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Real-time cryo-electron microscopy data preprocessing with Warp

Nature Methods volume 16, pages 1146–1152 (2019)Cite this article

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Abstract

The acquisition of cryo-electron microscopy (cryo-EM) data from biological specimens must be tightly coupled to data preprocessing to ensure the best data quality and microscope usage. Here we describe Warp, a software that automates all preprocessing steps of cryo-EM data acquisition and enables real-time evaluation. Warp corrects micrographs for global and local motion, estimates the local defocus and monitors key parameters for each recorded micrograph or tomographic tilt series in real time. The software further includes deep-learning-based models for accurate particle picking and image denoising. The output from Warp can be fed into established programs for particle classification and 3D-map refinement. Our benchmarks show improvement in the nominal resolution, which went from 3.9 Å to 3.2 Å, of a published cryo-EM data set for influenza virus hemagglutinin. Warp is easy to install from http://github.com/cramerlab/warp and computationally inexpensive, and has an intuitive, streamlined user interface.

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Fig. 1: Warp handles all preprocessing steps in the 2D cryo-EM pipeline.
Fig. 2: Automated particle picking with Warp’s deep-learning-based BoxNet.
Fig. 3: Warp’s 2D pipeline improves cryo-EM density for the influenza hemagglutinin trimer.
Fig. 4: Warp’s 2D pipeline in combination with RELION 3.0 improves cryo-EM density for β-galactosidase (using the published EMPIAR-10061 dataset).
Fig. 5: Effect of using the full local 3D CTF for template matching in tomograms.
Fig. 6: Sub-tomogram averaging results obtained by using Warp’s tilt series CTF estimation and sub-tomogram export.

Data availability

Figure 1 and Supplementary Fig. 1 use exemplary data from EMPIAR-10078. Figure 2 uses a cryo-EM image of RNA Pol II complexes, available from the authors upon request. Figure 3 and the benchmark section use data from EMPIAR-10097 re-analyzed in this study. The refined maps shown in Fig. 3a are available in Supplementary Data 1–4. The ‘Full Warp pipeline’ map shown in Fig. 3a has been deposited in EMDB as EMD-0025. Figure 4 and the benchmark section use data from EMPIAR-10061 re-analyzed in this study, the 1.86 Å map shown in Fig. 4a is available as Supplementary Data 5. Figure 5a uses a tomogram reconstructed from data from EMPIAR-10045. Figure 6 and the benchmark section use data from EMPIAR-10045 and EMPIAR-10164 re-analyzed in this study, the maps shown in Fig. 6a,b are available in Supplementary Data 6 and 7, respectively. Supplementary Fig. 2 uses exemplary data from EMPIAR-10061. Supplementary Fig. 3 uses exemplary data from EMPIAR-10097. Supplementary Fig. 5 uses in-house data, available upon request. Supplementary Fig. 6 uses exemplary data from EMPIAR-10078. Supplementary Fig. 7 uses exemplary data from (left) EMPIAR-10078, (center) in-house data available upon request, and (right) EMPIAR-10153. Training data for BoxNet can be accessed through https://github.com/cramerlab/boxnet.

Code availability

Warp binaries, source code and user guide are available as Supplementary Software and can be downloaded from https://github.com/cramerlab/warp. BoxNet source code can be downloaded from https://github.com/cramerlab/boxnet.

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Acknowledgements

We thank members of the Cramer lab for beta-testing early versions of Warp and providing feedback on bugs in the software. We thank C. Bernecky, S. Dodonova, W. Hagen, D. Lyumkis, C. Plaschka, J. Söding and Y. Z. Tan for critical reading of the manuscript. PC was supported by ERC Advanced Grant TRANSREGULON (grant agreement no. 693023) of the European Research Council, the Deutsche Forschungsgemeinschaft (SFB 860) and the Volkswagen Foundation.

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Authors and Affiliations

  1. Max Planck Institute for Biophysical Chemistry, Department of Molecular Biology, Göttingen, Germany

    Dimitry Tegunov & Patrick Cramer

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Contributions

D.T. designed Warp’s architecture and all algorithms, and carried out all implementation and application. P.C. provided scientific environment, funding and additional interpretations and implications. D.T. and P.C. wrote the manuscript.

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Correspondence to Dimitry Tegunov or Patrick Cramer.

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Peer review information Allison Doerr was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Integrated supplementary information

Supplementary Figure 1 User interface of Warp.

a, The processing settings (left) specify all steps and parameters for online data evaluation, correction and processing. The ‘Overview’ tab (right) presents all important processing results and lets the user specify selection filters to remove low-quality data. b, View of a single micrograph. In Fourier space (left), the simulated 2D CTF (i), the 1D power spectrum (PS) and its fit (ii), and the 2D PS (iii) are presented. The real space view (right) shows the aligned movie average with particle positions (green dots), motion tracks (white curves) and the defocus variation (transparent magenta-cyan overlay), and applies a deconvolution filter as well as denoising. Individual display elements can be shown or hidden. The navigation bar (bottom) shows the processing status for all items and allows to quickly switch between them as well as to manually exclude single items from processing.

Supplementary Figure 2 Deconvolution and denoising of a low-defocus micrograph.

a, A raw micrograph from EMPIAR-10061 acquired at 0.8 μm defocus. b, Same micrograph after applying deconvolution. Low-resolution contrast is boosted and the defocused signal is more localized, allowing to distinguish the particles better. c, Same micrograph after applying deconvolution and denoising with a noise2noise model retrained on this dataset. The shapes of individual 400-kDa proteins nearly invisible in the raw image can be distinguished clearly against the background. d, Shape and effect of the deconvolution filter. The filter largely reverses the effect of the first CTF peak, while also suppressing the lowest and higher frequencies.

Supplementary Figure 3 Motion and CTF model fitting by Warp.

The unaligned, defocused movie (i) is parametrized with a coarse grid (black dots), divided into patches for the alignment (ii), and power spectra of these patches are computed (iii) for CTF fitting. The motion model (iv) includes 2 components: global motion (cyan trajectory) with fine temporal and no spatial resolution, and local motion (magenta trajectories) with coarse temporal, and fine spatial resolution. Both components are optimized to minimize the squared difference between the individual patch frames and their aligned average. The spatially resolved CTF model (v) is optimized to minimize the squared difference between the power spectra (iii, upper left part of each patch) and the simulated local 2D CTF (iii, bottom right part of each patch). Here, the defocus gradient follows the 40° tilt of the specimen, with the notable exception of the hole edge in the bottom left corner.

Supplementary Figure 4 CTF fitting of flat, tilted and tilt series data.

Fitted spectra without (left column) and with (right column) a spatially resolved model. The samples are (a) flat (EMPIAR-10078), (b) tilted at 40° (EMPIAR-10097) and (c) a tilt series ranging from –60° to +60° (EMPIAR-10045). In all three cases, using a spatially resolved model allowed to fit the sample geometry more accurately, as evidenced by the clearer Thon rings in the rescaled, averaged 1D spectra. The fitting range (grey rectangle in the 1D spectra) was chosen well below the estimated resolution to avoid overfitting the higher number of parameters in the spatially resolved model.

Supplementary Figure 5 Unbiased particle picking with Warp’s BoxNet.

Examples of automated particle picking on samples not seen by BoxNet in training. For comparison, the same micrographs were picked with crYOLO’s generic model, and RELION’s Laplacian of Gaussian (LoG) method. Micrographs were selected from in-house data to make sure they were absent in crYOLO’s knowledge base. BoxNet reliably recognizes almost all particles (yellow), and masks out all artifacts (purple). LoG is often confused by high-contrast edges and ethane impurities. crYOLO performs better than LoG, but is also routinely confused by ethane impurities and protein aggregates, and misses many of the small particles (bottom row).

Supplementary Figure 6 Neural network architecture of BoxNet.

Rectangles depict the intermediate tensor dimensions. Their width and height are proportional to the number of channels and the spatial extent, respectively. Thick arrows represent convolution operations. Their format is encoded as ‘(Kx R), LxMxN /O’, where K is the number of consecutive ResNet blocks, or absent in case of a single convolution operation; L and M are the dimensions of the convolution kernel; N is the number of kernels, resulting in N channels in the output; O is the stride length (1 = no change, 2 = downsampling by factor of 2, 0.5 = upsampling by factor of 2 through transposed convolution). The stride parameter is only applied to the first convolution in a chain of ResNet blocks, whereas all subsequent convolutions use stride = 1. The contractive part of the network is colored in cyan, the expanding part in magenta. The final image shows the result of applying a per-pixel ArgMax operator to the result of the last convolution to obtain the spatial distribution of the three labels the model is trained to predict: background (black), particle (yellow), artifact (purple).

Supplementary Figure 7 Examples of data used to train BoxNet.

Examples of micrographs presented to BoxNet as input (top row), and the per-pixel labels used as the desired output during training (bottom row). The pixel classes predicted by BoxNet are background (black), particles (yellow), and artifacts (purple).

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Tegunov, D., Cramer, P. Real-time cryo-electron microscopy data preprocessing with Warp. Nat Methods 16, 1146–1152 (2019). https://doi.org/10.1038/s41592-019-0580-y

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