Raster Image Correlation Spectroscopy Performance Evaluation.

Raster image correlation spectroscopy (RICS) is a fluorescence image analysis method for extracting the mobility, concentration, and stoichiometry of diffusing fluorescent molecules from confocal image stacks. The method works by calculating a spatial correlation function for each image and analyzing the average of those by model fitting. Rules of thumb exist for RICS image acquisitioning, yet a rigorous theoretical approach to predict the accuracy and precision of the recovered parameters has been lacking. We outline explicit expressions to reveal the dependence of RICS results on experimental parameters. In terms of imaging settings, we observed that a twofold decrease of the pixel size, e.g., from 100 to 50 nm, decreases the error on the translational diffusion constant (D) between three- and fivefold. For D = 1 µm2 s-1, a typical value for intracellular measurements, ∼25-fold lower mean-squared relative error was obtained when the optimal scan speed was used, although more drastic improvements were observed for other values of D. We proposed a slightly modified RICS calculation that allows correcting for the significant bias of the autocorrelation function at small (≪50 × 50 pixels) sizes of the region of interest. In terms of sample properties, at molecular brightness E = 100 kHz and higher, RICS data quality was sufficient using as little as 20 images, whereas the optimal number of frames for lower E scaled pro rata. RICS data quality was constant over the nM-µM concentration range. We developed a bootstrap-based confidence interval of D that outperformed the classical least-squares approach in terms of coverage probability of the true value of D. We validated the theory via in vitro experiments of enhanced green fluorescent protein at different buffer viscosities. Finally, we outline robust practical guidelines and provide free software to simulate the parameter effects on recovery of the diffusion coefficient.

Colloidal particle aggregation in three dimensions.

Colloidal systems are of importance not only for everyday products, but also for the development of new advanced materials. In many applications, it is crucial to understand and control colloidal interaction. In this paper, we study colloidal particle aggregation of silica nanoparticles, where the data are given in a three-dimensional micrograph obtained by high-angle annular dark field scanning transmission electron microscopy tomography. We investigate whether dynamic models for particle aggregation, namely the diffusion limited cluster aggregation and the reaction limited cluster aggregation models, can be used to construct structures present in the scanning transmission electron microscopy data. We compare the experimentally obtained silica aggregate to the simulated postaggregated structures obtained by the dynamic models. In addition, we fit static Gibbs point process models, which are commonly used models for point patterns with interactions, to the silica data. We were able to simulate structures similar to the silica structures by using Gibbs point process models. By fitting Gibbs models to the simulated cluster aggregation patterns, we saw that a smaller probability of aggregation would be needed to construct structures similar to the observed silica particle structure.

Fluorescence recovery after photobleaching in material and life sciences: putting theory into practice.

Fluorescence recovery after photobleaching (FRAP) is a versatile tool for determining diffusion and interaction/binding properties in biological and material sciences. An understanding of the mechanisms controlling the diffusion requires a deep understanding of structure-interaction-diffusion relationships. In cell biology, for instance, this applies to the movement of proteins and lipids in the plasma membrane, cytoplasm and nucleus. In industrial applications related to pharmaceutics, foods, textiles, hygiene products and cosmetics, the diffusion of solutes and solvent molecules contributes strongly to the properties and functionality of the final product. All these systems are heterogeneous, and accurate quantification of the mass transport processes at the local level is therefore essential to the understanding of the properties of soft (bio)materials. FRAP is a commonly used fluorescence microscopy-based technique to determine local molecular transport at the micrometer scale. A brief high-intensity laser pulse is locally applied to the sample, causing substantial photobleaching of the fluorescent molecules within the illuminated area. This causes a local concentration gradient of fluorescent molecules, leading to diffusional influx of intact fluorophores from the local surroundings into the bleached area. Quantitative information on the molecular transport can be extracted from the time evolution of the fluorescence recovery in the bleached area using a suitable model. A multitude of FRAP models has been developed over the years, each based on specific assumptions. This makes it challenging for the non-specialist to decide which model is best suited for a particular application. Furthermore, there are many subtleties in performing accurate FRAP experiments. For these reasons, this review aims to provide an extensive tutorial covering the essential theoretical and practical aspects so as to enable accurate quantitative FRAP experiments for molecular transport measurements in soft (bio)materials.

Valid and efficient manual estimates of intracranial volume from magnetic resonance images.

BACKGROUND: Manual segmentations of the whole intracranial vault in high-resolution magnetic resonance images are often regarded as very time-consuming. Therefore it is common to only segment a few linearly spaced intracranial areas to estimate the whole volume. The purpose of the present study was to evaluate how the validity of intracranial volume estimates is affected by the chosen interpolation method, orientation of the intracranial areas and the linear spacing between them. METHODS: Intracranial volumes were manually segmented on 62 participants from the Gothenburg MCI study using 1.5 T, T1-weighted magnetic resonance images. Estimates of the intracranial volumes were then derived using subsamples of linearly spaced coronal, sagittal or transversal intracranial areas from the same volumes. The subsamples of intracranial areas were interpolated into volume estimates by three different interpolation methods. The linear spacing between the intracranial areas ranged from 2 to 50 mm and the validity of the estimates was determined by comparison with the entire intracranial volumes. RESULTS: A progressive decrease in intra-class correlation and an increase in percentage error could be seen with increased linear spacing between intracranial areas. With small linear spacing (≤15 mm), orientation of the intracranial areas and interpolation method had negligible effects on the validity. With larger linear spacing, the best validity was achieved using cubic spline interpolation with either coronal or sagittal intracranial areas. Even at a linear spacing of 50 mm, cubic spline interpolation on either coronal or sagittal intracranial areas had a mean absolute agreement intra-class correlation with the entire intracranial volumes above 0.97. CONCLUSION: Cubic spline interpolation in combination with linearly spaced sagittal or coronal intracranial areas overall resulted in the most valid and robust estimates of intracranial volume. Using this method, valid ICV estimates could be obtained in less than five minutes per patient.

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.

Interactions and diffusion in fine-stranded ß-lactoglobulin gels determined via FRAP and binding.

The effects of electrostatic interactions and obstruction by the microstructure on probe diffusion were determined in positively charged hydrogels. Probe diffusion in fine-stranded gels and solutions of ß-lactoglobulin at pH 3.5 was determined using fluorescence recovery after photobleaching (FRAP) and binding, which is widely used in biophysics. The microstructures of the ß-lactoglobulin gels were characterized using transmission electron microscopy. The effects of probe size and charge (negatively charged Na2-fluorescein (376Da) and weakly anionic 70kDa FITC-dextran), probe concentration (50 to 200 ppm), and ß-lactoglobulin concentration (9% to 12% w/w) on the diffusion properties and the electrostatic interaction between the negatively charged probes and the positively charged gels or solutions were evaluated. The results show that the diffusion of negatively charged Na2-fluorescein is strongly influenced by electrostatic interactions in the positively charged ß-lactoglobulin systems. A linear relationship between the pseudo-on binding rate constant and the ß-lactoglobulin concentration for three different probe concentrations was found. This validates an important assumption of existing biophysical FRAP and binding models, namely that the pseudo-on binding rate constant equals the product of the molecular binding rate constant and the concentration of the free binding sites. Indicators were established to clarify whether FRAP data should be analyzed using a binding-diffusion model or an obstruction-diffusion model.

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.

Automatic particle detection in microscopy using temporal correlations.

One of the fundamental problems in the analysis of single particle tracking data is the detection of individual particle positions from microscopy images. Distinguishing true particles from noise with a minimum of false positives and false negatives is an important step that will have substantial impact on all further analysis of the data. A common approach is to obtain a plausible set of particles from a larger set of candidate particles by filtering using manually selected threshold values for intensity, size, shape, and other parameters describing a particle. This introduces subjectivity into the analysis and hinders reproducibility. In this paper, we introduce a method for automatic selection of these threshold values based on maximizing temporal correlations in particle count time series. We use Markov Chain Monte Carlo to find the threshold values corresponding to the maximum correlation, and we study several experimental data sets to assess the performance of the method in practice by comparing manually selected threshold values from several independent experts with automatically selected threshold values. We conclude that the method produces useful results, reducing subjectivity and the need for manual intervention, a great benefit being its easy integratability into many already existing particle detection algorithms.

MS risk genes are transcriptionally regulated in CSF leukocytes at relapse.

BACKGROUND: Infiltrating T-helper cells, cytotoxic T-cells, B-cells and monocytes are thought to mediate the damage to myelin, oligodendrocytes and axons in multiple sclerosis (MS), which results in progressive disability. OBJECTIVE: The objective of this paper is to explore gene expression profiles of leukocytes in the cerebrospinal fluid (CSF) compartment of MS patients during relapse. METHODS: Global gene expression was analyzed by DNA microarray analysis of cells in CSF from MS patients and controls, and verifications were performed with real-time polymerase chain reaction (PCR) and enzyme-linked immunosorbent assay (ELISA). RESULTS: Fifty percent of the recently described risk genes for MS and 28% of non-risk genes were differently expressed in MS patients compared to controls (χ(2)-test, p=7.7 × 10(-5)). Genes involved in T- and NK-cell processes were up-regulated, and genes involved in processes targeting innate immunity or B-cells were down-regulated in MS. Increased expression of EDN1 and CXCL11 and decreased expression of HMOX1 was verified with real-time PCR and increased expression of CXCL13 was verified with ELISA in CSF. CONCLUSION: DNA microarray analysis is useful in identifying differently expressed genes in CSF leukocytes, which may be important in MS in vivo. Our findings suggest that many of the risk genes for MS are differently expressed in the disease-mediating leukocytes that penetrate the blood-brain barrier.

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.

Measuring absolute number concentrations of nanoparticles using single-particle tracking.

Single-particle tracking (SPT) microscopy is increasingly used to characterize nanoparticulate systems. We introduce a concept for estimation of particle number concentration in Brownian particle dispersions using SPT based on a model for the trajectory length distribution of particles to estimate the detection region volume. The resulting method is independent of precalibration reference measurements, and robust with respect to image processing settings. Experimentally estimated concentrations of different dilutions of 0.19- and 0.52-µm polymer nanospheres are in excellent agreement with estimates computed from the concentrations of the stock solutions.

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.

Identification of adipocyte genes regulated by caloric intake.

CONTEXT: Changes in energy intake have marked and rapid effects on metabolic functions, and some of these effects may be due to changes in adipocyte gene expression that precede alterations in body weight. OBJECTIVE: The aim of the study was to identify adipocyte genes regulated by changes in caloric intake independent of alterations in body weight. RESEARCH DESIGN AND METHODS: Obese subjects given a very low-caloric diet followed by gradual reintroduction of ordinary food and healthy subjects subjected to overfeeding were investigated. Adipose tissue biopsies were taken at multiple time-points, and gene expression was measured by DNA microarray. Genes regulated in the obese subjects undergoing caloric restriction followed by refeeding were identified using two-way ANOVA corrected with Bonferroni. From these, genes regulated by caloric restriction and oppositely during the weight-stable refeeding phase were identified in the obese subjects. The genes that were also regulated, in the same direction as the refeeding phase, in the healthy subjects after overfeeding were defined as being regulated by caloric intake. Results were confirmed using real-time PCR or immunoassay. RESULTS: Using a significance level of P < 0.05 for all comparisons, 52 genes were down-regulated, and 50 were up-regulated by caloric restriction and regulated in the opposite direction by refeeding and overfeeding. Among these were genes involved in lipogenesis (ACLY, ACACA, FASN, SCD), control of protein synthesis (4EBP1, 4EBP2), ß-oxidation (CPT1B), and insulin resistance (PEDF, SPARC). CONCLUSIONS: Metabolic genes involved in lipogenesis, protein synthesis, and insulin resistance are central in the transcriptional response of adipocytes to changes in caloric intake.

Straightforward FRAP for quantitative diffusion measurements with a laser scanning microscope.

Confocal or multi-photon laser scanning microscopes are convenient tools to perform FRAP diffusion measurements. Despite its popularity, accurate FRAP remains often challenging since current methods are either limited to relatively large bleach regions or can be complicated for non-specialists. In order to bring reliable quantitative FRAP measurements to the broad community of laser scanning microscopy users, here we have revised FRAP theory and present a new pixel based FRAP method relying on the photo bleaching of rectangular regions of any size and aspect ratio. The method allows for fast and straightforward quantitative diffusion measurements due to a closed-form expression for the recovery process utilizing all available spatial and temporal data. After a detailed validation, its versatility is demonstrated by diffusion studies in heterogeneous biopolymer mixtures.

Metabolomic, transcriptional, hormonal, and signaling cross-talk in superroot2.

Auxin homeostasis is pivotal for normal plant growth and development. The superroot2 (sur2) mutant was initially isolated in a forward genetic screen for auxin overproducers, and SUR2 was suggested to control auxin conjugation and thereby regulate auxin homeostasis. However, the phenotype was not uniform and could not be described as a pure high auxin phenotype, indicating that knockout of CYP83B1 has multiple effects. Subsequently, SUR2 was identified as CYP83B1, a cytochrome P450 positioned at the metabolic branch point between auxin and indole glucosinolate metabolism. To investigate concomitant global alterations triggered by knockout of CYP83B1 and the countermeasures chosen by the mutant to cope with hormonal and metabolic imbalances, 10-day-old mutant seedlings were characterized with respect to their transcriptome and metabolome profiles. Here, we report a global analysis of the sur2 mutant by the use of a combined transcriptomic and metabolomic approach revealing pronounced effects on several metabolic grids including the intersection between secondary metabolism, cell wall turnover, hormone metabolism, and stress responses. Metabolic and transcriptional cross-talks in sur2 were found to be regulated by complex interactions between both positively and negatively acting transcription factors. The complex phenotype of sur2 may thus not only be assigned to elevated levels of auxin, but also to ethylene and abscisic acid responses as well as drought responses in the absence of a water deficiency. The delicate balance between these signals explains why minute changes in growth conditions may result in the non-uniform phenotype. The large phenotypic variation observed between and within the different surveys may be reconciled by the complex and intricate hormonal balances in sur2 seedlings decoded in this study.

Changes in adipose tissue gene expression and plasma levels of adipokines and acute-phase proteins in patients with critical illness.

Insulin resistance develops rapidly during critical illness. The release of adipokines from adipose tissue is thought to play a key role in the development of insulin resistance, as are elevated levels of acute-phase proteins. The aim of this study was to identify changes in adipose tissue gene expression and plasma levels of adipokines and acute-phase proteins during critical illness. From 8 patients with subarachnoidal hemorrhage, consecutive blood samples and adipose tissue biopsies were obtained at 3 time points, twice during intensive care (1-2 days [IC1] and 7-9 days after subarachnoidal hemorrhage) and once after 8 months (recovery). The patients received a continuous insulin infusion to maintain normal glucose levels reflecting insulin resistance. The DNA microarray analysis showed increased zink-alpha2 glycoprotein (ZAG) and phospholipase A2, group IIA messenger RNA levels during intensive care compared with recovery (P < .05). Real-time polymerase chain reaction confirmed the increased expression of ZAG and phospholipase A2, group IIA. Plasma levels of ZAG, serum amyloid A, and C-reactive protein were higher at 7 to 9 days after subarachnoidal hemorrhage compared with either IC1 or recovery (P = .0001); and plasma levels of retinol-binding protein 4 and adiponectin were lower at IC1 compared with recovery (P = .05). The described changes in adipose tissue gene expression and plasma levels of adipokines and acute-phase proteins may influence the development of insulin resistance during critical illness.

Empirical Bayes models for multiple probe type microarrays at the probe level.

BACKGROUND: When analyzing microarray data a primary objective is often to find differentially expressed genes. With empirical Bayes and penalized t-tests the sample variances are adjusted towards a global estimate, producing more stable results compared to ordinary t-tests. However, for Affymetrix type data a clear dependency between variability and intensity-level generally exists, even for logged intensities, most clearly for data at the probe level but also for probe-set summarizes such as the MAS5 expression index. As a consequence, adjustment towards a global estimate results in an intensity-level dependent false positive rate. RESULTS: We propose two new methods for finding differentially expressed genes, Probe level Locally moderated Weighted median-t (PLW) and Locally Moderated Weighted-t (LMW). Both methods use an empirical Bayes model taking the dependency between variability and intensity-level into account. A global covariance matrix is also used allowing for differing variances between arrays as well as array-to-array correlations. PLW is specially designed for Affymetrix type arrays (or other multiple-probe arrays). Instead of making inference on probe-set summaries, comparisons are made separately for each perfect-match probe and are then summarized into one score for the probe-set. CONCLUSION: The proposed methods are compared to 14 existing methods using five spike-in data sets. For RMA and GCRMA processed data, PLW has the most accurate ranking of regulated genes in four out of the five data sets, and LMW consistently performs better than all examined moderated t-tests when used on RMA, GCRMA, and MAS5 expression indexes.

Improved covariance matrix estimators for weighted analysis of microarray data.

Empirical Bayes models have been shown to be powerful tools for identifying differentially expressed genes from gene expression microarray data. An example is the WAME model, where a global covariance matrix accounts for array-to-array correlations as well as differing variances between arrays. However, the existing method for estimating the covariance matrix is very computationally intensive and the estimator is biased when data contains many regulated genes. In this paper, two new methods for estimating the covariance matrix are proposed. The first method is a direct application of the EM algorithm for fitting the multivariate t-distribution of the WAME model. In the second method, a prior distribution for the log fold-change is added to the WAME model, and a discrete approximation is used for this prior. Both methods are evaluated using simulated and real data. The first method shows equal performance compared to the existing method in terms of bias and variability, but is superior in terms of computer time. For large data sets (>15 arrays), the second method also shows superior computer run time. Moreover, for simulated data with regulated genes the second method greatly reduces the bias. With the proposed methods it is possible to apply the WAME model to large data sets with reasonable computer run times. The second method shows a small bias for simulated data, but appears to have a larger bias for real data with many regulated genes.

Weighted analysis of general microarray experiments.

BACKGROUND: In DNA microarray experiments, measurements from different biological samples are often assumed to be independent and to have identical variance. For many datasets these assumptions have been shown to be invalid and typically lead to too optimistic p-values. A method called WAME has been proposed where a variance is estimated for each sample and a covariance is estimated for each pair of samples. The current version of WAME is, however, limited to experiments with paired design, e.g. two-channel microarrays. RESULTS: The WAME procedure is extended to general microarray experiments, making it capable of handling both one- and two-channel datasets. Two public one-channel datasets are analysed and WAME detects both unequal variances and correlations. WAME is compared to other common methods: fold-change ranking, ordinary linear model with t-tests, LIMMA and weighted LIMMA. The p-value distributions are shown to differ greatly between the examined methods. In a resampling-based simulation study, the p-values generated by WAME are found to be substantially more correct than the alternatives when a relatively small proportion of the genes is regulated. WAME is also shown to have higher power than the other methods. WAME is available as an R-package. CONCLUSION: The WAME procedure is generalized and the limitation to paired-design microarray datasets is removed. The examined other methods produce invalid p-values in many cases, while WAME is shown to produce essentially valid p-values when a relatively small proportion of genes is regulated. WAME is also shown to have higher power than the examined alternative methods.

Identification of the three-dimensional gel microstructure from transmission electron micrographs.

Mass transport in gels depends crucially on local properties of the gel network. We propose a method for identifying the three-dimensional (3D) gel microstructure from statistical information in transmission electron micrographs. The gel strand network is modelled as a random graph with nodes and edges (branches). The distribution of edge length, the number of edges at nodes and the angles between edges at a node are estimated from transmission electron micrographs by image analysis methods. The 3D network is simulated by Markov chain Monte Carlo, with a probability function based on the statistical information found from the micrographs. The micrographs are projections of stained gel strands in slices, and we derive a formula for estimating the thickness of the stained gel slice based on the total projected gel strand length and the number of times that gel strands enter or exit the slice.