Insights into HIV uncoating from single-particle imaging techniques

2022 ◽  
Author(s):  
Margaret J. Zhang ◽  
Jeffrey H. Stear ◽  
David A. Jacques ◽  
Till Böcking
Keyword(s):  
Single Particle ◽  
Imaging Techniques ◽  
Particle Imaging ◽  
Single Particle Imaging

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 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 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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