scholarly journals An encryption–decryption framework to validating single-particle imaging

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhou Shen ◽  
Colin Zhi Wei Teo ◽  
Kartik Ayyer ◽  
N. Duane Loh
Keyword(s):  
Conceptual Framework ◽  
Single Particle ◽  
Latent Variables ◽  
Ground Truth ◽  
Resolving Power ◽  
Validation Measure ◽  
Encryption Decryption ◽  
Diffraction Patterns ◽  
Particle Imaging ◽  
Single Particle Imaging

AbstractWe propose an encryption–decryption framework for validating diffraction intensity volumes reconstructed using single-particle imaging (SPI) with X-ray free-electron lasers (XFELs) when the ground truth volume is absent. This conceptual framework exploits each reconstructed volumes’ ability to decipher latent variables (e.g. orientations) of unseen sentinel diffraction patterns. Using this framework, we quantify novel measures of orientation disconcurrence, inconsistency, and disagreement between the decryptions by two independently reconstructed volumes. We also study how these measures can be used to define data sufficiency and its relation to spatial resolution, and the practical consequences of focusing XFEL pulses to smaller foci. This conceptual framework overcomes critical ambiguities in using Fourier Shell Correlation (FSC) as a validation measure for SPI. Finally, we show how this encryption-decryption framework naturally leads to an information-theoretic reformulation of the resolving power of XFEL-SPI, which we hope will lead to principled frameworks for experiment and instrument design.

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

IUCrJ ◽  
2021 ◽  
Vol 8 (6) ◽  
Author(s):  
Miklós Tegze ◽  
Gábor Bortel
Keyword(s):  
Intensity Distribution ◽  
Single Particle ◽  
Free Electron ◽  
Free Electron Laser ◽  
Biological Molecules ◽  
Crucial Step ◽  
X Ray ◽  
Diffraction Patterns ◽  
Particle Imaging ◽  
Single Particle Imaging

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.

scholarly journals Single-particle imaging without symmetry constraints at an X-ray free-electron laser

IUCrJ ◽  
2018 ◽  
Vol 5 (6) ◽  
pp. 727-736 ◽  
Author(s):  
Max Rose ◽  
Sergey Bobkov ◽  
Kartik Ayyer ◽  
Ruslan P. Kurta ◽  
Dmitry Dzhigaev ◽  
...  
Keyword(s):  
Single Particle ◽  
Three Dimensional ◽  
Icosahedral Symmetry ◽  
Dimensional Reconstruction ◽  
Symmetry Constraints ◽  
Diffraction Patterns ◽  
Particle Imaging ◽  
Single Particle Imaging ◽  
Nanoscale Features ◽  
Better Than

The analysis of a single-particle imaging (SPI) experiment performed at the AMO beamline at LCLS as part of the SPI initiative is presented here. A workflow for the three-dimensional virus reconstruction of the PR772 bacteriophage from measured single-particle data is developed. It consists of several well defined steps including single-hit diffraction data classification, refined filtering of the classified data, reconstruction of three-dimensional scattered intensity from the experimental diffraction patterns by orientation determination and a final three-dimensional reconstruction of the virus electron density without symmetry constraints. The analysis developed here revealed and quantified nanoscale features of the PR772 virus measured in this experiment, with the obtained resolution better than 10 nm, with a clear indication that the structure was compressed in one direction and, as such, deviates from ideal icosahedral symmetry.

scholarly journals The correlation of single-particle diffraction patterns as a continuous function of particle orientation

2014 ◽  
Vol 369 (1647) ◽  
pp. 20130329 ◽  
Author(s):  
Andrew V. Martin
Keyword(s):  
Continuous Function ◽  
Single Particle ◽  
Imaging Techniques ◽  
Particle Orientation ◽  
X Ray ◽  
X Ray Scattering ◽  
Pattern Similarity ◽  
Diffraction Patterns ◽  
Particle Imaging ◽  
Single Particle Imaging

A statistical model for X-ray scattering of a non-periodic sample to high angles is introduced. It is used to calculate analytically the correlation of distinct diffraction measurements of a particle as a continuous function of particle orientation. Diffraction measurements with shot-noise are also considered. This theory provides a general framework for a deeper understanding of single particle imaging techniques used at X-ray free-electron lasers. Many of these techniques use correlations as a measure of diffraction-pattern similarity in order to determine properties of the sample, such as particle orientation.

scholarly journals Evaluation of the performance of classification algorithms for XFEL single-particle imaging data

IUCrJ ◽  
2019 ◽  
Vol 6 (2) ◽  
pp. 331-340 ◽  
Author(s):  
Yingchen Shi ◽  
Ke Yin ◽  
Xuecheng Tai ◽  
Hasan DeMirci ◽  
Ahmad Hosseinizadeh ◽  
...  
Keyword(s):  
Single Particle ◽  
Three Dimensional ◽  
Real Space ◽  
Classification Algorithms ◽  
Imaging Data ◽  
Network Graph ◽  
Manifold Embedding ◽  
Diffraction Patterns ◽  
Particle Imaging ◽  
Single Particle Imaging

Using X-ray free-electron lasers (XFELs), it is possible to determine three-dimensional structures of nanoscale particles using single-particle imaging methods. Classification algorithms are needed to sort out the single-particle diffraction patterns from the large amount of XFEL experimental data. However, different methods often yield inconsistent results. This study compared the performance of three classification algorithms: convolutional neural network, graph cut and diffusion map manifold embedding methods. The identified single-particle diffraction data of the PR772 virus particles were assembled in the three-dimensional Fourier space for real-space model reconstruction. The comparison showed that these three classification methods lead to different datasets and subsequently result in different electron density maps of the reconstructed models. Interestingly, the common dataset selected by these three methods improved the quality of the merged diffraction volume, as well as the resolutions of the reconstructed maps.

scholarly journals Dragonfly: an implementation of the expand–maximize–compress algorithm for single-particle imaging

2016 ◽  
Vol 49 (4) ◽  
pp. 1320-1335 ◽  
Author(s):  
Kartik Ayyer ◽  
Ti-Yen Lan ◽  
Veit Elser ◽  
N. Duane Loh
Keyword(s):  
Single Particle ◽  
Parallel Implementation ◽  
Three Dimensional ◽  
Reconstruction Algorithm ◽  
X Ray ◽  
Resultant Data ◽  
Linac Coherent Light Source ◽  
Diffraction Patterns ◽  
Particle Imaging ◽  
Single Particle Imaging

Single-particle imaging (SPI) with X-ray free-electron lasers has the potential to change fundamentally how biomacromolecules are imaged. The structure would be derived from millions of diffraction patterns, each from a different copy of the macromolecule before it is torn apart by radiation damage. The challenges posed by the resultant data stream are staggering: millions of incomplete, noisy and un-oriented patterns have to be computationally assembled into a three-dimensional intensity map and then phase reconstructed. In this paper, theDragonflysoftware package is described, based on a parallel implementation of the expand–maximize–compress reconstruction algorithm that is well suited for this task. Auxiliary modules to simulate SPI data streams are also included to assess the feasibility of proposed SPI experiments at the Linac Coherent Light Source, Stanford, California, USA.

scholarly journals An advanced workflow for single-particle imaging with the limited data at an X-ray free-electron laser

IUCrJ ◽  
2020 ◽  
Vol 7 (6) ◽  
pp. 1102-1113 ◽  
Author(s):  
Dameli Assalauova ◽  
Young Yong Kim ◽  
Sergey Bobkov ◽  
Ruslan Khubbutdinov ◽  
Max Rose ◽  
...  
Keyword(s):  
Single Particle ◽  
3D Structure ◽  
Limited Data ◽  
Mode Decomposition ◽  
Analysis Workflow ◽  
Linac Coherent Light Source ◽  
Symmetry Constraints ◽  
Diffraction Patterns ◽  
Particle Imaging ◽  
Single Particle Imaging

An improved analysis for single-particle imaging (SPI) experiments, using the limited data, is presented here. Results are based on a study of bacteriophage PR772 performed at the Atomic, Molecular and Optical Science instrument at the Linac Coherent Light Source as part of the SPI initiative. Existing methods were modified to cope with the shortcomings of the experimental data: inaccessibility of information from half of the detector and a small fraction of single hits. The general SPI analysis workflow was upgraded with the expectation-maximization based classification of diffraction patterns and mode decomposition on the final virus-structure determination step. The presented processing pipeline allowed us to determine the 3D structure of bacteriophage PR772 without symmetry constraints with a spatial resolution of 6.9 nm. The obtained resolution was limited by the scattering intensity during the experiment and the relatively small number of single hits.

scholarly journals Virus detection and identification in minutes using single-particle imaging and deep learning

2020 ◽  
Author(s):  
Nicolas Shiaelis ◽  
Alexander Tometzki ◽  
Leon Peto ◽  
Andrew McMahon ◽  
Christof Hepp ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Single Particle ◽  
Virus Detection ◽  
Diagnostic Methods ◽  
Clinical Samples ◽  
Promising Alternative ◽  
Detection And Identification ◽  
Particle Imaging ◽  
Single Particle Imaging

AbstractThe increasing frequency and magnitude of viral outbreaks in recent decades, epitomized by the current COVID-19 pandemic, has resulted in an urgent need for rapid and sensitive viral diagnostic methods. Here, we present a methodology for virus detection and identification that uses a convolutional neural network to distinguish between microscopy images of single intact particles of different viruses. Our assay achieves labeling, imaging and virus identification in less than five minutes and does not require any lysis, purification or amplification steps. The trained neural network was able to differentiate SARS-CoV-2 from negative clinical samples, as well as from other common respiratory pathogens such as influenza and seasonal human coronaviruses, with high accuracy. Single-particle imaging combined with deep learning offers a promising alternative to traditional viral diagnostic methods, and has the potential for significant impact.

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