2D image classification and alignment in single-particle Cryo-EM

ZiJian, Bai

2D image classification and alignment in single-particle Cryo-EM

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Files

BaiZ_PhD2024.pdf(46.22 MB)

Full Text E-thesis

Date

2024

Authors

ZiJian, Bai

Publisher

University College Cork

Abstract

Background and Objective: Single-particle Cryo-EM is a powerful tool for structural biologists to determine structures of macro-molecules at atomic resolution. In single-particle Cryo-EM, many individual biological molecular particles with identical structures are rapidly frozen at unknown, random orientations, and then imaged by transmission electron microscopy to generate a set of two-dimensional (2D) projection images. These generated images are treated as projections of one particle from various directions and used to reconstruct the 3D structure of the particle. Reconstruction in single-particle Cryo-EM is extremely challenging due to the lack of knowledge on the projection directions and low signal to noise ratios (SNR). To tackle this difficulty, most methods start by estimating an ab-initio model from class-averaged particle images. Then, this initial model is refined iteratively until a high resolution map is obtained, a task named ‘3D refinement’. There are two challenges in reconstructing a 3D particle structure from projection images in single-particle Cryo-EM. One is that as the particles are oriented randomly within the ice, the projection angle of the image is unknown. Another is the very low SNR, it is extremely hard to estimate projection angles. The main reason for the low SNR is the low electron doses allowed to be used for imaging. To improve the SNR in single-particle Cryo-EM, projection images are classified and averaged to generate the class-average images. This thesis focus on identifying and developing methods that can accurately and effectively compute class-averages. Methodology: Two important steps in class averaging are: (1) classifying images with similar viewing directions but with different in-plane rotations; (2) rotation alignment of images in each class. To perform rotation-invariant classification and rotation alignment, we first propose a non-uniform discrete Fourier transform (NUDFT) to calculate the Fourier transform of an image in polar coordinates. Based on the proposed NUDFT, we develop a rotation-invariant feature extraction algorithm. After using principal component analysis to reduce the dimension of the extracted rotation-invariant features and defining a distance between images using their corresponding features, K-means is employed to classify images. We also investigate combining spectral clustering with our NUDFT based rotation-invariant features for the image classification and compare its performance with the K-means. We build an algorithm for estimation of rotation between a pair of images on the base of proposed NUDFT. We also develop a novel spectral clustering method to improve the accuracy of image alignment in the same class. To demonstrate the efficiency of the algorithms, extensive simulations and experiments are performed to compare with rotation-invariant classification and rotation alignment based conventional FT and some existing algorithms in single-particle Cryo-EM. Results and conclusions: The results for the first set of simulation studies show that combining NUDFT with K-means has superior performance in rotation-invariant classification compared with K-means using features extracted by the classical FT and CL2D, displaying enhanced noise resistance, particularly in very low signal-to-noise ratio environments. The second set of simulation studies show that combining NUDFT with spectral clustering can further improve classification performance. In third set of simulation studies, the results show that NUDFT has superior performance in image rotation estimation compared with classical FT, it shows enhanced noise resistance in different SNR levels. The fourth set of simulation studies show that applying spectral clustering and Z-score method on the frequency information generated from NUDFT can further enhanced alignment performance. The overall studies show that NUDFT is effective and efficient in 2D class-averaging, and proposed algorithms can realize the potential of NUDFT and improve its performance.

Keywords

Rotation-invariant feature extraction , 2D classification , Single-particle Cryo-EM , Non-uniform Fourier transform

Citation

Bai, Z. 2024. 2D image classification and alignment in single-particle Cryo-EM. PhD Thesis, University College Cork.