Holographic single particle imaging for weakly scattering, heterogeneous nanoscale objects

TL;DR

This paper introduces an advanced holographic single particle imaging method with a new phase retrieval algorithm, MaxLP, that enhances robustness to heterogeneity and improves low-signal performance for nanoscale structure determination.

Contribution

It presents a novel phase retrieval algorithm, MaxLP, with improved scalability and performance, and demonstrates enhanced robustness to heterogeneity in holographic SPI.

Findings

MaxLP outperforms previous algorithms in low signal conditions.

Structural heterogeneity is averaged out, increasing robustness.

Potential to achieve sub-nanometer resolution in biomolecule imaging.

Abstract

Single particle imaging (SPI) at X-ray free electron lasers (XFELs) is a technique to determine the 3D structure of nanoscale objects like biomolecules from a large number of diffraction patterns of copies of these objects in random orientations. Millions of low signal-to-noise diffraction patterns with unknown orientation are collected during an X-ray SPI experiment. The patterns are then analyzed and merged using a reconstruction algorithm to retrieve the full 3D-structure of particle. The resolution of reconstruction is limited by background noise, signal-to-noise ratio in diffraction patterns and total amount of data collected. We recently introduced a reference-enhanced holographic single particle imaging methodology [Optica 7,593-601(2020)] to collect high enough signal-to-noise and background tolerant patterns and a reconstruction algorithm to recover missing parameters beyond orientation and then directly retrieve the full Fourier model of the sample of interest. Here we describe a phase retrieval algorithm based on maximum likelihood estimation using pattern search dubbed as MaxLP, with better scalability for fine sampling of latent parameters and much better performance in the low signal limit. Furthermore, we show that structural variations within the target particle are averaged in real space, significantly improving robustness to conformational heterogeneity in comparison to conventional SPI. With these computational improvements, we believe reference-enhanced SPI is capable of reaching sub-nm resolution biomolecule imaging.