A Sample Preparation Pipeline for Microcrystals at the VMXm Beamline.

The mounting of microcrystals (<10 µm) for single crystal cryo-crystallography presents a non-trivial challenge. Improvements in data quality have been seen for microcrystals with the development of beamline optics, beam stability and variable beam size focusing from submicron to microns, such as at the VMXm beamline at Diamond Light Source. Further improvements in data quality will be gained through improvements in sample environment and sample preparation. Microcrystals inherently generate weaker diffraction, therefore improving the signal-to-noise is key to collecting quality X-ray diffraction data and will predominantly come from reductions in background noise. Major sources of X-ray background noise in a diffraction experiment are from their interaction with the air path before and after the sample, excess crystallization solution surrounding the sample, the presence of crystalline ice and scatter from any other beamline instrumentation or X-ray windows. The VMXm beamline comprises instrumentation and a sample preparation protocol to reduce all these sources of noise. Firstly, an in-vacuum sample environment at VMXm removes the air path between X-ray source and sample. Next, sample preparation protocols for macromolecular crystallography at VMXm utilize a number of processes and tools adapted from cryoTEM. These include copper grids with holey carbon support films, automated blotting and plunge cooling robotics making use of liquid ethane. These tools enable the preparation of hundreds of microcrystals on a single cryoTEM grid with minimal surrounding liquid on a low-noise support. They also minimize the formation of crystalline ice from any remaining liquid surrounding the crystals. We present the process for preparing and assessing the quality of soluble protein microcrystals using visible light and scanning electron microscopy before mounting the samples on the VMXm beamline for X-ray diffraction experiments. We will also provide examples of good quality samples as well as those which require further optimization and strategies to do so.