![]() ![]() Over the past years attempts were made to develop routine methods for grid characterization by tomography 8 or using an energy filter or scattering outside the objective aperture 9 to measure ice thickness. We reason that sample quality diagnostic -in particular regarding the ice thickness and its uniformity across the grid- is a bottleneck that could substantially benefit from a faster imaging method. Thus, this procedure constitutes a significant rate-limiting step in cryo-EM, especially for new users with limited access to measurement time at facilities. In addition, electron microscope usage is expensive and the machines are usually extensively booked. ![]() Even with automated sample loading and acquisition the screening of 12 grids requires a day of measurement time. Here, the loading of the sample within the measurement chamber and the image acquisition are relatively slow processes. ![]() In addition, it is necessary to image each of the coated grids for quality assessment, which is time consuming with an electron microscope. First, the visual assessment of ice quality from the EM images requires much experience, and it is therefore strongly dependent on the experimenter. This procedure has, however, limitations. Since a thicker ice layer transmits fewer electrons than a thinner film, the user is then able to select suitable samples based on the brightness of the images inside the holes of the grids, for which high-resolution cryo-EM can be performed. Usually the grids are then imaged at low-magnification using an electron microscope. This leads to a concentration range of protein embedded in an ice layer with varying thicknesses and uniformity across the support structure. In practice, for a given protein or complex an experimenter prepares a series of samples using various conditions for buffer composition, additives like detergents, blotting time and strength, grid type and glow discharge conditions. Samples are commonly prepared by blotting an aqueous solution containing the biological material (usually a purified protein or protein/nucleic acid complex) onto a grid support (often in copper or gold), followed by plunge freezing into liquid ethane or propane, fast enough to prevent ice formation 5, 6, 7. Hence, for every sample there is an optimal ice thickness that ideally should be just thick enough to support all orientations of the particles, which roughly corresponds to the particle size. A particularly critical experimental factor is the thickness of the ice layer in which the particles of interest are commonly embedded: the particles would not fit or could be distorted in a too thin film compared to their size, whereas image contrast would be severely impaired with a too thick ice layer because of electron absorption. However, in cryo-EM the procedure leading from purified sample to the final reconstructed biological structure is complex and time consuming, mainly due to the computationally intensive data processing, but also due to challenging sample preparation. ![]() Over the last decades cryo-EM has proven to be a powerful approach for the structure determination of proteins and biological complexes, now routinely delivering structures at near-atomic resolution 1, 2, 3, 4 The method is based on imaging randomly oriented single particles embedded in thin vitreous ice with an electron microscope, followed by image processing and 3D reconstruction of the macromolecular complex. We expect that its throughput and its versatility will contribute to facilitate the sample optimization process for structural biologists. This technique is moderate in cost and can be easily performed on a laboratory bench. We present and validate this approach for different protein complexes and grid types, and demonstrate its performance for the assessment of ice quality. The experimental setup comprises an optical interferometric microscope equipped with a cryogenic stage and image analysis software based on artificial neural networks (ANN) for an unbiased sample selection. To facilitate and accelerate the selection procedure of probes suitable for high-resolution cryo-EM, we devised a method to assess the vitreous ice layer thickness of sample coated grids. Evaluating sample quality requires access to cryo-electron microscopes and a strong expertise in EM. A critical time factor is sample optimization that requires the use of an electron microscope to screen grids prepared under different conditions to achieve the ideal vitreous ice thickness containing the particles. While recent technological developments contributed to breakthrough advances in single particle cryo-electron microscopy (cryo-EM), sample preparation remains a significant bottleneck for the structure determination of macromolecular complexes. ![]()
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