Cryo-ZSSR: multiple-image super-resolution based on deep internal learning

TL;DR

Cryo-ZSSR introduces a deep internal learning super-resolution method tailored for cryo-EM images, enhancing resolution without ground-truth data, thus potentially accelerating data collection in structural biology.

Contribution

The paper presents a novel multiple-image super-resolution algorithm based on deep internal learning specifically designed for low-SNR cryo-EM images, without requiring external training data.

Findings

Resolution of 3D structures surpasses imaging system limits.

SR micrographs enable faster cryo-EM data collection.

Method improves resolution in low-SNR cryo-EM images.

Abstract

Single-particle cryo-electron microscopy (cryo-EM) is an emerging imaging modality capable of visualizing proteins and macro-molecular complexes at near-atomic resolution. The low electron-doses used to prevent sample radiation damage, result in images where the power of the noise is 100 times greater than the power of the signal. To overcome the low-SNRs, hundreds of thousands of particle projections acquired over several days of data collection are averaged in 3D to determine the structure of interest. Meanwhile, recent image super-resolution (SR) techniques based on neural networks have shown state of the art performance on natural images. Building on these advances, we present a multiple-image SR algorithm based on deep internal learning designed specifically to work under low-SNR conditions. Our approach leverages the internal image statistics of cryo-EM movies and does not require training on ground-truth data. When applied to a single-particle dataset of apoferritin, we show that the resolution of 3D structures obtained from SR micrographs can surpass the limits imposed by the imaging system. Our results indicate that the combination of low magnification imaging with image SR has the potential to accelerate cryo-EM data collection without sacrificing resolution.