Enhanced EMC-Advantages of partially known orientations in x-ray single particle imaging.

Single particle imaging of proteins in the gas phase with x-ray free-electron lasers holds great potential to study fast protein dynamics, but is currently limited by weak and noisy data. A further challenge is to discover the proteins' orientation as each protein is randomly oriented when exposed to x-rays. Algorithms such as the expand, maximize, and compress (EMC) exist that can solve the orientation problem and reconstruct the three-dimensional diffraction intensity space, given sufficient measurements. If information about orientation were known, for example, by using an electric field to orient the particles, the reconstruction would benefit and potentially reach better results. We used simulated diffraction experiments to test how the reconstructions from EMC improve with particles' orientation to a preferred axis. Our reconstructions converged to correct maps of the three-dimensional diffraction space with fewer measurements if biased orientation information was considered. Even for a moderate bias, there was still significant improvement. Biased orientations also substantially improved the results in the case of missing central information, in particular in the case of small datasets. The effects were even more significant when adding a background with 50% the strength of the averaged diffraction signal photons to the diffraction patterns, sometimes reducing the data requirement for convergence by a factor of 10. This demonstrates the usefulness of having biased orientation information in single particle imaging experiments, even for a weaker bias than what was previously known. This could be a key component in overcoming the problems with background noise that currently plague these experiments.

Reproducibility of single protein explosions induced by X-ray lasers.

Single particle imaging (SPI) using X-ray pulses has become increasingly attainable with the advent of high-intensity free electron lasers. Eliminating the need for crystallized samples enables structural studies of molecules previously inaccessible by conventional crystallography. While this emerging technique already demonstrates substantial promise, some obstacles need to be overcome before SPI can reach its full potential. One such problem is determining the spatial orientation of the sample at the time of X-ray interaction. Existing solutions rely on diffraction data and are computationally demanding and sensitive to noise. In this in silico study, we explore the possibility of aiding these methods by mapping the ion distribution as the sample undergoes a Coulomb explosion following the intense ionization. By detecting the ions ejected from the fragmented sample, the orientation of the original sample should be possible to determine. Knowledge of the orientation has been shown earlier to be of substantial advantage in the reconstruction of the original structure. 150 explosions of each of twelve separate systems - four polypeptides with different amounts of surface bound water - were simulated with molecular dynamics (MD) and the average angular distribution of carbon and sulfur ions was investigated independently. The results show that the explosion maps are reproducible in both cases, supporting the idea that orientation information is preserved. Additional water seems to restrict the carbon ion trajectories further through a shielding mechanism, making the maps more distinct. For sulfurs, water has no significant impact on the trajectories, likely due to their higher mass and greater ionization cross section, indicating that they could be of particular interest. Based on these findings, we conclude that explosion data can aid spatial orientation in SPI experiments and could substantially improve the capabilities of the novel technique.

Single mimivirus particles intercepted and imaged with an X-ray laser.

X-ray lasers offer new capabilities in understanding the structure of biological systems, complex materials and matter under extreme conditions. Very short and extremely bright, coherent X-ray pulses can be used to outrun key damage processes and obtain a single diffraction pattern from a large macromolecule, a virus or a cell before the sample explodes and turns into plasma. The continuous diffraction pattern of non-crystalline objects permits oversampling and direct phase retrieval. Here we show that high-quality diffraction data can be obtained with a single X-ray pulse from a non-crystalline biological sample, a single mimivirus particle, which was injected into the pulsed beam of a hard-X-ray free-electron laser, the Linac Coherent Light Source. Calculations indicate that the energy deposited into the virus by the pulse heated the particle to over 100,000 K after the pulse had left the sample. The reconstructed exit wavefront (image) yielded 32-nm full-period resolution in a single exposure and showed no measurable damage. The reconstruction indicates inhomogeneous arrangement of dense material inside the virion. We expect that significantly higher resolutions will be achieved in such experiments with shorter and brighter photon pulses focused to a smaller area. The resolution in such experiments can be further extended for samples available in multiple identical copies.

Lipidic phase membrane protein serial femtosecond crystallography.

X-ray free electron laser (X-FEL)-based serial femtosecond crystallography is an emerging method with potential to rapidly advance the challenging field of membrane protein structural biology. Here we recorded interpretable diffraction data from micrometer-sized lipidic sponge phase crystals of the Blastochloris viridis photosynthetic reaction center delivered into an X-FEL beam using a sponge phase micro-jet.

In vivo protein crystallization opens new routes in structural biology.

Protein crystallization in cells has been observed several times in nature. However, owing to their small size these crystals have not yet been used for X-ray crystallographic analysis. We prepared nano-sized in vivo-grown crystals of Trypanosoma brucei enzymes and applied the emerging method of free-electron laser-based serial femtosecond crystallography to record interpretable diffraction data. This combined approach will open new opportunities in structural systems biology.

Three-dimensional reconstruction of the giant mimivirus particle with an x-ray free-electron laser.

We present a proof-of-concept three-dimensional reconstruction of the giant mimivirus particle from experimentally measured diffraction patterns from an x-ray free-electron laser. Three-dimensional imaging requires the assembly of many two-dimensional patterns into an internally consistent Fourier volume. Since each particle is randomly oriented when exposed to the x-ray pulse, relative orientations have to be retrieved from the diffraction data alone. We achieve this with a modified version of the expand, maximize and compress algorithm and validate our result using new methods.

Automated identification and classification of single particle serial femtosecond X-ray diffraction data.

The first hard X-ray laser, the Linac Coherent Light Source (LCLS), produces 120 shots per second. Particles injected into the X-ray beam are hit randomly and in unknown orientations by the extremely intense X-ray pulses, where the femtosecond-duration X-ray pulses diffract from the sample before the particle structure is significantly changed even though the sample is ultimately destroyed by the deposited X-ray energy. Single particle X-ray diffraction experiments generate data at the FEL repetition rate, resulting in more than 400,000 detector readouts in an hour, the data stream during an experiment contains blank frames mixed with hits on single particles, clusters and contaminants. The diffraction signal is generally weak and it is superimposed on a low but continually fluctuating background signal, originating from photon noise in the beam line and electronic noise from the detector. Meanwhile, explosion of the sample creates fragments with a characteristic signature. Here, we describe methods based on rapid image analysis combined with ion Time-of-Flight (ToF) spectroscopy of the fragments to achieve an efficient, automated and unsupervised sorting of diffraction data. The studies described here form a basis for the development of real-time frame rejection methods, e.g. for the European XFEL, which is expected to produce 100 million pulses per hour.

Explosion dynamics of sucrose nanospheres monitored by time of flight spectrometry and coherent diffractive imaging at the split-and-delay beam line of the FLASH soft X-ray laser.

We use a Mach-Zehnder type autocorrelator to split and delay XUV pulses from the FLASH soft X-ray laser for triggering and subsequently probing the explosion of aerosolised sugar balls. FLASH was running at 182 eV photon energy with pulses of 70 fs duration. The delay between the pump-probe pulses was varied between zero and 5 ps, and the pulses were focused to reach peak intensities above 10¹6W/cm² with an off-axis parabola. The direct pulse triggered the explosion of single aerosolised sucrose nano-particles, while the delayed pulse probed the exploding structure. The ejected ions were measured by ion time of flight spectrometry, and the particle sizes were measured by coherent diffractive imaging. The results show that sucrose particles of 560-1000 nm diameter retain their size for about 500 fs following the first exposure. Significant sample expansion happens between 500 fs and 1 ps. We present simulations to support these observations.

Introduction to the virtual collection of papers on Artificial neural networks: applications in X-ray photon science and crystallography.

Artificial intelligence is more present than ever, both in our society in general and in science. At the center of this development has been the concept of deep learning, the use of artificial neural networks that are many layers deep and can often reproduce human-like behavior much better than other machine-learning techniques. The articles in this collection are some recent examples of its application for X-ray photon science and crystallography that have been published in Journal of Applied Crystallography.

Resolving Nonequilibrium Shape Variations among Millions of Gold Nanoparticles.

Nanoparticles, exhibiting functionally relevant structural heterogeneity, are at the forefront of cutting-edge research. Now, high-throughput single-particle imaging (SPI) with X-ray free-electron lasers (XFELs) creates opportunities for recovering the shape distributions of millions of particles that exhibit functionally relevant structural heterogeneity. To realize this potential, three challenges have to be overcome: (1) simultaneous parametrization of structural variability in real and reciprocal spaces; (2) efficiently inferring the latent parameters of each SPI measurement; (3) scaling up comparisons between 105 structural models and 106 XFEL-SPI measurements. Here, we describe how we overcame these three challenges to resolve the nonequilibrium shape distributions within millions of gold nanoparticles imaged at the European XFEL. These shape distributions allowed us to quantify the degree of asymmetry in these particles, discover a relatively stable "shape envelope" among nanoparticles, discern finite-size effects related to shape-controlling surfactants, and extrapolate nanoparticles' shapes to their idealized thermodynamic limit. Ultimately, these demonstrations show that XFEL SPI can help transform nanoparticle shape characterization from anecdotally interesting to statistically meaningful.

Observation of a single protein by ultrafast X-ray diffraction.

The idea of using ultrashort X-ray pulses to obtain images of single proteins frozen in time has fascinated and inspired many. It was one of the arguments for building X-ray free-electron lasers. According to theory, the extremely intense pulses provide sufficient signal to dispense with using crystals as an amplifier, and the ultrashort pulse duration permits capturing the diffraction data before the sample inevitably explodes. This was first demonstrated on biological samples a decade ago on the giant mimivirus. Since then, a large collaboration has been pushing the limit of the smallest sample that can be imaged. The ability to capture snapshots on the timescale of atomic vibrations, while keeping the sample at room temperature, may allow probing the entire conformational phase space of macromolecules. Here we show the first observation of an X-ray diffraction pattern from a single protein, that of Escherichia coli GroEL which at 14 nm in diameter is the smallest biological sample ever imaged by X-rays, and demonstrate that the concept of diffraction before destruction extends to single proteins. From the pattern, it is possible to determine the approximate orientation of the protein. Our experiment demonstrates the feasibility of ultrafast imaging of single proteins, opening the way to single-molecule time-resolved studies on the femtosecond timescale.

Sensing the wavefront of x-ray free-electron lasers using aerosol spheres.

Characterizing intense, focused x-ray free electron laser (FEL) pulses is crucial for their use in diffractive imaging. We describe how the distribution of average phase tilts and intensities on hard x-ray pulses with peak intensities of 10(21) W/m(2) can be retrieved from an ensemble of diffraction patterns produced by 70 nm-radius polystyrene spheres, in a manner that mimics wavefront sensors. Besides showing that an adaptive geometric correction may be necessary for diffraction data from randomly injected sample sources, our paper demonstrates the possibility of collecting statistics on structured pulses using only the diffraction patterns they generate and highlights the imperative to study its impact on single-particle diffractive imaging.

Toward unsupervised single-shot diffractive imaging of heterogeneous particles using X-ray free-electron lasers.

Single shot diffraction imaging experiments via X-ray free-electron lasers can generate as many as hundreds of thousands of diffraction patterns of scattering objects. Recovering the real space contrast of a scattering object from these patterns currently requires a reconstruction process with user guidance in a number of steps, introducing severe bottlenecks in data processing. We present a series of measures that replace user guidance with algorithms that reconstruct contrasts in an unsupervised fashion. We demonstrate the feasibility of automating the reconstruction process by generating hundreds of contrasts obtained from soot particle diffraction experiments.

3D-printed sheet jet for stable megahertz liquid sample delivery at X-ray free-electron lasers.

X-ray free-electron lasers (XFELs) can probe chemical and biological reactions as they unfold with unprecedented spatial and temporal resolution. A principal challenge in this pursuit involves the delivery of samples to the X-ray interaction point in such a way that produces data of the highest possible quality and with maximal efficiency. This is hampered by intrinsic constraints posed by the light source and operation within a beamline environment. For liquid samples, the solution typically involves some form of high-speed liquid jet, capable of keeping up with the rate of X-ray pulses. However, conventional jets are not ideal because of radiation-induced explosions of the jet, as well as their cylindrical geometry combined with the X-ray pointing instability of many beamlines which causes the interaction volume to differ for every pulse. This complicates data analysis and contributes to measurement errors. An alternative geometry is a liquid sheet jet which, with its constant thickness over large areas, eliminates the problems related to X-ray pointing. Since liquid sheets can be made very thin, the radiation-induced explosion is reduced, boosting their stability. These are especially attractive for experiments which benefit from small interaction volumes such as fluctuation X-ray scattering and several types of spectroscopy. Although their use has increased for soft X-ray applications in recent years, there has not yet been wide-scale adoption at XFELs. Here, gas-accelerated liquid sheet jet sample injection is demonstrated at the European XFEL SPB/SFX nano focus beamline. Its performance relative to a conventional liquid jet is evaluated and superior performance across several key factors has been found. This includes a thickness profile ranging from hundreds of nanometres to 60 nm, a fourfold increase in background stability and favorable radiation-induced explosion dynamics at high repetition rates up to 1.13 MHz. Its minute thickness also suggests that ultrafast single-particle solution scattering is a possibility.

Noise reduction and mask removal neural network for X-ray single-particle imaging.

Free-electron lasers could enable X-ray imaging of single biological macromolecules and the study of protein dynamics, paving the way for a powerful new imaging tool in structural biology, but a low signal-to-noise ratio and missing regions in the detectors, colloquially termed 'masks', affect data collection and hamper real-time evaluation of experimental data. In this article, the challenges posed by noise and masks are tackled by introducing a neural network pipeline that aims to restore diffraction intensities. For training and testing of the model, a data set of diffraction patterns was simulated from 10 900 different proteins with molecular weights within the range of 10-100 kDa and collected at a photon energy of 8 keV. The method is compared with a simple low-pass filtering algorithm based on autocorrelation constraints. The results show an improvement in the mean-squared error of roughly two orders of magnitude in the presence of masks compared with the noisy data. The algorithm was also tested at increasing mask width, leading to the conclusion that demasking can achieve good results when the mask is smaller than half of the central speckle of the pattern. The results highlight the competitiveness of this model for data processing and the feasibility of restoring diffraction intensities from unknown structures in real time using deep learning methods. Finally, an example is shown of this preprocessing making orientation recovery more reliable, especially for data sets containing very few patterns, using the expansion-maximization-compression algorithm.

Unsupervised learning approaches to characterizing heterogeneous samples using X-ray single-particle imaging.

One of the outstanding analytical problems in X-ray single-particle imaging (SPI) is the classification of structural heterogeneity, which is especially difficult given the low signal-to-noise ratios of individual patterns and the fact that even identical objects can yield patterns that vary greatly when orientation is taken into consideration. Proposed here are two methods which explicitly account for this orientation-induced variation and can robustly determine the structural landscape of a sample ensemble. The first, termed common-line principal component analysis (PCA), provides a rough classification which is essentially parameter free and can be run automatically on any SPI dataset. The second method, utilizing variation auto-encoders (VAEs), can generate 3D structures of the objects at any point in the structural landscape. Both these methods are implemented in combination with the noise-tolerant expand-maximize-compress (EMC) algorithm and its utility is demonstrated by applying it to an experimental dataset from gold nanoparticles with only a few thousand photons per pattern. Both discrete structural classes and continuous deformations are recovered. These developments diverge from previous approaches of extracting reproducible subsets of patterns from a dataset and open up the possibility of moving beyond the study of homogeneous sample sets to addressing open questions on topics such as nanocrystal growth and dynamics, as well as phase transitions which have not been externally triggered.

Unsupervised classification of single-particle X-ray diffraction snapshots by spectral clustering.

Single-particle experiments using X-ray Free Electron Lasers produce more than 10(5) snapshots per hour, consisting of an admixture of blank shots (no particle intercepted), and exposures of one or more particles. Experimental data sets also often contain unintentional contamination with different species. We present an unsupervised method able to sort experimental snapshots without recourse to templates, specific noise models, or user-directed learning. The results show 90% agreement with manual classification.

Ab initio phasing of the diffraction of crystals with translational disorder.

To date X-ray protein crystallography is the most successful technique available for the determination of high-resolution 3D structures of biological molecules and their complexes. In X-ray protein crystallography the structure of a protein is refined against the set of observed Bragg reflections from a protein crystal. The resolution of the refined protein structure is limited by the highest angle at which Bragg reflections can be observed. In addition, the Bragg reflections alone are typically insufficient (by a factor of two) to determine the structure ab initio, and so prior information is required. Crystals formed from an imperfect packing of the protein molecules may also exhibit continuous diffraction between and beyond these Bragg reflections. When this is due to random displacements of the molecules from each crystal lattice site, the continuous diffraction provides the necessary information to determine the protein structure without prior knowledge, to a resolution that is not limited by the angular extent of the observed Bragg reflections but instead by that of the diffraction as a whole. This article presents an iterative projection algorithm that simultaneously uses the continuous diffraction as well as the Bragg reflections for the determination of protein structures. The viability of this method is demonstrated on simulated crystal diffraction.

Erratum: Experimental strategies for imaging bioparticles with femtosecond hard X-ray pulses. Corrigendum.

[This corrects the article DOI: 10.1107/S2052252517003591.].

Considerations for three-dimensional image reconstruction from experimental data in coherent diffractive imaging.

Diffraction before destruction using X-ray free-electron lasers (XFELs) has the potential to determine radiation-damage-free structures without the need for crystallization. This article presents the three-dimensional reconstruction of the Melbournevirus from single-particle X-ray diffraction patterns collected at the LINAC Coherent Light Source (LCLS) as well as reconstructions from simulated data exploring the consequences of different kinds of experimental sources of noise. The reconstruction from experimental data suffers from a strong artifact in the center of the particle. This could be reproduced with simulated data by adding experimental background to the diffraction patterns. In those simulations, the relative density of the artifact increases linearly with background strength. This suggests that the artifact originates from the Fourier transform of the relatively flat background, concentrating all power in a central feature of limited extent. We support these findings by significantly reducing the artifact through background removal before the phase-retrieval step. Large amounts of blurring in the diffraction patterns were also found to introduce diffuse artifacts, which could easily be mistaken as biologically relevant features. Other sources of noise such as sample heterogeneity and variation of pulse energy did not significantly degrade the quality of the reconstructions. Larger data volumes, made possible by the recent inauguration of high repetition-rate XFELs, allow for increased signal-to-background ratio and provide a way to minimize these artifacts. The anticipated development of three-dimensional Fourier-volume-assembly algorithms which are background aware is an alternative and complementary solution, which maximizes the use of data.