Phase retrieval from single biomolecule diffraction pattern

Refinement for single-nanoparticle structure determination from low-quality single-shot coherent diffraction data

With the emergence of X-ray free-electron lasers, it is possible to investigate the structure of nanoscale samples by employing coherent diffractive imaging in the X-ray spectral regime. In this work, we developed a refinement method for structure reconstruction applicable to low-quality coherent diffraction data. The method is based on the gradient search method and considers the missing region of a diffraction pattern and the small number of detected photons. We introduced an initial estimate of the structure in the method to improve the convergence. The present method is applied to an experimental diffraction pattern of an Xe cluster obtained in an X-ray scattering experiment at the SPring-8 Angstrom Compact free-electron LAser (SACLA) facility. It is found that the electron density is successfully reconstructed from the diffraction pattern with a large missing region, with a good initial estimate of the structure. The diffraction pattern calculated from the reconstructed electron density reproduced the observed diffraction pattern well, including the characteristic intensity modulation in each ring. Our refinement method enables structure reconstruction from diffraction patterns under difficulties such as missing areas and low diffraction intensity, and it is potentially applicable to the structure determination of samples that have low scattering power.

Deep neural networks for classifying complex features in diffraction images

Intense short-wavelength pulses from free-electron lasers and high-harmonic-generation sources enable diffractive imaging of individual nanosized objects with a single x-ray laser shot. The enormous data sets with up to several million diffraction patterns present a severe problem for data analysis because of the high dimensionality of imaging data. Feature recognition and selection is a crucial step to reduce the dimensionality. Usually, custom-made algorithms are developed at a considerable effort to approximate the particular features connected to an individual specimen, but because they face different experimental conditions, these approaches do not generalize well. On the other hand, deep neural networks are the principal instrument for today's revolution in automated image recognition, a development that has not been adapted to its full potential for data analysis in science. We recently published [Langbehn et al., Phys. Rev. Lett. 121, 255301 (2018)PRLTAO0031-900710.1103/PhysRevLett.121.255301] the application of a deep neural network as a feature extractor for wide-angle diffraction images of helium nanodroplets. Here we present the setup, our modifications, and the training process of the deep neural network for diffraction image classification and its systematic bench marking. We find that deep neural networks significantly outperform previous attempts for sorting and classifying complex diffraction patterns and are a significant improvement for the much-needed assistance during postprocessing of large amounts of experimental coherent diffraction imaging data.

Perspectives for imaging single protein molecules with the present design of the European XFEL

The Single Particles, Clusters and Biomolecules & Serial Femtosecond Crystallography (SPB/SFX) instrument at the European XFEL is located behind the SASE1 undulator and aims to support imaging and structure determination of biological specimen between about 0.1 μm and 1 μm size. The instrument is designed to work at photon energies from 3 keV up to 16 keV. Here, we propose a cost-effective proof-of-principle experiment, aiming to demonstrate the actual feasibility of a single molecule diffraction experiment at the European XFEL. To this end, we assume self-seeding capabilities at SASE1 and we suggest to make use of the baseline European XFEL accelerator complex-with the addition of a slotted-foil setup-and of the SPB/SFX instrument. As a first step towards the realization of an actual experiment, we developed a complete package of computational tools for start-to-end simulations predicting its performance. Single biomolecule imaging capabilities at the European XFEL can be reached by exploiting special modes of operation of the accelerator complex and of the SASE1 undulator. The output peak power can be increased up to more than 1.5 TW, which allows to relax the requirements on the focusing efficiency of the optics and to reach the required fluence without changing the present design of the SPB/SFX instrument. Explicit simulations are presented using the 15-nm size RNA Polymerase II molecule as a case study. Noisy diffraction patterns were generated and they were processed to generate the 3D intensity distribution. We discuss requirements to the signal-to-background ratio needed to obtain a correct pattern orientation. When these are fulfilled, our results indicate that one can achieve diffraction without destruction with about 0.1 photons per Shannon pixel per shot at 4 Å resolution with 10(13) photons in a 4 fs pulse at 4 keV photon energy and in a 0.3 μm focus, corresponding to a fluence of 10(14) photons/μm(2). We assume negligible structured background. At this signal level, one needs only about 30 000 diffraction patterns to recover full 3D information. At the highest repetition rate manageable by detectors at European XFEL, one will be able to accumulate these data within a fraction of an hour, even assuming a relatively low hit probability of about a percent.

KOTOBUKI-1 apparatus for cryogenic coherent X-ray diffraction imaging

We have developed an experimental apparatus named KOTOBUKI-1 for use in coherent X-ray diffraction imaging experiments of frozen-hydrated non-crystalline particles at cryogenic temperature. For cryogenic specimen stage with small positional fluctuation for a long exposure time of more than several minutes, we here use a cryogenic pot cooled by the evaporation cooling effect for liquid nitrogen. In addition, a loading device is developed to bring specimens stored in liquid nitrogen to the specimen stage in vacuum. The apparatus allows diffraction data collection for frozen-hydrated specimens at 66 K with a positional fluctuation of less than 0.4 μm and provides an experimental environment to easily exchange specimens from liquid nitrogen storage to the specimen stage. The apparatus was developed and utilized in diffraction data collection of non-crystalline particles with dimensions of μm from material and biological sciences, such as metal colloid particles and chloroplast, at BL29XU of SPring-8. Recently, it has been applied for single-shot diffraction data collection of non-crystalline particles with dimensions of sub-μm using X-ray free electron laser at BL3 of SACLA.