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A pixel-based approach for extracting structure factors from X-ray free-electron laser crystal diffraction measurements is presented., Most crystallographic data processing methods use pixel integration. In serial femtosecond crystallography (SFX), the intricate interaction between the reciprocal lattice point and the Ewald sphere is integrated out by averaging symmetrically equivalent observations recorded across a large number (104−106) of exposures. Although sufficient for generating biological insights, this approach converges slowly, and using it to accurately measure anomalous differences has proved difficult. This report presents a novel approach for increasing the accuracy of structure factors obtained from SFX data. A physical model describing all observed pixels is defined to a degree of complexity such that it can decouple the various contributions to the pixel intensities. Model dependencies include lattice orientation, unit-cell dimensions, mosaic structure, incident photon spectra and structure factor amplitudes. Maximum likelihood estimation is used to optimize all model parameters. The application of prior knowledge that structure factor amplitudes are positive quantities is included in the form of a reparameterization. The method is tested using a synthesized SFX dataset of ytterbium(III) lysozyme, where each X-ray laser pulse energy is centered at 9034 eV. This energy is 100 eV above the Yb3+ L-III absorption edge, so the anomalous difference signal is stable at 10 electrons despite the inherent energy jitter of each femtosecond X-ray laser pulse. This work demonstrates that this approach allows the determination of anomalous structure factors with very high accuracy while requiring an order-of-magnitude fewer shots than conventional integration-based methods would require to achieve similar results.

It is demonstrated that a convolutional neural network denoising algorithm can be used to significantly enhance the signal-to-noise ratio and generate contrast in cryo-EM images. It also provides a quantitative evaluation of the bias introduced by the denoising procedure and its influence on image processing and three-dimensional reconstructions., In cryogenic electron microscopy (cryo-EM) of radiation-sensitive biological samples, both the signal-to-noise ratio (SNR) and the contrast of images are critically important in the image-processing pipeline. Classic methods improve low-frequency image contrast experimentally, by imaging with high defocus, or computationally, by applying various types of low-pass filter. These contrast improvements typically come at the expense of the high-frequency SNR, which is suppressed by high-defocus imaging and removed by low-pass filtration. Recently, convolutional neural networks (CNNs) trained to denoise cryo-EM images have produced impressive gains in image contrast, but it is not clear how these algorithms affect the information content of the image. Here, a denoising CNN for cryo-EM images was implemented and a quantitative evaluation of SNR enhancement, induced bias and the effects of denoising on image processing and three-dimensional reconstructions was performed. The study suggests that besides improving the visual contrast of cryo-EM images, the enhanced SNR of denoised images may be used in other parts of the image-processing pipeline, such as classification and 3D alignment. These results lay the groundwork for the use of denoising CNNs in the cryo-EM image-processing pipeline beyond particle picking.

MicroED data can be collected with the fastest and most sensitive cameras that are typically reserved for single-particle electron microscopy., Microcrystal electron diffraction (MicroED) combines crystallography and electron cryo-microscopy (cryo-EM) into a method that is applicable to high-resolution structure determination. In MicroED, nanosized crystals, which are often intractable using other techniques, are probed by high-energy electrons in a transmission electron microscope. Diffraction data are recorded by a camera in movie mode: the nanocrystal is continuously rotated in the beam, thus creating a sequence of frames that constitute a movie with respect to the rotation angle. Until now, diffraction-optimized cameras have mostly been used for MicroED. Here, the use of a direct electron detector that was designed for imaging is reported. It is demonstrated that data can be collected more rapidly using the Falcon III for MicroED and with markedly lower exposure than has previously been reported. The Falcon III was operated at 40 frames per second and complete data sets reaching atomic resolution were recorded in minutes. The resulting density maps to 2.1 Å resolution of the serine protease proteinase K showed no visible signs of radiation damage. It is thus demonstrated that dedicated diffraction-optimized detectors are not required for MicroED, as shown by the fact that the very same cameras that are used for imaging applications in electron microscopy, such as single-particle cryo-EM, can also be used effectively for diffraction measurements.

Methods are described for processing XFEL data from individual crystalline fibrils. The methods are applied to data collected at the Linac Coherent Light Source from an amyloid-forming oligopeptide from the adenovirus shaft., Serial diffraction data collected at the Linac Coherent Light Source from crystalline amyloid fibrils delivered in a liquid jet show that the fibrils are well oriented in the jet. At low fibril concentrations, diffraction patterns are recorded from single fibrils; these patterns are weak and contain only a few reflections. Methods are developed for determining the orientation of patterns in reciprocal space and merging them in three dimensions. This allows the individual structure amplitudes to be calculated, thus overcoming the limitations of orientation and cylindrical averaging in conventional fibre diffraction analysis. The advantages of this technique should allow structural studies of fibrous systems in biology that are inaccessible using existing techniques.