Statistical predictions on the encapsulation of single molecule binding pairs into sized-dispersed nanocontainers.

Single molecule experiments have recently attracted enormous interest. Many of these studies involve the encapsulation of a single molecule into nanoscale containers (such as vesicles, droplets and nanowells). In such cases, the single molecule encapsulation efficiency is a key parameter to consider in order to get a statistically significant quantitative information. It has been shown that such encapsulation typically follows a Poisson distribution and such theory of encapsulation has only been applied to the encapsulation of single molecules into perfectly sized monodispersed containers. However, experimentally nanocontainers are usually characterized by a size distribution, and often just a single binding pair (rather than a single molecule) is required to be encapsulated. Here the use of Poisson distribution is extended to predict the encapsulation efficiency of two different molecules in an association equilibrium. The Poisson distribution is coupled with a log-normal distribution in order to consider the effect of the container size distribution, and the effect of adsorption to the container is also considered. This theory will allow experimentalists to determine what single molecule encapsulation efficiency can be expected as a function of the experimental conditions. Two case studies, based on experimental data, are given to support the theoretical predictions.

Biomolecular Binding under Confinement: Statistical Predictions of Steric Influence in Absence of Long-Distance Interactions.

We propose a theoretical model for the influence of confinement on biomolecular binding at the single-molecule scale at equilibrium, based on the change of the number of microstates (localization and orientation) upon reaction. Three cases are discussed: DNA sequences shorter and longer than the single strain DNA Kuhn length and spherical proteins, confined into a spherical container (liposome, droplet, etc.). The influence of confinement is found to be highly dependent on the molecular structure and significant for large molecules (relative to container size).

Nitric Oxide to Fight Viral Infections.

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) that has quickly and deeply affected the world, with over 60 million confirmed cases. There has been a great effort worldwide to contain the virus and to search for an effective treatment for patients who become critically ill with COVID-19. A promising therapeutic compound currently undergoing clinical trials for COVID-19 is nitric oxide (NO), which is a free radical that has been previously reported to inhibit the replication of several DNA and RNA viruses, including coronaviruses. Although NO has potent antiviral activity, it has a complex role in the immunological host responses to viral infections, i.e., it can be essential for pathogen control or detrimental for the host, depending on its concentration and the type of virus. In this Essay, the antiviral role of NO against SARS-CoV, SARS-CoV-2, and other human viruses is highlighted, current development of NO-based therapies used in the clinic is summarized, existing challenges are discussed and possible further developments of NO to fight viral infections are suggested.

Investigating Spatial Heterogeneity of Nanoparticles Movement in Live Cells with Pair-Correlation Microscopy and Phasor Analysis.

How nanoparticles distribute in living cells and overcome cellular barriers are important criteria in the design of drug carriers. Pair-correlation microscopy is a correlation analysis of fluctuation in the fluorescence intensity obtained by a confocal line scan that can quantify the dynamic properties of nanoparticle diffusion including the number of mobile nanoparticles, diffusion coefficient, and transit time across a spatial distance. Due to the potential heterogeneities in nanoparticle properties and the complexity within the cellular environment, quantification of averaged auto- and pair-correlation profiles may obscure important insights into the ability of nanoparticles to deliver drugs. To overcome this issue, we used phasor analysis to develop a data standardizing method, which can segment the scanned line into several subregions according to diffusion and address the spatial heterogeneity of nanoparticles moving inside cells. The phasor analysis is a fit-free method that represents autocorrelation profiles for each pixel relative to free diffusion on the so-called phasor plots. Phasor plots can then be used to select subpopulations for which the auto- and pair-correlation analysis can be performed separately. We demonstrate the phasor analysis for pair-correlation microscopy for investigating 16 nm, Cy5-labeled silica nanoparticles diffusing across the plasma membrane and green fluorescent proteins (GFP) diffusing across nuclear envelope in MCF-7 cells.

Impact of the Coverage of Aptamers on a Nanoparticle on the Binding Equilibrium and Kinetics between Aptamer and Protein.

Knowledge of the interaction between aptamer and protein is integral to the design and development of aptamer-based biosensors. Nanoparticles functionalized with aptamers are commonly used in these kinds of sensors. As such, studies into how the number of aptamers on the nanoparticle surface influence both kinetics and thermodynamics of the binding interaction are required. In this study, aptamers specific for interferon gamma (IFN-γ) were immobilized on the surface of gold nanoparticles (AuNPs), and the effect of surface coverage of aptamer on the binding interaction with its target was investigated using fluorescence spectroscopy. The number of aptamers were adjusted from an average of 9.6 to 258 per particle. The binding isotherm between AuNPs-aptamer conjugate and protein was modeled with the Hill-Langmuir equation, and the determined equilibrium dissociation constant (K'D) decreased 10-fold when increasing the coverage of aptamer. The kinetics of the reaction as a function of coverage of aptamer were also investigated, including the association rate constant (kon) and the dissociation rate constant (koff). The AuNPs-aptamer conjugate with 258 aptamers per particle had the highest kon, while the koff was similar for AuNPs-aptamer conjugates with different surface coverages. Therefore, the surface coverage of aptamers on AuNPs affects both the thermodynamics and the kinetics of the binding. The AuNPs-aptamer conjugate with the highest surface coverage is the most favorable in biosensors considering the limit of detection, sensitivity, and response time of the assay. These findings deepen our understanding of the interaction between aptamer and target protein on the particle surface, which is important to both improve the scientific design and increase the application of aptamer-nanoparticle based biosensor.

Metal-Organic Framework-Enhanced Solid-Phase Microextraction Mass Spectrometry for the Direct and Rapid Detection of Perfluorooctanoic Acid in Environmental Water Samples.

We report the development of metal-organic framework (MOF)-based probes for the direct and rapid detection and quantification of perfluorooctanoic acid (PFOA) by mass spectrometry. Four water-resistant MOFs-ZIF-8, UiO-66, MIL88-A, and Tb2(BDC)3-were coated on poly(dopamine) precoated stainless steel needles and used to rapidly preconcentrate PFOA from water for direct analysis by nanoelectrospray ionization mass spectrometry. The analytical performance of each MOF for detecting PFOA was correlated with both the calculated binding energy of the MOF for PFOA and the relative change in the surface area of the MOF upon exposure to PFOA. MOF-functionalized probes can be used for the rapid (<5 min) and sensitive quantification of PFOA molecules at low ng L-1 levels in environmental water samples (i.e., tap water, rainwater, and seawater) with no sample preparation. The limit of detection of PFOA in ultrapure water was 11.0 ng L-1. Comparable accuracy to an accredited analytical method was achieved, despite the MOF-functionalized probe approach being ∼40 times quicker and requiring ∼10 times less sample. These features indicate that MOF-coated probes are promising for the direct and rapid monitoring of polyfluorinated substances and other pollutants in the field.

The application of personal glucose meters as universal point-of-care diagnostic tools.

Personal glucose meters (PGMs) have been used for the measurement of blood glucose for decades now such that they have become the most used analytical method in the world. They are also well placed to be repurposed for point-of-care testing of other analytes as they are inexpensive, portable and quantitative. Efforts to repurpose PGMs for the detection of any analyte at the point-of-care have been one focus of biosensor research for several years now with a number of successful efforts in the detection of a wide range of analytes. This article reviews the published methods to repurpose a PGM to detect analytes other than glucose, and analyses the potential and the challenges to be overcome in developing a PGM-based biosensor and bring it to market.

The use of a personal glucose meter for detecting procalcitonin through glucose encapsulated within liposomes.

Herein, a glucose meter-based immunosensing platform is developed that allows the quantification of procalcitonin (PCT) in whole blood samples. PCT is a biomarker for sepsis and its early detection would improve the safety of the patient, as the diagnostic process will be easier and faster. The method employs liposomes with encapsulated glucose as a signal generation tag, which are then used in a sandwich immunoassay by conjugating an antibody to the liposome. The optimal liposomes' size and concentration of encapsulated glucose is determined experimentally to be 200 nm and 27.8 mM, respectively. Upon the addition of a surfactant (Triton X-100), the glucose is released and a signal is detected with a personal glucose meter (PGM). This signal is directly proportional to the concentration of the PCT in the sample. The dynamic range of the assay developed was 0.153-15.38 nM, and could allow the detection of PCT as low as 0.15 nM. The assay showed a high selectivity toward PCT against other proteins such as C-reactive protein and human serum albumin and good reproducibility. This assay was able to quantitatively determine the amount of PCT in whole blood samples at clinically-relevant concentrations.

Rapid detection of hendra virus using magnetic particles and quantum dots.

A proof-of-concept for the development of a fast and portable Hendra virus biosensor is presented. Hendra virus, a deadly emerging pathogen in Australia, can be co-localized, concentrated and revealed using simultaneously magnetic and luminescent functional particles. This method should be applicable for the early detection of any other virus by targeting the specific virus with the corresponding antibody.

Method for optimizing coating properties based on an evolutionary algorithm approach.

In industry as well as many areas of scientific research, data collected often contain a number of responses of interest for a chosen set of exploratory variables. Optimization of such multivariable multiresponse systems is a challenge well suited to genetic algorithms as global optimization tools. One such example is the optimization of coating surfaces with the required absolute and relative sensitivity for detecting analytes using devices such as sensor arrays. High-throughput synthesis and screening methods can be used to accelerate materials discovery and optimization; however, an important practical consideration for successful optimization of materials for arrays and other applications is the ability to generate adequate information from a minimum number of experiments. Here we present a case study to evaluate the efficiency of a novel evolutionary model-based multiresponse approach (EMMA) that enables the optimization of a coating while minimizing the number of experiments. EMMA plans the experiments and simultaneously models the material properties. We illustrate this novel procedure for materials optimization by testing the algorithm on a sol-gel synthetic route for production and optimization of a well studied amino-methyl-silane coating. The response variables of the coating have been optimized based on application criteria for micro- and macro-array surfaces. Spotting performance has been monitored using a fluorescent dye molecule for demonstration purposes and measured using a laser scanner. Optimization is achieved by exploring less than 2% of the possible experiments, resulting in identification of the most influential compositional variables. Use of EMMA to optimize control factors of a product or process is illustrated, and the proposed approach is shown to be a promising tool for simultaneously optimizing and modeling multivariable multiresponse systems.