Ice thickness monitoring for cryo-EM grids by interferometry imaging

• Published in: Scientific reports Vol. 12; no. 1; p. 15330
• Main Authors: Hohle, Markus Matthias, Lammens, Katja, Gut, Fabian, Wang, Bingzhi, Kahler, Sophia, Kugler, Kathrin, Till, Michael, Beckmann, Roland, Hopfner, Karl-Peter, Jung, Christophe
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 12.09.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 631/535/1258/1259; 639/624/1107/328/1650; Automation; Electron microscopy; Humanities and Social Sciences; Ice; Ice thickness; Image processing; Interferometry; Macromolecules; Methods; Microscopes; Microscopy; multidisciplinary; Neural networks; Proteins; Sample preparation; Science; Science (multidisciplinary); Software
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-022-16978-7

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. 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. Evaluating sample quality requires access to cryo-electron microscopes and a strong expertise in EM. 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. 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. We present and validate this approach for different protein complexes and grid types, and demonstrate its performance for the assessment of ice quality. This technique is moderate in cost and can be easily performed on a laboratory bench. We expect that its throughput and its versatility will contribute to facilitate the sample optimization process for structural biologists.