Suppressed twist in droplets of cholesteric rod-like virus as identified by 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.

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Correlative Microscopy: Site-Specific SERS Assay for Survivin Protein Dimer: From Ensemble Experiments to Correlative Single-Particle Imaging (Small 32/2017)

Small ◽

10.1002/smll.201770176 ◽

2017 ◽

Vol 13 (32) ◽

Author(s):

Jörg Wissler ◽

Sandra Bäcker ◽

Alessandro Feis ◽

Shirley K. Knauer ◽

Sebastian Schlücker

Keyword(s):

Single Particle ◽

Correlative Microscopy ◽

Site Specific ◽

Survivin Protein ◽

Protein Dimer ◽

Particle Imaging ◽

Single Particle Imaging ◽

Ensemble Experiments

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

IUCrJ ◽

10.1107/s205225252100868x ◽

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.

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

10.1101/2020.10.13.20212035 ◽

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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