Abstract
Recent evidence suggests that the beam-induced motion of the sample during tilt-series acquisition is a major resolution-limiting factor in electron cryo-tomography (cryoET). It causes suboptimal tilt-series alignment and thus deterioration of the reconstruction quality. Here we present a novel approach to tilt-series alignment and tomographic reconstruction that considers the beam-induced sample motion through the tilt-series. It extends the standard fiducial-based alignment approach in cryoET by introducing quadratic polynomials to model the sample motion. The model can be used during reconstruction to yield a motion-compensated tomogram. We evaluated our method on various datasets with different sample sizes. The results demonstrate that our method could be a useful tool to improve the quality of tomograms and the resolution in cryoET.
Highlights
Recent advancements have established single particle electron cryomicroscopy as a high-resolution structure determination technique (Kuhlbrandt, 2014; Bai et al, 2015; Vinothkumar and Henderson, 2016; Crowther, 2016)
We demonstrate that the new method is able to improve the accuracy of the tilt-series alignment, the quality of tomograms and the resolution of the subtomogram averages
Each tilt image was dose-fractionated into three image frames that were aligned with the Digital Micrograph software
Summary
Recent advancements have established single particle electron cryomicroscopy (cryoEM) as a high-resolution structure determination technique (Kuhlbrandt, 2014; Bai et al, 2015; Vinothkumar and Henderson, 2016; Crowther, 2016). Fractionated over the tilt-series (typically 40–120 images) and the sample thickness increases at high tilts Under those SNR conditions, whole-frame motion correction methods are commonly used for cryoET, though potentially the recently developed patch-based approach could be used if enough signal is preserved (Zheng et al, 2017). The current workflow in standard cryoET compensates for the sample deformation within each tilt in the tilt-series by correcting for the projection of motion observed at the image plane, but still ignores the deformation that the sample may undergo through the different tilts This ignored deformation will translate into suboptimal tilt-series alignment and deterioration of the quality of the tomogram and its high-resolution information (Voortman et al, 2014; Bharat et al, 2015). We demonstrate that the new method is able to improve the accuracy of the tilt-series alignment, the quality of tomograms and the resolution of the subtomogram averages
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