CryoEM screening with a blotting instrument: quantifying parameters affecting ice thickness using semi-automated image analysis

Cryo-electron microscopy (cryoEM) grid preparation is one of the bottlenecks in using cryoEM for macromolecular structure determination. Despite significant advancements in the development of blot-free instruments, their high cost limits their widespread usage. Understanding the effects of the different parameters of the blotting instruments, particularly the Vitrobot, can lead to producing high-quality grids while saving both time and resources. In this study, we focus specifically on low magnification cryoEM images as a rapid way to evaluate ice thickness and screen the effects of different grid preparation parameters. By employing a machine learning-based approach and semi-automated image analysis, we analysed large datasets of low-magnification atlas images to quantify ice thickness and distribution across grids prepared under varied conditions. Our results show that detectable changes in ice quality often require substantial adjustments in parameters, and even then, considerable grid-to-grid variability can persist. Notably, we observed that the presence of detergent improved consistency in ice thickness. While our approach does not assess protein distribution or particle behaviour at high magnification, it offers a scalable and efficient tool for early-stage grid screening and protocol optimization.