Single-shot diffraction data from the Mimivirus particle using an X-ray free-electron laser

Tomas Ekeberg, Martin Svenda, M. Marvin Seibert, Chantal Abergel, Filipe R.N.C. Maia, Virginie Seltzer, Daniel P. DePonte, Andrew Aquila, Jakob Andreasson, Bianca Iwan, Olof Jonsson, Daniel Westphal, Dusko Odic, Inger Andersson, Anton Barty, Meng Liang, Andrew V. Martin, Lars Gumprecht, Holger Fleckenstein, Saša BajtMiriam Barthelmess, Nicola Coppola, Jean Michel Claverie, N. Duane Loh, Christoph Bostedt, John D. Bozek, Jacek Krzywinski, Marc Messerschmidt, Michael J. Bogan, Christina Y. Hampton, Raymond G. Sierra, Matthias Frank, Robert L. Shoeman, Lukas Lomb, Lutz Foucar, Sascha W. Epp, Daniel Rolles, Artem Rudenko, Robert Hartmann, Andreas Hartmann, Nils Kimmel, Peter Holl, Georg Weidenspointner, Benedikt Rudek, Benjamin Erk, Stephan Kassemeyer, Ilme Schlichting, Lothar Struder, Joachim Ullrich, Carlo Schmidt, Faton Krasniqi, Gunter Hauser, Christian Reich, Heike Soltau, Sebastian Schorb, Helmut Hirsemann, Cornelia Wunderer, Heinz Graafsma, Henry Chapman, Janos Hajdu

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


Free-electron lasers (FEL) hold the potential to revolutionize structural biology by producing X-ray pules short enough to outrun radiation damage, thus allowing imaging of biological samples without the limitation from radiation damage. Thus, a major part of the scientific case for the first FELs was three-dimensional (3D) reconstruction of non-crystalline biological objects. In a recent publication we demonstrated the first 3D reconstruction of a biological object from an X-ray FEL using this technique. The sample was the giant Mimivirus, which is one of the largest known viruses with a diameter of 450 nm. Here we present the dataset used for this successful reconstruction. Data-analysis methods for single-particle imaging at FELs are undergoing heavy development but data collection relies on very limited time available through a highly competitive proposal process. This dataset provides experimental data to the entire community and could boost algorithm development and provide a benchmark dataset for new algorithms.

Original languageEnglish (US)
Article number160060
JournalScientific Data
StatePublished - Aug 1 2016
Externally publishedYes

ASJC Scopus subject areas

  • Statistics and Probability
  • Information Systems
  • Education
  • Computer Science Applications
  • Statistics, Probability and Uncertainty
  • Library and Information Sciences


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