Automated data acquisition is used widely for single-particle reconstruction of three-dimensional (3D) volumes of biological complexes preserved in vitreous ice and imaged in a transmission electron microscope. Automation has become integral to this method because of the very large number of particle images required in order to overcome the typically low signal-to-noise ratio of these images. For optimal efficiency, automated data acquisition software packages typically employ some beam-image shift targeting as this method is both fast and accurate (±0.1 µm). In contrast, using only stage movement, relocation to a targeted area under low-dose conditions can only be achieved in combination with multiple iterations or long relaxation times, both reducing efficiency. Nevertheless it is well known that applying beam-image shift induces beam-tilt and with it a potential structure phase error with a phase error π/4 the highest acceptable value. This theory has been used as an argument against beam-image shift for high resolution data collection. Nevertheless, in practice many small beam-image shift datasets have resulted in 3D reconstructions beyond the π/4 phase error limit. To address this apparent contradiction, we performed cryo-EM single-particle reconstructions on a T20S proteasome sample using applied beam-image shifts corresponding to beam tilts from 0 to 10 mrad. To evaluate the results we compared the FSC values, and examined the water density peaks in the 3D map. We conclude that the phase error does not limit the validity of the 3D reconstruction from single-particle averaging beyond the π/4 resolution limit.

Automated data acquisition is now used widely for the single-particle averaging approach to reconstruction of three-dimensional (3D) volumes of biological complexes preserved in vitreous ice and imaged in a transmission electron microscope (cryo-EM). Automation has become integral to this method because of the very large number of particle images required to be averaged in order to overcome the typically low signal-to-noise ratio of these images.For optimal efficiency, all automated data acquisition software packages employ some degree of beam-image shift because this method is fast and accurate (+/-0.1 µm). Relocation to a targeted area under low-dose conditions can only be achieved using stage movements in combination with multiple iterations or long relaxation times, both reducing efficiency. It is, however, well known that applying beam-image shift induces beam-tilt and hence structure phase error. A π/4 phase error is considered as the worst that can be accepted, and is used as an argument against the use of any beam-image shift for high resolution data collection.In this study, we performed cryo-EM single-particle reconstructions on a T20S proteasome sample using applied beam-image shifts corresponding to beam tilts from 0 to 10 mrad. To evaluate the results we compared the FSC values, and examined the water density peaks in the 3D map. We conclude that the π/4 phase error does not limit the validity of the 3D reconstruction from single-particle averaging beyond the π/4 resolution limit.

The most widely used sample preparation method for single particle cryo-electron microscopy (cryo-EM) today involves the application of 3-4 μl of sample onto a cryo-EM grid, removing most of the liquid by blotting with filter paper, then rapidly plunging into liquid ethane to vitrify the sample. To determine if the grid has appropriate ice thicknesses and sufficient area for cryo-EM imaging, the grid must be inserted into a transmission electron microscope (TEM) and screened. This process to evaluate grid quality is costly and time consuming. Here, we present our initial attempt to image the sample preparation process in one of the most commonly used plunge freezing devices, the Vitrobot. We do this by building the Vitrocam, a Raspberry Pi high-speed camera, that captures images of grids mid-plunge. Images from the Vitrocam can be correlated to TEM atlases and show promise for providing preliminary feedback on grid quality and ice thickness.

Recent developments in cryo-electron microscopy (cryoEM) have led to the routine determination of structures at near atomic resolution and greatly increased the number of biomedical researchers wanting access to high-end cryoEM instrumentation. The high costs and long wait times for gaining access encourages facilities to maximize instrument uptime for data collection. To support these goals, we developed a System Environmental Metrics Collector for facilities (SEMCf) that serves as a laboratory performance and management tool. SEMCf consists of an architecture of automated and robust sensors that track, organize and report key facility metrics. The individual sensors are connected to Raspberry Pi (RPi) single board computers installed in close proximity to the input metrics being measured. The system is controlled by a central server that may be installed on a RPi or existing microscope support PC. Tracking the system and the environment provides feedback of imminent issues, suggestions for interventions that are needed to optimize data production, and indications as to when preventative maintenance should be scheduled. The sensor components are relatively inexpensive and widely commercially available, and the open-source design and software enables straightforward implementation, customization, and optimization by any facility that would benefit from real time environmental monitoring and reporting.

No abstract