Single particle raster image analysis of diffusion for particle mixtures.

Recently we complemented the raster image correlation spectroscopy (RICS) method of analysing raster images via estimation of the image correlation function with the method single particle raster image analysis (SPRIA). In SPRIA, individual particles are identified and the diffusion coefficient of each particle is estimated by a maximum likelihood method. In this paper, we extend the SPRIA method to analyse mixtures of particles with a finite set of diffusion coefficients in a homogeneous medium. In examples with simulated and experimental data with two and three different diffusion coefficients, we show that SPRIA gives accurate estimates of the diffusion coefficients and their proportions. A simple technique for finding the number of different diffusion coefficients is also suggested. Further, we study the use of RICS for mixtures with two different diffusion coefficents and investigate, by plotting level curves of the correlation function, how large the quotient between diffusion coefficients needs to be in order to allow discrimination between models with one and two diffusion coefficients. We also describe a minor correction (compared to published papers) of the RICS autocorrelation function.

Single particle raster image analysis of diffusion.

As a complement to the standard RICS method of analysing Raster Image Correlation Spectroscopy images with estimation of the image correlation function, we introduce the method SPRIA, Single Particle Raster Image Analysis. Here, we start by identifying individual particles and estimate the diffusion coefficient for each particle by a maximum likelihood method. Averaging over the particles gives a diffusion coefficient estimate for the whole image. In examples both with simulated and experimental data, we show that the new method gives accurate estimates. It also gives directly standard error estimates. The method should be possible to extend to study heterogeneous materials and systems of particles with varying diffusion coefficient, as demonstrated in a simple simulation example. A requirement for applying the SPRIA method is that the particle concentration is low enough so that we can identify the individual particles. We also describe a bootstrap method for estimating the standard error of standard RICS.

From static micrographs to particle aggregation dynamics in three dimensions.

Studies on colloidal aggregation have brought forth theories on stability of colloidal gels and models for aggregation dynamics. Still, a complete link between developed frameworks and obtained laboratory observations has to be found. In this work, aggregates of silica nanoparticles (20 nm) are studied using diffusion limited cluster aggregation (DLCA) and reaction limited cluster aggregation (RLCA) models. These processes are driven by the probability of particles to aggregate upon collision. This probability of aggregation is one in the DLCA and close to zero in the RLCA process. We show how to study the probability of aggregation from static micrographs on the example of a silica nanoparticle gel at 9 wt%. The analysis includes common summary functions from spatial statistics, namely the empty space function and Ripley's K-function, as well as two newly developed summary functions for cluster analysis based on graph theory. One of the new cluster analysis functions is related to the clustering coefficient in communication networks and the other to the size of a cluster. All four topological summary statistics are used to quantitatively compare in plots and in a least-square approach experimental data to cluster aggregation simulations with decreasing probabilities of aggregation. We study scanning transmission electron micrographs and utilize the intensity-mass thickness relation present in such images to create comparable micrographs from three-dimensional simulations. Finally, a characterization of colloidal silica aggregates and simulated structures is obtained, which allows for an evaluation of the cluster aggregation process for different aggregation scenarios. As a result, we find that the RLCA process fits the experimental data better than the DLCA process.

Estimation of mass thickness response of embedded aggregated silica nanospheres from high angle annular dark-field scanning transmission electron micrographs.

In this study, we investigate the functional behaviour of the intensity in high-angle annular dark field scanning transmission electron micrograph images. The model material is a silica particle (20 nm) gel at 5 wt%. By assuming that the intensity response is monotonically increasing with increasing mass thickness of silica, an estimate of the functional form is calculated using a maximum likelihood approach. We conclude that a linear functional form of the intensity provides a fair estimate but that a power function is significantly better for estimating the amount of silica in the z-direction. The work adds to the development of quantifying material properties from electron micrographs, especially in the field of tomography methods and three-dimensional quantitative structural characterization from a scanning transmission electron micrograph. It also provides means for direct three-dimensional quantitative structural characterization from a scanning transmission electron micrograph.

Self-calibrated concentration measurements of polydisperse nanoparticles.

Quantitative characterization of nanoparticles, e.g. accurate estimation of concentration distributions, is critical to many pharmaceutical and biological applications. We present a method that enables for the first time highly accurate size and absolute concentration measurements of polydisperse nanoparticles in solution, based on fluorescence single particle tracking, that are self-calibrated in the sense that the detection region volume is estimated based on the tracking data. The method is evaluated using simulations and experimental data of polystyrene nanospheres in water/sucrose solution. In addition, the method is used to quantify aggregation and clearance of different types of liposomes after intravenous injection in rats, where additional and more accurate information can be obtained that was previously unavailable, which can help elucidate their usefulness as drug carriers.

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.

Pixel-based analysis of FRAP data with a general initial bleaching profile.

In Jonasson et al. (2008), we presented a new pixel-based maximum likelihood framework for the estimation of diffusion coefficients from data on fluorescence recovery after photobleaching (FRAP) with confocal laser scanning microscopy (CLSM). The main method there, called the Gaussian profile method below, is based on the assumption that the initial intensity profile after photobleaching is approximately Gaussian. In the present paper, we introduce a method, called the Monotone profile method, where the maximum likelihood framework is extended to a general initial bleaching profile only assuming that the profile is a non-decreasing function of the distance to the bleaching centre. The statistical distribution of the image noise is further assumed to be Poisson instead of normal, which should be a more realistic description of the noise in the detector. The new Monotone profile method and the Gaussian profile method are applied to FRAP data on swelling of super absorbent polymers (SAP) in water with a Fluorescein probe. The initial bleaching profile is close to a step function at low degrees of swelling and close to a Gaussian profile at high degrees of swelling. The results obtained from the analysis of the FRAP data are corroborated with NMR diffusometry analysis of SAP with a polyethylene glycol probe having size similar to the Fluorescein. The comparison of the Gaussian and Monotone profile methods is also performed by use of simulated data. It is found that the new Monotone profile method is accurate for all types of initial profiles studied, but it suffers from being computationally slow. The fast Gaussian profile method is sufficiently accurate for most of the profiles studied, but underestimates the diffusion coefficient for profiles close to a step function. We also provide a diagnostic plot, which indicates whether the Gaussian profile method is acceptable or not.

A pixel-based likelihood framework for analysis of fluorescence recovery after photobleaching data.

A new framework for the estimation of diffusion coefficients from data on fluorescence recovery after photobleaching (FRAP) with confocal laser scanning microscopy (CLSM) is presented. It is a pixel-based statistical methodology that efficiently utilizes all information about the diffusion process in the available set of images. The likelihood function for a series of images is maximized which gives both an estimate of the diffusion coefficient and a corresponding error. This framework opens up possibilities (1) to obtain localized diffusion coefficient estimates in both homogeneous and heterogeneous materials, (2) to account for time differences between the registrations at the pixels within each image, and (3) to plan experiments optimized with respect to the number of replications, the number of bleached regions for each replicate, pixel size, the number of pixels, the number of images in each series etc. To demonstrate the use of the new framework, we have applied it to a simple system with polyethylene glycol (PEG) and water where we find good agreement with diffusion coefficient estimates from NMR diffusometry. In this experiment, it is also shown that the effect of the point spread function is negligible, and we find fluorochrome-concentration levels that give a linear response function for the fluorescence intensity.

Connectivity can be used to identify key genes in DNA microarray data: a study based on gene expression in nasal polyps before and after treatment with glucocorticoids.

CONCLUSIONS: The presented analysis of nasal polyposis using connectivity based on the PubGene literature co-citation network demonstrates that this tool can be used to identify key genes in DNA microarray studies of human polygenic diseases. OBJECTIVES: DNA microarray studies of complex diseases may reveal differential expression of hundreds of genes. According to network theory and studies of yeast cells, genes that are connected with several other genes appear to have key regulatory roles. This study aimed to examine if this principle can be translated to DNA microarray studies of human disease, using nasal polyposis as a base for the analysis. MATERIALS AND METHODS: The connectivity of differentially expressed genes from a previously described microarray study of nasal polyposis before and after treatment with glucocorticoids was determined. This was done using the literature co-citation network PubGene. RESULTS: In all, 166 genes were differentially expressed; 39 of these were previously defined as inflammatory and considered important for nasal polyposis. The connectivity of all differentially expressed genes was analysed using the PubGene literature co-citation network. Seventy-four of the 166 genes were connected to other genes. By contrast, the average number of connected genes among 100 sets of 166 randomly chosen genes was 31.5. A small number of the differentially expressed genes were highly connected, while most genes had few or no connections. This indicated a scale-free network. The most connected gene was interleukin-8, an inflammatory gene of known importance for nasal polyposis. Twenty-eight of the 74 connected genes were inflammatory (38%), compared with 11 of the 92 unconnected genes (12%), p < 0.0001. Since most evidence suggests that nasal polyps are inflammatory in their nature, this supports the hypothesis that connected genes have more disease relevance than unconnected genes.

Increased expression of aquaporin 3 in atopic eczema.

BACKGROUND: Dry skin in atopic eczema depends on increased water loss. The mechanisms behind this are poorly understood. The aim of this work was to identify genes that may contribute to water loss in eczema. METHODS: Affymetrix DNA microarrays U133A were used to analyse gene expression in skin biopsies from 10 patients with atopic eczema and 10 healthy controls. RESULTS: DNA microarray analysis showed up-regulation of 262 genes and down-regulation of 129 genes in atopic eczema. The known functions of these genes were analysed using Gene Ontology to identify genes that could contribute to increased water loss. This led to identification of aquaporin 3 (AQP3), which has a key role in hydrating healthy epidermis. Increased expression of AQP3 was found in eczema compared with healthy skin. This was confirmed with real-time polymerase chain reaction (P<0.001). In healthy skin, epidermal AQP3 immunoreactivity was weak and mainly found in the stratum basale. A gradient was formed with decreasing AQP3 staining in the lower layers of the stratum spinosum. By contrast, in acute and chronic atopic eczema strong AQP3 staining was found in both the stratum basale and the stratum spinosum. CONCLUSIONS: Aquaporin 3 is the predominant aquaporin in human skin. Increased expression and altered cellular distribution of AQP3 is found in eczema and this may contribute to water loss.

A network-based analysis of allergen-challenged CD4+ T cells from patients with allergic rhinitis.

We performed a network-based analysis of DNA microarray data from allergen-challenged CD4(+) T cells from patients with seasonal allergic rhinitis. Differentially expressed genes were organized into a functionally annotated network using the Ingenuity Knowledge Database, which is based on manual review of more than 200,000 publications. The main function of this network is the regulation of lymphocyte apoptosis, a role associated with several genes of the tuber necrosis factor superfamily. The expression of TNFRSF4, one of the genes in this family, was found to be 48 times higher in allergen-challenged cells than in diluent-challenged cells. TNFRSF4 is known to inhibit apoptosis and to enhance Th2 proliferation. Examination of a different material of allergen-stimulated peripheral blood mononuclear cells showed a higher number of interleukin-4(+) type 2 CD4(+) T (Th2) cells in patients than in controls (P<0.01), as well as a higher number of non-apoptotic Th2 cells in patients (P<0.01). The number of Th2 cells expressing TNFRSF4, TNFSF7 and TNFRSF1B was also significantly higher in patients. Treatment with anti-TNFSF4 resulted in a significantly decreased number of Th2 cells (P<0.05). A logical inference from all this is that the proliferation of allergen-challenged Th2 cells is associated with a decreased apoptosis of Th2 cells and an increase in TNFRSF4 signalling.

Multiple change-point analysis applied to the monitoring of Salmonella prevalence in Danish pigs and pork.

In the nation-wide Salmonella Control Program in Denmark, the occurrence of Salmonella enterica in pork, pigs at slaughter and herds is monitored. The objective of the present study was to retrospectively evaluate changes in sero-prevalence of meat juice samples and in the occurrence of Salmonella enterica in pork in 1995 and 1996. Three sets of data were used in this work: (1) serological test results of meat juice samples from pigs at slaughter (approximately 14000 samples per week); (2) bacteriological test results of pork (approximately 550 samples per week); and (3) data on the salmonella level of all Danish herds with an expected kill of over 100 pigs per year. The change-point analysis was applied to detect the change-points that divided the study period into intervals in which the prevalence was constant and to estimate the average prevalences in those intervals. Progress in the Danish Salmonella Control Program was visualised when using the results of the change-point analysis (1995-96) as baseline prevalences and compared with the current year (1997). The change-point analysis provided an indication of a seasonal pattern in salmonella occurrence with lower sero-prevalence in summer than in winter. The sero-prevalence (percent positive meat juice samples) might be a better predictor of prevalence in pork than the proportion of herds with moderate or high sero-prevalence.