Maximum Entropy Technique and Regularization Functional for Determining the Pharmacokinetic Parameters in DCE-MRI.

This paper aims to solve the arterial input function (AIF) determination in dynamic contrast-enhanced MRI (DCE-MRI), an important linear ill-posed inverse problem, using the maximum entropy technique (MET) and regularization functionals. In addition, estimating the pharmacokinetic parameters from a DCE-MR image investigations is an urgent need to obtain the precise information about the AIF-the concentration of the contrast agent on the left ventricular blood pool measured over time. For this reason, the main idea is to show how to find a unique solution of linear system of equations generally in the form of [Formula: see text] named an ill-conditioned linear system of equations after discretization of the integral equations, which appear in different tomographic image restoration and reconstruction issues. Here, a new algorithm is described to estimate an appropriate probability distribution function for AIF according to the MET and regularization functionals for the contrast agent concentration when applying Bayesian estimation approach to estimate two different pharmacokinetic parameters. Moreover, by using the proposed approach when analyzing simulated and real datasets of the breast tumors according to pharmacokinetic factors, it indicates that using Bayesian inference-that infer the uncertainties of the computed solutions, and specific knowledge of the noise and errors-combined with the regularization functional of the maximum entropy problem, improved the convergence behavior and led to more consistent morphological and functional statistics and results. Finally, in comparison to the proposed exponential distribution based on MET and Newton's method, or Weibull distribution via the MET and teaching-learning-based optimization (MET/TLBO) in the previous studies, the family of Gamma and Erlang distributions estimated by the new algorithm are more appropriate and robust AIFs.

Modified Maximum Entropy Method and Estimating the AIF via DCE-MRI Data Analysis.

Background: For the kinetic models used in contrast-based medical imaging, the assignment of the arterial input function named AIF is essential for the estimation of the physiological parameters of the tissue via solving an optimization problem. Objective: In the current study, we estimate the AIF relayed on the modified maximum entropy method. The effectiveness of several numerical methods to determine kinetic parameters and the AIF is evaluated-in situations where enough information about the AIF is not available. The purpose of this study is to identify an appropriate method for estimating this function. Materials and Methods: The modified algorithm is a mixture of the maximum entropy approach with an optimization method, named the teaching-learning method. In here, we applied this algorithm in a Bayesian framework to estimate the kinetic parameters when specifying the unique form of the AIF by the maximum entropy method. We assessed the proficiency of the proposed method for assigning the kinetic parameters in the dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), when determining AIF with some other parameter-estimation methods and a standard fixed AIF method. A previously analyzed dataset consisting of contrast agent concentrations in tissue and plasma was used. Results and Conclusions: We compared the accuracy of the results for the estimated parameters obtained from the MMEM with those of the empirical method, maximum likelihood method, moment matching ("method of moments"), the least-square method, the modified maximum likelihood approach, and our previous work. Since the current algorithm does not have the problem of starting point in the parameter estimation phase, it could find the best and nearest model to the empirical model of data, and therefore, the results indicated the Weibull distribution as an appropriate and robust AIF and also illustrated the power and effectiveness of the proposed method to estimate the kinetic parameters.

Cohesin depleted cells rebuild functional nuclear compartments after endomitosis.

Cohesin plays an essential role in chromatin loop extrusion, but its impact on a compartmentalized nuclear architecture, linked to nuclear functions, is less well understood. Using live-cell and super-resolved 3D microscopy, here we find that cohesin depletion in a human colon cancer derived cell line results in endomitosis and a single multilobulated nucleus with chromosome territories pervaded by interchromatin channels. Chromosome territories contain chromatin domain clusters with a zonal organization of repressed chromatin domains in the interior and transcriptionally competent domains located at the periphery. These clusters form microscopically defined, active and inactive compartments, which likely correspond to A/B compartments, which are detected with ensemble Hi-C. Splicing speckles are observed nearby within the lining channel system. We further observe that the multilobulated nuclei, despite continuous absence of cohesin, pass through S-phase with typical spatio-temporal patterns of replication domains. Evidence for structural changes of these domains compared to controls suggests that cohesin is required for their full integrity.

Co-localization analysis in fluorescence microscopy via maximum entropy copula.

Co-localization analysis is a popular method for quantitative analysis in fluorescence microscopy imaging. The localization of marked proteins in the cell nucleus allows a deep insight into biological processes in the nucleus. Several metrics have been developed for measuring the co-localization of two markers, however, they depend on subjective thresholding of background and the assumption of linearity. We propose a robust method to estimate the bivariate distribution function of two color channels. From this, we can quantify their co- or anti-colocalization. The proposed method is a combination of the Maximum Entropy Method (MEM) and a Gaussian Copula, which we call the Maximum Entropy Copula (MEC). This new method can measure the spatial and nonlinear correlation of signals to determine the marker colocalization in fluorescence microscopy images. The proposed method is compared with MEM for bivariate probability distributions. The new colocalization metric is validated on simulated and real data. The results show that MEC can determine co- and anti-colocalization even in high background settings. MEC can, therefore, be used as a robust tool for colocalization analysis.

The impact of model assumptions in scalar-on-image regression.

Complex statistical models such as scalar-on-image regression often require strong assumptions to overcome the issue of nonidentifiability. While in theory, it is well understood that model assumptions can strongly influence the results, this seems to be underappreciated, or played down, in practice. This article gives a systematic overview of the main approaches for scalar-on-image regression with a special focus on their assumptions. We categorize the assumptions and develop measures to quantify the degree to which they are met. The impact of model assumptions and the practical usage of the proposed measures are illustrated in a simulation study and in an application to neuroimaging data. The results show that different assumptions indeed lead to quite different estimates with similar predictive ability, raising the question of their interpretability. We give recommendations for making modeling and interpretation decisions in practice based on the new measures and simulations using hypothetic coefficient images and the observed data.

Joint Spatio-Temporal Shared Component Model with an Application in Iran Cancer Data

Background: Among the proposals for joint disease mapping, the shared component model has become more popular. Another advance to strengthen inference of disease data is the extension of purely spatial models to include time aspect. We aim to combine the idea of multivariate shared components with spatio-temporal modelling in a joint disease mapping model and apply it for incidence rates of seven prevalent cancers in Iran which together account for approximately 50% of all cancers. Methods: In the proposed model, each component is shared by different subsets of diseases, spatial and temporal trends are considered for each component, and the relative weight of these trends for each component for each relevant disease can be estimated. Results: For esophagus and stomach cancers the Northern provinces was the area of high risk. For colorectal cancer Gilan, Semnan, Fars, Isfahan, Yazd and East-Azerbaijan were the highest risk provinces. For bladder and lung cancer, the northwest were the highest risk area. For prostate and breast cancers, Isfahan, Yazd, Fars, Tehran, Semnan, Mazandaran and Khorasane-Razavi were the highest risk part. The smoking component, shared by esophagus, stomach, bladder and lung, had more effect in Gilan, Mazandaran, Chaharmahal and Bakhtiari, Kohgilouyeh and Boyerahmad, Ardebil and Tehran provinces, in turn. For overweight and obesity component, shared by esophagus, colorectal, prostate and breast cancers the largest effect was found for Tehran, Khorasane-Razavi, Semnan, Yazd, Isfahan, Fars, Mazandaran and Gilan, in turn. For low physical activity component, shared by colorectal and breast cancers North-Khorasan, Ardebil, Golestan, Ilam, Khorasane-Razavi and South-Khorasan had the largest effects, in turn. The smoking component is significantly more important for stomach than for esophagus, bladder and lung. The overweight and obesity had significantly more effect for colorectal than of esophagus cancer. Conclusions: The presented model is a valuable model to model geographical and temporal variation among diseases and has some interesting potential features and benefits over other joint models.

Initial high-resolution microscopic mapping of active and inactive regulatory sequences proves non-random 3D arrangements in chromatin domain clusters.

BACKGROUND: The association of active transcription regulatory elements (TREs) with DNAse I hypersensitivity (DHS[+]) and an 'open' local chromatin configuration has long been known. However, the 3D topography of TREs within the nuclear landscape of individual cells in relation to their active or inactive status has remained elusive. Here, we explored the 3D nuclear topography of active and inactive TREs in the context of a recently proposed model for a functionally defined nuclear architecture, where an active and an inactive nuclear compartment (ANC-INC) form two spatially co-aligned and functionally interacting networks. RESULTS: Using 3D structured illumination microscopy, we performed 3D FISH with differently labeled DNA probe sets targeting either sites with DHS[+], apparently active TREs, or DHS[-] sites harboring inactive TREs. Using an in-house image analysis tool, DNA targets were quantitatively mapped on chromatin compaction shaped 3D nuclear landscapes. Our analyses present evidence for a radial 3D organization of chromatin domain clusters (CDCs) with layers of increasing chromatin compaction from the periphery to the CDC core. Segments harboring active TREs are significantly enriched at the decondensed periphery of CDCs with loops penetrating into interchromatin compartment channels, constituting the ANC. In contrast, segments lacking active TREs (DHS[-]) are enriched toward the compacted interior of CDCs (INC). CONCLUSIONS: Our results add further evidence in support of the ANC-INC network model. The different 3D topographies of DHS[+] and DHS[-] sites suggest positional changes of TREs between the ANC and INC depending on their functional state, which might provide additional protection against an inappropriate activation. Our finding of a structural organization of CDCs based on radially arranged layers of different chromatin compaction levels indicates a complex higher-order chromatin organization beyond a dichotomic classification of chromatin into an 'open,' active and 'closed,' inactive state.

Quantitative analyses of the 3D nuclear landscape recorded with super-resolved fluorescence microscopy.

Recent advancements of super-resolved fluorescence microscopy have revolutionized microscopic studies of cells, including the exceedingly complex structural organization of cell nuclei in space and time. In this paper we describe and discuss tools for (semi-) automated, quantitative 3D analyses of the spatial nuclear organization. These tools allow the quantitative assessment of highly resolved different chromatin compaction levels in individual cell nuclei, which reflect functionally different regions or sub-compartments of the 3D nuclear landscape, and measurements of absolute distances between sites of different chromatin compaction. In addition, these tools allow 3D mapping of specific DNA/RNA sequences and nuclear proteins relative to the 3D chromatin compaction maps and comparisons of multiple cell nuclei. The tools are available in the free and open source R packages nucim and bioimagetools. We discuss the use of masks for the segmentation of nuclei and the use of DNA stains, such as DAPI, as a proxy for local differences in chromatin compaction. We further discuss the limitations of 3D maps of the nuclear landscape as well as problems of the biological interpretation of such data.

Maximum Entropy Approach in Dynamic Contrast-Enhanced Magnetic Resonance Imaging.

BACKGROUND: In the estimation of physiological kinetic parameters from Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) data, the determination of the arterial input function (AIF) plays a key role. OBJECTIVES: This paper proposes a Bayesian method to estimate the physiological parameters of DCE-MRI along with the AIF in situations, where no measurement of the AIF is available. METHODS: In the proposed algorithm, the maximum entropy method (MEM) is combined with the maximum a posterior approach (MAP). To this end, MEM is used to specify a prior probability distribution of the unknown AIF. The ability of this method to estimate the AIF is validated using the Kullback-Leibler divergence. Subsequently, the kinetic parameters can be estimated with MAP. The proposed algorithm is evaluated with a data set from a breast cancer MRI study. RESULTS: The application shows that the AIF can reliably be determined from the DCE-MRI data using MEM. Kinetic parameters can be estimated subsequently. CONCLUSIONS: The maximum entropy method is a powerful tool to reconstructing images from many types of data. This method is useful for generating the probability distribution based on given information. The proposed method gives an alternative way to assess the input function from the existing data. The proposed method allows a good fit of the data and therefore a better estimation of the kinetic parameters. In the end, this allows for a more reliable use of DCE-MRI.

Stage-dependent remodeling of the nuclear envelope and lamina during rabbit early embryonic development.

Utilizing 3D structured illumination microscopy, we investigated the quality and quantity of nuclear invaginations and the distribution of nuclear pores during rabbit early embryonic development and identified the exact time point of nucleoporin 153 (NUP153) association with chromatin during mitosis. Contrary to bovine early embryonic nuclei, featuring almost exclusively nuclear invaginations containing a small volume of cytoplasm, nuclei in rabbit early embryonic stages show additionally numerous invaginations containing a large volume of cytoplasm. Small-volume invaginations frequently emanated from large-volume nuclear invaginations but not vice versa, indicating a different underlying mechanism. Large- and small-volume nuclear envelope invaginations required the presence of chromatin, as they were restricted to chromatin-positive areas. The chromatin-free contact areas between nucleolar precursor bodies (NPBs) and large-volume invaginations were free of nuclear pores. Small-volume invaginations were not in contact with NPBs. The number of invaginations and isolated intranuclear vesicles per nucleus peaked at the 4-cell stage. At this stage, the nuclear surface showed highly concentrated clusters of nuclear pores surrounded by areas free of nuclear pores. Isolated intranuclear lamina vesicles were usually NUP153 negative. Cytoplasmic, randomly distributed NUP153-positive clusters were highly abundant at the zygote stage and decreased in number until they were almost absent at the 8-cell stage and later. These large NUP153 clusters may represent a maternally provided NUP153 deposit, but they were not visible as clusters during mitosis. Major genome activation at the 8- to 16-cell stage may mark the switch from a necessity for a deposit to on-demand production. NUP153 association with chromatin is initiated during metaphase before the initiation of the regeneration of the lamina. To our knowledge, the present study demonstrates for the first time major remodeling of the nuclear envelope and its underlying lamina during rabbit preimplantation development.

Remodeling of the Nuclear Envelope and Lamina during Bovine Preimplantation Development and Its Functional Implications.

The present study demonstrates a major remodeling of the nuclear envelope and its underlying lamina during bovine preimplantation development. Up to the onset of major embryonic genome activation (MGA) at the 8-cell stage nuclei showed a non-uniform distribution of nuclear pore complexes (NPCs). NPCs were exclusively present at sites where DNA contacted the nuclear lamina. Extended regions of the lamina, which were not contacted by DNA, lacked NPCs. In post-MGA nuclei the whole lamina was contacted rather uniformly by DNA. Accordingly, NPCs became uniformly distributed throughout the entire nuclear envelope. These findings shed new light on the conditions which control the integration of NPCs into the nuclear envelope. The switch from maternal to embryonic production of mRNAs was accompanied by multiple invaginations covered with NPCs, which may serve the increased demands of mRNA export and protein import. Other invaginations, as well as interior nuclear segments and vesicles without contact to the nuclear envelope, were exclusively positive for lamin B. Since the abundance of these invaginations and vesicles increased in concert with a massive nuclear volume reduction, we suggest that they reflect a mechanism for fitting the nuclear envelope and its lamina to a shrinking nuclear size during bovine preimplantation development. In addition, a deposit of extranuclear clusters of NUP153 (a marker for NPCs) without associated lamin B was frequently observed from the zygote stage up to MGA. Corresponding RNA-Seq data revealed deposits of spliced, maternally provided NUP153 mRNA and little unspliced, newly synthesized RNA prior to MGA, which increased strongly at the initiation of embryonic expression of NUP153 at MGA.

Bayesian mixed-effects model for the analysis of a series of FRAP images.

The binding behavior of molecules in nuclei of living cells can be studied through the analysis of images from fluorescence recovery after photobleaching experiments. However, there is still a lack of methodology for the statistical evaluation of FRAP data, especially for the joint analysis of multiple dynamic images. We propose a hierarchical Bayesian nonlinear model with mixed-effect priors based on local compartment models in order to obtain joint parameter estimates for all nuclei as well as to account for the heterogeneity of the nuclei population. We apply our method to a series of FRAP experiments of DNA methyltransferase 1 tagged to green fluorescent protein expressed in a somatic mouse cell line and compare the results to the application of three different fixed-effects models to the same series of FRAP experiments. With the proposed model, we get estimates of the off-rates of the interactions of the molecules under study together with credible intervals, and additionally gain information about the variability between nuclei. The proposed model is superior to and more robust than the tested fixed-effects models. Therefore, it can be used for the joint analysis of data from FRAP experiments on various similar nuclei.

Reprogramming of fibroblast nuclei in cloned bovine embryos involves major structural remodeling with both striking similarities and differences to nuclear phenotypes of in vitro fertilized embryos.

Nuclear landscapes were studied during preimplantation development of bovine embryos, generated either by in vitro fertilization (IVF), or generated as cloned embryos by somatic cell nuclear transfer (SCNT) of bovine fetal fibroblasts, using 3-dimensional confocal laser scanning microscopy (3D-CLSM) and structured illumination microscopy (3D-SIM). Nuclear landscapes of IVF and SCNT embryonic nuclei were compared with each other and with fibroblast nuclei. We demonstrate that reprogramming of fibroblast nuclei in cloned embryos requires changes of their landscapes similar to nuclei of IVF embryos. On the way toward the 8-cell stage, where major genome activation occurs, a major lacuna, enriched with splicing factors, was formed in the nuclear interior and chromosome territories (CTs) were shifted toward the nuclear periphery. During further development the major lacuna disappeared and CTs were redistributed throughout the nuclear interior forming a contiguous higher order chromatin network. At all stages of development CTs of IVF and SCNT embryonic nuclei were built up from chromatin domain clusters (CDCs) pervaded by interchromatin compartment (IC) channels. Quantitative analyses revealed a highly significant enrichment of RNA polymerase II and H3K4me3, a marker for transcriptionally competent chromatin, at the periphery of CDCs. In contrast, H3K9me3, a marker for silent chromatin, was enriched in the more compacted interior of CDCs. Despite these striking similarities, we also detected major differences between nuclear landscapes of IVF and cloned embryos. Possible implications of these differences for the developmental potential of cloned animals remain to be investigated. We present a model, which integrates generally applicable structural and functional features of the nuclear landscape.

Three-dimensional super-resolution microscopy of the inactive X chromosome territory reveals a collapse of its active nuclear compartment harboring distinct Xist RNA foci.

BACKGROUND: A Xist RNA decorated Barr body is the structural hallmark of the compacted inactive X territory in female mammals. Using super-resolution three-dimensional structured illumination microscopy (3D-SIM) and quantitative image analysis, we compared its ultrastructure with active chromosome territories (CTs) in human and mouse somatic cells, and explored the spatio-temporal process of Barr body formation at onset of inactivation in early differentiating mouse embryonic stem cells (ESCs). RESULTS: We demonstrate that all CTs are composed of structurally linked chromatin domain clusters (CDCs). In active CTs the periphery of CDCs harbors low-density chromatin enriched with transcriptionally competent markers, called the perichromatin region (PR). The PR borders on a contiguous channel system, the interchromatin compartment (IC), which starts at nuclear pores and pervades CTs. We propose that the PR and macromolecular complexes in IC channels together form the transcriptionally permissive active nuclear compartment (ANC). The Barr body differs from active CTs by a partially collapsed ANC with CDCs coming significantly closer together, although a rudimentary IC channel system connected to nuclear pores is maintained. Distinct Xist RNA foci, closely adjacent to the nuclear matrix scaffold attachment factor-A (SAF-A) localize throughout Xi along the rudimentary ANC. In early differentiating ESCs initial Xist RNA spreading precedes Barr body formation, which occurs concurrent with the subsequent exclusion of RNA polymerase II (RNAP II). Induction of a transgenic autosomal Xist RNA in a male ESC triggers the formation of an 'autosomal Barr body' with less compacted chromatin and incomplete RNAP II exclusion. CONCLUSIONS: 3D-SIM provides experimental evidence for profound differences between the functional architecture of transcriptionally active CTs and the Barr body. Basic structural features of CT organization such as CDCs and IC channels are however still recognized, arguing against a uniform compaction of the Barr body at the nucleosome level. The localization of distinct Xist RNA foci at boundaries of the rudimentary ANC may be considered as snap-shots of a dynamic interaction with silenced genes. Enrichment of SAF-A within Xi territories and its close spatial association with Xist RNA suggests their cooperative function for structural organization of Xi.

Spatially regularized estimation for the analysis of dynamic contrast-enhanced magnetic resonance imaging data.

Competing compartment models of different complexities have been used for the quantitative analysis of dynamic contrast-enhanced magnetic resonance imaging data. We present a spatial elastic net approach that allows to estimate the number of compartments for each voxel such that the model complexity is not fixed a priori. A multi-compartment approach is considered, which is translated into a restricted least square model selection problem. This is done by using a set of basis functions for a given set of candidate rate constants. The form of the basis functions is derived from a kinetic model and thus describes the contribution of a specific compartment. Using a spatial elastic net estimator, we chose a sparse set of basis functions per voxel, and hence, rate constants of compartments. The spatial penalty takes into account the voxel structure of an image and performs better than a penalty treating voxels independently. The proposed estimation method is evaluated for simulated images and applied to an in vivo dataset.

Fully Bayesian inference for structural MRI: application to segmentation and statistical analysis of T2-hypointensities.

Aiming at iron-related T2-hypointensity, which is related to normal aging and neurodegenerative processes, we here present two practicable approaches, based on Bayesian inference, for preprocessing and statistical analysis of a complex set of structural MRI data. In particular, Markov Chain Monte Carlo methods were used to simulate posterior distributions. First, we rendered a segmentation algorithm that uses outlier detection based on model checking techniques within a Bayesian mixture model. Second, we rendered an analytical tool comprising a Bayesian regression model with smoothness priors (in the form of Gaussian Markov random fields) mitigating the necessity to smooth data prior to statistical analysis. For validation, we used simulated data and MRI data of 27 healthy controls (age: [Formula: see text]; range, [Formula: see text]). We first observed robust segmentation of both simulated T2-hypointensities and gray-matter regions known to be T2-hypointense. Second, simulated data and images of segmented T2-hypointensity were analyzed. We found not only robust identification of simulated effects but also a biologically plausible age-related increase of T2-hypointensity primarily within the dentate nucleus but also within the globus pallidus, substantia nigra, and red nucleus. Our results indicate that fully Bayesian inference can successfully be applied for preprocessing and statistical analysis of structural MRI data.

Sensitivity to prior specification in Bayesian genome-based prediction models.

Different statistical models have been proposed for maximizing prediction accuracy in genome-based prediction of breeding values in plant and animal breeding. However, little is known about the sensitivity of these models with respect to prior and hyperparameter specification, because comparisons of prediction performance are mainly based on a single set of hyperparameters. In this study, we focused on Bayesian prediction methods using a standard linear regression model with marker covariates coding additive effects at a large number of marker loci. By comparing different hyperparameter settings, we investigated the sensitivity of four methods frequently used in genome-based prediction (Bayesian Ridge, Bayesian Lasso, BayesA and BayesB) to specification of the prior distribution of marker effects. We used datasets simulated according to a typical maize breeding program differing in the number of markers and the number of simulated quantitative trait loci affecting the trait. Furthermore, we used an experimental maize dataset, comprising 698 doubled haploid lines, each genotyped with 56110 single nucleotide polymorphism markers and phenotyped as testcrosses for the two quantitative traits grain dry matter yield and grain dry matter content. The predictive ability of the different models was assessed by five-fold cross-validation. The extent of Bayesian learning was quantified by calculation of the Hellinger distance between the prior and posterior densities of marker effects. Our results indicate that similar predictive abilities can be achieved with all methods, but with BayesA and BayesB hyperparameter settings had a stronger effect on prediction performance than with the other two methods. Prediction performance of BayesA and BayesB suffered substantially from a non-optimal choice of hyperparameters.

Automatic post-picking using MAPPOS improves particle image detection from cryo-EM micrographs.

Cryo-electron microscopy (cryo-EM) studies using single particle reconstruction are extensively used to reveal structural information on macromolecular complexes. Aiming at the highest achievable resolution, state of the art electron microscopes automatically acquire thousands of high-quality micrographs. Particles are detected on and boxed out from each micrograph using fully- or semi-automated approaches. However, the obtained particles still require laborious manual post-picking classification, which is one major bottleneck for single particle analysis of large datasets. We introduce MAPPOS, a supervised post-picking strategy for the classification of boxed particle images, as additional strategy adding to the already efficient automated particle picking routines. MAPPOS employs machine learning techniques to train a robust classifier from a small number of characteristic image features. In order to accurately quantify the performance of MAPPOS we used simulated particle and non-particle images. In addition, we verified our method by applying it to an experimental cryo-EM dataset and comparing the results to the manual classification of the same dataset. Comparisons between MAPPOS and manual post-picking classification by several human experts demonstrated that merely a few hundred sample images are sufficient for MAPPOS to classify an entire dataset with a human-like performance. MAPPOS was shown to greatly accelerate the throughput of large datasets by reducing the manual workload by orders of magnitude while maintaining a reliable identification of non-particle images.

The potential of 3D-FISH and super-resolution structured illumination microscopy for studies of 3D nuclear architecture: 3D structured illumination microscopy of defined chromosomal structures visualized by 3D (immuno)-FISH opens new perspectives for studies of nuclear architecture.

Three-dimensional structured illumination microscopy (3D-SIM) has opened up new possibilities to study nuclear architecture at the ultrastructural level down to the ~100 nm range. We present first results and assess the potential using 3D-SIM in combination with 3D fluorescence in situ hybridization (3D-FISH) for the topographical analysis of defined nuclear targets. Our study also deals with the concern that artifacts produced by FISH may counteract the gain in resolution. We address the topography of DAPI-stained DNA in nuclei before and after 3D-FISH, nuclear pores and the lamina, chromosome territories, chromatin domains, and individual gene loci. We also look at the replication patterns of chromocenters and the topographical relationship of Xist-RNA within the inactive X-territory. These examples demonstrate that an appropriately adapted 3D-FISH/3D-SIM approach preserves key characteristics of the nuclear ultrastructure and that the gain in information obtained by 3D-SIM yields new insights into the functional nuclear organization.

Effect of aging on lung structure in vivo: assessment with densitometric and fractal analysis of high-resolution computed tomography data.

PURPOSE: To test the hypothesis that there is a difference between the lung computed tomography (CT) microstructure of asymptomatic older individuals and that of young individuals as evaluated by objective indices of complexity and density. MATERIALS AND METHODS: Two study groups of nonsmoking urban-dwelling individuals over 75 years and under 55 years were prospectively identified. Thirty-three consecutive volunteers (21 older than 75 y and 12 less than 55 y) were included, and CTs were performed with concurrent pulmonary function testing. Pulmonary regions of interest (ROIs) were evaluated with fractal dimension (FD) analysis (an index of complexity), mean lung density (MLD), and percentage of pixels with lung density (LD) less than thresholds of -910 HU and -950 HU. The Student t test and the Mann-Whitney test were used to evaluate for differences in mean values between groups. The Pearson correlation coefficient was used to correlate mean FD value and LD data with pulmonary function. RESULTS: Significant correlations of ROI MLD, LD -910 HU, and LD -950 HU with age and sex were shown (P = 0.029-0.003). The ROI mean FD value was greater in younger individuals compared with older individuals (76.5 ± 1.7 vs. 70.3 ± 1.2; P = 0.004). There was a correlation between Kco (gas-diffusing capacity adjusted for alveolar volume) and mean FD value (P = 0.006) and MLD (P = 0.015). CONCLUSION: The lung parenchyma of nonsmoking older urban-dwelling asymptomatic individuals has significantly different CT density and complexity compared with younger individuals.