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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. 

Single particle imaging at x-ray free electron lasers (XFELs) has the potential to determine the structure and dynamics of single biomolecules at room temperature. Two major hurdles have prevented this potential from being reached, namely, the collection of sufficient high-quality diffraction patterns and robust computational purification to overcome structural heterogeneity. We report the breaking of both of these barriers using gold nanoparticle test samples, recording around 10 million diffraction patterns at the European XFEL and structurally and orientationally sorting the patterns to obtain better than 3-nm-resolution 3D reconstructions for each of four samples. With these new developments, integrating advancements in x-ray sources, fast-framing detectors, efficient sample delivery, and data analysis algorithms, we illuminate the path towards sub-nanometer biomolecular imaging. The methods developed here can also be extended to characterize ensembles that are inherently diverse to obtain their full structural landscape. 

Abstract The world’s first superconducting megahertz repetition rate hard X-ray free-electron laser (XFEL), the European XFEL, began operation in 2017, featuring a unique pulse train structure with 886 ns between pulses. With its rapid pulse rate, the European XFEL may alleviate some of the increasing demand for XFEL beamtime, particularly for membrane protein serial femtosecond crystallography (SFX), leveraging orders-of-magnitude faster data collection. Here, we report the first membrane protein megahertz SFX experiment, where we determined a 2.9 Å-resolution SFX structure of the large membrane protein complex, Photosystem I, a > 1 MDa complex containing 36 protein subunits and 381 cofactors. We address challenges to megahertz SFX for membrane protein complexes, including growth of large quantities of crystals and the large molecular and unit cell size that influence data collection and analysis. The results imply that megahertz crystallography could have an important impact on structure determination of large protein complexes with XFELs.