Focus integrates software resources to create a user-friendly environment that allows the tasks and processes of electron microscopy data processing to be carried out in a high-throughput manner. It features tools such as a parallel batch-queue processor that can be used for high-
4.6. DISCUSSION
throughput processing, a configurable website for remote monitoring, and a project library that can summarize image statistics and provide several overviews of the image. Focus implements ready-to-use scripts with easy parameter handling, and can be customized with additional scripts as required. When Focus is supported by the appropriate hardware, the image processing speed is faster than the recording speed.
Focus can be installed on a Linux or macOS system, and can run and complete image processing tasks in parallel to image acquisition. The overview of the images provided by the project library is continuously updated, and the performance of the recording session can be monitored using time series of mean intensity values, drift, defocus and resolution on a web server (Appendix A, Figure A.1). During or after the data collection session, images can be organized or moved to a TRASH folder (data pruning), and remaining images can be exported to another hard drive or network location together with the processing results in the form of customizable metadata files, such as for example STAR files prepared for subsequent processing, e.g., with RELION [Scheres, 2012] for single particle datasets.
Often, processing parameters need to be determined and optimized using a small set of high-quality images before they are applied to the rest of the dataset. Focus supports this type of operation, as images can easily be divided into groups, for which specific tasks can be run again by submission to the batch queue. For instance, parameters like particle diameter, box size, cross-correlation cutoff, have to be optimized when particles are ‘picked’ for single particle projects. In this case, one could sort the acquired images by parameters such as defocus, to select a set of images, for which particle picking is then launched again with specific optimized parameters, which then could also be used as default values for subsequently imported images. Focus for macOS (10.10 or higher) and Linux (Ubuntu/Fedora/Redhat) can be downloaded from the website: http://www.focus-em.org. The source code is available under the Gnu Public License (GPL) at Github: http://www.github.com/C-CINA/focus.
PART III
HIGH RESOLUTION
2D ELECTRON
C
H A P T E R5
I
MAGE PROCESSING TECHNIQUES FOR HIGH RESOLUTION2D
ELECTRON CRYSTALLOGRAPHYThe following chapter is a part of the submitted manuscript:
Image processing techniques for high-resolution structure determination from badly ordered 2D crystals
Nikhil Biyani∗, Sebastian Scherer∗, Ricardo Righetto, Julia Kowal, Mohamed Chami and Henning Stahlberg.
* equal contribution in
Journal of Structural Biology.
Contribution:
Development of some algorithms and adaptation to the user-friendly environment; image processing of the dataset, final map generation
Abstract
2
D electron crystallography can be used to study small membrane proteins in their na- tive environment. Obtaining highly-ordered 2D crystals is difficult and time-consuming. However, 2D crystals diffracting to only 10-12 Å can be prepared relatively conveniently in most cases. We have therefore developed image processing algorithms allowing to generate a high resolution 3D structure from cryo-EM images of badly ordered crystals. These include movie-mode unbending, that corrects for crystal imperfections and locally-varying beam-induced sample deformations, while preventing over-fitting and computationally optimizing the electron dose used for each resolution range individually; refinement over sub-tiles of the frames in order to locally refine the crystal tilt geometry and electron beam tilt within different tile locations on the images; analysis of various CTF correction schemes to improve amplitudes and phases. We applied the new procedure to a three-dimensional (3D) dataset consisting of ∼400 images of the voltage-gated ion-channel MloK1 crystallized in 2D. The 3D density map and its quality measurements improved significantly. The refined map shows more distinct densities than the 3D reconstruction obtained by classical crystal unbending and is easier to interpret.Keywords:
Direct electron detector; Gatan K2 Summit; Movie-mode processing; Dose-fractionation; Membrane proteins; Electron crystallography; Tiled image processing; CTF correction
5.1. INTRODUCTION
5.1
Introduction
Direct electron detectors (DEDs) equipped with a radiation-hardened complementary metal-oxide- semiconductor (CMOS) sensor [Bammes et al., 2012; Glaeser et al., 2011; Milazzo et al., 2011] tremendously increase the signal-to-noise ratio (SNR) of cryo-electron microscopy images. Instead of converting incoming electrons into light via a scintillator, these new devices detect them directly, which significantly reduces detector background noise. Beside the superior detector quantum efficiency (DQE) of DEDs [Ruskin et al., 2013], this new generation of detectors feature an enhanced sensor readout frequency. This makes it possible to perform counting of single electrons, resulting in recording of images with minimal dark background signal. This along with the fast readout speed allows to record image frames within exposure times that are short enough to prevent physical drift of the specimen stage. Consequently, movies of the sample can be recorded while it is continuously exposed to the electron beam. Algorithms like Zorro [McLeod et al., 2016], MotionCor2 [Zheng et al., 2016] efficiently measure and correct for translational offsets between dose-fractionated movie-frames recorded on DEDs. The impact of sample movements such as those caused by specimen stage-drift, can thereby be strongly reduced, which increases the efficiency of data acquisition significantly. These new hardware and software developments have led to several recent breakthroughs in high-resolution cryo-EM 3D structure determinations that reach and surpass atomic resolution [Liu et al., 2016; Merk et al., 2016; Yu et al., 2016]. Thus, cryo-EM has become a powerful and fast method for atomic-resolution protein structure determination.
2D electron crystallography is a branch of cryo-EM that is used to determine the electron density maps by imaging 2D crystals of desired biological structures [Stahlberg et al., 2015]. 2D electron crystallography is advantageous as: (i) the hydrophobic core is contained in the lipid bilayer providing a more native environment to the membrane proteins, (ii) it can be used to study proteins that are small in size, and (iii) the image processing is quite automated [Scherer et al., 2014b]. Creation of homogeneous sample with high crystallinity becomes quite important to generate high resolution structures in 2D electron crystallography. Obtaining highly ordered crystals is tedious and time consuming which involves testing stability in various detergents along with varying conditions. However, 2D crystals diffracting to only 10-12 Å can be prepared relatively conveniently in most cases.
Besides sample quality, beam-induced specimen movement further reduces the crystalline order, especially when the images of highly tilted 2D crystal samples are to be recorded [Glaeser et al., 2011]. The behavior of the sample under the electron beam was investigated in multiple studies [Bai et al., 2013; Brilot et al., 2012; Campbell et al., 2012; Glaeser et al., 2011; Veesler et al., 2013]. By analyzing virus or ribosome particles during the exposure, these studies reported locally correlated movements that differ among the particles of an image. It is commonly believed that these local movements are due to irreversible deformations of the ice layer and also the sample, caused by the electron beam [Brilot et al., 2012]. The amount of beam-induced motion
depends on the microscope settings and the properties of the sample and grid. For 2D crystals, Glaeser et al. proposed firmly attaching the crystals to an appropriate strong and conductive support in order to overcome beam-induced sample movements [Glaeser et al., 2011]. However, such treatment may not be optimal for membrane proteins that show structural alterations when adsorbed to carbon film. A more general approach is to allow the 2D crystal membrane to move under the beam, and then computationally retrieve the structural data from recorded movies. Here, algorithms accounting for universal motion of all movie-frames, e.g., caused by stage drift, cannot correct for spatially varying beam-induced motion as they neglect locally varying movements within the frames. An image processing approach that locally treats sub-regions of a 2D crystal sample in each movie frame is needed.
Another resolution-limiting factor is electron beam-induced radiation damage. A high cu- mulative electron dose destroys fine structural details, whereas a low cumulative electron dose preserves the high-resolution information but reduces the SNR of the recorded image. Before de- tectors capable of movie-mode imaging were available, the full electron dose was captured within one single image and the EM operator had to carefully choose the electron dose depending on the target resolution. In 2010, before the advent of DEDs, Baker et al. recorded dose-fractionated image series of crystals and analyzed the resolution dependent vanishing of computed diffraction spots. Their findings suggest to record dose-fractionated image series of the sample and add these frames together using resolution-dependent frequency filters, in order to optimally exploit each image frequency at its maximal SNR [Baker et al., 2010].
The fact that a 2D crystal contains many regularly arranged identical copies of the same protein, allows electron crystallography to improve the SNR by using Fourier-filtering methods. Processing individual micrographs of 2D crystals generally involves the following six steps [Arheit et al., 2013]: (i) Defocus and (ii) tilt-geometry estimation, (iii) lattice determination, (iv) correcting for crystal imperfections, termed unbending, (v) Fourier extraction of amplitude and phase values from computed reflections, (vi) Contrast transfer function (CTF) correction and (vii) generating a projection map of one unit cell at higher SNR. So far, this procedure was applied to the drift-corrected average gained from a movie-mode exposure of a 2D crystal and thus did not account for locally varying beam-induced sample movements. Additionally, the CTF determination and correction is done after the unbending procedure. With the images obtained from new detectors, Thon rings up to 3 Å can be correctly identified. GCTF [Zhang, 2016] can quickly and precisely calculate the CTF and defocus on GPUs. This creates a possibility to correct the CTF as a preprocessing step before the unbending procedure.
In this study we developed image processing algorithms and techniques allowing to generate a high resolution 3D structure from cryo-EM images of badly ordered crystals. These include: (i) movie-mode unbending that corrects for locally varying beam-induced sample deformations and computationally optimize the electron dose for each frequency, along with reducing the risk of overfitting the data at low SNR, (ii) tiled image processing, by dividing it into sub-images called