Plasma membranes (PMs) are highly dynamic structures where lipids and proteins can theoretically diffuse freely. However, reports indicate that PM proteins do not freely diffuse within their planes but are constrained by cytoskeleton networks, though the mechanisms for how the cytoskeleton restricts lateral diffusion of plant PM proteins are unclear. Through single-molecule tracking, we investigated the dynamics of six Arabidopsis (Arabidopsis thaliana) PM proteins with diverse structures and found distinctions in sizes and dynamics among these proteins. Moreover, we showed that the cytoskeleton, particularly microtubules, limits the diffusion of PM proteins, including transmembrane and membrane-anchoring proteins. Interestingly, the microfilament skeleton regulates intracellular transport of endocytic cargo. Therefore, these findings indicate that the cytoskeleton controls signal transduction by limiting diffusion of PM proteins in specific membrane compartments and participating in transport of internalized cargo vesicles, thus actively regulating plant signal transduction.

Nanosized ultrafine particles (UFPs) from natural and anthropogenic sources are widespread and pose serious health risks when inhaled by humans. However, tracing the inhaled UFPs in vivo is extremely difficult, and the distribution, translocation, and metabolism of UFPs remain unclear. Here, we report a label-free, machine learning-aided single-particle inductively coupled plasma mass spectrometry (spICP-MS) approach for tracing the exposure pathways of airborne magnetite nanoparticles (MNPs), including external emission sources, and distribution and translocation in vivo using a mouse model. Our results provide quantitative analysis of different metabolic pathways in mice exposed to MNPs, revealing that the spleen serves as the primary site for MNP metabolism (84.4%), followed by the liver (11.4%). The translocation of inhaled UFPs across different organs alters their particle size. This work provides novel insights into the in vivo fate of UFPs as well as a versatile and powerful platform for nanotoxicology and risk assessment.

Lipid-based nanomedicines (LBNMs), including liposomes, lipid nanoparticles (LNPs) and extracellular vesicles (EVs), are recognized as one of the most clinically acceptable nano-formulations. However, the bench-to-bedside translation efficiency is far from satisfactory, mainly due to the lack of in-depth understanding of their physical and biochemical attributes at the single-particle level. In this review, we first give a brief introduction of LBNMs, highlighting some milestones and related scientific and clinical achievements in the past several decades, as well as the grand challenges in the characterization of LBNMs. Next, we present an overview of each category of LBNMs as well as the core properties that largely dictate their biological characteristics and clinical performance, such as size distribution, particle concentration, morphology, drug encapsulation and surface properties. Then, the recent applications of several analytical techniques including electron microscopy, atomic force microscopy, fluorescence microscopy, Raman microscopy, nanoparticle tracking analysis, tunable resistive pulse sensing and flow cytometry on the single-particle characterization of LBNMs are thoroughly discussed. Particularly, the comparative advantages of the newly developed nano-flow cytometry that enables quantitative analysis of both the physical and biochemical characteristics of LBNMs smaller than 40 nm with high throughput and statistical robustness are emphasized. The overall aim of this review article is to illustrate the importance, challenges and achievements associated with single-particle characterization of LBNMs.

Spike-like nanostructures are omnipresent in natural and artificial systems. Although biorecognition of nanostructures to cellular receptors has been indicated as the primary factor for virus infection pathways, how the spiky morphology of DNA-modified nanoparticles affects their cellular uptake and intracellular fate remains to be explored. Here, we design dually emissive gold nanoparticles with varied spikiness (from 0 to 2) to probe the interactions of spiky nanoparticles with cells. We discovered that nanospikes at the nanoparticle regulated myosin IIA recruitment at the cell membrane during cellular uptake, thereby enhancing cellular uptake efficiency, as revealed by dual-modality (plasmonic and fluorescence) imaging. Furthermore, the spiky nanoparticles also exhibited facilitated endocytosis dynamics, as revealed by real-time dark-field microscopy (DFM) imaging and colorimetry-based classification algorithms. These findings highlight the crucial role of the spiky morphology in regulating the intracellular fate of nanoparticles, which may shed light on engineering theranostic nanocarriers.

Recent progress in cryo-EM research has ignited a revolution in biological macromolecule structure determination. Resolution is an essential parameter for quality assessment of a cryo-EM density map, and it is known that resolution varies in different regions of a map. Currently available methods for local resolution estimation require manual adjustment of parameters and in some cases necessitate acquisition or de novo generation of so-called \"half maps\". Here, we developed CryoRes, a deep-learning algorithm to estimate local resolution directly from a single final cryo-EM density map, specifically by learning resolution-aware patterns of density map voxels through supervised training on a large dataset comprising 1,174 experimental cryo-EM density maps. CryoRes significantly outperforms all of the state-of-the-art competing resolution estimation methods, achieving an average RMSE of 2.26 Å for local resolution estimation relative to the currently most reliable FSC-based method blocres, yet requiring only the single final map as input. Further, CryoRes is able to generate a molecular mask for each map, with accuracy 12.12% higher than the masks generated by ResMap. CryoRes is ultra-fast, fully automatic, parameter-free, applicable to cryo-EM subtomogram data, and freely available at https://cryores.zhanglab.net.

Aryl hydrocarbon receptor (AhR) is an important ligand-activated transcription factor involved in the regulation of various important physiological functions. Here, we report the cryo-EM structures of the Hsp90-AhR-p23 complex with or without bound XAP2, where the structure of the mouse AhR PAS-B domain is resolved. A highly conserved bridge motif of AhR is responsible for the interaction with the Hsp90 dimeric lumen. The ligand-free AhR PAS-B domain is attached to the Hsp90 dimer and is stabilized in the complex with bound XAP2. In addition, the DE-loop and a group of conserved pocket inner residues in the AhR PAS-B domain are found to be important for ligand binding. These results reveal the structural basis of the biological functions of AhR. Moreover, the protein purification method presented here allows the isolation of stable mouse AhR protein, which could be used to develop high-sensitivity biosensors for environmental pollutant detection.

The rapid development of cryogenic electron microscopy (cryo-EM) enables the structure determination of macromolecules without the need for crystallization. Protein, protein-lipid, and protein-nucleic acid complexes can now be routinely resolved by cryo-EM single-particle analysis (SPA) to near-atomic or atomic resolution. Here we describe the structure determination of pure RNAs by SPA, from cryo-specimen preparation to data collection and 3D reconstruction. This protocol is useful to yield many cryo-EM structures of RNA, here exemplified by the Tetrahymena L-21 ScaI ribozyme at 3.1-Å resolution.

Extracellular vesicles (EVs) have emerged as an attractive drug delivery system owing to their natural roles in intercellular communication. On account of the large intrinsic heterogeneity of EVs, it is highly desirable to evaluate not only the encapsulation efficiency but also the alteration of biological functionality after the drug-loading process at the single-particle level. However, the nanoscale size of EVs poses a great challenge. Taking advantage of nano-flow cytometry (nFCM) in the multiparameter analysis of single EVs as small as 40 nm, six commonly used drug-loading strategies (coincubation, electroporation, extrusion, freeze-thawing, sonication, and surfactant treatment) were exploited by employing doxorubicin (Dox) as the model drug. Encapsulation ratio, EV concentration, drug content, and membrane proteins of Dox-loaded EVs were measured at the single-particle level. Our data indicated that coincubation and electroporation outperformed other methods with an encapsulation ratio of approximately 45% and a higher Dox content in single EVs. Interestingly, the labeling ratios of membrane proteins indicated that varying degrees of damage to the surface proteins of EVs occurred upon extrusion, freeze-thawing, sonication, and surfactant treatment. Confocal fluorescence microscopy and flow cytometry analysis revealed that Dox-loaded EVs prepared by electroporation induced the strongest apoptosis followed by coincubation. These results correlated well with their cellular uptake rate and fundamentally with the Dox encapsulation efficiency of single EVs. nFCM provides a rapid and sensitive platform for single-particle assessment of drug-loading strategies for incorporating drugs into EVs.

Single-particle cryo-electron microscopy (cryo-EM) has become one of the mainstream technologies in the field of structural biology to determine the three-dimensional (3D) structures of biological macromolecules. Heterogeneous cryo-EM projection image classification is an effective way to discover conformational heterogeneity of biological macromolecules in different functional states. However, due to the low signal-to-noise ratio of the projection images, the classification of heterogeneous cryo-EM projection images is a very challenging task. In this paper, two novel distance measures between projection images integrating the reliability of common lines, pixel intensity and class averages are designed, and then a two-stage spectral clustering algorithm based on the two distance measures is proposed for heterogeneous cryo-EM projection image classification. In the first stage, the novel distance measure integrating common lines and pixel intensities of projection images is used to obtain preliminary classification results through spectral clustering. In the second stage, another novel distance measure integrating the first novel distance measure and class averages generated from each group of projection images is used to obtain the final classification results through spectral clustering. The proposed two-stage spectral clustering algorithm is applied on a simulated and a real cryo-EM dataset for heterogeneous reconstruction. Results show that the two novel distance measures can be used to improve the classification performance of spectral clustering, and using the proposed two-stage spectral clustering algorithm can achieve higher classification and reconstruction accuracy than using RELION and XMIPP.

Understanding the dynamic behavior of charged particles driven by flow and electric field in nanochannels/pores is highly important for both fundamental study and practical applications. While a great breakthrough has been made in understanding the translocation dynamics of charged particles within the nanochannels/pores, studies on the dynamics of particles at the orifice of nanochannels/pores are scarcely reported. Here, we study particle motion at a smaller-sized orifice of a nanopipette by combining experimentally observed current transients with simulated force conditions. The theoretical force analysis reveals that dielectrophoretic force plays an equally important role as electrophoretic force and electroosmotic force, although it has often been neglected in understanding the particle translocation dynamics within the nanopipette. Under the combined action of these forces, it thus becomes difficult for particles to physically collide with the orifice of the nanopipette, resulting in a relatively low decrease in the current transients, which coincides with experimental results. We then regulate the dynamic behavior by altering experimental conditions (i.e., bias potential, nanopipette surface charge, and particle size), and the results further validate the presence and influence of forces being considered. This study improves the understanding of the relationship between particle properties and observed current transients, providing more possibilities for accurate single-particle analysis and single-entity regulation.