Cryo-TEM simulations of amorphous radiation-sensitive samples using multislice wave propagation

Image simulation plays a central role in the development and practice of high-resolution electron microscopy, including transmission electron microscopy of frozen-hydrated specimens (cryo-EM). Simulating images with contrast that matches the contrast observed in experimental images remains challenging, especially for amorphous samples. Current state-of-the-art simulators apply post hoc scaling to approximate empirical solvent contrast, attenuated image intensity due to specimen thickness and amplitude contrast. This practice fails for images that require spatially variable scaling, e.g. simulations of a crowded or cellular environment. Modeling both the signal and the noise accurately is necessary to simulate images of biological specimens with contrast that is correct on an absolute scale. The 'frozen plasmon' method is introduced to explicitly model spatially variable inelastic scattering processes in cryo-EM specimens. This approach produces amplitude contrast that depends on the atomic composition of the specimen, reproduces the total inelastic mean free path as observed experimentally and allows for the incorporation of radiation damage in the simulation. These improvements are quantified using the matched filter concept to compare simulation and experiment. The frozen plasmon method, in combination with a new mathematical formulation for accurately sampling the tabulated atomic scattering potentials onto a Cartesian grid, is implemented in the open-source software package cisTEM.

Automated summary

Image simulation is crucial in the development of high-resolution electron microscopy, but challenges remain in matching contrast, modeling signal and noise accurately, and accommodating spatially variable scaling. The 'frozen plasmon' method introduces an approach to simulate spatially variable inelastic scattering processes in cryo-EM specimens, allowing for accurate amplitude contrast and incorporating radiation damage in the simulation. By quantifying these improvements using the matched filter concept, the method shows potential for accurately simulating biological specimen images.