搜索结果：Extracellular vesicles and circulating nucleic acids

Extracellular vesicles (EVs) and plasma membrane-derived exosome-mimetic nanovesicles demonstrate significant potential for drug delivery. Latter synthetic provides higher throughput over physiological EVs. However they face size-stability and self-agglomeration challenges in physiological solutions to be properly characterized and addressed. Here we demonstrate a fast and high-throughput nanovesicle screening methodology relying on dynamic light scattering (DLS) complemented by atomic force microscopy (AFM) measurements, suitable for the evaluation of hydrodynamic size instabilities and aggregation effects in nanovesicle solutions under varying experimental conditions and apply it to the analysis of bio-engineered nanovesicles derived from erythrocytes as well as physiological extracellular vesicles isolated from animal seminal plasma. The synthetic vesicles exhibit a significantly higher degree of agglomeration, with only 8 % of them falling within the typical extracellular vesicle size range (30-200 nm) in their original preparation conditions. Concurrent zeta potential measurements performed on both physiological and synthetic nanovesicles yielded values in the range of -17 to

Super-Resolution Microscopy (SRM) is emerging as a powerful and innovative tool for imaging, characterizing, and understanding the structure of Extracellular Vesicles (EVs). By addressing the need for single-particle analysis with the high resolution required to study the composition and organization of these nanoparticles, SRM provides unique insights into EV biology. However, its application is accompanied by significant challenges, ranging from experimental setup to data analysis. This review outlines the fundamentals of SRM and its position within the broader field of EV research. We then explore its applications in evaluating (i) the morphological structure of EVs, (ii) their molecular composition, and (iii) their roles in biological systems. By offering practical guidance and an overview of critical parameters for standardization, this review aims at providing researchers with the tools and insights necessary to effectively apply SRM to EV investigation.

Achieving high-throughput, comprehensive analysis of single nanoparticles to determine their size, shape, and composition is essential for understanding particle heterogeneity with applications ranging from drug delivery to environmental monitoring. Existing techniques are hindered by low throughput, lengthy trapping times, irreversible particle adsorption, or limited characterization capabilities. Here, we introduce Interferometric Electrohydrodynamic Tweezers (IET), an integrated platform that rapidly traps single nanoparticles in parallel within three seconds. IET enables label-free characterization of particle size and shape via interferometric imaging and identifies molecular composition through Raman spectroscopy, all without the need for fluorescent labeling. We demonstrate the platform's capabilities by trapping and imaging colloidal polymer beads, nanoscale extracellular vesicles (EVs), and newly discovered extracellular nanoparticles known as supermeres. By monitoring their interferometric contrast images while trapped, we accurately determine the sizes of EVs and supermeres. Our IET represents a powerful optofluidics platform for comprehensive characterization of nanosca

Phase separation is as familiar as watching vinegar separating from oil in vinegrette. The observation that phase separation of proteins and nucleic acids is widespread in living cells has opened an entire field of research into the biological significance and the biophysical mechanisms of phase separation and protein condensation in biology. Recent evidence indicate that certain proteins and nucleic acids condensates are not simple liquids and instead display both viscous and elastic behaviours, which in turn may have biological significance. The aim of this perspective is to review the state-of-the-art of this quickly emerging field focusing on the material and rheological properties of protein condensates. Finally, we discuss the different techniques that can be employed to quantify the viscoelasticity of condensates and highlight potential future directions and opportunities for interdisciplinary cross-talk between chemists, physicists and biologists.

Artificial analogues of the natural nucleic acids have attracted recent interest as a diverse class of information storage molecules capable of self-replication. In the present study, we use the computational potential energy landscape framework to investigate the structural and dynamical properties of xylo- and deoxyxylo-nucleic acids (XyNA and dXyNA), which are derived from their respective RNA and DNA analogues by an inversion of configuration at a single chiral center in the sugar moiety of the nucleotide unit. The free energy landscapes of an octameric XyNA sequence and its dXyNA analogue demonstrate the existence of a facile conformational transition between a left-handed helix that is the global free energy minimum, and a closely competing ladder-type structure with approximately zero helicity. The separation of the competing conformational ensembles is better-defined for the dXyNA system, whereas the XyNA analogue is inherently more flexible. The former therefore appear more suitable candidates for a molecular switch. The landscapes differ qualitatively from those reported in previous studies for evolved biomolecules: they are significantly more frustrated, so that XyNAs pr

Helicases are molecular motors which unwind double-stranded nucleic acids (dsNA) in cells. Many helicases move with directional bias on single-stranded (ss) nucleic acids, and couple their directional translocation to strand separation. A model of the coupling between translocation and unwinding uses an interaction potential to represent passive and active helicase mechanisms. A passive helicase must wait for thermal fluctuations to open dsNA base pairs before it can advance and inhibit NA closing. An active helicase directly destabilizes dsNA base pairs, accelerating the opening rate. Here we extend this model to include helicase unbinding from the nucleic-acid strand. The helicase processivity depends on the form of the interaction potential. A passive helicase has a mean attachment time which does not change between ss translocation and ds unwinding, while an active helicase in general shows a decrease in attachment time during unwinding relative to ss translocation. In addition, we describe how helicase unwinding velocity and processivity vary if the base-pair binding free energy is changed.

The interaction between proteins and nucleic acids is crucial for processes that sustain cellular function, including DNA maintenance and the regulation of gene expression and translation. Amino acid mutations in protein-nucleic acid complexes often lead to vital diseases. Experimental techniques have their own specific limitations in predicting mutational effects in protein-nucleic acid complexes. In this study, we compiled a large dataset of 1951 mutations including both protein-DNA and protein-RNA complexes and integrated structural and sequential features to build a deep learning-based regression model named DeepPNI. This model estimates mutation-induced binding free energy changes in protein-nucleic acid complexes. The structural features are encoded via edge-aware RGCN and the sequential features are extracted using protein language model ESM-2. We have achieved a high average Pearson correlation coefficient (PCC) of 0.76 in the large dataset via five-fold cross-validation. Consistent performance across individual dataset of protein-DNA, protein-RNA complexes, and different experimental temperature split dataset make the model generalizable. Our model showed good performance

To understand and engineer biological and artificial nucleic acid systems, algorithms are employed for prediction of secondary structures at thermodynamic equilibrium. Dynamic programming algorithms are used to compute the most favoured, or Minimum Free Energy (MFE), structure, and the Partition Function (PF), a tool for assigning a probability to any structure. However, in some situations, such as when there are large numbers of strands, or pseudoknoted systems, NP-hardness results show that such algorithms are unlikely, but only for MFE. Curiously, algorithmic hardness results were not shown for PF, leaving two open questions on the complexity of PF for multiple strands and single strands with pseudoknots. The challenge is that while the MFE problem cares only about one, or a few structures, PF is a summation over the entire secondary structure space, giving theorists the vibe that computing PF should not only be as hard as MFE, but should be even harder. We answer both questions. First, we show that computing PF is #P-hard for systems with an unbounded number of strands, answering a question of Condon Hajiaghayi, and Thachuk [DNA27]. Second, for even a single strand, but allowin

Nucleic acids theoretically possess a Szilard engine function that can convert the energy associated with the Shannon entropy of molecules for which they have coded recognition, into the useful work of geometric reconfiguration of the nucleic acid molecule. This function is logically reversible because its mechanism is literally and physically constructed out of the information necessary to reduce the Shannon entropy of such molecules, which means that this information exists on both sides of the theoretical engine, and because information is retained in the geometric degrees of freedom of the nucleic acid molecule, a quantum gate is formed through which multi-state nucleic acid qubits can interact. Entangled biophotons emitted as a consequence of symmetry breaking nucleic acid Szilard engine (NASE) function can be used to coordinate relative positioning of different nucleic acid locations, both within and between cells, thus providing the potential for quantum coherence of an entire biological system. Theoretical implications of understanding biological systems as such "quantum adaptive systems" include the potential for multi-agent based quantum computing, and a better understand

The precise quantification of nucleic acids is pivotal in molecular biology, underscored by the rising prominence of nucleic acid amplification tests (NAAT) in diagnosing infectious diseases and conducting genomic studies. This review examines recent advancements in digital Polymerase Chain Reaction (dPCR) and digital Loop-mediated Isothermal Amplification (dLAMP), which surpass the limitations of traditional NAAT by offering absolute quantification and enhanced sensitivity. In this review, we summarize the compelling advancements of dNNAT in addressing pressing public health issues, especially during the COVID-19 pandemic. Further, we explore the transformative role of artificial intelligence (AI) in enhancing dNAAT image analysis, which not only improves efficiency and accuracy but also addresses traditional constraints related to cost, complexity, and data interpretation. In encompassing the state-of-the-art (SOTA) development and potential of both software and hardware, the all-encompassing Point-of-Care Testing (POCT) systems cast new light on benefits including higher throughput, label-free detection, and expanded multiplex analyses. While acknowledging the enhancement of AI-

Extracellular vesicles (EVs) are cell-derived secretions that mediate tissue homeostasis and intercellular communication through their diverse cargos, such as proteins. Distinct EV biogenesis pathways suggest specific association and co-enrichment of proteins sharing a biogenesis pathway, and non-association and co-depletion of proteins segregated into distinct pathways. Yet these associations elude conventional protein expression or co-expression measurements. Here, we propose and define pairwise protein co-enrichment (CoEn) to quantify whether a given protein is co-enriched or co-depleted with another protein relative to its overall expression. We measure CoEn, and differential CoEn (dCoEn) between a stimulus and a reference condition, of up to 240 protein pairs in EVs using antibody microarrays. We validate CoEn by modulating well-known EV biogenesis pathways, and find that dCoEn quantifies expected changes between perturbed and reference conditions while uncovering new ones; CoEn and dCoEn in three model cell lines and parental and organotropic breast cancer progeny cell lines reveals both preserved and variable CoEn that may warrant further studies. Collectively, our result su

Mechanical properties of nucleic acids play an important role in many biological processes which often involve physical deformations of these molecules. At sufficiently long length scales (say above $\sim 20-30$ base pairs) the mechanics of DNA and RNA double helices is described by a homogeneous Twistable Wormlike Chain (TWLC), a semiflexible polymer model characterized by twist and bending stiffnesses. At shorter scales this model breaks down for two reasons: the elastic properties become sequence-dependent and the mechanical deformations at distal sites gets coupled. We discuss in this paper the origin of the latter effect using the framework of a non-local Twistable Wormlike Chain (nlTWLC). We show, by comparing all-atom simulations data for DNA and RNA double helices, that the non-local couplings are of very similar nature in these two molecules: couplings between distal sites are strong for tilt and twist degrees of freedom and weak for roll. We introduce and analyze a simple double-stranded polymer model which clarifies the origin of this universal distal couplings behavior. In this model, referred to as the ladder model, a nlTWLC description emerges from the coarsening of l

Loops are essential secondary structure elements in folded DNA and RNA molecules and proliferate close to the melting transition. Using a theory for nucleic acid secondary structures that accounts for the logarithmic entropy c ln m for a loop of length m, we study homopolymeric single-stranded nucleic acid chains under external force and varying temperature. In the thermodynamic limit of a long strand, the chain displays a phase transition between a low temperature / low force compact (folded) structure and a high temperature / high force molten (unfolded) structure. The influence of c on phase diagrams, critical exponents, melting, and force extension curves is derived analytically. For vanishing pulling force, only for the limited range of loop exponents 2 < c < 2.479 a melting transition is possible; for c <= 2 the chain is always in the folded phase and for 2.479 < c always in the unfolded phase. A force induced melting transition with singular behavior is possible for all loop exponents c < 2.479 and can be observed experimentally by single molecule force spectroscopy. These findings have implications for the hybridization or denaturation of double stranded nucl

Nucleic acids such as mRNA have emerged as a promising therapeutic modality with the capability of addressing a wide range of diseases. Lipid nanoparticles (LNPs) as a delivery platform for nucleic acids were used in the COVID-19 vaccines and have received much attention. While modern manufacturing processes which involve rapidly mixing an organic stream containing the lipids with an aqueous stream containing the nucleic acids are conceptually straightforward, detailed understanding of LNP formation and structure is still limited and scale-up can be challenging. Mathematical and computational methods are a promising avenue for deepening scientific understanding of the LNP formation process and facilitating improved process development and control. This article describes strategies for the mechanistic modeling of LNP formation, starting with strategies to estimate and predict important physicochemical properties of the various species such as diffusivities and solubilities. Subsequently, a framework is outlined for constructing mechanistic models of reactor- and particle-scale processes. Insights gained from the various models are mapped back to product quality attributes and proces

We present an experimental demonstration of near-field optical trapping and dynamic manipulation of a single extracellular vesicle using a plasmonic dielectric nanoantenna that supports an optical anapole state. The optical anapole is a non-radiating optical state generated by the destructive interference between electric and toroidal dipoles in the far-field. To enhance the trapping capabilities, we employ a plasmonic mirror to enhance the anapole state. By harnessing the enhanced electromagnetic hotspot resulting from the mirror-enhanced anapole state, we achieve a high trapping potential of approximately 3.5 KbT. The dynamic manipulation of the vesicle is achieved by inducing a thermoelectric field in the presence of an ionic surfactant and the resulting plasmonic heating. Specifically, we introduce cetyltrimethylammonium chloride (CTAC) as the ionic surfactant and utilize the local heating generated by the plasmonic reflector to create a thermoelectric field. This enables active transport, stable trapping, and dynamic manipulation of a single extracellular vesicle. Moreover, the thermoelectric field contributes to an increase in the overall trapping potential.

The structural flexibility of nucleic acids plays a key role in many fundamental life processes, such as gene replication and expression, DNA-protein recognition, and gene regulation. To obtain a thorough understanding of nucleic acid flexibility, extensive studies have been performed using various experimental methods and theoretical models. In this review, we will introduce the progress that has been made in understanding the flexibility of nucleic acids including DNAs and RNAs, and will emphasize the experimental findings and the effects of salt, temperature, and sequence. Finally, we will discuss the major unanswered questions in understanding the flexibility of nucleic acids.

Extracellular vesicles (EVs) have drawn rapidly increasing attention as the next-generation diagnostic biomarkers and therapeutic agents. However, the heterogeneous nature of EVs necessitates advanced methods for profiling EVs at the single-particle level. While nanoparticle tracking analysis (NTA) is a widely used technique for quantifying particle size and concentration, conventional scattering-based systems are non-specific. In this study, we present an optimised protocol for quantitative profiling of EVs at the single-particle level by fluorescent NTA (F-NTA). The protocol integrates fluorescent immunolabeling of EVs with size-exclusion chromatography (SEC) to efficiently remove unbound labels, enabling the precise quantification of EV concentration, size distribution, and surface immunophenotype. We first validated this approach using biotinylated liposomes and EVs from cultured human cell lines, confirming effective removal of unbound labels and assessing labelling efficiency. We then demonstrated that F-NTA can distinguish EV subpopulations with distinct surface marker expression, exemplified by the differentiation of EpCAM-positive EVs derived from HT29 and HEK293 cells. Fi

A new formalism for calculation of the partition function of single stranded nucleic acids is presented. Secondary structures and the topology of structure elements are the level of resolution that is used. The folding model deals with matches, mismatches, symmetric and asymmetric interior loops, stacked pairs in loop and dangling end regions, multi-branched loops, bulges and single base stacking that might exist at duplex ends or at the ends of helices. Calculations on short and long sequences show, that for short oligonucleotides, a duplex formation often displays a two-state transition. However, for longer oligonucleotides, the thermodynamic properties of the single self-folding transition affects the transition nature of the duplex formation, resulting in a population of intermediate hairpin species in the solution. The role of intermediate hairpin species is analyzed in the case when a short oligonucleotides (molecular beacons) have to reliably identify and hybridize to accessible nucleotides within their targeted mRNA sequences. It is shown that the enhanced specificity of the molecular beacons is a result of their constrained conformational flexibility and the all-or-none me

Far from being a passive information store, the genome is a mechanically dynamic and diverse system in which torsion and tension fluctuate and combine to determine structure and help regulate gene expression. Much of this mechanical perturbation is due to molecular machines such as topoisomerases which must stretch and twist DNA as part of various functions including DNA repair and replication. While the broad-scale mechanical response of nucleic acids to tension and torsion is well characterized, detail at the single base pair level is beyond the limits of even super-resolution imaging. Here, we present a straightforward, flexible, and extensible umbrella-sampling protocol to twist and stretch nucleic acids in silico using the popular biomolecular simulation package Amber -- though the principles we describe are applicable also to other packages such as GROMACS. We discuss how to set up the simulation system, decide forcefields and solvation models, and equilibrate. We then introduce the torsionally-constrained stretching protocol, and finally we present some analysis techniques we have used to characterize structural motif formation. Rather than define forces or fictional pseudoa