Radiation damage to biological samples: still a pertinent issue.
Elspeth F GarmanMartin WeikPublished in: Journal of synchrotron radiation (2021)
An understanding of radiation damage effects suffered by biological samples during structural analysis using both X-rays and electrons is pivotal to obtain reliable molecular models of imaged molecules. This special issue on radiation damage contains six papers reporting analyses of damage from a range of biophysical imaging techniques. For X-ray diffraction, an in-depth study of multi-crystal small-wedge data collection single-wavelength anomalous dispersion phasing protocols is presented, concluding that an absorbed dose of 5 MGy per crystal was optimal to allow reliable phasing. For small-angle X-ray scattering, experiments are reported that evaluate the efficacy of three radical scavengers using a protein designed to give a clear signature of damage in the form of a large conformational change upon the breakage of a disulfide bond. The use of X-rays to induce OH radicals from the radiolysis of water for X-ray footprinting are covered in two papers. In the first, new developments and the data collection pipeline at the NSLS-II high-throughput dedicated synchrotron beamline are described, and, in the second, the X-ray induced changes in three different proteins under aerobic and low-oxygen conditions are investigated and correlated with the absorbed dose. Studies in XFEL science are represented by a report on simulations of ultrafast dynamics in protic ionic liquids, and, lastly, a broad coverage of possible methods for dose efficiency improvement in modalities using electrons is presented. These papers, as well as a brief synopsis of some other relevant literature published since the last Journal of Synchrotron Radiation Special Issue on Radiation Damage in 2019, are summarized below.
Keyphrases
- ionic liquid
- high resolution
- oxidative stress
- high throughput
- dual energy
- systematic review
- electronic health record
- big data
- molecular dynamics
- emergency department
- magnetic resonance
- computed tomography
- randomized controlled trial
- magnetic resonance imaging
- public health
- molecular dynamics simulations
- machine learning
- binding protein
- room temperature
- optical coherence tomography
- artificial intelligence
- data analysis
- quantum dots
- fluorescence imaging