No abstract

Accurate concentration determination of subpopulations of extracellular vesicles (EVs), such as exosomes, is of importance both in the context of understanding their fundamental biological role and of potentially using them as disease biomarkers. In principle, this can be achieved by measuring the rate of diffusion-limited mass uptake to a sensor surface modified with a receptor designed to only bind the subpopulation of interest. However, a significant error is introduced if the targeted EV subpopulation has a size, and thus hydrodynamic diffusion coefficient, that differs from the mean size and diffusion coefficient of the whole EV population and/or if the EVs become deformed upon binding to the surface. We here demonstrate a new approach to determine the mean size (or effective film thickness) of bound nanoparticles, in general, and EV subpopulation carrying a marker of interest, in particular. The method is based on operating surface plasmon resonance simultaneously at two wavelengths with different sensing depths and using the ratio of the corresponding responses to extract the particle size on the surface. By estimating in this way the degree of deformation of adsorbed EVs, we markedly improved their bulk concentration determination and showed that EVs carrying the exosomal marker CD63 correspond to not more than around 10% of the EV sample.

Ultrasensitive detectors based on localized surface plasmon resonance refractive index sensing are capable of detecting very low numbers of molecules for biochemical analysis. It is well known that the sensitivity of such sensors crucially depends on the spatial distribution of the electromagnetic field around the metal surface. However, the precise connection between local field enhancement and resonance shift is seldom discussed. Using a quasistatic approximation, we developed a model that relates the sensitivity of a nanoplasmonic resonator to the local field in which the analyte is placed. The model, corroborated by finite-difference time-domain simulations, may be used to estimate the magnitude of the shift as a function of the properties of the sensed object - permittivity and volume - and its location on the surface of the resonator. It requires only a computation of the resonant field induced by the metal structure and is therefore suitable for numerical optimization of nanoplasmonic sensors.

The sensitivity of a surface plasmon to the dielectric environment makes it a viable tool in detecting single molecules. To be able to precisely determine sensed molecular concentrations and carry out precise analyses of single-molecule binding/ unbinding events in real time it is necessary to quantify rigorously the relation between the number of bound molecules and the spectral response of the plasmonic sensor. However, this is challenging as this relation is subject to an uncertainty which is highly dependent on the spatially varying response of the plasmonic nanosensor of choice. The origin of this uncertainty is little understood, and its effect is often disregarded in quantitative sensing experiments. Here, we employ stochastic diffusion-reaction simulations of biomolecular interactions on a sensor's surface combined with electromagnetic calculations of the plasmon resonance peak shift of three metal nanosensors (disk, cone, dimer) to clarify the interplay between position-dependent binding probability and inhomogeneous sensitivity distribution in determining the statistical characteristics of the total signal upon molecular binding. This approach is generally applicable regardless of the specific transduction mechanism at the basis of sensing. Here we identify how this interplay affects the feasibility of using certain plasmonic sensors for sensing low concentrations or real-time monitoring of individual binding reactions and how illumination conditions may affect the level of uncertainty of the measured signal upon molecular binding.