Single-Particle Spectroscopic Chromatography Reveals Heterogeneous RNA Loading and Size Correlations in Lipid Nanoparticles.

Lipid nanoparticles (LNP) have emerged as pivotal delivery vehicles for RNA therapeutics. Previous research and development usually assumed that LNPs are homogeneous in population, loading density, and composition. Such perspectives are difficult to examine due to the lack of suitable tools to characterize these physicochemical properties at the single-nanoparticle level. Here, we report an integrated spectroscopy-chromatography approach as a generalizable strategy to dissect the complexities of multicomponent LNP assembly. Our platform couples cylindrical illumination confocal spectroscopy (CICS) with single-nanoparticle free solution hydrodynamic separation (SN-FSHS) to simultaneously profile population identity, hydrodynamic size, RNA loading levels, and distributions of helper lipid and PEGylated lipid of LNPs at the single-particle level and in a high-throughput manner. Using a benchmark siRNA LNP formulation, we demonstrate the capability of this platform by distinguishing seven distinct LNP populations, quantitatively characterizing size distribution and RNA loading level in wide ranges, and more importantly, resolving composition-size correlations. This SN-FSHS-CICS analysis provides critical insights into a substantial degree of heterogeneity in the packing density of RNA in LNPs and size-dependent loading-size correlations, explained by kinetics-driven assembly mechanisms of RNA LNPs.

Dissipative particle dynamics for coarse-grained models.

We develop a computational method based on Dissipative Particle Dynamics (DPD) that introduces solvent hydrodynamic interactions to coarse-grained models of solutes, such as ions, molecules, or polymers. DPD-solvent (DPDS) is a fully off-lattice method that allows straightforward incorporation of hydrodynamics at desired solvent viscosity, compressibility, and solute diffusivity with any particle-based solute model. Solutes interact with the solvent only through the DPD thermostat, which ensures that the equilibrium properties of the solute system are not affected by the introduction of the DPD solvent, while the thermostat coupling strength sets the desired solute diffusivity. Thus, DPDS can be used as a replacement for traditional molecular dynamics thermostats such as Nosé-Hoover and Langevin. We demonstrate the applicability of DPDS in the case of polymer dynamics and electroosmotic flow through a nanopore. The method should be broadly useful as a means to introduce hydrodynamic interactions to existing coarse-grained models of solutes and soft materials.

Erratum: Spontaneous Domain Formation in Spherically Confined Elastic Filaments [Phys. Rev. Lett. 123, 047801 (2019)].

This corrects the article DOI: 10.1103/PhysRevLett.123.047801.

A Study of the Drift Phenomena of Gate-Functionalized Biosensors and Dual-Gate-Functionalized Biosensors in Human Serum.

In this paper, we study the drift behavior of organic electrochemical transistor (OECT) biosensors in a phosphate-buffered saline (PBS) buffer solution and human serum. Theoretical and experimental methods are illustrated in this paper to understand the origin of the drift phenomenon and the mechanism of ion diffusion in the sensing layer. The drift phenomenon is explained using a first-order kinetic model of ion adsorption into the gate material and shows very good agreement with experimental data on drift in OECTs. We show that the temporal current drift can be largely mitigated using a dual-gate OECT architecture and that dual-gate-based biosensors can increase the accuracy and sensitivity of immuno-biosensors compared to a standard single-gate design. Specific binding can be detected at a relatively low limit of detection, even in human serum.

Collapse and expansion kinetics of a single polyelectrolyte chain with hydrodynamic interactions.

We investigate the collapse and expansion dynamics of a linear polyelectrolyte (PE) with hydrodynamic interactions. Using dissipative particle dynamics with a bead-spring PE model, long-range electrostatics, and explicit ions, we examine how the timescales of collapse tcol and expansion texp depend on the chain length N and obtain scaling relationships tcol ∼ Nα and texp ∼ Nß. For neutral polymers, we derive values of α = 0.94 ± 0.01 and ß = 1.97 ± 0.10. Interestingly, the introduction of electrostatic interaction markedly shifts α to α ≈ 1.4 ± 0.1 for salt concentrations within c = 10-4 to 10-2 M. A reduction in the ion-to-monomer size ratio noticeably reduces α. On the other hand, the expansion scaling remains approximately constant, ß ≈ 2, regardless of the salt concentration or ion size considered. We find ß > α for all conditions considered, implying that expansion is always slower than collapse in the limit of long polymers. This asymmetry is explained by distinct kinetic pathways of collapse and expansion processes.

Rigidity-Induced Controlled Aggregation of Binary Colloids.

Here, we report the proof-of-concept for controlled aggregation in a binary colloidal system. The binary systems are studied by varying bond flexibility of only one species, while the other species' bonds remain fully flexible. By establishing the underlying relation between gelation and bond rigidity, we demonstrate how the interplay among bond flexibility, critical concentration, and packing volume fraction influenced the aggregation kinetics. Our result shows that rigidity in bonds increases the critical concentration for gels to be formed in the binary mixture. Furthermore, the average number of bonded neighbor analyses reveal the influence of bond rigidity both above and below critical concentrations and show that variation in bond flexibility in only one species alters the kinetics of aggregation of both species. This finding improves our understanding of colloidal aggregation in soft and biological systems.

Phase separation and ripening in a viscoelastic gel.

The process of phase separation in elastic solids and viscous fluids is of fundamental importance to the stability and function of soft materials. We explore the dynamics of phase separation and domain growth in a viscoelastic material such as a polymer gel. Using analytical theory and Monte Carlo simulations, we report a domain growth regime in which the domain size increases algebraically with a ripening exponent [Formula: see text] that depends on the viscoelastic properties of the material. For a prototypical Maxwell material, we obtain [Formula: see text], which is markedly different from the well-known Ostwald ripening process with [Formula: see text]. We generalize our theory to systems with arbitrary power-law relaxation behavior and discuss our findings in the context of the long-term stability of materials as well as recent experimental results on phase separation in cross-linked networks and cytoskeleton.

Unravelling crystal growth of nanoparticles.

Crystal growth from nanoscale constituents is a ubiquitous phenomenon in biology, geology and materials science. Numerous studies have focused on understanding the onset of nucleation and on producing high-quality crystals by empirically sampling constituents with different attributes and varying the growth conditions. However, the kinetics of post-nucleation growth processes, an important determinant of crystal morphology and properties, have remained underexplored due to experimental challenges associated with real-space imaging at the nanoscale. Here we report the imaging of the crystal growth of nanoparticles of different shapes using liquid-phase transmission electron microscopy, resolving both lateral and perpendicular growth of crystal layers by tracking individual nanoparticles. We observe that these nanoscale systems exhibit layer-by-layer growth, typical of atomic crystallization, as well as rough growth prevalent in colloidal systems. Surprisingly, the lateral and perpendicular growth modes can be independently controlled, resulting in two mixed crystallization modes that, until now, have received only scant attention. Combining analytical considerations with molecular dynamics and kinetic Monte Carlo simulations, we develop a comprehensive framework for our observations, which are fundamentally determined by the size and shape of the building blocks. These insights unify the understanding of crystal growth across four orders of magnitude in particle size and suggest novel pathways to crystal engineering.

Determinants of Superselectivity─Practical Concepts for Application in Biology and Medicine.

Multivalent interactions are common in biological systems and are also widely deployed for targeting applications in biomedicine. A unique feature of multivalent binding is "superselectivity". Superselectivity refers to the sharp discrimination of surfaces (e.g., on cells or cell compartments) by their comparative surface densities of a given receptor. This feature is different from the conventional "type" selectivity, which discriminates surfaces by their distinct receptor types. In a broader definition, a probe is superselective if it converts a gradual change in any one interaction parameter into a sharp on/off dependency in probe binding.This Account describes our systematic experimental and theoretical efforts over the past decade to analyze the determinants of superselective binding. It aims to offer chemical biologists, biophysicists, biologists, and biomedical scientists a set of guidelines for the interpretation of multivalent binding data, and design rules for tuning superselective targeting. We first provide a basic introduction that identifies multiple low-affinity interactions and combinatorial entropy as the minimal set of conditions required for superselective recognition. We then introduce the main experimental and theoretical tools and analyze how salient features of the multivalent probes (i.e., their concentration, size, ligand valency, and scaffold type), of the surface receptors (i.e., their affinity for ligands, surface density, and mobility), and of competitors and cofactors (i.e., their concentration and affinity for the ligands and/or receptors) influence the sharpness and the position of the threshold for superselective recognition.Emerging from this work are a set of relatively simple yet quantitative data analysis guidelines and superselectivity design rules that apply to a broad range of probe types and interaction systems. The key finding is the scaling variable xS which faithfully predicts the influence of the surface receptor density, probe ligand valency, receptor-ligand affinity, and competitor/cofactor concentrations and affinities on superselective recognition. The scaling variable is a simple yet versatile tool to quantitatively tune the on/off threshold of superselective probes. We exemplify its application by reviewing and reinterpreting literature data for selected biological and biomedical interaction systems where superselectivity clearly is important.Our guidelines can be deployed to generate a new mechanistic understanding of multivalent recognition events inside and outside cells and the downstream physiological/pathological implications. Moreover, the design rules can be harnessed to develop novel superselective probes for analytical purposes in the life sciences and for diagnostic/therapeutic intervention in biomedicine.

Amplification Free Detection of SARS-CoV-2 Using Multi-Valent Binding.

We present the development of electrochemical impedance spectroscopy (EIS)-based biosensors for sensitive detection of SARS-CoV-2 RNA using multi-valent binding. By increasing the number of probe-target binding events per target molecule, multi-valent binding is a viable strategy for improving the biosensor performance. As EIS can provide sensitive and label-free measurements of nucleic acid targets during probe-target hybridization, we used multi-valent binding to build EIS biosensors for targeting SARS-CoV-2 RNA. For developing the biosensor, we explored two different approaches including probe combinations that individually bind in a single-valent fashion and the probes that bind in a multi-valent manner on their own. While we found excellent biosensor performance using probe combinations, we also discovered unexpected signal suppression. We explained the signal suppression theoretically using inter- and intra-probe hybridizations which confirmed our experimental findings. With our best probe combination, we achieved a LOD of 182 copies/µL (303 aM) of SARS-CoV-2 RNA and used these for successful evaluation of patient samples for COVID-19 diagnostics. We were also able to show the concept of multi-valent binding with shorter probes in the second approach. Here, a 13-nt-long probe has shown the best performance during SARS-CoV-2 RNA binding. Therefore, multi-valent binding approaches using EIS have high utility for direct detection of nucleic acid targets and for point-of-care diagnostics.

Controlling Superselectivity of Multivalent Interactions with Cofactors and Competitors.

Moieties that compete with multivalent interactions or act as cofactors are common in living systems, but their effect on multivalent binding remains poorly understood. We derive a theoretical model that shows how the superselectivity of multivalent interactions is modulated by the presence of cofactors or competitors. We find that the role of these participating moieties can be fully captured by a simple rescaling of the affinity constant of the individual ligand-receptor bonds. Theoretical predictions are supported by experimental data of the membrane repair protein annexin A5 binding to anionic lipid membranes in the presence of Ca2+ cofactors and of the extracellular matrix polysaccharide hyaluronan (HA) binding to CD44 cell surface receptors in the presence of HA oligosaccharide competitors. The obtained findings should facilitate understanding of multivalent recognition in biological systems and open new routes for fine-tuning the selectivity of multivalent nanoprobes in medicinal chemistry.

Hybrid Nanocrystals of Small Molecules and Chemically Disordered Polymers.

Organic crystals formed by small molecules can be highly functional but are often brittle or insoluble structures with limited possibilities for use or processing from a liquid phase. A possible solution is the nanoscale integration of polymers into organic crystals without sacrificing long-range order and therefore function. This enables the organic crystals to benefit from the advantageous mechanical and chemical properties of the polymeric component. We report here on a strategy in which small molecules cocrystallize with side chains of chemically disordered polymers to create hybrid nanostructures containing a highly ordered lattice. Synchrotron X-ray scattering, absorption spectroscopy, and coarse-grained molecular dynamics simulations reveal that the polymer backbones form an "exo-crystalline" layer of disordered chains that wrap around the nanostructures, becoming a handle for interesting properties. The morphology of this "hybrid bonding polymer" nanostructure is dictated by the competition between the polymers' entropy and the enthalpy of the lattice allowing for control over the aspect ratio of the nanocrystal by changing the degree of polymer integration. We observed that nanostructures with an exo-crystalline layer of polymer exhibit enhanced fracture strength, self-healing capacity, and dispersion in water, which benefits their use as light-harvesting assemblies in photocatalysis. Guided by computation, future work could further explore these hybrid nanostructures as components for functional materials.

Accelerated simulation method for charge regulation effects.

The net charge of solvated entities, ranging from polyelectrolytes and biomolecules to charged nanoparticles and membranes, depends on the local dissociation equilibrium of individual ionizable groups. Incorporation of this phenomenon, charge regulation (CR), in theoretical and computational models requires dynamic, configuration-dependent recalculation of surface charges and is therefore typically approximated by assuming constant net charge on particles. Various computational methods exist that address this. We present an alternative, particularly efficient CR Monte Carlo method (CR-MC), which explicitly models the redistribution of individual charges and accurately samples the correct grand-canonical charge distribution. In addition, we provide an open-source implementation in the large-scale Atomic/Molecular Massively Parallel Simulator molecular dynamics (MD) simulation package, resulting in a hybrid MD/CR-MC simulation method. This implementation is designed to handle a wide range of implicit-solvent systems that model discreet ionizable groups or surface sites. The computational cost of the method scales linearly with the number of ionizable groups, thereby allowing accurate simulations of systems containing thousands of individual ionizable sites. By matter of illustration, we use the CR-MC method to quantify the effects of CR on the nature of the polyelectrolyte coil-globule transition and on the effective interaction between oppositely charged nanoparticles.

Charge Regulation Effects in Nanoparticle Self-Assembly.

Nanoparticles in solution acquire charge through the dissociation or association of surface groups. Thus, a proper description of their electrostatic interactions requires the use of charge-regulating boundary conditions rather than the commonly employed constant-charge approximation. We implement a hybrid Monte Carlo/molecular dynamics scheme that dynamically adjusts the charges of individual surface groups of objects while evolving their trajectories. Charge regulation effects are shown to qualitatively change self-assembled structures due to global charge redistribution, stabilizing asymmetric constructs. We delineate under which conditions the conventional constant-charge approximation may be employed and clarify the interplay between charge regulation and dielectric polarization.

Computational design of probes to detect bacterial genomes by multivalent binding.

Rapid methods for diagnosis of bacterial infections are urgently needed to reduce inappropriate use of antibiotics, which contributes to antimicrobial resistance. In many rapid diagnostic methods, DNA oligonucleotide probes, attached to a surface, bind to specific nucleotide sequences in the DNA of a target pathogen. Typically, each probe binds to a single target sequence; i.e., target-probe binding is monovalent. Here we show using computer simulations that the detection sensitivity and specificity can be improved by designing probes that bind multivalently to the entire length of the pathogen genomic DNA, such that a given probe binds to multiple sites along the target DNA. Our results suggest that multivalent targeting of long pieces of genomic DNA can allow highly sensitive and selective binding of the target DNA, even if competing DNA in the sample also contains binding sites for the same probe sequences. Our results are robust to mild fragmentation of the bacterial genome. Our conclusions may also be relevant for DNA detection in other fields, such as disease diagnostics more broadly, environmental management, and food safety.

First-order 'hyper-selective' binding transition of multivalent particles under force.

Multivalent particles bind to targets via many independent ligand-receptor bonding interactions. This microscopic design spans length scales in both synthetic and biological systems. Classic examples include interactions between cells, virus binding, synthetic ligand-coated micrometer-scale vesicles or smaller nano-particles, functionalised polymers, and toxins. Equilibrium multivalent binding is a continuous yet super-selective transition with respect to the number of ligands and receptors involved in the interaction. Increasing the ligand or receptor density on the two particles leads to sharp growth in the number of bound particles at equilibrium. Here we present a theory and Monte Carlo simulations to show that applying mechanical force to multivalent particles causes their adsorption/desorption isotherm on a surface to become sharper and more selective, with respect to variation in the number of ligands and receptors on the two objects. When the force is only applied to particles bound to the surface by one or more ligands, then the transition can become infinitely sharp and first-order-a new binding regime which we term 'hyper-selective'. Force may be imposed by, e.g. flow of solvent around the particles, a magnetic field, chemical gradients, or triggered uncoiling of inert oligomers/polymers tethered to the particles to provide a steric repulsion to the surface. This physical principle is a step towards 'all or nothing' binding selectivity in the design of multivalent constructs.

Spontaneous Domain Formation in Spherically Confined Elastic Filaments.

Although the free energy of a genome packing into a virus is dominated by DNA-DNA interactions, ordering of the DNA inside the capsid is elasticity driven, suggesting general solutions with DNA organized into spool-like domains. Using analytical calculations and computer simulations of a long elastic filament confined to a spherical container, we show that the ground state is not a single spool as assumed hitherto, but an ordering mosaic of multiple homogeneously ordered domains. At low densities, we observe concentric spools, while at higher densities, other morphologies emerge, which resemble topological links. We discuss our results in the context of metallic wires, viral DNA, and flexible polymers.

Multivalent Recognition at Fluid Surfaces: The Interplay of Receptor Clustering and Superselectivity.

The interaction between a biological membrane and its environment is a complex process, as it involves multivalent binding between ligand/receptor pairs, which can self-organize in patches. Any description of the specific binding of biomolecules to membranes must account for the key characteristics of multivalent binding, namely, its unique ability to discriminate sharply between high and low receptor densities (superselectivity), but also for the effect of the lateral mobility of membrane-bound receptors to cluster upon binding. Here we present an experimental model system that allows us to compare systematically the effects of multivalent interactions on fluid and immobile surfaces. A crucial feature of our model system is that it allows us to control the membrane surface chemistry, the properties of the multivalent binder, and the binding affinity. We find that multivalent probes retain their superselective binding behavior at fluid interfaces. Supported by numerical simulations, we demonstrate that, as a consequence of receptor clustering, superselective binding is enhanced and shifted to lower receptor densities at fluid interfaces. To translate our findings into a simple, predictive tool, we propose an analytical model that enables rapid predictions of how the superselective binding behavior is affected by the lateral receptor mobility as a function of the physicochemical characteristics of the multivalent probe. We believe that our model, which captures the key physical mechanisms underpinning multivalent binding to biological membranes, will greatly facilitate the rational design of nanoprobes for the superselective targeting of cells.

Coarse-grained simulation of DNA using LAMMPS : An implementation of the oxDNA model and its applications.

During the last decade coarse-grained nucleotide models have emerged that allow us to study DNA and RNA on unprecedented time and length scales. Among them is oxDNA, a coarse-grained, sequence-specific model that captures the hybridisation transition of DNA and many structural properties of single- and double-stranded DNA. oxDNA was previously only available as standalone software, but has now been implemented into the popular LAMMPS molecular dynamics code. This article describes the new implementation and analyses its parallel performance. Practical applications are presented that focus on single-stranded DNA, an area of research which has been so far under-investigated. The LAMMPS implementation of oxDNA lowers the entry barrier for using the oxDNA model significantly, facilitates future code development and interfacing with existing LAMMPS functionality as well as other coarse-grained and atomistic DNA models.

Controlling Cargo Trafficking in Multicomponent Membranes.

Biological membranes typically contain a large number of different components dispersed in small concentrations in the main membrane phase, including proteins, sugars, and lipids of varying geometrical properties. Most of these components do not bind the cargo. Here, we show that such "inert" components can be crucial for the precise control of cross-membrane trafficking. Using a statistical mechanics model and molecular dynamics simulations, we demonstrate that the presence of inert membrane components of small isotropic curvatures dramatically influences cargo endocytosis, even if the total spontaneous curvature of such a membrane remains unchanged. Curved lipids, such as cholesterol, as well as asymmetrically included proteins and tethered sugars can, therefore, actively participate in the control of the membrane trafficking of nanoscopic cargo. We find that even a low-level expression of curved inert membrane components can determine the membrane selectivity toward the cargo size and can be used to selectively target membranes of certain compositions. Our results suggest a robust and general method of controlling cargo trafficking by adjusting the membrane composition without needing to alter the concentration of receptors or the average membrane curvature. This study indicates that cells can prepare for any trafficking event by incorporating curved inert components in either of the membrane leaflets.