Clustering cryo-EM images of helical protein polymers for helical reconstructions

Practical considerations for plunge freezing samples over 40 °C for Cryo-EM

Cryo-EM is now an established tool for examining samples in their native, hydrated states-a leap made possible by vitrification. Utilising this sample preparation method to directly visualise temperature-responsive samples allows for deeper insights into their structural behaviours under functional conditions. This requires samples to be plunge-frozen at elevated temperatures and presents additional challenges, including condensation within the blotting chamber and difficulties in maintaining a stable sample temperatures. Here, we address these challenges and suggest practical strategies to minimise condensation and reduce temperature fluctuations during the plunge-freezing of samples at elevated temperatures (>40 °C). By preheating equipment and reducing chamber humidity and blotting times, we can improve sample preservation and grid reproducibility. These considerations are then demonstrated on poly(N-isopropylacrylamide) microgels, which exhibit a volume phase transition due to temperature changes.

Evaluating Cryo-TEM Reconstruction Accuracy of Self-Assembled Polymer Nanostructures

Cryogenic transmission electron microscopy (cryo-TEM) combined with single particle analysis (SPA) is an emerging imaging approach for soft materials. However, the accuracy of SPA-reconstructed nanostructures, particularly those formed by synthetic polymers, remains uncertain due to potential packing heterogeneity of the nanostructures. In this study, the combination of molecular dynamics (MD) simulations and image simulations is utilized to validate the accuracy of cryo-TEM 3D reconstructions of self-assembled polypeptoid fibril nanostructures. Using CryoSPARC software, image simulations, 2D classifications, ab initio reconstructions, and homogenous refinements are performed. By comparing the results with atomic models, the recovery of molecular details is assessed, heterogeneous structures are identified, and the influence of extraction location on the reconstructions is evaluated. These findings confirm the fidelity of single particle analysis in accurately resolving complex structural characteristics and heterogeneous structures, exhibiting its potential as a valuable tool for detailed structural analysis of synthetic polymers and soft materials.

Classification of helical polymers with deep-learning language models

Many macromolecules in biological systems exist in the form of helical polymers. However, the inherent polymorphism and heterogeneity of samples complicate the reconstruction of helical polymers from cryo-EM images. Currently, available 2D classification methods are effective at separating particles of interest from contaminants, but they do not effectively differentiate between polymorphs, resulting in heterogeneity in the 2D classes. As such, it is crucial to develop a method that can computationally divide a dataset of polymorphic helical structures into homogenous subsets. In this work, we utilized deep-learning language models to embed the filaments as vectors in hyperspace and group them into clusters. Tests with both simulated and experimental datasets have demonstrated that our method - HLM (Helical classification with Language Model) can effectively distinguish different types of filaments, in the presence of many contaminants and low signal-to-noise ratios. We also demonstrate that HLM can isolate homogeneous subsets of particles from a publicly available dataset, resulting in the discovery of a previously unreported filament variant with an extra density around the tau filaments.

An Unsupervised Classification Algorithm for Heterogeneous Cryo-EM Projection Images Based on Autoencoders

Heterogeneous three-dimensional (3D) reconstruction in single-particle cryo-electron microscopy (cryo-EM) is an important but very challenging technique for recovering the conformational heterogeneity of flexible biological macromolecules such as proteins in different functional states. Heterogeneous projection image classification is a feasible solution to solve the structural heterogeneity problem in single-particle cryo-EM. The majority of heterogeneous projection image classification methods are developed using supervised learning technology or require a large amount of a priori knowledge, such as the orientations or common lines of the projection images, which leads to certain limitations in their practical applications. In this paper, an unsupervised heterogeneous cryo-EM projection image classification algorithm based on autoencoders is proposed, which only needs to know the number of heterogeneous 3D structures in the dataset and does not require any labeling information of the projection images or other a priori knowledge. A simple autoencoder with multi-layer perceptrons trained in iterative mode and a complex autoencoder with residual networks trained in one-pass learning mode are implemented to convert heterogeneous projection images into latent variables. The extracted high-dimensional features are reduced to two dimensions using the uniform manifold approximation and projection dimensionality reduction algorithm, and then clustered using the spectral clustering algorithm. The proposed algorithm is applied to two heterogeneous cryo-EM datasets for heterogeneous 3D reconstruction. Experimental results show that the proposed algorithm can effectively extract category features of heterogeneous projection images and achieve high classification and reconstruction accuracy, indicating that the proposed algorithm is effective for heterogeneous 3D reconstruction in single-particle cryo-EM.

Quaternary structure of patient-homogenate amplified α-synuclein fibrils modulates seeding of endogenous α-synuclein

Parkinson's disease (PD) and Multiple System Atrophy (MSA) are progressive and unremitting neurological diseases that are neuropathologically characterized by α-synuclein inclusions. Increasing evidence supports the aggregation of α-synuclein in specific brain areas early in the disease course, followed by the spreading of α-synuclein pathology to multiple brain regions. However, little is known about how the structure of α-synuclein fibrils influence its ability to seed endogenous α-synuclein in recipient cells. Here, we aggregated α-synuclein by seeding with homogenates of PD- and MSA-confirmed brain tissue, determined the resulting α-synuclein fibril structures by cryo-electron microscopy, and characterized their seeding potential in mouse primary oligodendroglial cultures. The combined analysis shows that the two patient material-amplified α-synuclein fibrils share a similar protofilament fold but differ in their inter-protofilament interface and their ability to recruit endogenous α-synuclein. Our study indicates that the quaternary structure of α-synuclein fibrils modulates the seeding of α-synuclein pathology inside recipient cells. It thus provides an important advance in the quest to understand the connection between the structure of α-synuclein fibrils, cellular seeding/spreading, and ultimately the clinical manifestations of different synucleinopathies.

Challenges in sample preparation and structure determination of amyloids by cryo-EM

Amyloids share a common architecture but play disparate biological roles in processes ranging from bacterial defense mechanisms to protein misfolding diseases. Their structures are highly polymorphic, which makes them difficult to study by X-ray diffraction or NMR spectroscopy. Our understanding of amyloid structures is due in large part to recent advances in the field of cryo-EM, which allows for determining the polymorphs separately. In this review, we highlight the main stepping stones leading to the substantial number of high-resolution amyloid fibril structures known today as well as recent developments regarding automation and software in cryo-EM. We discuss that sample preparation should move closer to physiological conditions to understand how amyloid aggregation and disease are linked. We further highlight new approaches to address heterogeneity and polymorphism of amyloid fibrils in EM image processing and give an outlook to the upcoming challenges in researching the structural biology of amyloids.

Clustering polymorphs of tau and IAPP fibrils with the CHEP algorithm

Recent steps towards automation have improved the quality and efficiency of the entire cryo-electron microscopy workflow, from sample preparation to image processing. Most of the image processing steps are now quite automated, but there are still a few steps which need the specific intervention of researchers. One such step is the identification and separation of helical protein polymorphs at early stages of image processing. Here, we tested and evaluated our recent clustering approach on three datasets containing amyloid fibrils, demonstrating that the proposed unsupervised clustering method automatically and effectively identifies the polymorphs from cryo-EM images. As an automated polymorph separation method, it has the potential to complement automated helical picking, which typically cannot easily distinguish between polymorphs with subtle differences in morphology, and is therefore a useful tool for the image processing and structure determination of helical proteins.

Helical Membrane Protein Crystallization in the New Era of Electron Cryo-Microscopy

Helical assemblies of proteins, which consist of a two-dimensional lattice of identical subunits arranged with helical symmetry, are a common structural motif in nature. For membrane proteins, crystallization protocols can induce helical arrangements and take advantage of the symmetry found in these assemblies for the structural determination of target proteins. Modern advances in the field of electron cryo-microscopy (cryo-EM), in particular the advent of direct electron detectors, have opened the potential for structure determination of membrane proteins in such assemblies at high resolution. The nature of the symmetry in helical crystals of membrane proteins means that a single image potentially contains enough information for three-dimensional structural determination. With the current direct electron detectors, we have never been closer to making this a reality. Here, we present a protocol detailing the preparation of helical crystals, with an emphasis on further cryo-EM analysis and structural determination of the sarco(endo)plasmic reticulum Ca²⁺-ATPase in the presence of regulatory subunits such as phospholamban.

Polymer Free Volume Effects on Protein Dynamics in Polystyrene Revealed by Single-Molecule Spectroscopy

Protein-polymer interactions are critical to applications ranging from biomedical devices to chromatographic separations. The mechanistic relationship between the microstructure of polymer chains and protein interactions is challenging to quantify and not well studied. Here, single-molecule microscopy is used to compare the dynamics of two model proteins, α-lactalbumin and lysozyme, at the interface of uncharged polystyrene with varied molecular weights. The two proteins exhibit different surface interaction mechanisms despite having a similar size and structure. α-Lactalbumin exhibits interfacial adsorption-desorption with residence times that depend on polymer molecular weight. Lysozyme undergoes a continuous time random walk at the polystyrene surface with residence times that also depend on the molecular weight of polystyrene. Single-molecule observables suggest that the hindered continuous time random walk dynamics displayed by lysozyme are determined by the polystyrene free volume, a finding supported by thermal annealing and solvent quality studies. Hindered dynamics are dominated by short-range hydrophobic interactions where the contributions of electrostatic forces are negligible. This work establishes a relationship between the microscale structure (i.e., free volume) of polystyrene polymer chains to nanoscale interfacial protein dynamics.