Beam-induced motion correction for sub-megadalton cryo-EM particles - PubMed (original) (raw)

Beam-induced motion correction for sub-megadalton cryo-EM particles

Sjors Hw Scheres. Elife. 2014.

Abstract

In electron cryo-microscopy (cryo-EM), the electron beam that is used for imaging also causes the sample to move. This motion blurs the images and limits the resolution attainable by single-particle analysis. In a previous Research article (Bai et al., 2013) we showed that correcting for this motion by processing movies from fast direct-electron detectors allowed structure determination to near-atomic resolution from 35,000 ribosome particles. In this Research advance article, we show that an improved movie processing algorithm is applicable to a much wider range of specimens. The new algorithm estimates straight movement tracks by considering multiple particles that are close to each other in the field of view, and models the fall-off of high-resolution information content by radiation damage in a dose-dependent manner. Application of the new algorithm to four data sets illustrates its potential for significantly improving cryo-EM structures, even for particles that are smaller than 200 kDa.

Keywords: cryo-EM; image analysis; single-particle analysis.

Copyright © 2014, Scheres.

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Conflict of interest statement

SHWS: Reviewing editor, eLife.

Figures

Figure 1.

Figure 1.. Beam-induced movement tracks.

A representative micrograph for each of the four test cases is shown, on top of which 50-fold exaggerated beam-induced particle movements are plotted. The original tracks as estimated for running averages of several movie frames for each particle independently are shown in blue; the fitted linear tracks are shown in white. The start and end points of the fitted tracks are indicated with green and red dots, respectively. The orange circles indicate the 2σNB distance for one of the particles on the micrographs. Note that tracks are only shown for those particles that were selected for the final reconstruction after 2D and 3D classification. Also note that the relatively small movement tracks for γ-secretase only represent the beam-induced motion that was not already corrected for in the algorithm by Li et al. (2013). DOI:

http://dx.doi.org/10.7554/eLife.03665.002

Figure 2.

Figure 2.. Radiation-damage weighting.

For each of the four test cases, estimated values for B f and C f (top) and the resulting frequency-dependent relative weights (bottom) are shown for all movie frames. The first, third, and last movie frames of each data set are highlighted in green, red, and blue, respectively. For these movie frames, the relative Guinier plots as described in the main text and the linear fits through them are shown in Figure 2—figure supplement 1. For example, in the γ-secretase case, the third movie frame has the least negative relative B-factor (B f), and therefore this frame contributes the most of all movie frames to the weighted average at the high frequencies (and hence the red band gets broader towards the right-hand side of the relative-weight figure). In contrast, the first and last movie frames have much larger negative B-factors because they suffer from large initial beam-induced motion and radiation damage, respectively. Therefore, these movie frames contribute relatively little to the weighted average at the higher frequencies (and hence the green and blue bands decrease in width towards the right-hand side of the relative-weight figure). Because beam-induced motion and radiation damage affect the low frequencies to a much smaller extent, for the low frequencies all movie frames contribute more or less equally to the weighted average. Therefore, each band is more or less the same width on the left-hand side of the relative-weight figure, although the exact relative weights are dominated by C f on this side of the plot. DOI:

http://dx.doi.org/10.7554/eLife.03665.004

Figure 2—figure supplement 1.

Figure 2—figure supplement 1.. Relative Guinier plots (solid lines) and the linear fits through those (dashed lines) for the first, third, and last movie frames of each data set in green, red, and blue, respectively.

DOI:

http://dx.doi.org/10.7554/eLife.03665.005

Figure 3.

Figure 3.. Map improvement.

Representative parts of the density maps for all four test cases before (left of the arrow) and after the new movie processing approach (right of the arrow). DOI:

http://dx.doi.org/10.7554/eLife.03665.006

Figure 3—figure supplement 1.

Figure 3—figure supplement 1.. The same part of the mitoribosome large sub-unit map as shown in Figure 3, but after application of the original movie processing approach, as described in Bai et al. (2013).

DOI:

http://dx.doi.org/10.7554/eLife.03665.007

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