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Review
. 2019 Oct:58:249-258.
doi: 10.1016/j.sbi.2019.05.021. Epub 2019 Jul 5.

Advances in cryo-electron tomography and subtomogram averaging and classification

Peijun Zhang  1
Affiliations
Review

Advances in cryo-electron tomography and subtomogram averaging and classification

Peijun Zhang. Curr Opin Struct Biol. 2019 Oct.

Abstract

Cryo-electron tomography (cryoET) can provide 3D reconstructions, or tomograms, of pleomorphic objects such as organelles or cells in their close-to-native states. Subtomograms that contain repetitive structures can be further extracted and subjected to averaging and classification to improve resolution, and this process has become an emerging structural biology method referred to as cryoET subtomogram averaging and classification (cryoSTAC). Recent technical advances in cryoSTAC have had a profound impact on many fields in biology. Here, I review recent exciting work on several macromolecular assemblies demonstrating the power of cryoSTAC for in situ structure analysis and discuss challenges and future directions.

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Figures

Figure 1
Figure 1
The overall workflow for cryoET subtomogram averaging and classification (cryoSTAC). The processes dealing with tilt series are in blue, subtomograms in orange, and 3D classification in green. The final structures at the bottom of the flowchart are yeast ribosomes.
Figure 2
Figure 2
CryoSTAC of a reconstituted bacterial chemotaxis signaling array. (a) and (b) Tomographic slices of the in vitro reconstituted array of the chemotaxis core signaling complex (a) and of a native array in an Escherichia coli cell (b). Scale bar, 100 nm. (c) The density map of a threefold assembly unit by subtomogram averaging, with a core signaling complex boxed in red and a rotated view shown on the right corresponding to the black box. The receptor, P3 and P4 domains of Che A are labeled as ‘R’, ‘P3’ and ‘P4’, respectively. (d) Pseudo-atomic model of the core signaling complex, consisting of six chemoreceptor dimers (red, labeled ‘TOD’), one CheA dimer (blue, labeled ‘P3–P5’), and four CheW monomers (green, labeled ‘W1’ and ‘W2’). (e) CheA conformational dynamics with a dipping motion, determined by large scale MD simulation. Arrows point to the interacting amino acids in the dipped state [44••].
Figure 3
Figure 3
CryoSTAC of HIV-1 immature Gag particles. (a) Tomographic slices of immature HIV-1 particles (with D25A mutation), ΔMACANCSP2 VLPs, in the absence and presence of the maturation inhibitor bevirimat (BVM). Scale bar, 50 nm. (b) CA-SP1 densities from the samples shown in (a) by subtomogram averaging. One CA-SP1 monomer is highlighted, with the CA-NTD in cyan and the CA-CTD and SP1 in orange. (c) The refined atomic model. (d) An improved CA-SP1 density map at 3.1 Å resolution by emClarity (right), compared to the previous structure (left, EMD-3782). (e) Enlarged views of boxed area in (d) overlaid with a real-space refined model [18••,26••].
Figure 4
Figure 4
CryoSTAC of in situ Poly-GA aggregates with proteasomes recruitment in neurons. (a) and (b) Correlative cryo-light and cryoFIB/SEM of rat cortical neurons cultured on EM grids and transduced with (GA)175-GFP. SEM (a) and FIB (b) images were aligned and superimposed with the GFP signal from the cryo-LM image. (c) Cryo-TEM low magnification image of the lamella superimposed with the GFP signal. (d) A tomographic slice recorded in the area with GFP signal (white square in (c)). Red arrowheads mark a dense network of poly-GA-GFP. (e) 3D rendering of an aggregate within a neuron transduced with (GA)175-GFP showing different macromolecules found either within or at the periphery of the aggregate. (f) Subtomogram classification of 26S proteasomes reveals enrichment of substrate processing conformations. GS, ground state; SPS, substrate-processing state [7••].
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