A Java tool for dynamic web-based 3D visualization of anatomy and overlapping gene or protein expression patterns

The Gene Expression Viewer is a web-launched three-dimensional visualization tool, tailored to compare surface reconstructions of multi-channel image volumes generated by confocal microscopy or micro-CT.     

Improvement of MRI probes to allow efficient detection of gene expression

Recently, it has been demonstrated that magnetic resonance imaging (MRI) utilizing monocrystalline iron oxide nanoparticles (MIONs) targeted to an engineered transferrin receptor enables imaging of gene expression. However, the relatively high doses of iron oxides used indicated the need for improved MR imaging probes to monitor changes in gene expression in vivo. Using alternative conjugation chemistries to link targeting ligands and iron oxide nanoparticles, we present the development and characterization as well as improved receptor binding and MRI detection of a novel imaging probe. Iron oxide nanoparticles with a cross-linked dextran coat were conjugated to transferrin (Tf) through the linker molecule N-succinimidyl 3-(2-pyridyldithio)propionate (SPDP) to yield Tf-S-S-CLIO. The characteristics of this conjugate were evaluated in comparison to Tf-MION and Tf-CLIO generated by oxidative activation of the dextran-coat with subsequent reduction of Schiff's base. SPDP conjugation allowed approximately a 4-fold increase in the number of Tf molecules attached per iron oxide nanoparticle and resulted in a more than 10-fold improvement of binding and uptake by cells. This translated into an imaging probe that was 16 times better for imaging gene expression in a cellular MRI assay. This novel probe for MRI may substantially increase the sensitivity for the detection of endogenous or genetically induced transferrin receptor expression in small numbers of cells and may significantly reduce the imaging dose from over 100 mg/kg to doses of iron oxides that are currently used in clinical imaging.     

VizStruct: exploratory visualization for gene expression profiling

MOTIVATION: DNA arrays provide a broad snapshot of the state of the cell by measuring the expression levels of thousands of genes simultaneously. Visualization techniques can enable the exploration and detection of patterns and relationships in a complex data set by presenting the data in a graphical format in which the key characteristics become more apparent. The dimensionality and size of array data sets however present significant challenges to visualization. The purpose of this study is to present an interactive approach for visualizing variations in gene expression profiles and to assess its usefulness for classifying samples. RESULTS: The first Fourier harmonic projection was used to map multi-dimensional gene expression data to two dimensions in an implementation called VizStruct. The visualization method was tested using the differentially expressed genes identified in eight separate gene expression data sets. The samples were classified using the oblique decision tree (OC1) algorithm to provide a procedure for visualization-driven classification. The classifiers were evaluated by the holdout and the cross-validation techniques. The proposed method was found to achieve high accuracy. AVAILABILITY: Detailed mathematical derivation of all mapping properties as well as figures in color can be found as supplementary on the web page http://www.cse.buffalo.edu/DBGROUP/bioinformatics/supplementary/vizstruct. All programs were written in Java and Matlab and software code is available by request from the first author.     

Erythropoietin gene expression in haemopoietic cell lines

Erythropoietin (Epo) gene expression was studied in a number of different haemopoietic cell lines by in situ hybridization and Northern Blot analysis using a radioisotope-labelled monkey Epo DNA probe. A positive message was expressed by a human cell line, CM-S, derived from a patient with congenital hypoplastic anemia, and by a murine erythro-leukaemic cell line, clone 707, derived from the spleen of Friend virus-infected mice. No message was detected in two megakaryoblastic cell lines, and in a monocytic cell line, derived from a patient with acute monocytic leukaemia. These data may fit with the hypothesis of expression of Epo and other growth factors by haemopoietic cells through a mechanism of so-called autocrine secretion.     

In vivo visualization of gene expression using magnetic resonance imaging

High-resolution in vivo imaging of gene expression is not possible in opaque animals by existing techniques. Here we present a new approach for obtaining such images by magnetic resonance imaging (MRI) using an MRI contrast agent that can indicate reporter gene expression in living animals. We have prepared MRI contrast agents in which the access of water to the first coordination sphere of a chelated paramagnetic ion is blocked with a substrate that can be removed by enzymatic cleavage. Following cleavage, the paramagnetic ion can interact directly with water protons to increase the MR signal. Here, we report an agent where galactopyranose is the blocking group. This group renders the MRI contrast agent sensitive to expression of the commonly used marker gene, beta-galactosidase. To cellular resolution, regions of higher intensity in the MR image correlate with regions expressing marker enzyme. These results offer the promise of in vivo mapping of gene expression in transgenic animals and validate a general approach for constructing a family of MRI contrast agents that respond to biological activity.     

Statistical inference for simultaneous clustering of gene expression data

Current methods for analysis of gene expression data are mostly based on clustering and classification of either genes or samples. We offer support for the idea that more complex patterns can be identified in the data if genes and samples are considered simultaneously. We formalize the approach and propose a statistical framework for two-way clustering. A simultaneous clustering parameter is defined as a function theta=Phi(P) of the true data generating distribution P, and an estimate is obtained by applying this function to the empirical distribution P(n). We illustrate that a wide range of clustering procedures, including generalized hierarchical methods, can be defined as parameters which are compositions of individual mappings for clustering patients and genes. This framework allows one to assess classical properties of clustering methods, such as consistency, and to formally study statistical inference regarding the clustering parameter. We present results of simulations designed to assess the asymptotic validity of different bootstrap methods for estimating the distribution of Phi(P(n)). The method is illustrated on a publicly available data set.     

Multidimensional support vector machines for visualization of gene expression data

MOTIVATION: Since DNA microarray experiments provide us with huge amount of gene expression data, they should be analyzed with statistical methods to extract the meanings of experimental results. Some dimensionality reduction methods such as Principal Component Analysis (PCA) are used to roughly visualize the distribution of high dimensional gene expression data. However, in the case of binary classification of gene expression data, PCA does not utilize class information when choosing axes. Thus clearly separable data in the original space may not be so in the reduced space used in PCA. RESULTS: For visualization and class prediction of gene expression data, we have developed a new SVM-based method called multidimensional SVMs, that generate multiple orthogonal axes. This method projects high dimensional data into lower dimensional space to exhibit properties of the data clearly and to visualize a distribution of the data roughly. Furthermore, the multiple axes can be used for class prediction. The basic properties of conventional SVMs are retained in our method: solutions of mathematical programming are sparse, and nonlinear classification is implemented implicitly through the use of kernel functions. The application of our method to the experimentally obtained gene expression datasets for patients' samples indicates that our algorithm is efficient and useful for visualization and class prediction. CONTACT: komura@hal.rcast.u-tokyo.ac.jp.     

SeqExpress: desktop analysis and visualization tool for gene expression experiments

SUMMARY: SeqExpress is a stand-alone desktop application for the identification of relevant genes within collections of microarray or SAGE experiments. A number of analysis, filtering and visualization tools are provided to aid in the selection of groups of genes. If R is installed then the application can use this to provide further analysis. AVAILABILITY: SeqExpress is available at: http://www.seqexpress.com     

Fast track in vitro mycorrhization of potato plantlets allow studies on gene expression dynamics

Root colonization by arbuscular mycorrhizal (AM) fungi is a dynamic process involving major changes in plant gene expression. Here, the expression of a phosphate transporter gene (PT3) and several defense genes, already known to be involved in the various stages of AM establishment, were monitored in the mycelium donor plant (MDP) in vitro culture system associating potato plantlets with an AM fungus. This system allows fast and homogenous mycorrhization of seedlings at their early stage of development by growing the plantlets in active mycelial networks, but has never been validated for gene expression analysis. Here, QRT-PCR analyses were conducted in parallel to pre- (1 day), early (2 and 3 days), and late (6, 9, and 15 days) stages of root colonization. We observed the induction of a plant gene marker of AM root colonization (PT3) at the late stage and the induction of MAPK and PAL genes at the early and late stages of root colonization. We also demonstrated the induction of PR1 and PR2 genes at pre- and late stages and of GST1 and Lox genes at a late stage of root colonization. These results validated the MDP in vitro culture system as an optimal tool to study gene expression analysis during the AM fungi establishment. This system further opened the door to investigate gene networks associated with the plants–AM fungi symbiosis.     

An interactive tool for visualization of relationships between gene expression profiles

Background  

Application of phenetic methods to gene expression analysis proved to be a successful approach. Visualizing the results in a 3-dimentional space may further enhance these techniques.     

GAAS: gene array analyzer software for management,analysis and visualization of gene expression data

SUMMARY: GAAS, Gene Array Analyzer Software supports multi-user efficient management and suitable analyses of large amounts of gene expression data across replicated experiments. Its management framework handles input data generated by different technologies. A multi-user environment allows each user to store his/her own data visualization scheme, analysis parameters used, values and formats of the output data. The analysis engine performs: background and spot quality evaluation, data normalization, differential gene expression analyses in single and multiple replica experiments. Results of expression profiles can be interactively navigated through graphical interfaces and stored into output databases.     

A unified approach for simultaneous gene clustering and differential expression identification

Although both clustering and identification of differentially expressed genes are equally essential in most microarray studies, the two tasks are often conducted without regard to each other. This is clearly not the most efficient way of extracting information. The main aim of this article is to develop a coherent statistical method that can simultaneously cluster and detect differentially expressed genes. Through information sharing between the two tasks, the proposed approach gives more sensible clustering among genes and is more sensitive in identifying differentially expressed genes. The improvement over existing methods is illustrated in both our simulation results and a case study.     

Genetic inheritance of gene expression in human cell lines

Combining genetic inheritance information, for both molecular profiles and complex traits, is a promising strategy not only for detecting quantitative trait loci (QTLs) for complex traits but for understanding which genes, pathways, and biological processes are also under the influence of a given QTL. As a primary step in determining the feasibility of such an approach in humans, we present the largest survey to date, to our knowledge, of the heritability of gene-expression traits in segregating human populations. In particular, we measured expression for 23,499 genes in lymphoblastoid cell lines for members of 15 Centre d'Etude du Polymorphisme Humain (CEPH) families. Of the total set of genes, 2,340 were found to be expressed, of which 31% had significant heritability when a false-discovery rate of 0.05 was used. QTLs were detected for 33 genes on the basis of at least one P value <.000005. Of these, 13 genes possessed a QTL within 5 Mb of their physical location. Hierarchical clustering was performed on the basis of both Pearson correlation of gene expression and genetic correlation. Both reflected biologically relevant activity taking place in the lymphoblastoid cell lines, with greater coherency represented in Kyoto Encyclopedia of Genes and Genomes database (KEGG) pathways than in Gene Ontology database pathways. However, more pathway coherence was observed in KEGG pathways when clustering was based on genetic correlation than when clustering was based on Pearson correlation. As more expression data in segregating populations are generated, viewing clusters or networks based on genetic correlation measures and shared QTLs will offer potentially novel insights into the relationship among genes that may underlie complex traits.