Magnus Röding

Hemocompatibility of siRNA loaded dextran nanogels

Although the behavior of nanoscopic delivery systems in blood is an important parameter when contemplating their intravenous injection, this aspect is often poorly investigated when advancing from in vitro to in vivo experiments. In this paper, the behavior of siRNA loaded dextran nanogels in human plasma and blood is examined using fluorescence fluctuation spectroscopy, platelet aggregometry, flow cytometry and single particle tracking. Our results show that, in contrast to their negatively charged counterparts, positively charged siRNA loaded dextran nanogels cause platelet aggregation and show increased binding to human blood cells. Although PEGylating the nanogels did not have a significant effect on their interaction with blood cells, single particle tracking revealed that it is necessary to prevent their aggregation in human plasma. We therefore conclude that PEGylated negatively charged dextran nanogels are the most suited for further in vivo studies as they do not aggregate in human plasma and exhibit minimal interactions with blood cells.

The gamma distribution model for pulsed-field gradient NMR studies of molecular-weight distributions of polymers

Self-diffusion in polymer solutions studied with pulsed-field gradient nuclear magnetic resonance (PFG NMR) is typically based either on a single self-diffusion coefficient, or a log-normal distribution of self-diffusion coefficients, or in some cases mixtures of these. Experimental data on polyethylene glycol (PEG) solutions and simulations were used to compare a model based on a gamma distribution of self-diffusion coefficients to more established models such as the single exponential, the stretched exponential, and the log-normal distribution model with regard to performance and consistency. Even though the gamma distribution is very similar to the log-normal distribution, its NMR signal attenuation can be written in a closed form and therefore opens up for increased computational speed. Estimates of the mean self-diffusion coefficient, the spread, and the polydispersity index that were obtained using the gamma model were in excellent agreement with estimates obtained using the log-normal model. Furthermore, we demonstrate that the gamma distribution is by far superior to the log-normal, and comparable to the two other models, in terms of computational speed. This effect is particularly striking for multi-component signal attenuation. Additionally, the gamma distribution as well as the log-normal distribution incorporates explicitly a physically plausible model for polydispersity and spread, in contrast to the single exponential and the stretched exponential. Therefore, the gamma distribution model should be preferred in many experimental situations.

Injectable insulin-lysozyme-loaded nanogels with enzymatically-controlled degradation and release for basal insulin treatment: In vitro characterization and in vivo observation

Diabetes is a common global disease that causes immense suffering for individuals and huge costs for the health care system. To minimize complications such as organ degeneration, diabetic patients are required to undergo treatments to maintain the blood glucose level in the normal range, ideally mimicking normal insulin secretion. The normal physiological insulin secretion pattern in healthy individuals consists of a base (basal) level through the day and increased secretion after meals (bolus insulin). Thus effective treatments may combine long acting, low-level insulin therapy with boosts of short acting insulin and/or oral agents. To achieve long term management of basal insulin level, an injectable insulin-loaded gel composed of self-assembled nanoparticles from carboxymethyl-hexanoyl chitosan (CHC) and integrated lysozyme for controlled biodegradation and insulin release was developed. In vitro characterizations and evaluations confirmed that lysozyme was active on CHC and that the amount of lysozyme in a CHC hydrogel determined the degradation and insulin release rate. The degradation products were found to be highly cytocompatible using a cell assay. In vivo evaluation of the system in a diabetic mouse model revealed that the fasted blood glucose level could be maintained in the normal range for 10days with a single injection of insulin-loaded CHC-lysozyme gel. The insulin-loaded CHC-lysozyme gels clearly show promise for use as a novel injectable long-acting insulin delivery system, with potential to manage the basal insulin level for many days with a single injection.

On-chip light sheet illumination enables diagnostic size and concentration measurements of membrane vesicles in biofluids

Cell-derived membrane vesicles that are released in biofluids, like blood or saliva, are emerging as potential non-invasive biomarkers for diseases, such as cancer. Techniques capable of measuring the size and concentration of membrane vesicles directly in biofluids are urgently needed. Fluorescence single particle tracking microscopy has the potential of doing exactly that by labelling the membrane vesicles with a fluorescent label and analysing their Brownian motion in the biofluid. However, an unbound dye in the biofluid can cause high background intensity that strongly biases the fluorescence single particle tracking size and concentration measurements. While such background intensity can be avoided with light sheet illumination, current set-ups require specialty sample holders that are not compatible with high-throughput diagnostics. Here, a microfluidic chip with integrated light sheet illumination is reported, and accurate fluorescence single particle tracking size and concentration measurements of membrane vesicles in cell culture medium and in interstitial fluid collected from primary human breast tumours are demonstrated.

Heterogeneity in the fluorescence of graphene and graphene oxide quantum dots

AbstractHeterogeneity is an inherent property of a wealth of real-world nanomaterials and yet rarely in the reporting of new properties is its effect sufficiently addressed. Graphene quantum dots (GQDs) – fluorescent, nanoscale fragments of graphene - are an extreme example of a heterogeneous nanomaterial. Here, top-down approaches – by far the most predominant – produce batches of particles with a distribution of sizes, shapes, extent of oxidation, chemical impurities and more. This makes characterization of these materials using bulk techniques particularly complex and comparisons of properties across different synthetic methods uninformative. In particular, it hinders the understanding of the structural origin of their fluorescence properties. We present a simple synthetic method, which produces graphene quantum dots with very low oxygen content that can be suspended in organic solvents, suggesting a very pristine material. We use this material to illustrate the limitations of interpreting complex data sets generated by heterogeneous materials and we highlight how misleading this “pristine” interpretation is by comparison with graphene oxide quantum dots synthesized using an established protocol. In addition, we report on the solvatochromic properties of these particles, discuss common characterization techniques and their limitations in attributing properties to heterogeneous materials. Graphical abstractGraphene quantum dots with very low oxygen content were synthesized using a simple method, suggesting a very pristine material. We highlight how misleading this “pristine” term is when applied to a heterogeneous material through comparison with graphene oxide quantum dots.

Gamma convolution models for self-diffusion coefficient distributions in PGSE NMR

We introduce a closed-form signal attenuation model for pulsed-field gradient spin echo (PGSE) NMR based on self-diffusion coefficient distributions that are convolutions of n gamma distributions, n⩾1. Gamma convolutions provide a general class of uni-modal distributions that includes the gamma distribution as a special case for n=1 and the lognormal distribution among others as limit cases when n approaches infinity. We demonstrate the usefulness of the gamma convolution model by simulations and experimental data from samples of poly(vinyl alcohol) and polystyrene, showing that this model provides goodness of fit superior to both the gamma and lognormal distributions and comparable to the common inverse Laplace transform.

Measuring absolute nanoparticle number concentrations from particle count time series.

Single‐particle microscopy is important for characterization of nanoparticulate matter for which accurate concentration measurements are crucial. We introduce a method for estimating absolute number concentrations in nanoparticle dispersions based on a fluctuating time series of particle counts, known as a Smoluchowski process. Thus, unambiguous tracking of particles is not required and identification of single particles is sufficient. However, the diffusion coefficient of the particles must be estimated separately. The proposed method does not require precalibration of the detection region volume, as this can be estimated directly from the observations. We evaluate the method in a simulation study and on experimental data from a series of dilutions of 0.2‐ and 0.5‐μm polymer nanospheres in water, obtaining very good agreement with reference values.

The lognormal and gamma distribution models for estimating molecular weight distributions of polymers using PGSE NMR

We present comprehensive derivations for the statistical models and methods for the use of pulsed gradient spin echo (PGSE) NMR to characterize the molecular weight distribution of polymers via the well-known scaling law relating diffusion coefficients and molecular weights. We cover the lognormal and gamma distribution models and linear combinations of these distributions. Although the focus is on methodology, we illustrate the use experimentally with three polystyrene samples, comparing the NMR results to gel permeation chromatography (GPC) measurements, test the accuracy and noise-sensitivity on simulated data, and provide code for implementation.

Identifying directional persistence in intracellular particle motion using Hidden Markov Models

Particle tracking is a widely used and promising technique for elucidating complex dynamics of the living cell. The cytoplasm is an active material, in which the kinetics of intracellular structures are highly heterogeneous. Tracer particles typically undergo a combination of random motion and various types of directed motion caused by the activity of molecular motors and other non-equilibrium processes. Random switching between more and less directional persistence of motion generally occurs. We present a method for identifying states of motion with different directional persistence in individual particle trajectories. Our analysis is based on a multi-scale turning angle model to characterize motion locally, together with a Hidden Markov Model with two states representing different directional persistence. We define one of the states by the motion of particles in a reference data set where some active processes have been inhibited. We illustrate the usefulness of the method by studying transport of vesicles along microtubules and transport of nanospheres activated by myosin. We study the results using mean square displacements, durations, and particle speeds within each state. We conclude that the method provides accurate identification of states of motion with different directional persistence, with very good agreement in terms of mean-squared displacement between the reference data set and one of the states in the two-state model.

Obtaining T1-T2 distribution functions from 1-dimensional T1 and T2 measurements: The pseudo 2-D relaxation model.

We present the pseudo 2-D relaxation model (P2DRM), a method to estimate multidimensional probability distributions of material parameters from independent 1-D measurements. We illustrate its use on 1-D T1 and T2 relaxation measurements of saturated rock and evaluate it on both simulated and experimental T1-T2 correlation measurement data sets. Results were in excellent agreement with the actual, known 2-D distribution in the case of the simulated data set. In both the simulated and experimental case, the functional relationships between T1 and T2 were in good agreement with the T1-T2 correlation maps from the 2-D inverse Laplace transform of the full 2-D data sets. When a 1-D CPMG experiment is combined with a rapid T1 measurement, the P2DRM provides a double-shot method for obtaining a T1-T2 relationship, with significantly decreased experimental time in comparison to the full T1-T2 correlation measurement.