Megahertz single-particle imaging at the European XFEL

Sobolev, E. V.; Zolotarev, S. I.; Giewekemeyer, Klaus; Bielecki, Johan; Okamoto, Kenta; Reddy, Hemanth K. N.; Andreasson, Jakob; Ayyer, Kartik; Barák, Imrich; Bari, Sadia; Barty, Anton; Bean, Richard; Bobkov, Sergey; Chapman, Henry N.; Chojnowski, Grzegorz; Daurer, Benedikt J.; Dörner, Katerina; Ekeberg, Tomas; Flückiger, Leonie; Galzitskaya, Oxana V.; Gelisio, Luca; Hauf, Steffen; Hogue, Brenda G.; Horke, Daniel A.; Hosseinizadeh, Ahmad; Ilyin, V. A.; Jung, Chul-Ho; Kim, Chan; Kim, Yoonhee; Kirian, Richard A.; Kirkwood, Henry; Kulyk, Olena; Küpper, Jochen; Letrun, Romain; Loh, N. Duane; Lorenzen, Kristina; Messerschmidt, Marc; Mühlig, Kerstin; Ourmazd, A.; Raab, Natascha; Rode, Andrei; Rose, M. Franklin; Round, Adam; Sato, Takushi; Schubert, Robin; Schwander, Peter; Sellberg, Jonas A.; Sikorski, Marcin; Silenzi, A.; Song, Changyong; Spence, John C. H.; Stern, Stephan; Sztuk-Dambietz, J.; Teslyuk, Anton; Tı̂mneanu, Nicuşor; Trebbin, Martin; Uetrecht, Charlotte; Weinhausen, Britta; Williams, Garth J.; Xavier, P. Lourdu; Xu, Chen; Vartanyants, Ivan A.; Lamzin, Victor S.; Mancuso, Adrian P.; Maia, Filipe R. N. C.

doi:10.1038/s42005-020-0362-y

Cited by 69 publications

(37 citation statements)

References 38 publications

(70 reference statements)

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“…Theoretical work predicts that currently available XFEL sources generate enough scattered photons from single macromolecules to solve for their unknown orientations and reconstruct 3D structures of large reproducible biomolecules [5][6][7]. Proof-of-principle SPI experiments on biological particles [8][9][10][11][12][13][14] have highlighted the challenges of the approach i.e. the recording of a large number of patterns, all with sufficiently low background, and from structurally homogeneous samples.…”

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confidence: 99%

3D diffractive imaging of nanoparticle ensembles using an x-ray laser

Ayyer

Xavier

Bielecki

et al. 2020

Optica

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We report the 3D structure determination of gold nanoparticles (AuNPs) by X-ray single particle imaging (SPI). Around 10 million diffraction patterns from gold nanoparticles were measured in less than 100 hours of beam time, more than 100 times the amount of data in any single prior SPI experiment, using the new capabilities of the European X-ray free electron laser which allow measurements of 1500 frames per second. A classification and structural sorting method was developed to disentangle the heterogeneity of the particles and to obtain a resolution of better than 3 nm. With these new experimental and analytical developments, we have entered a new era for the SPI method and the path towards close-to-atomic resolution imaging of biomolecules is apparent.

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confidence: 99%

3D diffractive imaging of nanoparticle ensembles using an x-ray laser

Ayyer

Xavier

Bielecki

et al. 2020

Optica

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“…Ultimately, our encryption-decryption approach demonstrably overcomes the difficulties of using FSC as a validation measure for XFEL-SPI, in spite of FSC's popularity 13,16,[18][19][20][21][22][23][24][25][26][27][28][29] . The data throughput from XFELS will rapidly increase because of higher pulse repetition rates 46 , and more efficient sample injection techniques. This trend inevitably creates a larger data load, which in turn increases our reliance on statistical techniques to assign confidence to de novo structural reconstructions.…”

Section: Discussionmentioning

confidence: 99%

An encryption–decryption framework to validating single-particle imaging

Zhou

Teo

Ayyer

et al. 2021

Sci Rep

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We 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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“…Ongoing advances in sample delivery and reduction of background scattering are aimed to increase the yield and signal-to-noise ratio of the diffraction patterns. 46 These improvements are implemented together with the new generation of high-repetition rate XFELs 47,48 which can generate datasets of tens of millions of diffraction patterns during a single experimental beamtime. Our GML approach allows for efficient analysis of such large datasets and recalls significantly large numbers of single-particle diffraction patterns, as required for high-resolution reconstruction.…”

Section: Discussionmentioning

confidence: 99%

Selecting XFEL single-particle snapshots by geometric machine learning

Cruz-Chú

Hosseinizadeh

Mashayekhi

et al. 2021

Structural Dynamics

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A promising new route for structural biology is single-particle imaging with an X-ray Free-Electron Laser (XFEL). This method has the advantage that the samples do not require crystallization and can be examined at room temperature. However, high-resolution structures can only be obtained from a sufficiently large number of diffraction patterns of individual molecules, so-called single particles. Here, we present a method that allows for efficient identification of single particles in very large XFEL datasets, operates at low signal levels, and is tolerant to background. This method uses supervised Geometric Machine Learning (GML) to extract low-dimensional feature vectors from a training dataset, fuse test datasets into the feature space of training datasets, and separate the data into binary distributions of “single particles” and “non-single particles.” As a proof of principle, we tested simulated and experimental datasets of the Coliphage PR772 virus. We created a training dataset and classified three types of test datasets: First, a noise-free simulated test dataset, which gave near perfect separation. Second, simulated test datasets that were modified to reflect different levels of photon counts and background noise. These modified datasets were used to quantify the predictive limits of our approach. Third, an experimental dataset collected at the Stanford Linear Accelerator Center. The single-particle identification for this experimental dataset was compared with previously published results and it was found that GML covers a wide photon-count range, outperforming other single-particle identification methods. Moreover, a major advantage of GML is its ability to retrieve single particles in the presence of structural variability.

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Megahertz single-particle imaging at the European XFEL

Cited by 69 publications

References 38 publications

3D diffractive imaging of nanoparticle ensembles using an x-ray laser

3D diffractive imaging of nanoparticle ensembles using an x-ray laser

An encryption–decryption framework to validating single-particle imaging

Selecting XFEL single-particle snapshots by geometric machine learning

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