Gene expression analysis using single molecule detection

Recent developments of single molecule detection techniques and in particular the introduction of fluorescence correlation spectroscopy (FCS) led to a number of important applications in biological research. We present a unique approach for the gene expression analysis using dual-color cross-correlation. The expression assay is based on gene-specific hybridization of two dye-labeled DNA probes to a selected target gene. The counting of the dual-labeled molecules within the solution allows the quantification of the expressed gene copies in absolute numbers. As detection and analysis by FCS can be performed at the level of single molecules, there is no need for any type of amplification. We describe the gene expression assay and present data demonstrating the capacity of this novel technology. In order to prove the gene specificity, we performed experiments with gene-depleted total cDNA. The biological application was demonstrated by quantifying selected high, medium and low abundant genes in cDNA prepared from HL-60 cells.     

cDNA macroarray analysis of gene expression changes in rat brain after a single administration of a 2-aminoadamantane derivative

The Atlas Rat cDNA Expression Array (BD Biosciences, United States) has been used to analyze changes in the expression of 588 genes in rat brain cells in response to a single administration of Ladasten, a 2-aminoadamantane derivative that has psychostimulating and anxiolytic effects. The analysis of hybridization on macroarrays, confirmed by the results of real-time quantitative RT-PCR, has demonstrated that Ladasten alters the expression of 12 genes in the rat brain. The GAT3 and CARBH genes are presumed to be pharmacologically important targets of Ladasten. The changes in their activity explain the mechanisms of the anxiolytic and mood-stabilizing effects of the drug. Ladasten has been shown to induce the genes whose products are involved in various signal pathways (APC, Rb, PKCIP, and PMCA), as well as the genes of cytoskeletal proteins (Tub1 and actin), synaptic proteins (SynIA&IB and PLP), and enzymes (Gapdh and NSE). The proteins encoded by these genes are presumably involved in compensatory and/or neuroplastic adaptation to the effects of Ladasten.__________Translated from Molekulyarnaya Biologiya, Vol. 39, No. 2, 2005, pp. 276–285.Original Russian Text Copyright © 2005 by Vakhitova, Yamidanov, Vakhitov, Seredenin.     

TABASCO: A single molecule,base-pair resolved gene expression simulator

Background  

Experimental studies of gene expression have identified some of the individual molecular components and elementary reactions that comprise and control cellular behavior. Given our current understanding of gene expression, and the goals of biotechnology research, both scientists and engineers would benefit from detailed simulators that can explicitly compute genome-wide expression levels as a function of individual molecular events, including the activities and interactions of molecules on DNA at single base pair resolution. However, for practical reasons including computational tractability, available simulators have not been able to represent genome-scale models of gene expression at this level of detail.     

Optimizing a massive parallel sequencing workflow for quantitative miRNA expression analysis

BACKGROUND: Massive Parallel Sequencing methods (MPS) can extend and improve the knowledge obtained by conventional microarray technology, both for mRNAs and short non-coding RNAs, e.g. miRNAs. The processing methods used to extract and interpret the information are an important aspect of dealing with the vast amounts of data generated from short read sequencing. Although the number of computational tools for MPS data analysis is constantly growing, their strengths and weaknesses as part of a complex analytical pipe-line have not yet been well investigated. PRIMARY FINDINGS: A benchmark MPS miRNA dataset, resembling a situation in which miRNAs are spiked in biological replication experiments was assembled by merging a publicly available MPS spike-in miRNAs data set with MPS data derived from healthy donor peripheral blood mononuclear cells. Using this data set we observed that short reads counts estimation is strongly under estimated in case of duplicates miRNAs, if whole genome is used as reference. Furthermore, the sensitivity of miRNAs detection is strongly dependent by the primary tool used in the analysis. Within the six aligners tested, specifically devoted to miRNA detection, SHRiMP and MicroRazerS show the highest sensitivity. Differential expression estimation is quite efficient. Within the five tools investigated, two of them (DESseq, baySeq) show a very good specificity and sensitivity in the detection of differential expression. CONCLUSIONS: The results provided by our analysis allow the definition of a clear and simple analytical optimized workflow for miRNAs digital quantitative analysis.     

Laser-assisted preparation of single cells from stained histological slides for gene analysis

 Individual cells are prepared from histological tissue sections of routinely formalin-fixed and paraffin-embedded tissues using an ultraviolet laser micromanipulator. This technology, in combination with polymerase chain reaction (PCR)-based gene analysis, will enable researchers to routinely detect a variety of nucleic acid abnormalities underlying cancer, infection, and genetic disease with previously unknown sensitivity: at the single cell level. The utility of this technique is demonstrated by PCR amplification and sequencing of the E-cadherin gene, which codes for a homophilic cell-to-cell adhesion molecule, in early gastric carcinomas of the diffuse type of Lauren’s classification. The main characteristics of the laser-assisted microdissection technique are high precision without contamination and easy application. The assignment of individual gene sequences to single cells will now provide a direct link between molecular biology on the one hand and histology and pathology on the other. Accepted: 18 August 1997     

Determining gene expression on a single pair of microarrays

Background  

In microarray experiments the numbers of replicates are often limited due to factors such as cost, availability of sample or poor hybridization. There are currently few choices for the analysis of a pair of microarrays where N = 1 in each condition. In this paper, we demonstrate the effectiveness of a new algorithm called PINC (PINC is Not Cyber-T) that can analyze Affymetrix microarray experiments.     

单细胞基因表达分析技术在神经科学研究中的应用

单细胞的分子生物学是神经科学中较新的领域,研究对象包括单细胞DNA、RNA、蛋白质和线粒体DNA。单细胞基因表达分析技术具有传统技术难以相比的优势,正成为神经科学研究的重要工具。本文将介绍单细胞基因表达分析技术的操作流程、技术和方法的特点,概述其在神经科学研究中的应用,并展望其应用前景。     

Transcriptional analysis of the titin cap gene

Mutations in titin cap (Tcap), also known as telethonin, cause limb-girdle muscular dystrophy type 2G (LGMD2G). Tcap is one of the titin interacting Z-disc proteins involved in the regulation and development of normal sarcomeric structure. Given the essential role of Tcap in establishing and maintaining normal skeletal muscle architecture, we were interested in determining the regulatory elements required for expression of this gene in myoblasts. We have defined a highly conserved 421 bp promoter proximal promoter fragment that contains two E boxes and multiple putative Mef2 binding sequences. This promoter can be activated by MyoD and myogenin in NIH3T3 fibroblast cells, and maintains the differentiated cell-specific expression pattern of the endogenous Tcap in C2C12 cells. We find that while both E boxes are required for full activation by MyoD or myogenin in NIH3T3 cells, the promoter proximal E box has a greater contribution to activation of this promoter in C2C12 cells and to activation by MyoD in NIH3T3 cells. Together, the data suggest an important role for MyoD in activating Tcap expression through the promoter proximal E box. We also show that myogenin is required for normal expression in vivo and physically binds to the Tcap promoter during embryogenesis.     

Multiple-testing strategy for analyzing cDNA array data on gene expression

An objective of many functional genomics studies is to estimate treatment-induced changes in gene expression. cDNA arrays interrogate each tissue sample for the levels of mRNA for hundreds to tens of thousands of genes, and the use of this technology leads to a multitude of treatment contrasts. By-gene hypotheses tests evaluate the evidence supporting no effect, but selecting a significance level requires dealing with the multitude of comparisons. The p-values from these tests order the genes such that a p-value cutoff divides the genes into two sets. Ideally one set would contain the affected genes and the other would contain the unaffected genes. However, the set of genes selected as affected will have false positives, i.e., genes that are not affected by treatment. Likewise, the other set of genes, selected as unaffected, will contain false negatives, i.e., genes that are affected. A plot of the observed p-values (1 - p) versus their expectation under a uniform [0, 1] distribution allows one to estimate the number of true null hypotheses. With this estimate, the false positive rates and false negative rates associated with any p-value cutoff can be estimated. When computed for a range of cutoffs, these rates summarize the ability of the study to resolve effects. In our work, we are more interested in selecting most of the affected genes rather than protecting against a few false positives. An optimum cutoff, i.e., the best set given the data, depends upon the relative cost of falsely classifying a gene as affected versus the cost of falsely classifying a gene as unaffected. We select the cutoff by a decision-theoretic method analogous to methods developed for receiver operating characteristic curves. In addition, we estimate the false discovery rate and the false nondiscovery rate associated with any cutoff value. Two functional genomics studies that were designed to assess a treatment effect are used to illustrate how the methods allowed the investigators to determine a cutoff to suit their research goals.     

cDNA macroarray analysis of gene expression in synoviocytes stimulated with TNFalpha

Gene expression of synoviocytes stimulated with tumor necrosis factor-alpha (TNFalpha) was studied by macroarray analysis to elucidate the cellular response and identify new biological functions of known and unknown genes. 10035 cDNA clones were used to make cDNA macroarrays of representative genes. Synoviocytes expressed large amounts of fibronectin and collagen mRNA. Statistical analysis of the macroarray data revealed 26 genes, including six new genes, which underwent significant alteration of gene expression in response to TNFalpha stimulation. These findings suggest that the synoviocyte response to TNFalpha stimulation forms the basis of development of various aspects of the pathophysiology of rheumatoid arthritis.     

Hierarchical Bayes models for cDNA microarray gene expression

cDNA microarrays are used in many contexts to compare mRNA levels between samples of cells. Microarray experiments typically give us expression measurements on 1000-20 000 genes, but with few replicates for each gene. Traditional methods using means and standard deviations to detect differential expression are not satisfactory in this context. A handful of alternative statistics have been developed, including several empirical Bayes methods. In the present paper we present two full hierarchical Bayes models for detecting gene expression, of which one (D) describes our microarray data very well. We also compare the full Bayes and empirical Bayes approaches with respect to model assumptions, false discovery rates and computer running time. The proposed models are compared to existing empirical Bayes models in a simulation study and for a set of data (Yuen et al., 2002), where 27 genes have been categorized by quantitative real-time PCR. It turns out that the existing empirical Bayes methods have at least as good performance as the full Bayes ones.     

cDNA microarray analysis of altered gene expression in Ara-C-treated leukemia cells

The acute lymphoblastic leukemia cell line CCRF-CEM is sensitive to Ara-C and undergoes apoptosis. In contrast, the chronic myelogenous leukemia (CML) cell line K562 is highly resistant to Ara-C, which causes the cells to differentiate into erythrocytes before undergoing apoptosis. We used cDNA microarrays to monitor the alterations in gene expression in these two cell lines under conditions leading to apoptosis or differentiation. Ara-C-treated CCRF-CEM cells were characterized by a cluster of down-regulated chaperone genes, whereas Ara-C-treated K562 cells were characterized by a cluster of up-regulated hemoglobin genes. In K562 cells, Ara-C treatment induced significant down-regulation of the asparagine synthetase gene, which is involved in resistance to L-asparaginase. Sequential treatment with Ara-C and L-asparaginase had a synergistic effect on the inhibition of K562 cell growth, and combination therapy with these two anticancer agents may prove effective in the treatment of CML, which cannot be cured by either drug alone.