Assessing the interplay of contrast, defocus, and resolution in cryo-EM: a benchmark experiment for limited dataset screening

Abstract

Abstract The selection of defocus ranges for small datasets in cryo-electron microscopy (cryo-EM) is under-researched. We present a comprehensive benchmark experiment that aimed to evaluate the relationship between contrast, defocus, and resolution, particularly in the context of limited datasets. We conducted a detailed analysis of beta-galactosidase, apo-ferritin, and connexin-46/50 datasets to optimize pre-screening strategies for cryo-EM. Our approach involved classifying micrographs based on image contrast using an artificial intelligence (AI) model without considering the defocus level. This method allowed us to investigate the optimal defocus range for pre-screening in a limited dataset and its impact on the overall image processing. The micrographs were categorized into good, moderate, and bad contrast groups. Subsequent analysis revealed that, contrary to the prevailing assumption that lower contrast (associated with lower defocus) leads to higher resolution, in scenarios with limited datasets higher contrast images yield superior resolution. This finding was consistent across all three protein samples, underscoring the critical role of contrast in determining the quality of 3D reconstructions in limited datasets. This significant finding challenges conventional cryo-EM methodologies. In conclusion, our study provides new benchmarks for selecting appropriate contrast and defocus levels in cryo-EM, particularly for screening approaches that use limited datasets. This strategy promises to enhance the data quality and efficiency in structural biology research, particularly in resource-constrained scenarios.

Keywords

• Cryo-electron microscopy (cryo-EM)
• Contrast
• Defocus
• Resolution
• Limited dataset
• Artificial intelligence (AI)