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.

Comparison of EMC and CM methods for orienting diffraction images in single-particle imaging experiments

In single-particle imaging (SPI) experiments, diffraction patterns of identical particles are recorded. The particles are injected into the X-ray free-electron laser (XFEL) beam in random orientations. The crucial step of the data processing of SPI is finding the orientations of the recorded diffraction patterns in reciprocal space and reconstructing the 3D intensity distribution. Here, two orientation methods are compared: the expansion maximization compression (EMC) algorithm and the correlation maximization (CM) algorithm. To investigate the efficiency, reliability and accuracy of the methods at various XFEL pulse fluences, simulated diffraction patterns of biological molecules are used.

Effects of radiation damage and inelastic scattering on single-particle imaging of hydrated proteins with an X-ray Free-Electron Laser

We present a computational case study of X-ray single-particle imaging of hydrated proteins on an example of 2-Nitrogenase–Iron protein covered with water layers of various thickness, using a start-to-end simulation platform and experimental parameters of the SPB/SFX instrument at the European X-ray Free-Electron Laser facility. The simulations identify an optimal thickness of the water layer at which the effective resolution for imaging the hydrated sample becomes significantly higher than for the non-hydrated sample. This effect is lost when the water layer becomes too thick. Even though the detailed results presented pertain to the specific sample studied, the trends which we identify should also hold in a general case. We expect these findings will guide future single-particle imaging experiments using hydrated proteins.

Abstract P-1: Analysis of Tick-borne Encephalitis Virus Single-particle Imaging on X-ray Free-electron Laser

Background: Tick-borne encephalitis virus (TBEV) is a dangerous human pathogen which envelope structure is already known from cryoEM study. TBEV mature viral particle size (~50 nm in diameter) makes it suitable for single-particle imaging (SPI) on X-ray free-electron laser (XFEL). XFEL SPI studies are at the early stages of development; thus, a well-described and conformationally homogeneous sample is required to develop approaches for experimental setup and data analysis. Here we present the image analysis results of data collected in October 2019 during the European XFEL experiment #2316. Methods: The detector was placed at 1.62 m from the injector; photon energy was around 6 keV, pulse energy 4 mJ, beam diameter ~ 500 nm. All runs were processed to detect hits with threshold filter (5th percentile of lit pixels) and further filtered to omit low-intensity images and images that lack detector modules. Filtered hits were background and geometry corrected with SPImage library and custom python scripts. Then hits were azimuthally integrated using PyFAI library. Scattering profiles were further clustered using the affinity propagation algorithm with cosine similarity metric in log space. Extracted classes were used to build averaged images. All hit profiles were fitted with model scattering to estimate the diameter of the particle. Simulated diffraction patterns were prepared using Condor from the cryoEM electron density map (EMDB ID 3752). Results: During the analysis after the filtering, only 276 clean and bright hits were collected per 135 min of injection (from 27287 hits detected via lit pixels threshold). Thus the hit rate was around ~ 2 hits/min, which is expected to rise in the future. The majority of hits correspond to the 40-50 nm particles (Fig. 1a), which is expected for TBEV. However, the exact size may vary due to solvent evaporation, ion condensation, and possible variability in the sample. Conclusion: The averaged images and their scattering profiles correlate with the simulated scattering patterns, though not ideally (Fig. 1 bc). Such discrepancy is expected due to the absence of electron density in the center of modeled viral structures.

The Role of Surface Chemistry in the Efficacy of Protein and DNA Microarrays for Label-Free Detection: An Overview

The importance of microarrays in diagnostics and medicine has drastically increased in the last few years. Nevertheless, the efficiency of a microarray-based assay intrinsically depends on the density and functionality of the biorecognition elements immobilized onto each sensor spot. Recently, researchers have put effort into developing new functionalization strategies and technologies which provide efficient immobilization and stability of any sort of molecule. Here, we present an overview of the most widely used methods of surface functionalization of microarray substrates, as well as the most recent advances in the field, and compare their performance in terms of optimal immobilization of the bioreceptor molecules. We focus on label-free microarrays and, in particular, we aim to describe the impact of surface chemistry on two types of microarray-based sensors: microarrays for single particle imaging and for label-free measurements of binding kinetics. Both protein and DNA microarrays are taken into consideration, and the effect of different polymeric coatings on the molecules’ functionalities is critically analyzed.