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We overcome two of the most daunting challenges in single-particle diffractive imaging: collecting many high-quality diffraction patterns on a small amount of sample and separating components from mixed samples. We demonstrate this on carboxysomes, which are polyhedral cell organelles that vary in size and facilitate up to 40% of Earth's carbon fixation. A new aerosol sample-injector allowed us to record 70,000 low-noise diffraction patterns in 12 min with the Linac Coherent Light Source running at 120 Hz. We separate different structures directly from the diffraction data and show that the size distribution is preserved during sample delivery. We automate phase retrieval and avoid reconstruction artefacts caused by missing modes. We attain the highest-resolution reconstructions on the smallest single biological objects imaged with an X-ray laser to date. These advances lay the foundations for accurate, high-throughput structure determination by flash-diffractive imaging and offer a means to study structure and structural heterogeneity in biology and elsewhere.

Ultra-intense femtosecond X-ray pulses from X-ray lasers permit structural studies on single particles and biomolecules without crystals. We present a large data set on inherently heterogeneous, polyhedral carboxysome particles. Carboxysomes are cell organelles that vary in size and facilitate up to 40% of Earth’s carbon fixation by cyanobacteria and certain proteobacteria. Variation in size hinders crystallization. Carboxysomes appear icosahedral in the electron microscope. A protein shell encapsulates a large number of Rubisco molecules in paracrystalline arrays inside the organelle. We used carboxysomes with a mean diameter of 115±26 nm from Halothiobacillus neapolitanus. A new aerosol sample-injector allowed us to record 70,000 low-noise diffraction patterns in 12 min. Every diffraction pattern is a unique structure measurement and high-throughput imaging allows sampling the space of structural variability. The different structures can be separated and phased directly from the diffraction data and open a way for accurate, high-throughput studies on structures and structural heterogeneity in biology and elsewhere.

Flash X-ray imaging has the potential to determine structures down to molecular resolution without the need for crystallization. The ability to accurately predict the diffraction signal and to identify the optimal experimental configuration within the limits of the instrument is important for successful data collection. This article introduces Condor, an open-source simulation tool to predict X-ray far-field scattering amplitudes of isolated particles for customized experimental designs and samples, which the user defines by an atomic or a refractive index model. The software enables researchers to test whether their envisaged imaging experiment is feasible, and to optimize critical parameters for reaching the best possible result. It also aims to support researchers who intend to create or advance reconstruction algorithms by simulating realistic test data. Condor is designed to be easy to use and can be either installed as a Python package or used from its web interface (http://lmb.icm.uu.se/condor). X-ray free-electron lasers have high running costs and beam time at these facilities is precious. Data quality can be substantially improved by using simulations to guide the experimental design and simplify data analysis.

The emerging technique of serial X-ray diffraction, in which diffraction data are collected from samples flowing across a pulsed X-ray source at repetition rates of 100 Hz or higher, has necessitated the development of new software in order to handle the large data volumes produced. Sorting of data according to different criteria and rapid filtering of events to retain only diffraction patterns of interest results in significant reductions in data volume, thereby simplifying subsequent data analysis and management tasks. Meanwhile the generation of reduced data in the form of virtual powder patterns, radial stacks, histograms and other meta data creates data set summaries for analysis and overall experiment evaluation. Rapid data reduction early in the analysis pipeline is proving to be an essential first step in serial imaging experiments, prompting the authors to make the tool described in this article available to the general community. Originally developed for experiments at X-ray free-electron lasers, the software is based on a modular facility-independent library to promote portability between different experiments and is available under version 3 or later of the GNU General Public License.