crystal.

In addition to reducing dynamic scattering through data collection strategies, the difference in atomic composition of biological crystals (light atoms) relative to inorganic crystals (heavier atoms) also plays an important role in reducing the dynamic scattering. When thin catalase crystals (~200 nm thick) were used to assess the scattering observed for MicroED data, it was found that the ratio of elastically scattered electrons relative to un-scattered electrons (direct beam) was low (0.2), indicating that the kinematic assumption is appropriate. The results of this analysis were very similar to those obtained in previous work on catalase (Dorset and Parsons, 1975; Unwin and Henderson, 1975). These also agree on the fact that the elastic scattering mean free path of the high energy electrons in the cryo-biological samples are at least 4 times longer than in the inorganic materials with atomic number bigger than 25 (Grimm et al., 1996; Iakoubovskii and Mitsuishi, 2009), which means that the multiple scattering events could be less frequent in the protein crystals than in the inorganic crystals. Together, this could explain why biological samples with CR method do not appear to suffer from dynamic scattering as much as is seen in inorganic crystals (Spence, 1993).

Using MicroED to Assess Radiation Damage

The radiation damage caused by high-energy electrons when they interact with beam sensitive material is the key reason for the resolution limitations in cryo-EM. Both protein crystals (Stark et al., 1996; Baker et al., 2010) and single particles (Bartesaghi et al., 2014; Grant and Grigorieff, 2015) have been used to estimate radiation damage. The radiation damages are more likely accumulated from high energy electron beam knocking out the electrons of protein molecules in vitreous ice, it would be important to assess the damage starting from lower dose. The signal of a diffraction experiment increases with the square of the number of unit cells in the crystal; therefore, MicroED can obtain accurate 3D density maps at high resolution using total doses of as low as ~1 e/Å2, which allows the analysis of radiation damage effects in both reciprocal space and real space (Hattne et al., 2018).

A study on the effects of radiation damage at these very low doses was conducted by collecting data over the same rotation range from the same crystals in increments of 1.6 e/Å2 total dose (Hattne et al., 2018). Figures 2A,B show the plots of the averaged intensities and the number of merged reflections in different resolution bins as the dose accumulates. The number of reflections rapidly decreases in the high-resolution shells as the fine features of the crystals lattice are lost to radiation damage, and the total intensities gradually decay at resolution-dependent rates as the dose accumulates. After a very small amount of dose has accumulated, there are no measurable reflections beyond 2 Å, which indicated the most of high-resolution information has already been lost. When the accumulated dose reaches > ~8 electron/Å2, most information at better than 3 Å in resolution is also lost. In this study, the effects of site-specific radiation damage were also found to follow a similar trend indicating that these effects are not solely do to a loss in crystalline order. This suggests that radiation damage at the atomic scale could occur at much higher rates than what has been estimated using single-particle measurements (Bartesaghi et al., 2014; Grant and Grigorieff, 2015).

FIGURE 2
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Figure 2. The plots of sweep number vs. (A) intensity of average per spot and (B) total number of measurable reflection numbers in the different resolution sphere shells of merged 3D reciprocal lattice, each resolution bin was colored differently.

Discussion

Since the initial presentation of MicroED, the data collection, and processing methods have evolved to continually improve the method. The use of MicroED has several advantages for structure determination including the ability to determine structures from a small number of crystals (in some cases a single crystal (Nannenga et al., 2014b) that are several orders of magnitude smaller than those needed by traditional crystallographic methods. A unique advantage of electron diffraction is that it is very sensitive to charge and chemical bonding (Chang et al., 1999; Wu and Spence, 2003; Yonekura and Maki-Yonekura, 2016). When these effects are properly accounted for and modeled, MicroED could be used to directly visualize charge and bonding in protein structures. Specimen preparation usually is the bottle neck for MicroED because of the fragileness of protein crystals, using cryo-FIB/SEM to reshape large proteins crystals embedded in ice without blotting is a very promising method (Duyvesteyn et al., 2018), few other groups are also working on the similar approach. Continued development of the method promises to cement electron diffraction's status as a unique and valuable tool for structural biology and materials characterization.

Author Contributions

DS, BN, and GB contributed to the writing of this paper.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We are grateful to Dr. Johan Hattne for generating the plots for Figure 2 and for useful discussions in regard to the MicroED radiation damage assessments. The Nannenga laboratory is supported by the National Institutes of Health (R01GM124152).

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmolb.2018.00114/full#supplementary-material

Supplementary Video. The first generation of continuous rotation device - from ‘low-tech' to high-resolution structures.

References

Baker, L. A., Smith, E. A., Bueler, S. A., and Rubinstein, J. L. (2010). The resolution dependence of optimal exposures in liquid nitrogen temperature electron cryomicroscopy of catalase crystals. J. Struct. Biol. 169, 431–437. doi: 10.1016/j.jsb.2009.11.014

PubMed Abstract | CrossRef Full Text | Google Scholar

Bartesaghi, A., Matthies, D., Banerjee, S., Merk, A., and Subramaniam, S. (2014). Structure of beta-galactosidase at 3.2-A resolution obtained by cryo-electron microscopy. Proc. Natl. Acad. Sci. U.S.A. 111, 11709–11714. doi: 10.1073/pnas.1402809111

PubMed Abstract | CrossRef Full Text | Google Scholar

Chang, S., Head-Gordon, T., Glaeser, R. M., and Downing, K. H. (1999). Chemical bonding effects in the determination of protein structures by electron crystallography. Acta Crystallogr A 55 (Pt 2 Pt 2), 305–313.

PubMed Abstract | Google Scholar

Cheng, Y., Grigorieff, N., Penczek, P. A., and Walz, T. (2015). A primer to single-particle cryo-electron microscopy. Cell 161, 438–449. doi: 10.1016/j.cell.2015.03.050

PubMed Abstract | CrossRef Full Text | Google Scholar

Clabbers, M. T. B., Gruene, T., Parkhurst, J. M., Abrahams, J. P., and Waterman, D. G. (2018). Electron diffraction data processing with DIALS. Acta Crystallogr. D Struct. Biol. 74(Pt 6), 506–518. doi: 10.1107/S2059798318007726

PubMed Abstract | CrossRef Full Text | Google Scholar

Dorset, D. L., and Parsons, D. F. (1975). Electron-diffraction from single, fully-hydrated, ox liver catalase microcrystals. Acta Crystallogr. Sec. A A 31, 210–215. doi: 10.1107/S0567739475000423

CrossRef Full Text | Google Scholar

Duyvesteyn, H. M. E., Kotecha, A., Ginn, H. M., Hecksel, C. W., Beale, E. V., de Haas, F., et al. (2018). Machining protein microcrystals for structure determination by electron diffraction. Proc. Natl. Acad. Sci. U.S.A. 115, 9569–9573. doi: 10.1073/pnas.1809978115

PubMed Abstract | CrossRef Full Text | Google Scholar

Frank, J. (2016). Generalized single-particle cryo-EM–a historical perspective. Microscopy 65, 3–8. doi: 10.1093/jmicro/dfv358

CrossRef Full Text | Google Scholar

Gallagher-Jones, M., Glynn, C., Boyer, D. R., Martynowycz, M. W., Hernandez, E., Miao, J., et al. (2018). Sub-angstrom cryo-EM structure of a prion protofibril reveals a polar clasp. Nat. Struct. Mol. Biol. 25, 131–134. doi: 10.1038/s41594-017-0018-0

PubMed Abstract | CrossRef Full Text | Google Scholar

Gemmi, M., La Placa, M. G. I., Galanis, A. S., Rauch, E. F., and Nicolopoulos, S. (2015). Fast electron diffraction tomography. J. Appl. Crystallogr. 48, 718–727. doi: 10.1107/S1600576715004604

CrossRef Full Text | Google Scholar

Gonen, T., Cheng, Y., Sliz, P., Hiroaki, Y., Fujiyoshi, Y., Harrison, S. C., et al. (2005). Lipid-protein interactions in double-layered two-dimensional AQP0 crystals. Nature 438, 633–638. doi: 10.1038/nature04321

PubMed Abstract | CrossRef Full Text | Google Scholar

Grant, T., and Grigorieff, N. (2015). Measuring the optimal exposure for single particle cryo-EM using a 2.6 A reconstruction of rotavirus VP6. Elife 4:e06980. doi: 10.7554/eLife.06980

PubMed Abstract | CrossRef Full Text | Google Scholar

Grigorieff, N. (2013). Direct detection pays off for electron cryo-microscopy. Elife 2:e00573. doi: 10.7554/eLife.00573

PubMed Abstract | CrossRef Full Text | Google Scholar

Grimm, R., Typke, D., Bärmann, M., and Baumeister, W. (1996). Determination of the inelastic mean free path in ice by examination of tilted vesicles and automated most probable loss imaging. Ultramicroscopy 63, 169–179.

PubMed Abstract | Google Scholar

Guenther, E. L., Ge, P., Trinh, H., Sawaya, M. R., Cascio, D., Boyer, D. R., et al. (2018). Atomic-level evidence for packing and positional amyloid polymorphism by segment from TDP-43 RRM2. Nat. Struct. Mol. Biol. 25, 311–319. doi: 10.1038/s41594-018-0045-5

PubMed Abstract | CrossRef Full Text | Google Scholar

Hattne, J., Reyes, F. E., Nannenga, B. L., Shi, D., de la Cruz, M. J., Leslie, A. G., et al. (2015). MicroED data collection and processing. Acta Crystallogr. A Found Adv. 71(Pt 4), 353–360. doi: 10.1107/S2053273315010669

PubMed Abstract | CrossRef Full Text | Google Scholar

Hattne, J., Shi, D., Glynn, C., Zee, C. T., Gallagher-Jones, M., Martynowycz, M. W., et al. (2018). Analysis of global and site-specific radiation damage in cryo-EM. Structure 26, 759–766 e754. doi: 10.1016/j.str.2018.03.021

PubMed Abstract | CrossRef Full Text | Google Scholar

Hughes, M. P., Sawaya, M. R., Boyer, D. R., Goldschmidt, L., Rodriguez, J. A., Cascio, D., et al. (2018). Atomic structures of low-complexity protein segments reveal kinked beta sheets that assemble networks. Science 359, 698–701. doi: 10.1126/science.aan6398

PubMed Abstract | CrossRef Full Text | Google Scholar

Iadanza, M. G., and Gonen, T. (2014). A suite of software for processing MicroED data of extremely small protein crystals. J. Appl. Crystallogr. 47(Pt 3), 1140–1145. doi: 10.1107/S1600576714008073

PubMed Abstract | CrossRef Full Text | Google Scholar

Iakoubovskii, K., and Mitsuishi, K. (2009). Elastic scattering of 200 keV electrons in elemental solids: experimental observation of atomic-number-dependent oscillatory behavior, J. Phys. Condens. Matter 21:155402. doi: 10.1088/0953-8984/21/15/155402

CrossRef Full Text

Jiang, L. H., Georgieva, D., Zandbergen, H. W., and Abrahams, J. P. (2009). Unit-cell determination from randomly oriented electron-diffraction patterns. Acta Crystallogr. Sec. D Biol. Crystallogr. 65, 625–632. doi: 10.1107/S0907444909003163

PubMed Abstract | CrossRef Full Text | Google Scholar

Krotee, P., Rodriguez, J. A., Sawaya, M. R., Cascio, D., Reyes, F. E., Shi, D., et al. (2017). Atomic structures of fibrillar segments of hIAPP suggest tightly mated beta-sheets are important for cytotoxicity. Elife 6:19273. doi: 10.7554/eLife.19273

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, X. M., Mooney, P., Zheng, S., Booth, C. R., Braunfeld, M. B., Gubbens, S., et al. (2013). Electron counting and beam-induced motion correction enable near-atomic-resolution single-particle cryo-EM. Nat. Methods 10:584. doi: 10.1038/nmeth.2472

CrossRef Full Text | Google Scholar

Liu, S., and Gonen, T. (2018). MicroED structure of the NaK ion channel reveals a Na+ partition process into the selectivity filter. Commun. Biol. 1:38. doi: 10.1038/s42003-018-0040-8

PubMed Abstract | CrossRef Full Text | Google Scholar

Mugnaioli, E., Gorelik, T., and Kolb, U. (2009). “Ab initio” structure solution from electron diffraction data obtained by a combination of automated diffraction tomography and precession technique. Ultramicroscopy 109, 758–765. doi: 10.1016/j.ultramic.2009.01.011

PubMed Abstract | CrossRef Full Text | Google Scholar

Nannenga, B. L., and Gonen, T. (2016). MicroED opens a new era for biological structure determination. Curr. Opin. Struct. Biol. 40, 128–135. doi: 10.1016/j.sbi.2016.09.007

PubMed Abstract | CrossRef Full Text | Google Scholar

Nannenga, B. L., Shi, D., Hattne, J., Reyes, F. E., and Gonen, T. (2014a). Structure of catalase determined by MicroED. Elife 3:e03600. doi: 10.7554/eLife.03600

PubMed Abstract | CrossRef Full Text | Google Scholar

Nannenga, B. L., Shi, D., Leslie, A. G., and Gonen, T. (2014b). High-resolution structure determination by continuous-rotation data collection in MicroED. Nat. Methods 11, 927–930. doi: 10.1038/nmeth.3043

PubMed Abstract | CrossRef Full Text | Google Scholar

Nederlof, I., van Genderen, E., Li, Y. W., and Abrahams, J. P. (2013). A Medipix quantum area detector allows rotation electron diffraction data collection from submicrometre three-dimensional protein crystals. Acta Crystallogr. D Biol. Crystallogr. 69(Pt 7), 1223–1230. doi: 10.1107/S0907444913009700

PubMed Abstract | CrossRef Full Text | Google Scholar

Oleynikov, P., Hovmoller, S., and Zou, X. D. (2007). Precession electron diffraction: observed and calculated intensities. Ultramicroscopy 107, 523–533. doi: 10.1016/j.ultramic.2006.04.032

PubMed Abstract | CrossRef Full Text | Google Scholar

Palatinus, L., Brazda, P., Boullay, P., Perez, O., Klementova, M., Petit, S., et al. (2017). Hydrogen positions in single nanocrystals revealed by electron diffraction. Science 355, 166–169. doi: 10.1126/science.aak9652

PubMed Abstract | CrossRef Full Text | Google Scholar

Rodriguez, J. A., Ivanova, M. I., Sawaya, M. R., Cascio, D., Reyes, F. E., Shi, D., et al. (2015). Structure of the toxic core of alpha-synuclein from invisible crystals. Nature 525, 486–490. doi: 10.1038/nature15368

PubMed Abstract | CrossRef Full Text | Google Scholar

Ruskin, R. S., Yu, Z., and Grigorieff, N. (2013). Quantitative characterization of electron detectors for transmission electron microscopy. J. Struct. Biol. 184, 385–393. doi: 10.1016/j.jsb.2013.10.016

PubMed Abstract | CrossRef Full Text | Google Scholar

Sawaya, M. R., Rodriguez, J., Cascio, D., Collazo, M. J., Shi, D., Reyes, F. E., et al. (2016). Ab initio structure determination from prion nanocrystals at atomic resolution by MicroED. Proc. Natl. Acad. Sci. U.S.A. 113, 11232–11236. doi: 10.1073/pnas.1606287113

PubMed Abstract | CrossRef Full Text | Google Scholar

Seidler, P. M., Boyer, D. R., Rodriguez, J. A., Sawaya, M. R., Cascio, D., Murray, K., et al. (2018). Structure-based inhibitors of tau aggregation. Nat. Chem. 10, 170–176. doi: 10.1038/nchem.2889

PubMed Abstract | CrossRef Full Text | Google Scholar

Shi, D., Lewis, M. R., Young, H. S., and Stokes, D. L. (1998). Three-dimensional crystals of Ca2+-ATPase from sarcoplasmic reticulum: merging electron diffraction tilt series and imaging the (h, k, 0) projection. J. Mol. Biol. 284, 1547–1564. doi: 10.1006/jmbi.1998.2283

PubMed Abstract | CrossRef Full Text | Google Scholar

Shi, D., Nannenga, B. L., de la Cruz, M. J., Liu, J., Sawtelle, S., Calero, G., et al. (2016). The collection of MicroED data for macromolecular crystallography. Nat. Protoc. 11, 895–904. doi: 10.1038/nprot.2016.046

PubMed Abstract | CrossRef Full Text | Google Scholar

Shi, D., Nannenga, B. L., Iadanza, M. G., and Gonen, T. (2013). Three-dimensional electron crystallography of protein microcrystals. Elife 2:e01345. doi: 10.7554/eLife.01345

PubMed Abstract | CrossRef Full Text | Google Scholar

Simancas, J., Simancas, R., Bereciartua, P. J., Jorda, J. L., Rey, F., Corma, A., et al. (2016). Ultrafast electron diffraction tomography for structure determination of the new zeolite ITQ-58. J. Am. Chem. Soc. 138, 10116–10119. doi: 10.1021/jacs.6b06394

PubMed Abstract | CrossRef Full Text | Google Scholar

Spence, J. (1993). On the accurate measurement of structure-factor amplitudes and phases by electron diffraction. Acta Crystallogr. Sec. A 49, 231–260. doi: 10.1107/S0108767392005087

CrossRef Full Text | Google Scholar

Stark, H., Zemlin, F., and Boettcher, C. (1996). Electron radiation damage to protein crystals of bacteriorhodopsin at different temperatures. Ultramicroscopy 63, 75–79. doi: 10.1016/0304-3991(96)00045-9

CrossRef Full Text | Google Scholar

Unwin, P. N., and Henderson, R. (1975). Molecular structure determination by electron microscopy of unstained crystalline specimens. J. Mol. Biol. 94, 425–440.

PubMed Abstract | Google Scholar

Vergara, S., Lukes, D. A., Martynowycz, M. W., Santiago, U., Plascencia-Villa, G., Weiss, S. C., et al. (2017). MicroED structure of Au146(p-MBA)57 at subatomic resolution reveals a twinned FCC cluster. J. Phys. Chem. Lett. 8, 5523–5530. doi: 10.1021/acs.jpclett.7b02621

PubMed Abstract | CrossRef Full Text | Google Scholar

Vincent, R., and Midgley, P. A. (1994). Double conical beam-rocking system for measurement of integrated electron diffraction intensities. Ultramicroscopy 53, 271–282. doi: 10.1016/0304-3991(94)90039-6

CrossRef Full Text | Google Scholar

Wan, W., Sun, J. L., Su, J., Hovmoller, S., and Zou, X. D. (2013). Three-dimensional rotation electron diffraction: software RED for automated data collection and data processing. J. Appl. Crystallogr. 46, 1863–1873. doi: 10.1107/S0021889813027714

PubMed Abstract | CrossRef Full Text | Google Scholar

Wu, J. S., and Spence, J. C. (2003). Structure and bonding in alpha-copper phthalocyanine by electron diffraction. Acta Crystallogr. A 59(Pt 5), 495–505. doi: 10.1107/S0108767303016866

PubMed Abstract | CrossRef Full Text | Google Scholar

Xu, H., Lebrette, H., Yang, T., Srinivas, V., Hovmoller, S., Hogbom, M., and Zou, X. D. (2018). A rare lysozyme crystal form solved using highly redundant multiple electron diffraction datasets from micron-sized crystals. Structure 26, 667–675. doi: 10.1016/j.str.2018.02.015

PubMed Abstract | CrossRef Full Text | Google Scholar

Yonekura, K., Kato, K., Ogasawara, M., Tomita, M., and Toyoshima, C. (2015). Electron crystallography of ultrathin 3D protein crystals: atomic model with charges. Proc. Natl. Acad. Sci. U.S.A. 112, 3368–3373. doi: 10.1073/pnas.1500724112

PubMed Abstract | CrossRef Full Text | Google Scholar

Yonekura, K., and Maki-Yonekura, S. (2016). Refinement of cryo-EM structures using scattering factors of charged atoms. J. Appl. Crystallogr. 49:5. doi: 10.1107/s1600576716011274

CrossRef Full Text | Google Scholar

Yuan, S., Qin, J.-S., Xu, H.-Q., Su, J., Rossi, D., Chen, Y., et al. (2018). [Ti8Zr2O12(COO)16] Cluster: an ideal inorganic building unit for photoactive metal–organic frameworks. ACS Central Sci. 4, 105–111. doi: 10.1021/acscentsci.7b00497

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhang, C., Kapaca, E., Li, J., Liu, Y., Yi, X., Zheng, A., et al. (2018). An extra-large-pore zeolite with 24 × 8 × 8-ring channels using a structure-directing agent derived from traditional Chinese medicine. Angew. Chem. Int. Edn. 57, 6486–6490. doi: 10.1002/anie.201801386

CrossRef Full Text | Google Scholar

Keywords: Cryo-EM, crystallography, MicroED, dynamic scattering, radiation damage

Citation: Nannenga BL, Bu G and Shi D (2018) The Evolution and the Advantages of MicroED. Front. Mol. Biosci. 5:114. doi: 10.3389/fmolb.2018.00114

Received: 13 July 2018; Accepted: 26 November 2018;
Published: 12 December 2018.

Edited by:

Marta Carroni, Science for Life Laboratory (SciLifeLab), Sweden

Reviewed by:

Hans Hebert, Royal Institute of Technology, Sweden
Hongyi Xu, Stockholm University, Sweden

Copyright © 2018 Nannenga, Bu and Shi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Dan Shi, dan.shi@nih.gov